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Functional papers are the subject of extensive research efforts and have already become an irreplaceable part of our modern society. Among other issues, they enable fast and inexpensive detection of a plethora of analytes and simplify laboratory work, for example in medical tests. This article focuses on the molecular and structural fundamentals of paper and the possibilities of functionalization, commercially available assays and their production, as well as on current and future challenges in research in this field. Graphic abstract

An enterprise imaging (EI) strategy is an organized plan to optimize the electronic health record (EHR) so that healthcare providers have intuitive and immediate access to all patient clinical images and their associated documentation, regardless of source. We describe ten steps recommended to achieve the goal of implementing EI for an institution. The first step is to define and access all images used for medical decision-making. Next, demonstrate how EI is a powerful strategy for enhancing patient and caregiver experience, improving population health, and reducing cost. Then, it is recommended that one must understand the specialties and their clinical workflow challenges as related to imaging. Step four is to create a strategy to improve quality of care and patient safety with EI. Step five demonstrates how EI can reduce costs. Then, show how EI can help enhance the patient experience. Step seven suggests how EI can enhance the work life of caregivers and step eight describes how to develop EI governance. Step nine describes the plan to implement an EI project, and finally, step 10, to understand cybersecurity from a patient safety perspective and to protect images from accidental and malicious intrusion.

Norm emergence is typically studied in the context of multiagent systems (MAS) where norms are implicit, and participating agents use simplistic decision-making mechanisms. These implicit norms are usually unconsciously shared and adopted through agent interaction. A norm is deemed to have emerged when a threshold or predetermined percentage of agents follow the “norm”. Conversely, in normative MAS, norms are typically explicit and agents deliberately share norms through communication or are informed about norms by an authority, following which an agent decides whether to adopt the norm or not. The decision to adopt a norm by the agent can happen immediately after recognition or when an applicable situation arises. In this paper, we make the case that, similarly, a norm has emerged in a normative MAS when a percentage of agents adopt the norm. Furthermore, we posit that agents themselves can and should be involved in norm synthesis, and hence influence the norms governing the MAS, in line with Ostrom’s eight principles. Consequently, we put forward a framework for the emergence of norms within a normative MAS, that allows participating agents to propose/request changes to the normative system, while special-purpose synthesizer agents formulate new norms or revisions in response to these requests. Synthesizers must collectively agree that the new norm or norm revision should proceed, and then finally be approved by an “Oracle”. The normative system is then modified to incorporate the norm.

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.

Clinical specialties have widely varied needs for diagnostic image interpretation, and clinical image and video image consumption. Enterprise viewers are being deployed as part of electronic health record implementations to present the broad spectrum of clinical imaging and multimedia content created in routine medical practice today. This white paper will describe the enterprise viewer use cases, drivers of recent growth, technical considerations, functionality differences between enterprise and specialty viewers, and likely future states. This white paper is aimed at CMIOs and CIOs interested in optimizing the image-enablement of their electronic health record or those who may be struggling with the many clinical image viewers their enterprises may employ today.

Mass unbalance is a major concern in modern day rotating machinery. For single and double disc rotor bearing systems, model based techniques have been used to identify the unbalance. The aim of this research is to predict the unbalance in a similar way for a paper machine’s tube roll using its virtual model. In this case study, the rotor is a large diameter continuous tube with thin walls and without any mounted disc. To identify the unbalance magnitude and phase, modal expansion and equivalent load minimization by least squares is used. The distributed equivalent load is sorted using two different methods. The first method considers only the isolated maximum load. In the second method, the load at all the nodes are taken into account. This first method is tested on a single disc rotor bearing system from literature and the unbalance parameters were predicted with good accuracy. However, for the paper machine’s tube roll, due to its continuous structure, the equivalent load is distributed more evenly across the rotor. Therefore the second method is able to predict the unbalance parameters with better accuracy.

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 recent Zika and Ebola virus outbreaks highlight the need for low-cost diagnostics that can be rapidly deployed and used outside of established clinical infrastructure. This demand for robust point-of-care (POC) diagnostics is further driven by the increasing burden of drug-resistant diseases, concern for food and water safety, and bioterrorism. As has been discussed in previous chapters, paper-based tests provide a simple and compelling solution to such needs.

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.

The decision to implement an orders-based versus an encounters-based imaging workflow poses various implications to image capture and storage. The impacts include workflows before and after an imaging procedure, electronic health record build, technical infrastructure, analytics, resulting, and revenue. Orders-based workflows tend to favor some imaging specialties while others require an encounters-based approach. The intent of this HIMSS-SIIM white paper is to offer lessons learned from early adopting institutions to physician champions and informatics leadership developing strategic planning and operational rollouts for specialties capturing clinical multimedia.

Temporary blockchain forks are part of the regular consensus process in permissionless blockchains such as Bitcoin. As forks can be caused by numerous factors such as latency and miner behavior, their analysis provides insights into these factors, which are otherwise unknown. In this paper we provide an empirical analysis of the announcement and propagation of blocks that led to forks of the Bitcoin blockchain. By analyzing the time differences in the publication of competing blocks, we show that the block propagation delay between miners can be of similar order as the block propagation delay of the average Bitcoin peer. Furthermore, we show that the probability of a block to become part of the main chain increases roughly linearly in the time the block has been published before the competing block. Additionally, we show that the observed frequency of short block intervals between two consecutive blocks mined by the same miner after a fork is conspicuously large. While selfish mining can be a cause for this observation, other causes are also possible. Finally, we show that not only the time difference of the publication of competing blocks but also their propagation speeds vary greatly.

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.

Traditionally, the way one evaluates the performance of an Artificial Intelligence (AI) system is via a comparison to human performance in specific tasks, treating humans as a reference for high-level cognition. However, these comparisons leave out important features of human intelligence: the capability to transfer knowledge and make complex decisions based on emotional and rational reasoning. These decisions are influenced by current inferences as well as prior experiences, making the decision process strongly subjective and apparently biased. In this context, a definition of compositional intelligence is necessary to incorporate these features in future AI tests. Here, a concrete implementation of this will be suggested, using recent developments in quantum cognition, natural language and compositional meaning of sentences, thanks to categorical compositional models of meaning.

Most visual code authentication schemes in the literature have been shown to be vulnerable to relay attacks: the attacker logs into the victim’s “account A” using credentials that the victim provides with the intent of logging into “account B”. Visual codes are not human-readable and therefore the victim cannot distinguish between the codes for A and B; on the other hand, codes must be machine-readable in order to automate the login process. We introduce a new type of visual code, the SAVVIcode, that contains an integrity-validated human-readable bitmap. With SAVVIcode, attackers have a harder time swapping visual codes surreptitiously because the integrity check prevents them from modifying or hiding the human-readable distinguisher.

Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work. Current approaches for this task rely primarily on the text of the papers and the citation network. In this paper, we propose to exploit an additional source of information, namely research knowledge graphs (KGs) that interlink research papers based on mentioned scientific concepts. Our experimental results demonstrate that the combination of information from research KGs with existing state-of-the-art approaches is beneficial. Experimental results are presented for the STM-KG (STM: Science, Technology, Medicine), which is an automatically populated knowledge graph based on the scientific concepts extracted from papers of ten domains. The proposed approach outperforms the state of the art with a mean average precision of 20.6% (+0.8) for the top-50 retrieved results.

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.

The purpose of this work is to explore the potentialities of a personalization platform in industrial settings. We report on a case study in the paper factory domain, in which the industrial aspects identified with relevant experts through interviews have been simulated and connected with a personalization platform. A first user test has been carried out with a representative set of users, which has provided useful and encouraging feedback in terms of the potentialities of the approach in industrial settings.

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.

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

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.

Generally, your paper will become available online as an “in press” pre-print within about a month after you confirm the final printer’s proofs. Depending on how unmodern and unprofessional the publisher is, it can be up to several months before the final print version of your paper becomes available. But when it does, you should read it again. At this point, it is too late to make any further corrections to the printed version, and most publishers will therefore also not alter the electronic version (except in cases of serious ethical, safety, or legal concerns). But journals can and will print a Corrigendum (your fault) or an Erratum (their fault), even for tiny details, especially if an error has meaningful implications [1–4]. So it is worth the effort to check the quality of your final paper, because that is the only version anyone else is ever going to read. Moreover, reading the final printed version (months later) may give you a different impression and/or a feel of satisfaction for your work.

While medical image analysis has seen extensive use of deep neural networks, learning over multiple tasks is a challenge for connectionist networks due to tendencies of degradation in performance over old tasks while adapting to novel tasks. It is pertinent that adaptations to new data distributions over time are tractable with automated analysis methods as medical imaging data acquisition is typically not a static problem. So, one needs to ensure that a continual learning paradigm be ensured in machine learning methods deployed for medical imaging. To explore interpretable lifelong learning for deep neural networks in medical imaging, we introduce a perspective of understanding forgetting in neural networks used in ultrasound image analysis through the notions of attention and saliency. Concretely, we propose quantification of forgetting as a decline in the quality of class specific saliency maps after each subsequent task schedule. We also introduce a knowledge transfer from past tasks to present by a saliency guided retention of past exemplars which improve the ability to retain past knowledge while optimizing parameters for current tasks. Experiments on a clinical fetal echocardiography dataset demonstrate a state-of-the-art performance for our protocols.

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

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.

The use of touch screen storybooks for children allows reading to be transformed into an interactive multimedia experience, in which text is augmented by animations, sound effects, and games. The present study is a follow-up to an earlier study [1] which found that touch screen storybooks negatively affected child readers’ comprehension but resulted in more emotional engagement. Ross et al.’s earlier study used visual observations to determine the level of emotional engagement. The current study extends those findings to examine the acoustic and prosodic indices of speech whilst children are reading. It was hypothesized that if touch screens were more emotionally engaging, this may express itself in greater pitch variability in the read speech. Also, if reading were more task-focused, then this might express in more careful (and hence more disfluent) paper-based material. Very preliminary analysis on a small selection of speech samples from 5 participants aged 6–7 years in the Ross et al. [1] study show greater pitch range variability with paper-based storybooks as compared to touch-screen interactive versions. On the other hand, there appeared to be less variation in speech and articulation rate in the paper-based books compared to touch screen books. This was also coupled by a tendency for greater overall phonation rate and an increased speech and articulation rate in the paper-based condition, which may reflect a more fluid style for paper-based book reading. Discussion of these preliminary findings focuses on the future lines of enquiry and reflections on children’s reading style using different mediums.

We apply stochastic model of citation dynamics of individual papers developed in Chap. 3 to forecast citation career of individual papers. We focus not only on the estimate of the future citations of a paper but on the probabilistic margins of such estimate as well.

Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a huge task-space for benchmarks to cover, and thus the widespread adoption of standard benchmarks has been slow, and barriers to releasing medical data exacerbate this issue. In this paper, we examine the role that publicly available data has played in MICCAI papers from the past five years. We find that more than half of these papers are based on private data alone, although this proportion seems to be decreasing over time. Additionally, we observed that after controlling for open access publication and the release of code, papers based on public data were cited over 60% more per year than their private-data counterparts. Further, we found that more than 20% of papers using public data did not provide a citation to the dataset or associated manuscript, highlighting the “second-rate” status that data contributions often take compared to theoretical ones. We conclude by making recommendations for MICCAI policies which could help to better incentivise data sharing and move the field toward more efficient and reproducible science.

This paper intends to review the reasons for the retraction over the last decade. The paper particularly aims at reviewing these reasons with reference to computer science field to assist authors in comprehending the style of writing. To do that, a total of 36 retracted papers found on the Web of Science within Jan 2007 through July 2017 are explored. Given the retraction notices which are based on 10 common reasons, this paper classifies the two main categories, namely, random and non-random retraction. Retraction due to the duplication of publications scored the highest proportion of all other reasons reviewed.

This paper describes a study comparing the use of paper strips with virtual flight strips depicted on a new user interface, the Ramp Traffic Console (RTC), designed for use by ramp controllers to be used in place of paper strips. A Human-In-the-Loop (HITL) experiment was performed as the fifth in a series of six HITL simulation studies designed to evaluate a pushback Decision Support Tool (DST) concept for Charlotte Douglas International Airport (CLT). Workload and usability were assessed in post-run and post-study questionnaires. In the RTC virtual flight strip condition, post-run questionnaire results show lower workload ratings across all aspects of workload; additionally, a trend is found toward increased usability ratings. Post-study questionnaire results indicate a preference for RTC over paper strips. Additional research is suggested with more training runs and a greater number of participants to increase statistical power. It is also suggested that this new technology be re-evaluated as a part of the ATD-2 field testing activities.

In this paper, we start by analyzing the presence of grey literature (GL) references in the bibliographies of different article-types using an extract of 122,406 articles from the ACM Digital Library. GL articles accounted for about 16% of the overall references with highest presence in proceedings (17.61%). Boosting techniques for promoting GL references are proposed for information retrieval systems for two specific scenarios. A simple IR experiment was conducted with 103,739 articles to validate the proposed boosting techniques with 10 research topics. Results show that GL references were consistently pushed to the top of the search results along with an increased visibility in top 20 results. NRF (Natl Research Foundation, S’pore) Accepted version

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.

Hardware and software lightweight solutions became the mainstream for current and future emerging technologies. Container-based virtualization provides more efficient and faster solutions than traditional virtual machines, offering good scalability, flexibility, and multi-tenancy. They are capable of serving in a heterogeneous and dynamic environment across multiple domains, including IoT, cloud, fog, and multi-access edge computing. In this paper, we propose a lightweight solution for LCC (Live Container Cloud) that permits the user to access live/remote cloud resources faster. LCC can be embedded as a fog/edge node to permit the users to allocate and deallocate cloud resources. The performance of such a containerization technology is presented.

In this paper we consider the quantum teleportation and entanglement swapping protocols used in quantum based networks for passing information between a sender and receiver. For the teleportation protocol we observe and identify relationships that exist between Einstein-Podolsky-Rosen (EPR) Bell states employed as quantum resources, measured sender values and the gates employed at the receiver side. For the entanglement swapping protocol we consider input and output EPR states and the relationship between the two. We include a review of the concepts and our findings from the analysis carried out.

ZJUNlict became the Small Size League Champion of RoboCup 2019 with 6 victories and 1 tie for their 7 games. The overwhelming ability of ball-handling and passing allows ZJUNlict to greatly threaten its opponent and almost kept its goal clear without being threatened. This paper presents the core technology of its ball-handling and robot movement which consist of hardware optimization, dynamic passing and shooting strategy, and multi-agent cooperation and formation. We first describe the mechanical optimization on the placement of the capacitors, the redesign of the damping system of the dribbler and the electrical optimization on the replacement of the core chip. We then describe our passing point algorithm. The passing and shooting strategy can be separated into two different parts, where we search the passing point on SBIP-DPPS and evaluate the point based on the ball model. The statements and the conclusion should be supported by the performances and log of games on Small Size League RoboCup 2019.

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.

In multi-label classification a datapoint can be labelled with more than one class at the same time. A common but trivial approach to multi-label classification is to train individual binary classifiers per label, but the performance can be improved by considering associations between the labels, and algorithms like classifier chains and RAKEL do this effectively. Like most machine learning algorithms, however, these approaches require accurate hyperparameter tuning, a computationally expensive optimisation problem. Tuning is important to train a good multi-label classifier model. There is a scarcity in the literature of effective multi-label classification approaches that do not require extensive hyperparameter tuning. This paper addresses this scarcity by proposing CascadeML, a multi-label classification approach based on cascade neural network that takes label associations into account and requires minimal hyperparameter tuning. The performance of the CasecadeML approach is evaluated using 10 multi-label datasets and compared with other leading multi-label classification algorithms. Results show that CascadeML performs comparatively with the leading approaches but without a need for hyperparameter tuning.

This papers aims to describe the historical introduction of paper prototyping techniques into interface development, what they basically consist of, and how such techniques can be applied on the development of Computer Digital Arts. With some examples applied on Digital Arts graduation courses at the Federal University of Minas Gerais in Brazil, this technique has proven to help the development of the artist’s intentionality, rather than blocking creativity. It helped students to focus on important concepts that needed to be transmitted by interaction, while usability made the piece friendly where it needed to be friendly. Thus, young art students have been acting in a traditional way when experimenting with materials, not only physically and visually, but also with the interactive environment that should be considered as material in Computer Digital Arts.

This article describes the designing process of an interactive visualization at BMW. Paper prototyping is introduced as a part of the user-centered-design process and put into practice as a novel method for software development. During the development process paper prototypes have been used as a tool for communicating and testing within the interdisciplinary team of operational experts, visualization experts, designers and software developers. In conclusion the integration of paper prototyping benefits the design process positively. The interactive visualization meets the expectations of the user.