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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

Electryo is a paper-based voting protocol that implements the Selene mechanism for individual verifiability. This short paper aims to provide the first formal model of Electryo, with security proofs for vote-privacy and individual verifiability. In general, voting protocols are complex constructs, involving advanced cryptographic primitives and strong security guarantees, posing a serious challenge when wanting to analyse and prove security with formal verification tools. Here we choose to use the \({{\,\mathrm{\textsc {Tamarin}}\,}}\) prover since it is one of the more advanced tools and is able to handle many of the primitives we encounter in the design and analysis of voting protocols.

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.

We demonstrate that XOR Arbiter PUFs with an even number of arbiter chains have inherently biased responses, even if all arbiter chains are perfectly unbiased. This rebukes the believe that XOR Arbiter PUFs are, like Arbiter PUFs, unbiased when ideally implemented and proves that independently manufactured Arbiter PUFs are not statistically independent.

The paper first examines the contours of artificial intelligence (AI) at its beginnings, more than sixty years ago, and points out the important place that machine learning already had at that time. The ambition of AI of making machines capable of performing any information processing task that the human mind can do, means that AI should cover the two modes of human thinking: the instinctive (reactive) one and the deliberative one. This also corresponds to the difference between mastering a skill without being able to articulate it and holding some pieces of knowledge that one can use to explain and teach. In case a function-based representation applies to a considered AI problem, the respective merits of learning a universal approximation of the function vs. a rule-based representation are discussed, with a view to better draw the contours of AI. Moreover, the paper reviews the relative positions of knowledge and data in reasoning and learning, and advocates the need for bridging the two tasks. The paper is also a plea for a unified view of the various facets of AI as a science.

Game-based learning represents a promising alternative to teach computing in higher education. This paper presents “BDD Assemble!”, a paper-based game proposal for teaching Behavior Driven Development (BDD) competences. For this, the proposed game and the evaluation approach with software engineering students are described. As a result, a simple, interactive and colaborative game was provided, able to teach BDD concepts in a practical, competitive and fun way.

SaaS (Software-as-a-Service) is a cloud computing model, which is sometimes referred to as “on-demand software”. Existing SaaS platforms are investigated before building new distributed SaaS platform. The service data mining and evaluation on existing SaaS platforms improve our new SaaS platform. For SaaS that provide various APIs, we analysis their website data in this paper by our data mining method and related software. We wrote a crawler program to obtain data from these websites. The websites include Baidu API and ProgrammableWeb API. After ETL (Extract-Transform-Load), the obtained and processed data is ready to be analyzed. Statistical methods including non-linear regression and outlier detection are used to evaluate the websites performance, and give suggestions to improve the design and development of our API website. All figures and tables in this paper are generated from IBM SPSS statistical software. The work helps us improve our own API website by comprehensively analyzing other successful API websites.

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.

Over the last decade, huge number of time series stream data are continuously being produced in diverse fields, including finance, signal processing, industry, astronomy and so on. Since time series data has high-dimensional, real-valued, continuous and other related properties, it is of great importance to do dimensionality reduction as a preliminary step. In this paper, we propose a novel online segmentation algorithm based on the importance of TPs to represent the time series into some continuous subsequences and maintain the corresponding local temporal features of the raw time series data. To demonstrate the advantage of our proposed algorithm, we provide extensive experimental results on different kinds of time series datasets for validating our algorithm and comparing it with other baseline methods of online segmentation.

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.

Smart applications are appealing an accurate matching between users and items, in which recommendation technologies are applied widely. Since recommendation serve for two roles, namely users and items, accuracy is not the only focus, the diversification and fairness should also be paid more attention for improving recommendation performance. The tradeoff among the accuracy, diversification and fairness on recommendation is bringing a big challenge. This paper proposed a novelty recommendation model to ensure the recommendation performance, which introduces a multi-variate linear regression model to cooperate with the collaborative filtering method. This study utilizes an improved similarity metrics to discover the closeness between users and item categories under the help of the collaborative filtering methods, and exploits the micro attribute information of items by a multi-variate linear regression model to decide the final recommended items. The experimental results show that our proposed method can provide better recommendation accuracy, diversification and fairness than the recommendation based on pure collaborative filtering method.

With the development of the Internet and smart phone, mobile data sharing have been attracted many researcher’s attentions. In this paper, we investigate the mobile data sharing problem in mobile crowdsensing. There are a large number of users, each user can be a mobile data acquisition, or can be a mobile data sharing, the problem is how to optimal choose users to collaborative sharing their idle mobile data to others. We consider two data sharing models, One-to-Many and Many-to-Many data sharing model when users share their mobile data. For One-to-Many model, we propose an OTM algorithm based on the greedy algorithm to share each one’s data. For Many-to-Many model, we translate the problem into the stable marriage problem (SMP), and we propose a MTM algorithm based on the SMP algorithm to solve this problem. Experimental results show that our methods are superior to the other approaches.

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.

Recently, Collective Spatial Keyword Querying (CoSKQ), which returns a group of objects that cover a set of given keywords collectively and have the smallest cost, has received extensive attention in spatial database community. However, no research so far focuses on a situation when the result of CoSKQ is taken as the input of a query. But this kind of query has many applications in location based services. In this paper, we introduce a new problem Reverse Collective Spatial Keyword Querying (RCoSKQ) that returns a region, in which the query objects are qualified objects with the highest spatial and textual similarity. We propose an efficient method which uses IR-tree to retrieve objects with text descriptions. To accelerate the query process, a pruning method that effectively reduces computing is proposed. The experiments over real and synthesis data sets demonstrate the efficiency of our approaches.

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.

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152.8 million clock cycles, which is about 29.03% faster than that of Meyer et al.

The clustering coefficient has been widely used for identifying the local structure of networks. In this paper, the weighted spectral distribution with 3-cycle (WSD3) that is similar (but not equal) to the clustering coefficient is studied on evolving social networks. It is demonstrated that the ratio of the WSD3 to the network size (i.e., the node number) provides a more sensitive discrimination for the size-independent local structure of social networks in contrast to the clustering coefficient. Moreover, the difference of the WSD3’s performances on social networks and communication networks is investigated, and it is found that the difference is induced by the different symmetrical features of the normalized Laplacian spectral densities on these networks.

Highly influential social users can guide public opinion and influence their emotional venting. Therefore, it is of great significance to identify high-impact users effectively. This paper starts with the users’ text content, users’ emotions, and fans’ behaviors. It combines the amount of information in the content and sentiment tendency with the fans’ forwarding, commenting, and Liking actions. And based on the principle of the three-degree influence, the users’ influence calculation model is constructed. Finally, the experimental results show that the three-degree force calculation model is more accurate and effective than other similar models.

Cyber-physical systems (CPSs) integrate programmable computing and communication capabilities to traditional physical environments. The use of SCADA (Supervisory Control And Data Acquisition) technologies to build such a new generation of CPSs plays an important role in current critical national-wide infrastructures. SCADA-driven CPSs can be disrupted by cyber-physical attacks, putting at risk human safety, environmental regulation and industrial work. In this paper, we address the aforementioned issues and provide a discussion on the mitigation techniques that aim to optimize the recovery response when a SCADA-driven CPS is under attack. Our discussion paves the way for novel cyber resilience techniques, focusing on the programmable computing and communication capabilities of CPSs, towards new research directions to tolerate cyber-physical attacks.

The EU General Data Protection Regulation (GDPR) recognizes data subject’s consent as a legitimate ground of data processing. At present, consent mechanisms in OSNs are either non-existent or not GDPR compliant. While the absence of consent means a lack of control of the OSN user (data subject) on his personal data, non-compliant consent mechanisms can give them a false sense of control, encouraging them to reveal more personal data than they would have otherwise. GDPR compliance is thus the only way to obtain meaningful consents, thereby protecting user privacy. In this paper, we discuss the characteristics of valid consent as per the GDPR, analyze the present status of consent in OSNs and propose some research directions to arrive at GDPR compliant consent models acceptable to users and OSN providers (data controller). We observe that evaluating privacy risks of consents to data processing activities can be an effective way to help users in their decision to give or refuse consents and hence is an important research direction.

WebView is commonly used by applications on the Android OS. Given that WebView is used as a browsing component on applications, they can be attacked via the web. Existing security mechanisms mainly focus on web browsers; hence, securing WebView is an important challenge. We proposed and implemented a method for preventing suspicious web access in Android WebView. Attackers distribute their malicious content including malicious applications, potentially unwanted programs, and coin miners, by inserting contents into a web page. Because loading malicious content involves HTTP communication, our proposed method monitors HTTP communication by WebView and blocks suspicious web accesses. To apply the proposed method to widely used applications, we implemented our method inside WebView. We also evaluated the proposed method with some popular applications and confirmed that the method can block designated web content without impeding the functionality of applications.

Patent terminology relation extraction is of great significance to the construction of patent Knowledge graph. In order to solve the problem of long-distance dependency in traditional depth learning, a new method of patent terminology relation extraction is proposed, which combines attention mechanism and bi-directional LSTM model and with keyword strategy. Category keyword features in each sentence obtained by the improved TextRank with the patent text information vectorization added. BiLSTM neural work and attention mechanism are employed to extract the temporal information and sentence-level global feature information. Moreover, pooling layer is added to obtain the local features of the text. Finally, we fuse the global features and local features, and output the final classification results through the softmax classifier. The addition of category keywords improves the distinction of categories. Substantial experimental results demonstrate that the proposed model outperform the state-of-art neural model in patent terminology relation extraction.

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.

There is a large number of high-profile cyberattacks identified in the year of 2017, i.e., Ransomware attacks are one of the areas of cybercrime growing the fastest. These increasingly sophisticated cyberattacks are forcing various organisations to face security challenges and invest money building security and trust models. There will also be an increase in the use of recent development of security solutions that can help improve the detection performance and react to malicious events. In this position paper, we mainly introduce recent development trends in cybersecurity, including legal issues (e.g., GDPR), Artificial intelligence (AI), Mobile security and Internet of Things.

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.

In disaster situations, people try to communicate with acquaintances for a variety of reasons. In general, they try to immediately communicate with family or important friends to confirm their safety. To understand the damage situation, they may try to communicate with neighbors whom they don’t often communicate with in daily life. This paper introduces the results of surveys of people who experienced the Great East Japan Earthquake, 2011 and the Kumamoto Earthquake, 2016 in Japan to discover what communication modalities were used and why they were chosen.

The task of keyphrase generation aims to generate the key phrases that capture the primary content of a document. An external domain-specific gazetteer can assist in generating keyphrases that are literally absent in the document (i.e., do not match any contiguous sub-sequence of source text) but relevant to the content of the document. In this paper, we present a technique to integrate knowledge from a gazetteer in order to improve keyphrase generation from research papers. We also present a copy mechanism that helps our model to utilize the gazetteer vocabulary to deal with the out-of-vocabulary words in keyphrases. Since constructing and maintaining relevant high-quality gazetteer by hand is very expensive, we also propose a method for automatic construction of a gazetteer given the input document, by leveraging similar documents in the training corpus. The thus constructed gazetteer helps focus on corpus-level information carried by other similar documents. Although this external information is crucial, it is never considered in previous studies. Experiments on real world datasets of research papers demonstrate that our proposed approach improves the performance of the state-of-the-art keyphrase generation models.

This paper shows the automatic classification of research papers published in Scopus. Our classification is based on the research lines of the university. We apply the K-nearest neighbor classifier and linear discriminant analysis (LDA). Various stages were used from information gathering, creating the vocabulary, pre-processing and data training, and supervised classification in this work. The experiment involved 596 research articles published in SCOPUS from 2003–2017. The results show an overall accuracy of 88.44%.

This paper shares our experience in developing information literacy skills through a framework based on writing papers in the IEEE format conference. The framework gave students, in an active learning style, a set of activities to identify the need for information, procure the information, evaluate the information and subsequently revise the strategy for obtaining the information, and to use it in an ethical manner to produce a technical paper. We followed the ACRL’s Information Literacy Competency Standards for Higher Education in the development of an assessment tool, course content, and exercises. Initial results show that the proposed framework developed information literacy skills in a maturing stage. We believe that this experience and the design of the framework could be replicated or adapted to different Engineering Careers.

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).

There have been a massive number of conferences and journals in computer science that create a lot of difficulties for scientists, especially for early-stage researchers, to find the most suitable venue for their scientific submission. In this paper, we present a novel approach for building a paper submission recommendation system by using two different types of embedding methods, GloVe and FastText, as well as Convolutional Neural Network (CNN) and LSTM to extract useful features for a paper submission recommendation model. We consider seven combinations of initial attributes from a given submission: title, abstract, keywords, title + keyword, title + abstract, keyword + abstract, and title + keyword + abstract. We measure these approaches’ performance on one dataset, presented by Wang et al., in terms of top K accuracy and compare our methods with the S2RSCS model, the state-of-the-art algorithm on this dataset. The experimental results show that CNN + FastText can outperform other approaches (CNN + GloVe, LSTM + GloVe, LSTM + FastText, S2RSCS) in term of top 1 accuracy for seven types of input data. Without using a list of keywords in the input data, CNN + GloVe/FastText can surpass other techniques. It has a bit lower performance than S2RSCS in terms of the top 3 and top 5 accuracies when using the keyword information. Finally, the combination of S2RSCS and CNN + FastText, namely S2CFT, can create a better model that bypasses all other methods by top K accuracy (K = 1,3,5,10).

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. \(\varTheta (n/k)\).

In this paper (This is a short paper accepted in the new ideas and work-in-progress section of SEFM 2017.) we introduce a technique to improve the efficiency of SAT calls in Bounded Model Checking (BMC) problems. The proposed technique is based on exploiting interpolation-based invariants as redundant constraints for BMC.

Domestic robots are vulnerable to hi-jacking and industrial robots are vulnerable to cyber-attacks. This paper proposes the integration of a security component into a robots’ system to minimise security risks. This objective is achieved through the inclusion of several monitors such as functional monitor, communication monitor and behavioural monitor, which assess the internal operations of the system at low levels of operation. Through this approach, the paper proposes a novel framework which will make it hard for robots to be hi-jacked or, at the very least, make it more difficult for attacks on their behaviour.

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.

In this paper we position Linear Time Temporal Logic (LTL), structural operational semantics (SOS), and a graphical generalization of BNF as central DSLs for program analysis and verification tasks in order to illustrate the impact of language to the mindset: (1) Specifying program analyses in LTL changes the classical algorithmic ‘HOW’ thinking into a property-oriented ‘WHAT’ thinking that allows one to logically combine analysis goals and eases proofs. (2) Playing with the original store component in SOS configurations allows one to elegantly realize variants of abstract program interpretations, and to align different aspects, like e.g., the symbolic values of variables and path conditions. (3) Specializing languages by refining their BNF-like meta models has the power to lift certain verification tasks from the program to the programming language level. We will illustrate the advantages of the change of mindset imposed by these three DSLs, as well as the fact that these advantages come at low price due to available adequate generator technology.

We discuss a new approach to interactive exploration of high-dimensional data sets which is aimed at building human’s understanding of the data by iterative additions of recommended attributes and objects that can together represent a context in which it may be useful to analyze the data. We identify challenges and expected benefits that our methodology can bring to the users. We also show how our ideas got inspired by Formal Concept Analysis (FCA) and Rough Set Theory (RST). It is though worth emphasizing that this particular paper is not aimed at investigating relationships between FCA and RST. Instead, the goal is to discuss which algorithmic methods developed within FCA and RST could be reused for the purpose of our approach.

Machine learning is widely used in malware detection systems as a core component. However, machine learning algorithm is based on the assumption that the underlying malware concept is stable for training and testing. The assumption is vulnerable to well-crafted concept drift attacks, such as mimicry attacks, gradient descent attacks, poisoning attacks and so on. This paper proposes an ensemble learning system which combines vertical and horizontal correlation learning models. The significant diversity among vertical and horizontal correlation models increases the difficulty of concept drift attacks. And average p-value assessment is applied to fortify the system to be sensitive to hidden concept drift. The experiment results show that the hybrid system could actively recognize the concept drift among different Miuref variants.

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.

There is a problem with the way we reason about problems in security. The justifications that we offer for many security measures reduce to unfalsifiable claims or circular statements. This position paper argues that reliance on less-than-solid arguments acts as a brake on progress in security.

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.

Location privacy has drawn much attention among mobile social network users, as the geo-location information can be used by the adversaries to launch localization attacks which focus on finding people’s sensitive locations such as home and office place. In this paper, we propose a community based information sharing scheme to help the users to protect their home locations. First, we study the existing home location prediction algorithms and conclude that they are all mainly based on the spatial and temporal features of the check-in data. Then we design the community based information sharing scheme which aggregates the check-ins of all community members, thus change the overall spatial and temporal features. Finally, our simulation results validate that our proposed scheme greatly reduces the home location predication accuracy and therefore can protect the user’s privacy effectively.

The goal of anti-phishing techniques is to reduce the delivery rate of phishemails, and anti-phishing training aims to decrease the phishing click-through rates. This paper presents the X-Platform Phishing Attack, a deceptive phishing attack with an alarmingly high delivery and click-through rates, and highlights a subclass of phishing attacks that existing anti-phishing methods do not seem to be able to address. The main characteristic of this attack is that an attacker is able to embed a malicious link within a legitimate message generated by service providers (e.g., Github, Google, Amazon) and sends it using their infrastructure to his targets. This technique results in the bypassing of existing anti-phishing filters because it utilizes reputable service providers to generate seemingly legitimate emails. This also makes it highly likely for the targets of the attack to click on the phishing link as the email id of a legitimate provider is being used. An X-Platform Phishing attack can use email-based messaging and notification mechanisms such as friend requests, membership invitations, status updates, and customizable gift cards to embed and deliver phishing links to their targets. We have tested the delivery and click-through rates of this attack experimentally, based on a customized phishing email tunneled through GitHub’s pull-request mechanism. We observed that 100% of X-Platform Phishing emails passed the anti-phishing systems and were delivered to the inbox of the target subjects. All of the participants clicked on phishing messages, and in some cases, forwarded the message to other project collaborators who also clicked on the phishing links.

Co-citation analysis can be exploited as a bibliometric technique used for mining information on the relationships between scientific papers. Proposed methods rely, however, on co-citation counting techniques that slightly take the semantic aspect into consideration. The present study proposes a new technique based on the measure of Semantic Similarity (SS) between the titles of co-cited papers. Several computational measures rely on knowledge resources to quantify the semantic similarity, such as the WordNet «is a» taxonomy. Our proposal analyzes the SS between the titles of co-cited papers using word-based SS measures. Two major analytical experiments are performed: the first includes the benchmarks designed for testing word-based SS measures; the second exploits the dataset DBLP (DBLP: Digital Bibliography & Library Project.) citation network. As a result, we found the SS measures behave the same as human judgement for the lexical similarity and can be consequently used for the automatic assessment of similarity between co-cited papers. The analysis of highly repeated co-citations demonstrates that the different SS measures display almost similar behaviours, with slight differences due to the distribution of the provided SS values. Furthermore, we note a low percentage of similar referred papers into the co-citations.

Technological solutions have been playing an important role in health care provision and their implementation in social care area, though a bit slower, promises good outcomes in terms of the quality of care delivery. The study reported in this paper analysed the shifting process from a typical paper registration to a registration performed by a web-based platform (i.e., the Ankira® platform) in a Portuguese social care institution. In order to gather evidence and best practices, 13630 registries were analysed, 6390 in paper format and 7240 from the web-based platform. The results show that the shifting from paper to the web-based platform lead to a significant error rate decline in all type of registers, which demonstrates the significant impact of the introduction of information technologies to support social care provision.

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.

The overabundance of academic papers makes it difficult for researchers to find relevant or interested papers. To address the problem, existing studies have developed many approaches to recommend academic papers effectively. Most of them mainly utilize content-based filtering or citation analysis to measure similarity or relatedness of two papers and recommend relevant papers to the given query. However, these recommended papers are usually discrete from each other, i.e., the relationship between recommended papers are omitted, which disables researchers from having an sight into the time-oriented development of the topic they are interested in. To overcome the drawbacks of existing work, we propose a novel academic paper recommendation method called PAPR (Path-based Academic Paper Recommendation). Our method aims to recommend an ordered path of relevant papers, which are of great benefit in helping researchers understand the development of a specific topic. During process, we take both content and network structure into account to learn the representation of a paper. Next, the similarity between papers are measured based on the representation. The experimental results based on real data show that the proposed method outperforms the state-of-art methods.