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

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 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.

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.

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.

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.

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.

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 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.

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

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.

Context and motivation. Collaborative AI systems aim at working together with humans in a shared space. Building these systems, which comply with quality requirements, domain specific standards and regulations is a challenging research direction. This challenge is even more exacerbated for new generation of systems that leverage on machine learning components rather than deductive (top-down programmed) AI.

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.

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.

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.

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.

This paper is a plea for developing possibilistic learning methods that would be consistent with if-then rule-based reasoning. The paper first recall the possibility theory-based handling of cascading sets of parallel if-then rules. This is illustrated by an example describing a classification problem. It is shown that the approach is both close to a possibilistic logic handling of the problem and can also be put under the form of a max-min-based matrix calculus describing a function underlying a structure somewhat similar to a max-min neural network. The second part of the paper discusses how possibility distributions can be obtained from precise or imprecise statistical data, and then surveys the few existing works on learning in a possibilistic setting. A final discussion emphasizes the interest of handling learning and reasoning in a consistent way.

The amount of data that produced is increased day after day especially data as a text, so with this massive production it would be difficult to analyze or extract information to discover the patterns from the unstructured text. Text mining is used for availing the massive amount of knowledge that is in the text and deriving high quality information from the text automatically. This Process would save effort and time. Text mining considered as a subset of data mining where data mining is more generic. This paper proposes a methodology of mining a text for a case study related to publication papers. Some of text mining approaches will be introduced for mining the publication papers using machine learning (ML) and natural language processing (NLP) techniques. Describing each phase as following: First phase is keywords extraction using natural language processing techniques, second phase named entity recognition and last phase is document classification. The last two phases are using the ML techniques. Then a case study is built to simulate the system phases, showing what is the input and the output in each phase.

This paper presents the development of a test paper checker that will recognize a handwritten text using Intelligent Character Recognition (ICR) for Alphanumeric Characters. An examination can be conducted in two ways—digital and manual—and each way has a different approach in checking. In this study, the main objective is to recognize alphanumeric handwritten characters accurately using intelligent character recognition. OpenCV is used in the Python programming language and Support Vector Machine as a tool in machine learning for ICR. Answer sheet was designed with 120 items for MCQ and problem-solving questions. Item analysis and printing of results are included in the device. Experiments were conducted by giving an actual examination from the 131 participants in Technological University of the Philippines for testing the accuracy of the device. The results obtained from comparing manual and machine checking had an accuracy of 93.0769%. Thus, the proposed method is applicable for the development of handwritten character recognition.

Paper manufacturing is energy demanding and improved modelling of the pulp bleach process is the main non-invasive means of reducing energy costs. In this paper, time it takes to bleach paper pulp to desired brightness is examined. The model currently used is analysed and benchmarked against two machine learning models Random Forest and TreeBoost. Results suggests that the current model can be superseded by the machine learning models and it does not use the optimal compact subset of features. Despite the differences between the machine learning models, a feature ranking correlation has been observed for the new models. One novel, yet unused, feature that both machine learning models found to be important is the concentration of bleach agent.

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.

Apps in the FINANCE category constitute approximately 2% of the 2,000,000 apps in the Google Play Store. These apps handle extremely sensitive data, such as online banking credentials, budgets, salaries, investments and the like. Although apps are automatically vetted for malicious activity before being admitted to the Google Play Store, it remains unclear whether app developers themselves check their apps for vulnerabilities before submitting them to be published. Additionally, it is not known how financial apps compare to other apps in terms of dangerous permission usage or how they evolve as they are updated. We analyse 10,400 apps to understand how apps in general and financial apps in particular have evolved over the past two years in terms of dangerous permission usage and the vulnerabilities they contain. Worryingly, we discover that both financial and non-financial apps are getting more vulnerable over time. Moreover, we discover that while financial apps tend to have less vulnerabilities, the rate of increase in vulnerabilities in financial apps is three times as much as that of other apps.

There are several frameworks for password-based authenticated key exchange (PAKE) protocols with common reference string following the work of Katz, Ostrovsky and Yung (Eurocrypt’01), and it seems that the IND-CCA secure encryption is inevitable when constructing PAKE in standard model.

Double-spending attacks on fast payments are one of the fatal architectural problems in Cryptocurrencies. Dmitrienko et al. proposed an offline fast payment scheme that relies on tamper-proof wallets produced by trustworthy manufacturers. With the wallets, the payee can immediately trust the transactions generated by the wallets without waiting for their registration to the blockchain. Secure coin-preloading to the wallet is important, while illegal coin-preloading can cause over/double-spending by the trusted wallets. For this, they proposed an interesting protocol that makes use of a fragment of the main blockchain to prove to the wallets the legitimacy of preloaded coins. One drawback is that, in proving that the fragment are from honest miners, their protocol requires a trusted online time-stamp server so that the wallets can verify the timestamps to see if the blocks in the fragment is mined with sufficiently large amount of computing resources. Otherwise, it sacrifices usability. In order to eliminate such an online trustee, in this paper we took the opposite approach that the payee (not the wallets) verifies the legitimacy of preloaded coins at the time of offline payment. As a consequence, our result shows that, with light-weight tamper-proof wallets, completely decentralized offline payment is possible without any modification to the existing Bitcoin network.

Physically unclonable function (PUF) is a technology to generate a device-unique identifier using process variation. PUF enables a cryptographic key that appears only when the chip is active, providing an efficient countermeasure against reverse-engineering attacks. In this paper, we explore the data conversion that digitizes a physical quantity representing PUF’s uniqueness into a numerical value as a new attack surface. We focus on time-to-digital converter (TDC) that converts time duration into a numerical value. We show the first signal injection attack on a TDC by manipulating its clock, and verify it through experiments on an off-the-shelf TDC chip. Then, we show how to leverage the attack to reveal a secret key protected by a PUF that uses a TDC for digitization.

In this paper, we report a study of an innovative mobile application to support grading paper-based programming exams. We call the app – Programming Grading Assistant (PGA). It scans pre-generated QR-codes of paper-based question-and-concepts associations and uses OCR to recognize handwritten answers. PGA provides interfaces for teachers to calibrate recognition results, as well as to adjust partial credit assignment according to conceptual incorrectness of the answers. We evaluate the mobile grading process and the quality of grading results based on the assessed semantic information. The results demonstrate that the mobile grading approach keeps persistent traces of students’ performance, including semantic feedback and ultimately enhances grading consistency.

Web of Things (WoT) is the most promising application model of Internet of Things (IoT). Current IoT systems urgently need extendibility and loose coupling, which are easily provided by WoT. However, some concerns about WoT security have been raised by academic researchers as well as industrial engineers. This paper provides a review of WoT literature especially on security issues. Moreover, this paper proposes an architecture that regards smart gateways as ideal devices to achieve WoT security. Smart gateways are classified into five types, and security functions are suggested for each type.

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.

A computing policy is a sequence of rules, where each rule consists of a predicate and a decision, and where each decision is either “accept” or “reject”. A policy P is said to accept (or reject, respectively) a request iif the decision of the first rule in P, that matches the request is “accept” (or “reject”, respectively). Examples of computing policies are firewalls, routing policies and software-defined networks in the Internet, and access control policies. A policy P is called adequate iff P accepts at least one request. It has been shown earlier that the problem of determining whether a given policy is adequate (called the policy adequacy problem) is NP-hard. In this paper, we present an efficient algorithm that use SAT solvers to solve the policy adequacy problem. Experimental results show that our algorithm can determine whether a given policy with 90 K rules is adequate in about 3 min.

This paper proposes a method to anonymize network trace data by utilizing a novel perturbation technique that has strong privacy guarantee and at the same time preserves data utility. The resulting dataset can be used for security analysis, retaining the utility of the original dataset, without revealing sensitive information. Our method utilizes a condensation based approach with strong privacy guarantees, suited for cloud environments. Experiments show that the method performs better than existing anonymization techniques in terms of privacy-utility trade off, and it surpasses existing techniques in attack prediction accuracy.

Physical attacks are a serious threat to the Internet of Things devices. Differential power analysis attacks are the most well-known physical attacks that exploit physical information leaked from hardware devices to retrieve secret information. Fault analysis attacks, a type of physical attack, are often considered more powerful than side-channel attacks if an attacker can inject the attacker’s intended faults. In fact, a few times of fault injections have enabled the attacker to retrieve the secret key. In this study, we propose a new model to validate the resistance of block ciphers to Differential Fault Analysis (DFA) attacks by assuming an ideal block cipher in which the differential probability is the same for all input and output differences. We show that Advanced Encryption Standard (AES) is near ideal for DFA attack resistance according to the experimental results.

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.

International audience; We study unlimited infinite churn in peer-to-peer overlay networks. Under this churn, arbitrary many peers may concurrently request to join or leave the overlay network; moreover these requests may never stop coming. We prove that unlimited adversarial churn, where processes may just exit the overlay network, is unsolvable. We focus on cooperative churn where exiting processes participate in the churn handling algorithm. We define the problem of unlimited infinite churn in this setting. We distinguish the fair version of the problem, where each request is eventually satisfied, from the unfair version that just guarantees progress. We focus on local solutions to the problem, and prove that a local solution to the Fair Infinite Unlimited Churn is impossible. We then present our algorithm UIUC that solves the Unfair Infinite Unlimited Churn Problem for a linearized peer-to-peer overlay network. We extend this solution to skip lists and skip graphs.

Gesture recognition is one of the most popular tasks in computer vision, where convolutional neural networks (CNNs) based method has obtained the state-of-the-art performance. However, it is generally acknowledged that CNNs need a large amount of data to achieve such performance. Event Camera is a kind of biologically inspired event-based camera, which can keep the information of moving objects and remove the redundant background data. In this paper, we set up a rock-paper-scissor hand gesture recognition system based on FlowNet and Event Camera. Event camera is used to acquire event data. Then we propose an algorithm for the proposed gesture recognition. Specifically, FlowNet2.0 is employed to extract the motion representation of the pre-processed visual data, and a CNN classification network is applied to recognize the symbols extracted according to the motion representation. As a comparison, we also apply the rock-paper-scissor gesture recognition algorithm on traditional camera. The experimental results show that the proposed system based on Event Camera gets better performance, and to some extent, weaken the dependence on the training data. The whole system achieves 94.0\(\%\) out-of-sample accuracy and allows computation at up to 30 fps.

Scientific papers are important for scholars to track trends in specific research areas. With the increase in the number of scientific papers, it is difficult for scholars to read all the papers to extract emerging or noteworthy knowledge. Paper modeling can help scholars master the key information in scientific papers, and relation classification (RC) between entity pairs is a major approach to paper modeling. To the best of our knowledge, most of the state-of-the-art RC methods are using entire sentence’s context information as input. However, long sentences have too much noise information, which is useless for classification. In this paper, a flexible context is selected as the input information for convolution neural network (CNN), which greatly reduces the noise. Moreover, we find that entity type is another important feature for RC. Based on these findings, we construct a typical CNN architecture to learn features from raw texts automatically, and use a softmax function to classify the entity pairs. Our experiment on SemEval-2018 task 7 dataset yields a macro-F1 value of 83.91%, ranking first among all participants.

E-commerce customers and general Web users should not believe that the products suggested by Recommender systems or results displayed by Web search engines are either complete or relevant to their search aspirations. The economic priority of Web related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers; furthermore, Web search engines and recommender systems revenue is obtained from advertisements and pay-per-click. This survey paper presents a review of Web Search Engines, Ranking Algorithms, Citation Analysis and Recommender Systems. In addition, Neural networks and Deep Learning are also analyzed including their use in learning relevance and ranking. Finally, this survey paper also introduces the Random Neural Network with its practical applications.

Near infrared (NIR) spectroscopy is used in many applications to gather information about chemical composition of materials. For paper waste sorting, a small number of scores computed from NIR-spectra and assuming more or less unimodal clustered data, a pixel classifier can still be crafted by hand using knowledge about chemical properties and a reasonable amount of intuition. Additional information can be gained by visual data (VIS). However, it is not obvious what features, e.g. based on color, saturation, textured areas, are finally important for successfully separating the paper classes in feature space. Hence, a rigorous feature analysis becomes inevitable. We have chosen a generic machine-learning approach to successfully fuse NIR and VIS information. By exploiting a classification tree and a variety of additional visual features, we could increase the recognition rate to 78% for 11 classes, compared to 63% only using NIR scores. A modified feature ranking measure, which takes redundancies of features into account, allows us to analyze the importance of features and reduce them effectively. While some visual features like color saturation and hue showed to be important, some NIR scores could even be dropped. Finally, we generalize this approach to analyze raw NIR-spectra instead of score values and apply it to plastic waste sorting.

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.

Based on the ZYNQ platform, this paper proposes a hardware/software co-design approach, and implements a binocular stereo vision system with high real-time performance and good human-computer interaction, which can be used to assist advanced driver assistance systems to improve driving safety. Combining the application characteristics of binocular stereo vision, the approach firstly modularizes the system’s functions to perform hardware/software partitioning, accelerates the data processing on FPGA, and performs the data control on ARM cores; then uses the ARM instruction set to configure the registers within FPGA to design relevant interfaces to complete the data interaction between hardware and software; finally, combines the implementation of specific algorithms and logical control to complete the binocular stereo vision system. The test results show that the frame rate with an image resolution of 640 * 480 can reach 121.43 frames per second when the FPGA frequency is 100M, and the frame rate is also high for large resolution images. At the same time, the system can achieve real-time display and human-computer interaction with the control of the graphical user interface.