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Many home IoT devices suffer from poor security design and confusing interfaces, lowering the bar for successful cyberattacks. A popular approach to identify compromised IoT devices is network-based detection, in which network traffic is analyzed to fingerprint and identify such devices. However, while several network-based techniques for identifying misbehaving devices have been proposed, the role of the user in remediating IoT security incidents has been conspicuously overlooked.

With the acceleration of education informatization, the social demand for online examination papers is increasing. However, there are some problems in the generation of online examination papers. Firstly, it is impossible to randomly generate examination papers quickly. Besides, it is impossible to dynamically adjust examination papers according to test results. Thirdly, it is impossible to generate examination papers based on individual characteristics of students. In order to solve these problems, this paper proposes a new auto-generation examination paper model based on genetic algorithm. The model dynamically adjusts the difficulty factor of individual test questions by analyzing the online learning data and historical user test result data, and then guarantees the difficulty of generating examination papers in line with the changes in the current educational environment. The simulation results show that the algorithm improves the efficiency and accuracy of the generation examination paper, and effectively controls the difficulty coefficient of the examination paper.

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.

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.

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.

At present, under the current quality education, the examination is still one of the main measure of teachers’ teaching ability and student achievement. At the same time, different levels of examination are different to the test paper. Aiming at the multi-combination of constraints in the test paper, an improved genetic algorithm is proposed, which combines the constraints of the papers effectively, so that the test papers can be maximized to meet the needs of the users.

With the upgrade of ICT infrastructure at a rocketed pace, diversification of applications and involvement of the Internet have increased. This evolution has led to the production, development, and implementation of various smart software/hardware solutions in multiple dimensions to make the process faster, smooth, accessible, and inclusive. The story of IoT (Internet of things) is a game-changer in several fields at different levels, starting from an office to home and industries. The term IoT was coined in 1999 by Kevin Ashton, since then it has seen an exponential growth, and now it has become ubiquitous. IoT may be summarized as the group of interrelated/interconnected devices embedded with sensors, software, actuator, and technology over the network for exchanging the data over the Internet without human involvement. Like any other technology, IoT has its pros and cons. In the last decade, cyber criminals have exploited many attack vectors, several of which can be used to exploit and launch attacks on IoT devices too. IoT attacks have increased substantially over the years, and there has been a jump of 900% in such attacks in 2019. Due to various constraints, IoT solutions don’t possess traditional security solutions or mechanisms to identify anomalies. Multiple security issues in IoT devices are persistent because of the limitation of computational power, hardware, and storage. These limitations make IoT devices more prone to cyber-attacks. IoT cyber-attacks are ranging from DDoS, MITM, brute-forcing, eavesdropping, privilege scaling to more sophisticated ransomware attacks and many more. This chapter will discuss multiple cyber-attacks, their mechanisms and TTPs, along with their impact on IoT infrastructure. This will also include security flaws that were exploited and related challenges in a holistic way at a single place. Statistical data leading to trend analysis along with shares of various attack campaigns shall also be highlighted in this study to give the readers more insight. In order to overcome these issues, remedial measures and mitigation policies will also be discussed.

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.

We tested four methods of making document representations cross-lingual for the task of semantic search for the similar papers based on the corpus of papers from three Russian conferences on NLP: Dialogue, AIST and AINL. The pipeline consisted of three stages: preprocessing, word-by-word vectorisation using models obtained with various methods to map vectors from two independent vector spaces to a common one, and search for the most similar papers based on the cosine similarity of text vectors. The four methods used can be grouped into two approaches: 1) aligning two pretrained monolingual word embedding models with a bilingual dictionary on our own (for example, with the VecMap algorithm) and 2) using pre-aligned cross-lingual word embedding models (MUSE). To find out, which approach brings more benefit to the task, we conducted a manual evaluation of the results and calculated the average precision of recommendations for all the methods mentioned above. MUSE turned out to have the highest search relevance, but the other methods produced more recommendations in a language other than the one of the target paper.

In this paper we show how the age of scientific papers can be predicted given a diachronic corpus of papers from a particular domain published over a certain time period. We first train ordinal regression models for the task of predicting the age of individual sentences by fine-tuning series of BERT models for binary classification. We then aggregate the prediction results on individual sentences into a final result for entire papers. Using two corpora of publications from the International World Wide Web Conference and the Journal of Artificial Societies and Social Simulation, we compare various result aggregation methods, and show that the sentence-based approach produces better results than the direct document-level method.

In this paper, we focus on the hierarchical multi-label classification task of scientific papers, which consists in assigning to a paper the set of relevant classes, which are organized in a hierarchy. The difficulty of manually constructing sufficient labeled datasets renders challenging the automatic classification task of research papers according to hierarchical labels. Multi-view active learning is a widely adopted method to address this issue, by iteratively selecting the most useful unlabeled samples for the multi-view classifiers exploiting disjoint data’ views, and querying an oracle on their real labels. However, none of the state of the art studies in this field is proposed for the hierarchical multi-label classification task. In this paper, we propose an effective multi-view active learning framework for the hierarchical multi-label classification task, applied on scientific papers. Our approach adopts a novel selection strategy that relies on both uncertainty and representativeness criteria when selecting the most informative unlabeled samples in each iteration. Experimental results on a real world dataset of ACM research papers show the efficiency of our approach over several baseline methods.

This paper gives an overview of the ongoing project STellaR – A stationary telepresence counselling system for collaborative work on paper documents. The system consists of dedicated rooms for video counselling, which clients can use to connect to a remotely located counselor without requiring any knowledge about computers or the internet. STellaR rooms are equipped with a large monitor, high quality microphone, camera, and sound system, which represent the counselor in life size.

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%.

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).

Paper-based books in university libraries are important learning materials for university students, but there are some drawbacks in traditional reading methods, and it is necessary to utilize scientific and technological means to solve the pain points of users, i.e. the students. With the development of information technology, augmented reality (AR) technology has gradually penetrated into all areas of the students’ lives. The fusion of AR technology and traditional paper books brings a potential new approach of reading and a sense of experience. At present, the application of AR technology in library is mainly for book navigation and book protection. AR technology has also appeared in some children’s books, but most of the books are still in the traditional way of reading. Therefore, this research aims to implement AR technology with traditional reading methods, optimize the reading experience of paper books, and enhance the interactivity and interest of paper book reading. This article selects university students in the library of South China University of Technology for the case study. Firstly, it finds the application method of AR technology in the library through the literature, and then observes and interviews the students to summarize the pain points and design opportunities from traditional reading. Based on AR technology, this paper designed a social reading application with a human-oriented design concept, thereby optimizing the reading experience of paper books for library.

This paper summarizes an empirical study of mining semantic information from the title/abstract of scientific papers’ dataset using the wordnet API. This summary includes description of the background, ideas, and research conducted using a program we wrote which uses Wordnet API and some other libraries. Semantic information from the abstract was obtained using wordnet (after stemming the words and selecting words belonging to certain tags), then these words were expanded, and then evaluated against the textual content of the same paper of which titles/abstracts were used as input. The semantic information is the hypernym (is-a relationship) of the stemmed words found in the title of each selected paper applied recursively (mined) using wordnet. That includes finding the hypernym of words and hypernyms of hypernyms recursively (until level 2). Every title and abstract, when written well, should contain a summary of the paper. By mining their semantic, one can get an insight of how much information can be obtained about the semantic of the paper using wordnet. Then the evaluation of that mining can be conducted by finding the percentage of matches between the semantic information found in the title/abstract and the content (body) of the same paper. It was found that the words/hypernyms: point, head, process are the most frequent matching in all papers. On the other hand, the word “value” is one of the least frequent but matching. It was also found that a paper about Grammars has the highest total number of matches (807 matches), and that the highest percentage of matches is 50%. That and other interesting results.

Peer review is an important means of academic evaluation. The evaluation words in peer review texts reflect the important viewpoints of reviewers. In this paper, An evaluation words recognition method for peer review texts (TransPeerBCC) is proposed based on transfer learning method and BiLSTM-CNN-CRF framework. TransPeerBCC first classifies direct evaluation word and indirect evaluation word, and then uses BiLSTM-CNN-CRF framework to identify these words. At the same time, in order to improve the recognition accuracy, the model parameters of public domain data are transferred to the peer review texts’ using the transfer learning method. The effectiveness of the method is verified by the experimental dataset, and the identified evaluation words are quantitatively analyzed.

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.

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.

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.

One of the biggest problems of agriculture is the reckless use of pesticides and their incorrect application with consequent waste of product and environmental pollution. The spray application characterization is a good preventive technique to limit the volume of a distributed product, to perform a more efficient application and to restrict the spray drift. Since there is no specific sampling technique useful for every context, it is necessary that every methodology is known in all its aspects before being employed. The colorimetry, fluorimetry and spectrometry methods are very accurate, but they are costly and time-consuming compared to a Water-Sensitive Paper (WSP) assessment performed by an image analysis software. This kind of software can detect and estimate many drops features using an image often obtained from a scanner. The objective of work was to develop a MATLAB code to evaluate the spray distribution over WSPs. After a pre-processing step in which the WSPs were isolated inside the image, the individual drops were identified using the difference between their color (blue) and the remaining dry part of the paper (yellow). Once the surface of every drop was estimated, it was possible to assess the number of the drops per cm2, the Normal Median Diameter (NMD) and the Normal Volume Diameter (VMD).

Body balance is an essential capability for an individual to perform functional activities. There are various performance-based balance measures available to occupational therapists. However, conventional balance measures are limited due to subjectivity. There is a prominent need for a more objective and accurate assessment. NIMBLE, using motion sensing and tracking system was developed for a more objective and accurate measure of body movement with high-resolution recording. A pilot study was conducted in 20 participants for functional sitting balance measures by using both paper-based assessment and the NIMBLE. Results showed substantial discrepancies when the NIMBLE was able to detect balance deficits when the paper-based measures failed. The NIMBLE system can accurately capture the extraction of joint centers and segment orientation, providing the ability to calculate joint kinematics and spatiotemporal aspects of the movement. With this low cost and friendly interface, it has great potential to be widely used in healthcare practices.

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 article aims to lay a foundation for learning and practicing music online. Massive Open Online Courses (MOOCs) are growing as we are moving to online classes. Current music courses through MOOCs mostly focus on peer evaluation for assessing the students’ performance. However, this technique may not be practical when it is applied to larger class sizes. Therefore, in this research, the main goal is to reduce the instructor’s load and provide online real-time performance feedback. As a contribution to music education, we propose a new technological framework to automate music lessons for learning how to play any favorite songs via existing machine learning (ML) techniques for adaptability to various learning styles.

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.

The Bitcoin whitepaper [1] states that security of the system is guaranteed as long as honest miners control more than half of the current total computational power. The whitepaper assumes a static difficulty, thus it is equally hard to solve a cryptographic proof-of-work puzzle for any given moment of system history. However, the real Bitcoin network is using an adaptive difficulty adjustment mechanism.

Under many circumstances, scholars need to identify new research directions by going through many different databases to identify the research gap and identify areas which have not yet been studied thus far. Checking all the electronic databases is tiresome, and one often misses the important pieces. In this paper, we propose to shorten the time required for identifying the research gap by using web scraping and natural language processing. We tested this approach by reviewing three distinct areas: (i) safety awareness, (ii) housing price, (iii) sentiment and artificial intelligence from 1988 to 2019. Tokenisation was used to parse the titles of the publications indexed on Google Scholar. We then ranked the collocations from the highest to the lowest frequency. Thus, we determined the sets of keywords that had not been stated in the title and identified the initial idea as a research void.

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 an automated multiple choice grader for paper-based exams is implemented. The system consists of two main parts, a software program and a document feeder scanner. The exam papers are fed to the scanner which scans them one by one and send them as an input to the software. The software program recognizes the student Identification Number (ID) and the answers for each exam paper and reports the final results in an Excel sheet. The system starts by applying an aligning procedure and segmenting the scanned image in order to extract form number, student ID, and answers boxes, then a pre-processing step that handles all irregular cases of input is implemented; where in this step a best possible shape that results in the highest recognition accuracy is gained. After getting a proper separated characters and numbers, a feature extraction process is applied on each character/number to calculate its feature vector. The feature vector is then compared with templates of feature vectors for each of the answers choices and numbers with their variations, where both characters and numbers are in English language. After recognizing all the answers and all ID number digits; the system starts grading the student paper and comparing student answer with the pre-entered key answers. A recognition rate of 95.58 % is attained.

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.

Clinical neuropsychologists develop comprehensive behavioral profiles on their patients primarily by using paper-and-pencil test stimuli. Despite these tests being significantly cheaper and faster than complex procedures such as MRI scans, multiple drawbacks remain. Constructing these behavioral profiles can take upwards of six hours to fully complete, and the analysis of the sketches from these pencil-and-paper tests is still largely subjective and qualitative. We developed SmartStrokes, a testing suite that implements digital versions of common clinical neuropsychology pencil-and-paper tests, with the purpose of helping to automate and analyze patient sketches using the principles of sketch recognition.

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.

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.

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

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.

Although there are a numerous of studies in the facilitative effects of dictionary consultation in promoting word learning, no research has ever been conducted to investigate the effectiveness of a hybrid use of paper-based and electronic dictionaries. The present research, therefore, responds to this call and compares the effectiveness of the pure use of either paper-based or electronic dictionary and the hybrid use of both. The empirical results demonstrate the superiority of the paper-based dictionary over the electronic dictionary, the usefulness of repetition and the greater effectiveness of the hybrid use of both paper-based and electronic dictionary than the pure use of either. We further conclude that the significance of processing for constructing memory, repetition for consolidating memory and diversity for reinforcing memory should be emphasized.

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.

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.

Within medical applications there are two main types of information design; paper-based and digital information. As technology is ever changing, healthcare is continually being transitioned from traditional paper documents to digital and online resources. This paper presents the findings of a study involving 26 participants who are familiar with Activity of Daily Living charts, and used three scenarios requiring them to complete both a paper ADL and a digital ADL. An evaluation was undertaken to discover if there were any ‘human errors’ in completing the paper ADL and also looked for similarities/differences through using the digital ADL. We also analyzed the variability of the decisions made by all subjects. Results illustrate that 22 participants agreed that the digital ADL is better, if not the same as a paper based ADL. Further positives include the added benefit of the digital ADL being easy to use and also that the final calculation is done automatically.

In the current architecture of the Internet, there is a strong asymmetry in terms of power between the entities that gather and process personal data (e.g., major Internet companies, telecom operators, cloud providers, ...) and the individuals from which this personal data is issued. In particular, individuals have no choice but to blindly trust that these entities will respect their privacy and protect their personal data. In this position paper, we address this issue by proposing an utopian crypto-democracy model based on existing scientific achievements from the field of cryptography. More precisely, our main objective is to show that cryptographic primitives, including in particular secure multiparty computation, offer a practical solution to protect privacy while minimizing the trust assumptions. In the crypto-democracy envisioned, individuals do not have to trust a single physical entity with their personal data but rather their data is distributed among several institutions. Together these institutions form a virtual entity called the Trustworthy that is responsible for the storage of this data but which can also compute on it (provided first that all the institutions agree on this). Finally, we also propose a realistic proof-of-concept of the Trustworthy, in which the roles of institutions are played by universities. This proof-of-concept would have an important impact in demonstrating the possibilities offered by the crypto-democracy paradigm.

The cloud is a rapidly expanding and increasingly prominent component of modern computing. Monolithic servers limit the flexibility of cloud-based systems, however, due to static memory limitations. Developments in OS design, distributed memory systems, and address translation have been crucial in aiding the progress of the cloud. In this paper, we discuss recent developments in virtualization, OS design and distributed memory structures with regards to their current impact and relevance to future work on eliminating memory limits in cloud computing. We argue that creating physical memory pools is essential for cheaper and more efficient cloud computing infrastructures, and we identify research challenges to implement these structures.

Data transaction in internet of vehicles is a transaction occurs between vehicle owner and data buyer. Blockchain is a new technology that brings decentralized ledger system for user, which means users could make payment without the third party. There are several projects combined internet of vehicles and Blockchain, however, none of them realize a trustworthy anonymous data transaction. In this paper, we first propose the concept of Super Nodes to guarantee data authenticity, then we construct the anonymity for the transaction base on zero-knowledge Succinct Non-interactive Argument of knowledge (zk-SNARKs) and DAP from Zerocash. Moreover, a smart contract is deployed for mutual benefits. Simulation experiment shows this scheme is practical.

The Small Size League is one of the important events in RoboCup Soccer. ZJUNlict got the first place in RoboCup 2018. In this paper, we introduce the new innovations and development we have made in the past year. These innovations include the mechanical part which accounted for most of our incredible goals and software part which enables us to play the game under a terrible vision situation. We also purpose an interception prediction algorithm to achieve some skills and improve our ball possession rate.

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