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Food safety and security have emerged as an issue of global public concern and trade implications. Food contamination and adulteration are major threats toward public health. Traditional and conventional benchtop technologies are being utilized all over the world for the identification and quantification of food contaminants and adulterants. But these technologies are expensive, labor-intensive, require highly skilled manpower, and resources for proper operation. There is an urgent need for developing point-of-care devices for food analysis and authentication, which can be cost-effective, reliable, simple to operate, and analyze. Bioactive paper is a promising tool that can be used over a wide range of applications involving food safety and security. Paper-based devices are typically rapid, cost-effective, and user-friendly, offering a high potential for rapid food safety analysis at point of need. In recent years, bioactive paper research has been a topic of great interest, as it is robust, simple, and affordable, and it can be an alternative to the traditional expensive instruments for detections, such as gas chromatography, mass spectroscopy, or high-performance liquid chromatography. This article focuses on paper-based point-of-care (POC) devices that are being developed for food analysis and authentication. It discusses paper-based devices as developed for the detection of foodborne and waterborne pathogens, organic and inorganic toxins, pesticides, and illegal food additives. Finally, this study also sheds light on the future aspect of bioactive papers in the food industry, limitations, and way forward.

Brain-machine interfaces (BMIs) decode neural activity in real-time, allowing control of sensors and actuators. However, real-time neural decoding requires high computational resources. Recently developed paper-based electronics (e.g., transistors) are likely to reduce the cost of computing devices. In this theoretical study, we propose a roadmap to develop BMIs that take advantage of cellulose based supercomputers. Major milestones will include: 1) an initial prototype with a small number of elements capable of forming a simple brain-controlled On/Off switch, and 2) scaling up the capabilities of the new computing device, such that paper-based electronics lined in the walls of people’s homes will form a supercomputer. The initial prototype will be used to control the lights in a room, but more advanced devices are expected to decode more complex brain commands. As paper-based electronics are very low cost, it is proposed here that paper-based supercomputing capabilities may significantly improve BMIs.

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

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.

Digitization of everything is the main driver in today’s economy. The constantly growing proliferation of digital tools makes it increasingly important to properly analyze and document which information is needed in which processes throughout a company. Information management competencies are a key enabler on the path to data generated value creation.

Considering the lack of consensus on the deployment of facial recognition technologies, many organizations, including public bodies, NGOs and private companies have alerted public opinion and called for a broad debate on facial recognition. We believe that such a debate is indeed necessary. However, in order to be really productive, it is essential to ensure that arguments can be expressed and confronted in a rigorous way. The main objective of this position paper is to help set the terms of this debate on a solid basis. To this aim, we present an incremental and comparative risk-analysis methodology for facial recognition systems. The methodology introduces, for a better separation of concerns, four levels of analysis: the purpose, the means, the use of facial recognition and its implementation. We discuss each of these levels and illustrate them with examples based on recent developments. Interested readers can find more details, in particular about the use of ethical matrices to facilitate the analysis, in an extended version of this position paper published as an Inria report [7].

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.

A possible practical engineering approach to creation of the general artificial intelligence is considered. The choice of approach is based on modular hierarchical representation of knowledge, where each module uses its own methods of representation and knowledge processing. Work with knowledge is done by a hierarchical multi-agent system. The description of system’s individual elements and information about the current development state are given.

In recent times, nanotechnology, which has been one of the main novelties to be developed in the 21st century, has been applied to many sectors, particularly to various industrial sectors including forest-based industry. An output of this is the development of nanomaterials of which nanocelluloses have been studied as high technology biopolymers for application in various materials through the development of films and as reinforcement in papers. With this background, the main objective of this Chapter is to present the use of nanocellulose in the paper making. Accordingly, the Chapter presents characteristics of the most used wood in the world for pulp and paper production, main methods of obtaining cellulose in nature, process of bleaching of pulp, paper making, processes to obtain different types of nanocellulose (microfibrillar nanofiber and cellulose nanocrystals), applications of nanocellulose in the paper making through coating and films as well as by nanocellulose-reinforced pulp and the resulting effects of the use of nanocellulose in paper production. These include increased tensile and burst strengths, weight loss, improved barrier properties for oils, oxygen and moisture, better printing surface, etc. In the end, marketing aspects, possible future opportunities and finally concluding remarks are given. These briefly mention the use of nanocelluloses in papermaking presenting interesting possibilities, which offer improvements in cost-benefit, energy efficiency and biocompatibility, in addition to generating new products with uses are not available today.

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.

Many companies across several industries have made the switch from paper to electronic documentation with the goals of improving efficiency, lowering costs, and providing employees with ways to easily access important information all at their fingertips. In the aviation industry, Electronic Flight Bags (EFBs) have transformed all required inflight, paper documentation into digital formats that are easily accessible to pilots on the flight deck. Unfortunately, in the development of first generation EFB documentation applications, and many other document-rich applications in industry, little attention has been paid to human factors design principles, resulting in applications that are inefficient and can burden user performance. This paper describes a usability analysis that was performed on a current EFB application used by a major U.S. air carrier, and provides redesign recommendations to increase usability of the EFB and streamline pilot performance. These results provide insight into potential problems to avoid in future flight deck application design, as well as for applications that serve to digitize paper-based process. Additionally, the results provide guidelines to help ensure effective and efficient user interfaces.

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.

Formal specification, model checking and model-based testing are recommended techniques for engineering of mission-critical systems. In the meantime, those techniques struggle to obtain wide adoption due to inherent learning barrier, i.e. it is considered difficult to use those methods. There is also a common difficulty in translating the specifications in natural language, a common practice nowadays, to formal specifications. In this position paper we discuss the concept of an end-to-end methodology that helps identify specifications from various sources, automatically create formal specifications and apply them to verification of cyber-physical systems. Thus, we intent to address the challenges of creation of formal specifications in an efficient automated and tool-supported manner. The novelty of the approach is analyzed through a survey of state of the art. It is currently planned to implement this concept and evaluate it with industrial case studies.

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.

The data hiding techniques have attracted a lot of attention in recent years and mainly with the intensive growth of multimedia and its possibility for covert communication. Steganography is one of the information hiding methods to confirm the ability of a multimedia carrier to exchange secret information between two end-points so that it is imperceptible, thus avoiding the detection of hidden information. The secret information can be embedded in several multimedia carriers, such as image or audio or video files. It works by embedding the message in a source cover which may make the observer feel it is the source cover itself. The type of multimedia carrier here is an image. However, this technique suffers from the problem of the carrier distortion. In this paper, we investigate the impact of some distortion types on the carrier images and discuss the possibility of using distraction images in steganography to protect the stego-image. Furthermore, we highlight the current challenges of image steganography. The experimentations show very interesting results.

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 contribution of this paper deals with IoT coordination. After a thorough review of the literature, we conclude that the challenge of coordination has not yet been met despite all the contributions already suggested. To address this challenge, we have proposed in a previous work an IoT contextual architecture, while the purpose of this article focuses on the behavior of the proposed multilayer IoT architecture by describing a flood case study. Three aspects are considered: functional aspect, informational aspect and behavioral aspect. When modeling the behavioral aspect, we focus mainly on the control flow throughout the workflow and we put our interest on relative ordering of sub-workflows.

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.

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

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.

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

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.

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.

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%.

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).

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.

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

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

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

The rapid growth of scientific literature is presenting several challenges for the search and discovery of research artifacts. Datasets are the backbone of scientific experiments. It is crucial to locate the datasets used or generated by previous research as building suitable datasets is costly in terms of time, money, and human labor. Hence automated mechanisms to aid the search and discovery of datasets from scientific publications can aid reproducibility and reusability of these valuable scientific artifacts. Here in this work, utilizing the next sentence prediction capability of language models, we show that a BERT-based entity recognition model with POS aware embedding can be effectively used to address this problem. Our investigation shows that identifying sentences containing dataset mentions in the first place proves critical to the task. Our method outperforms earlier ones and achieves an F1 score of 56.2 in extracting dataset mentions from research papers on a popular corpus of social science publications. We make our codes available at https://github.com/sandeep82945/data_discovery.

In the health sector, data analysis is typically performed by specialty using clinical data stored in a Clinical Data Registry (CDR), specific to that medical specialty. Therefore, if we want to analyze data from a new specialty, it is necessary to create a new CDR, which is usually done from scratch. Although the data stored in CDRs depends on the medical specialty, typically data has a common structure and the operations over it are similar (e.g., entering and viewing patient data). These characteristics make the creation of new CDRs possible to automate. In this paper, we present a software system for automatic CDR generation, called CDRGen, that relies on a metadata specification language to describe the data to be collected and stored, and the types of supported users as well as their permissions for accessing data. CDRGen parses the input specification language and generates the code needed for a functional CDR. The specification language is defined on top of a metamodel that describes the metadata of a generic CDR. The metamodel was designed taking into account the analysis of eleven existing CDRs. The experimental assessment of the CDRGen indicates that: (i) developers can create new CDRs more efficiently (in less than 2% of the typical time), (ii) CDRGen creates the user interface functionalities to enter and access data and the database to store that data, and finally, (iii) its specification language has a high expressiveness enabling the inclusion of a large variety of data types. Our solution will help developers creating new CDRs for different specialties in a fast and easy way, without the need to create everything from scratch.

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.

Multivariate cryptography plays an important role in post-quantum cryptography. Many signature schemes, such as Rainbow remain secure despite the development of several attempted attack algorithms. However, most multivariate signature schemes use relatively large public keys compared with those of other post-quantum signature schemes. In this paper, we present an approach for constructing a multivariate signature scheme based on matrix multiplication. At the same security level, our proposed signature scheme has smaller public key and signature sizes compared with the Rainbow signature scheme.

Artificial intelligence is a buzz word and even more when its accomplishments have challenged our intelligence. However, what is intelligence? Is there a consensus in its meaning for researchers and professionals? Is it just a sales word? What does it mean in practical terms? To answer these questions, we followed a systemic review of literature in most cited papers about intelligent systems in the Internet of Things (IoT) and discovered that only 58% were intelligent as we defined: “Intelligent Systems are systems conformed by algorithms that are programmed using some machine learning techniques and that can learn from data and perform tasks with a superior performance”. The rest 42% were just traditional systems with hardware or software enhancements.

Opinion mining is the use of analytic methods to extract subjective information. A study was conducted to apply spatial opinion mining in literary works to examine the writers’ opinions about how matters of space and place are experienced. For this reason, this paper conducts a review study to identify and compare different analytical techniques for opinion mining in fictional writings. This review study focused on sentiment analysis and topic modeling as two main techniques for spatial opinion mining in literary works. The comparison results are reported and the limitations of different techniques are mentioned. The results of this study can assist researchers in the field of opinion and text mining.

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.

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.

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.

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.

CSIDH is an isogeny-based post-quantum key establishment protocol proposed in 2018. In this work we analyze attacking implementations of CSIDH which use dummy isogeny operations using fault injections from a mathematical perspective. We detail an attack (implicit in prior works on implementations of CSIDH) by which a static private key can be learned (up to sign) by the attacker with certainty using \(\sum \lceil \log _2(b_i) + 1 \rceil \) faults using a binary search approach, where \(\boldsymbol{\mathbf {b}}\) is the bound vector defining the keyspace. A natural idea for a countermeasure to this attack is to randomly mix the real degree \(\ell _j\) isogenies together with the dummy ones, so that binary search becomes ineffective. In this work we evaluate the efficacy of this idea as a fault attack countermeasure; in particular, we give bounds (as a function of the bound vector entries) on the number of fault injections (of a particular relatively strong, hypothetical type) required for an attacker to have a given success probability for guessing an unknown key, and present the results of simulated attacks on keys sampled from 6 keyspaces found in the literature. We find that the number of faults required to reach any constant success probability in guessing a static key is quadratic in the bound vector entries, rather than logarithmic as in the “real-then-dummy” setting—concretely, the number of faults required increases from a few hundred to tens of thousands. Broadly, this behaviour is reflected in our simulations.