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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 chapter examines panic-buying (PB) as byproduct of pandemics. Panic buying is associated with disasters including pandemics. If and when disasters become more prominent and prevalent, understanding phenomena like PB becomes an important part of disaster communication and disaster management. Panic buying of toilet paper was examined during the international Covid-19 crisis of 2019–2020 in an effort to study and explain the behavior of PB, who panic buyers are, which drivers motivate PB as well as the socio-psychological explanations of panic-buying found in the existing literature. The chapter concludes with some recommendations to reduce PB in the future. This is a snapshot of a behavior intrinsic to disasters of all sorts and should serve as both explicative and proscriptive warnings.

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

The high cost and the limited availability of tests and application devices often restrict the benefits of many breakthroughs in biotechnology and environmental science. There is a high need for low-cost bioassays, in health, food, and environmental diagnostics. Bioactive paper is a promising technology which could be applicable on a wide range of areas like health, food, and environment. Bioactive papers and paper diagnostics are getting a reality for their low cost, selectivity, and application flexibility. The potential of bioactive papers for healthcare, environmental, and industrial applications has recently captured wide attention. The concept of bioactive paper is about 200 years old. However, low-cost paper-based tests are commercially available only since the last century. In recent years bioactive paper has considerably attracted the research community as it shows opportunity to replace traditional expensive instruments for detections like gas chromatography, mass spectroscopy, or high-performance liquid chromatography. Recently Bangladesh has also started limited research in the field of bioactive paper. Bangladesh envisions to ensure proper healthcare, safe food, water, and environment to all residence by 2041. Bioactive papers and diagnostics could be the future means to ensure better health and safer food and environment in Bangladesh at an affordable cost. With proper planning, fund allocation, and research capacity building, Bangladesh can take the opportunity to comply with local demands and become a global contributor in bioactive paper research, innovation, and product development by 2041.

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].

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.

The present research work deals with the study of some of the essential physicochemical parameters of water samples of “Taranga beel” (Beel is a local term which defines wetland) of Jagiroad, Assam, India. This study indicates a decline in the water quality of Taranga beel due to the paper industry’s influence in its vicinity. The collection of samples and the determination of various physicochemical parameters and heavy metal concentration was done according to the standard protocol. The physicochemical parameters such as Total Solids (TS), Total Dissolved Solids (TDS), Turbidity, pH, conductivity, Chemical Oxygen Demand (COD), Sulphate, Dissolved Oxygen (DO), Fluoride, and heavy metals such as Cd, Cr, Cu, Fe, Mg, Mn, Na, Ni and Pb were analyzed thoroughly. Among all the physicochemical parameters and heavy metals, the concentration of TS, TDS, Turbidity, DO, COD, Cr, Pb, Mn, and Fe were above the permissible limits prescribed by the Bureau of Indian Standards (BIS).

Quality outcomes in emergency surgery are achieved by knowledge and training in emergency surgery. This requires standards and KPIs which need to be measured and reported regularly.

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.

Paper reviews the method of deodorization and dehydration of sludge-lignin, which is based on the interaction of sludge-lignin with quicklime. CaO added to the sludge-lignin neutralizes lignin in the amount of around 15–20% and within 8–12 days binds volatile sulfur-containing components, functional organic groups and water. The proposed method can effectively reduce unpleasant odor to the level of 2–3 points based on a 5-point scale, as well as to diminish the humidity of the end product by 45–50%, making it look as a free-flowing powder, convenient for transportation and further use. Sludge lignin neutralized by the quicklime can be used for land recycling of municipal solid waste, as well as for other purposes. The recycling technology is characterized by the simple instrumental and the use of the inexpensive chemical reagent. The high economic efficiency of this method makes it promising for solving environmental problems of the ‘Baikal Pulp and Paper Mill’.

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.

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.

Dielectric permittivity is one of the most important parameters in the field of telecommunications, since it indicates an interaction value between the substrate and an electric field. Lately the paper has had a great acceptance for the development of new technologies, therefore, the permittivity of the Kraft paper was measured to be used as a substrate, in telecommunications applications. For the measurement of the dielectric permittivity of Kraft paper, a resonant ring structure was designed. The design of the ring requires a frequency of operation or resonance, moreover, to an impedance coupling between the measuring equipment and the resonator. The structure of the resonant ring was simulated obtaining the coefficient of direct transmission (S21), validating the design for its construction. S21 was acquired by the NI-PXI instrument in a practical way; this parameter allows the calculation of the effective dielectric permittivity and later the relative. Finally, the process revealed a value of 1.40, relative dielectric permittivity for Kraft 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.

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.

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.

Blockchain provides a secure and reliable guarantee for information transaction. The trading partners share information and benefits, as well as take risks together, which prompts the integration with blockchain. However, the establishment of trust could be the biggest challenge in this integration. In this article, a solution to address the challenge by building a supply chain financial service system based on the league blockchain is proposed. When small and medium-sized enterprises (SMEs) have financing needs, they can get a loan through blockchain system effectively. In the supply chain, with the help of consensus mechanism and encryption technology, the system provides enterprises, banks and regulators with more convenient financing audit and supervision methods, which improves the financing speed of non-core enterprises. At the same time, the system makes the financing process transparent, safe and traceable. On the one hand, this system changes the way information is stored from SMEs’ respective systems to the blockchain. On the other hand, regulators can supervise SMEs’ actual use of loan funds effectively. This, to some extent, has greatly solved the financing difficulties of SMEs.

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.

Effluents from the paper mill are highly toxic and are one of the major sources of aquatic pollution. Their toxic nature is derived from the presence of several naturally occurring and xenobiotic compounds, which are formed and released during various stages of papermaking. In the present study, mill effluents are collected from three different stations around the Cachar paper mill (CPM) in Assam and samples are analyzed for 17 parameters such as appearance, color, odour, conductivity, total dissolved solids, pH, total alkalinity, hardness (total and Ca), chloride, nitrite, BOD and COD along with the heavy metals As, Cu, Cd, Cr, Fe, Pb and Zn. The results are compared with water quality standards prescribed by World Health Organization. Multivariate statistical analyses are performed on data sets obtained to gain a better understanding of the nature of each parameter in the sampling stations. The results show variation amongst stations that may be due to spatial variations amongst them. The results from this study can be used for water quality monitoring purposes.

Many environmental contaminants may cause adverse reproductive effects through disruption of the neuroendocrine system. Pulp and paper mill effluent can contain up to 250 different contaminants and has been linked to reproductive impairment in fish. We used data from an in vitro study that characterized the potential neurochemical effects of pulp and paper mill effluent, to model vitellogenin production after exposure to different effluent fractions. We hypothesized that disrupted in vitro γ-aminobuytric acid and dopamine signaling could be modeled to simulate reproductive impairment in fish, specifically reduced vitellogenin production. These neurotransmitters are involved in the release of gonadotropin releasing hormone from the hypothalamus, which stimulates downstream processes related to vitellogenin production in the liver of female fish. In our approach, we integrated the in vitro results into a fish vitellogenesis model to predict adverse reproductive outcomes at the individual level. Our model results indicate that exposure to toxicants within the pulp and paper mill effluent, which interferes with neurotransmission, may cause harmful reproductive effects by impairing vitellogenin production. While the model has yet to be validated, our proof of principle approach highlights the use of computational models as a means to integrate results from in vitro studies that assess complex mixtures to potentially adverse effects on fish reproduction.

Pulp and Paper Companies collaborate to monitor and monetize waste and create value from their by-products. This process of Industrial Symbiosis requires the creation and maintenance of trusted and transparent relationships between all entities participating in these networks, which is a constant challenge. In this context, a blockchain-based system can help in establishing and maintaining these networks, serving as a ground truth between companies operating at a national or a global scale. This paper proposes a scalable and modular blockchain architecture design using smart contracts to enhance the industrial symbiosis process of the Pulp, Paper, and Cardboard Production Sector companies in Portugal. This design comprehends all entities participating in the network. The implementation of this design assumes the use of a permissioned ledger built using Hyperledger Fabric to provide the required trust and transparency between all entities.

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.

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.

This chapter aims to reposition paper as a commodity in the historiography of early modern book production by focusing on the markets for paper. This is done by means of a case study of an eighteenth-century paper dealer active in Amsterdam, the hub of the European paper trade. By examining the material flow of paper in and out of Amsterdam and through the city itself, it becomes clear that paper kept the whole ‘business of books’ running. Paper dealers were crucial figures within book production networks, performing pivotal functions such as providing access to supply chains. They also functioned in some unexpected ways, such as organizing the industry’s recycling processes of waste paper. It is argued here that these paper networks contributed to the formation of interconnected markets and constituted a significant component of the early modern book industry.

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.

Developing valuable materials from the by-products of paper industry can help to address some environmental and economic issues associated with traditional synthetic composites. Particularly, the management of paper mill sludge (PMS) waste remains an economic and environmental challenge for the pulp and paper industry. 11 million tons of PMS is generated annually in Europe from the wastewater treatment (WWT) process of paper mills. PMS is mostly used in low value applications. However, PMS contains fibers and minerals with physio-chemical properties that exhibit a high potential to substitute some conventional materials in other industries. The research presented in this paper aims to explore new directions for further investigation on PMS material applications by reviewing the literature on PMS materials and subsequently characterizing sludge from 6 different mills. The study shows the technical feasibility, opportunities and technological readiness of fiber and mineral based composites obtained from PMS, such as; cementitious products, polymer reinforcement and fiberboards.

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.

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

The Internet of Things (IoT) has been undergoing a rapid development and has obtained increasing visibility. A large number of scientific research achievements have been published. This study aims to provide a bibliometric review for highly cited papers in the field of IoT using the Essential Science Indicators, a widely used database to evaluate scientific outputs. Through the retrieval process, 388 papers were identified as highly cited papers. Based on these 388 papers, we analyze their characteristics from four perspectives: annual and discipline distributions; productive players in terms of journals, countries, institutes, and authors; top 15 most cited papers; and author keyword analysis. Interesting results are given after the analyses. Through author keyword analysis, we also provide research trends of the IoT for future study. It is hoped that our work will be helpful for practitioners and scholars who are interesting in the IoT.

This paper explores the characteristics of 106 award-winning papers from the Library and Information Science (LIS) journals published by Emerald Publishing between 2008 and 2019, focusing on collaboration type, paper type, topic, and citation count to illustrate the developmental trends of LIS scholarship. The findings show that the top three topics of the award-winning papers were information service activities, professions and information institutions, and user studies. More than half of the award-winning papers were written by teams, among which inter-institutional collaboration and intradepartmental collaboration accounted for the largest proportion, while interdepartmental collaboration within an institution accounted for the smallest proportion. There were 65 empirical research papers in the sample, among which qualitative studies were dominant, followed by quantitative research and mixed methods research. The award-winning papers had a higher mean and median in citation counts than the average papers concurrently published by the journals. The research results provide implications for researchers and can help them understand the trends in research topics and common analytical types in LIS for their future studies.

This final chapter includes some comments of mine about the papers in this volume and their connections with my work. I am very grateful to all the authors of these papers for their nice contributions!

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 is a conceptual paper on the effect of behavioral factors on job performance, and to investigate the mediating effect of employee engagement between behavioral factors and job performance in Jordanian commercial banks. This paper aims to present literature reviews related to investigating behavioral factors. Also, this paper proposed a model that enables the organization to achieve job performance by includes some factors which are talent management, quality of work-life, and organizational climate. However, employee performance refers to behaviors that are relevant to organizational goals and that are under the control of individual employees. Job performance is the feeling of the employee about his job. Likewise, all factors need to harmonize and align together to reach the desired progress, success, and sustainability. This paper suggests statistical techniques such as normality tests, multicollinearity test, and EFA using the SPSS. For the measurement model and hypothesis testing using smart PLS. Likewise, the conclusion that was drawn is that the effect of investigating Behavioral factors including talent management, quality of work-life, and organizational climate maybe help increase job performance. For future research, this paper recommends that all variables affecting the individual factors of employees should be included and applied to a service sector to confirm the results.