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First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person.This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.

European pharmaceutical companies have a legal requirement to provide non-promotional Medical Information (MI) services to support healthcare professionals (HCPs) who are using their medicinal products. While the industry has self-regulating bodies with established Codes of Practice, these mainly focus on promotional messaging and commercial activities. In the absence of similar frameworks for MI, such services struggle to understand how to meet HCP digital expectations, often in fear of breaching the promotional codes. This is limiting access to the wealth of non-promotional patient-focussed information held within the industry. Meanwhile, a large volume of unregulated, low-quality information can be readily found on the internet. To understand the current status, the Medical Information Leaders in Europe (MILE) industry association performed a benchmarking survey which explored the online MI service provision of 13 mid-large pharmaceutical companies across Europe. This highlighted a great diversity in approach in terms of geographical coverage and content. Visibility and access for HCPs is complex, compromising online engagement and website utilisation. This MILE position paper highlights the critical need to establish a clear governance model, which empowers pharmaceutical company MI functions to provide unbranded, non-promotional, medicinal product information sources to support HCP online information needs. It is essential to build confidence, transparency and trust by establishing a practical quality framework with principles and standards for online MI services for HCPs.

PURPOSE: Main insight of the study was to evaluate the potential of Perionyx excavatus, an indigenous earthworm of India towards bioconversion of toxic paper mill wastes. The study had considered a comprehensive study over succession of different enzymes and microbial biomasses alongside removal of heavy metals to reflect the qualitative enrichment of the toxic waste through valorisation procedure. METHODS: The process of vermicomposting was performed using ratio of paper mill sludge: cow dung: straw as 5:4:1 and Perionyx excavatus for a period of 60 days. Qualitative enrichment was considered through detailed evaluation of physicochemical parameters and succession of 19 enzymes using API ZYM technology. Heavy metal loads in terms of Zn, Cr, Cu, Pb along with bioaccumulation factor (BAF) were considered to establish functional role of earthworm in bioremediation. Microbial succession was measured in terms of PLFA biomarkers. RESULTS: Enzymatic enrichment of different isozymes of esterase, aminopeptidase and glycosyl hydrolase were found in the vermicompost. A significant reduction of heavy metals was noticed in vermicomposting of primary sludge as Zn > Pb > Cr > Cu and secondary sludge as Zn > Pb > Cu > Cr. An increase in density of Gram positive bacterial and fungal populations was found at the end phase of vermicomposting process which supports maturity of vermicompost. CONCLUSION: Presence of different enzyme activities, reduction of heavy metal loads along with rise of eukaryotic PLFA markers, had illustrated the efficiency of vermicomposting as a valorisation procedure in the qualitative enrichment of toxic organic paper mill sludge.

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

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).

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].

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.

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.

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.

This study reports the degradation and decolourization capability of a manganese peroxidase enzyme producing bacterium isolated from pulp and paper mill wastewater. The isolate was identified as Bacillus aryabhattai based on biochemical analysis and 16S rRNA gene sequencing. The strain was designated MG966493. This bacterium was able to reduce 67% and 54% colour and lignin, respectively from the pulp and paper mill wastewater after 144 h of treatment at 32 °C, pH 7.6 and 120 rpm. Further, FT-IR analysis showed that during the lignin degradation process a number of metabolites were produced comprising different functional groups such as carbonyl (C = C), carboxyl (-COOH), alkene (C=C), amines (-NH2), sulphonic (-SO3) and nitro (-NO2). In addition, the SEM analysis showed that the bacterial cells exposed to pulp and paper mill wastewater have rough surfaces with reduced size as compared to the unexposed cells with smooth surfaces. This study concluded that the isolated bacterium B. aryabhattai has significant potential for the bioremediation of pulp and paper mill wastewater and thus, can be applied for their treatment at an industrial scale.

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.

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.

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.

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.

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.

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.

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%.