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Analysis of roll products at the final stage of production is the main stage for identifying defects, disrupted integrity or homogeneity, etc. Paper production is a typical example of such an operation in an industrial setting. It is proposed to use the results of the assessment for opacity, described by several standardized and statistical estimates, as the main characteristic for the quality of paper products. Studies of the dependence of the quality of the paper web on the production conditions and the properties of raw materials produce considerable variance, so that it is impossible to make accurate predictions. For this reason, we used a neural network modeling technology to develop an intelligent system for monitoring the quality of the paper web. Online quality control allows to assess the efficiency of the paper machine and rapidly adjust the manufacturing execution system. Special technologies such as computer vision systems can be introduced for this purpose, making it possible to make a transition from subjective assessment of the structure and defects of the paper web to obtaining objective quantitative estimates of these indicators. We considered a procedure for determining the estimates of structural heterogeneity of the paper web at the final stage of its production. We suggest to expand the classification of finished product samples by using neural fuzzy interpolation of linguistic values of such indicators. The approach introduced is aimed at improving the efficiency of the production process.

Functional papers are the subject of extensive research efforts and have already become an irreplaceable part of our modern society. Among other issues, they enable fast and inexpensive detection of a plethora of analytes and simplify laboratory work, for example in medical tests. This article focuses on the molecular and structural fundamentals of paper and the possibilities of functionalization, commercially available assays and their production, as well as on current and future challenges in research in this field. Graphic abstract

An enterprise imaging (EI) strategy is an organized plan to optimize the electronic health record (EHR) so that healthcare providers have intuitive and immediate access to all patient clinical images and their associated documentation, regardless of source. We describe ten steps recommended to achieve the goal of implementing EI for an institution. The first step is to define and access all images used for medical decision-making. Next, demonstrate how EI is a powerful strategy for enhancing patient and caregiver experience, improving population health, and reducing cost. Then, it is recommended that one must understand the specialties and their clinical workflow challenges as related to imaging. Step four is to create a strategy to improve quality of care and patient safety with EI. Step five demonstrates how EI can reduce costs. Then, show how EI can help enhance the patient experience. Step seven suggests how EI can enhance the work life of caregivers and step eight describes how to develop EI governance. Step nine describes the plan to implement an EI project, and finally, step 10, to understand cybersecurity from a patient safety perspective and to protect images from accidental and malicious intrusion.

As one of the precious intangible cultural heritages, traditional Chinese paper cutting has a long history and profound cultural connotation. In the digital age, traditional paper cutting is gradually moving towards digital paper cutting in order for better inherit and development. In the research of paper cutting digitalization, computer has been normally adopted to make two-dimensional paper cutting, to focus on the restoration of paper cutting process. There is still a little difficult for users who are not familiar with paper cutting; three-dimensional model has also been used to improve the picture effect, but it is not easy to show the characteristics of paper cutting with three-dimensional models. In order to address such an issue, this paper innovates the artistic style of digital paper cutting by combining paper cutting with common visual effects in modern 2D games, improves the efficiency of scene construction by writing automatic generation algorithms, and perfects the interactive mode of digital paper cutting by introducing natural interaction. In this way, users can participate in the process of creating paper cutting works, and creation of paper cutting works becomes simple and efficient benefiting from the automatic generation algorithm and natural interaction. Finally, by comparing to works of other paper cutting style, the works achieved by the solution proposed in this paper illustrates the advantages of digitalization in three aspects: artistic style, fast generation, and natural interaction.

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.

Norm emergence is typically studied in the context of multiagent systems (MAS) where norms are implicit, and participating agents use simplistic decision-making mechanisms. These implicit norms are usually unconsciously shared and adopted through agent interaction. A norm is deemed to have emerged when a threshold or predetermined percentage of agents follow the “norm”. Conversely, in normative MAS, norms are typically explicit and agents deliberately share norms through communication or are informed about norms by an authority, following which an agent decides whether to adopt the norm or not. The decision to adopt a norm by the agent can happen immediately after recognition or when an applicable situation arises. In this paper, we make the case that, similarly, a norm has emerged in a normative MAS when a percentage of agents adopt the norm. Furthermore, we posit that agents themselves can and should be involved in norm synthesis, and hence influence the norms governing the MAS, in line with Ostrom’s eight principles. Consequently, we put forward a framework for the emergence of norms within a normative MAS, that allows participating agents to propose/request changes to the normative system, while special-purpose synthesizer agents formulate new norms or revisions in response to these requests. Synthesizers must collectively agree that the new norm or norm revision should proceed, and then finally be approved by an “Oracle”. The normative system is then modified to incorporate the norm.

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

This position paper presents preliminary findings from an ongoing systematic literature review over the usages of the ContSys standard ISO 13940 [7]. I show that the literature reveals several patterns of how “A system of concepts for the continuity of care” is understood and applied.

Blended learning environments offer a rich amount of data that encompasses various learning interactions. Despite advancements in technology, there have been several learning activities that remain offline, thus preventing us from fully understanding certain aspects of student learning. An example of this is how students review their paper-based assessments. Using a homegrown educational technology that addresses this gap, we analyzed students’ clickstream data to uncover latent profiles based on how they review these graded assessments. Such behavior could provide insight into their self-regulated learning strategies as they attempt to correct their misconceptions. We leveraged latent profile analysis and presented our preliminary findings and interpretations of the five student profiles we uncovered to understand the effects of their varying efforts to learn the course material.

Electryo is a paper-based voting protocol that implements the Selene mechanism for individual verifiability. This short paper aims to provide the first formal model of Electryo, with security proofs for vote-privacy and individual verifiability. In general, voting protocols are complex constructs, involving advanced cryptographic primitives and strong security guarantees, posing a serious challenge when wanting to analyse and prove security with formal verification tools. Here we choose to use the \({{\,\mathrm{\textsc {Tamarin}}\,}}\) prover since it is one of the more advanced tools and is able to handle many of the primitives we encounter in the design and analysis of voting protocols.

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

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

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

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

This paper offers a Talmudic norms solution to the paradox of the heap. The claim is that the paradox arises because philosophers use the wrong language to discuss it. We need a language about objects which is capable of expressing not only the declarative properties of the object (such as being a heap) but also how the object/heap was constructed. Such a view of objects comes from the Talmudic theory of mixtures.

When it comes to leaked credentials and credit card information, we observe the development and use of anti-fingerprinting browsers by malicious actors. These tools are carefully designed to evade detection, often by mimicking the browsing environment of the victim whose credentials were stolen. Even though these tools are popular in the underground markets, they have not received enough attention by researchers. In this paper, we report on the first evaluation of four underground, commercial, and research anti-fingerprinting browsers and highlight their high success rate in bypassing browser fingerprinting. Despite their success against well-known fingerprinting methods and libraries, we show that even slightest variation in the simulated fingerprint compared to the real ones can give away the presence of anti-fingerprinting tools. As a result, we provide techniques and fingerprint-based signatures that can be used to detect the current generation of anti-fingerprinting browsers.

We demonstrate that XOR Arbiter PUFs with an even number of arbiter chains have inherently biased responses, even if all arbiter chains are perfectly unbiased. This rebukes the believe that XOR Arbiter PUFs are, like Arbiter PUFs, unbiased when ideally implemented and proves that independently manufactured Arbiter PUFs are not statistically independent.

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

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

Clinical specialties have widely varied needs for diagnostic image interpretation, and clinical image and video image consumption. Enterprise viewers are being deployed as part of electronic health record implementations to present the broad spectrum of clinical imaging and multimedia content created in routine medical practice today. This white paper will describe the enterprise viewer use cases, drivers of recent growth, technical considerations, functionality differences between enterprise and specialty viewers, and likely future states. This white paper is aimed at CMIOs and CIOs interested in optimizing the image-enablement of their electronic health record or those who may be struggling with the many clinical image viewers their enterprises may employ today.

Despite a long history as an analytical tool, paper has come into significant academic and industrial focus over the last ∼10 years. This resurgence coincided with the seminal work by Whitesides group in 2007 on photoresist patterning of paper to create well-defined microfluidic channels [1]. The exponential growth can in large part be linked to key innovations, which have taken paper-based analytical devices from a niche apparatus in a handful analytical labs to a powerful tool used across a variety of disciplines around the world. Here, we outline some of the key advances and their impact.

Searching information by keywords is a simple and effective method used for a long time. Unfortunately, its usefulness is not well exploited in certain contexts. Indeed, in the case of scholarly resources (scientific papers, thesis, courses, etc.) what is generally done is a classification of documents by field (speciality), or by category (thesis, course materials, etc.). We consider the context of a user (student/teacher) who wants to search for resources on a particular field, but he does not know where to look, we consider also the context of a researcher who wants to search scientific papers. The existence of a dedicated search engine is thus useful in this case of problem. The purpose of this paper is to present an efficient engine of academic and scientific documents and more specifically a function to weight documents in the context of academic resources. Our approach is based on a multi-criteria weighting method that uses the average document score, the Okapi BM25 score and the number of downloads of documents. Alerts can also be programmed to keep the user informed about new publications.

The proliferation of scientific publications is a well-known phenomenon that was recently emphasized by the publications related to the Covid-19. The number of publications Covid-19 related that PubMed added in the period between January 17 and April 18, 2020 kept rising until it reached a number of 300 publications added in a single day. There are obvious issues related to this phenomenon, such as the difficulty for researchers to find papers strongly related to their applications. When searching for related papers, there could be issues with how a paper is preferred with respect to another. A paper could be recommended based on the greater number of citations, or on the connections between authors, that is as well related to the number of citations. For such reasons, the aim of this study is to build a recommendation system based exclusively on the abstracts of these publications. We provide a comparison between classical approaches—NLP-based such as TF-IDF and n-grams—and Deep Learning approaches for content-based recommendation systems, such as Transformers. We also provide an application to graphs that shows the relationships among related papers on the basis of the results obtained from the recommendation system developed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.