Search

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%.

This paper analyzes the significance of international conferences for higher education institutions’ (HEIs) promotion of sustainability by examining their published papers. This study not only allows the understanding of the state of the art but also, based on the identified gaps, sheds light and gives a north for future studies of Sustainable Development. The citations of works published from 2015 to 2019 were analyzed in a sample of 489 articles published in international periodicals included in the Web of Science database related to the subject of sustainable development in higher education. Classic text statistics, similarity analysis and word cloud are used to analyze the articles published on the World Sustainability Symposium, Bridge International Conference 2019 exploring its results and contributions to promote sustainability. The findings suggest that in addition to cooperation, participation and collaboration between institutions, international conferences sponsored or organized by HEIs have the potential to promulgate interdisciplinary and multidisciplinary approaches to addressing global sustainability challenges. In the international conferences analyzed the diverse array of disciplines and research teams illustrate this point. International conferences may be used for communicating the scientific results to stakeholders and send strong messages on sustainability-related issues.

This paper shares our experience in developing information literacy skills through a framework based on writing papers in the IEEE format conference. The framework gave students, in an active learning style, a set of activities to identify the need for information, procure the information, evaluate the information and subsequently revise the strategy for obtaining the information, and to use it in an ethical manner to produce a technical paper. We followed the ACRL’s Information Literacy Competency Standards for Higher Education in the development of an assessment tool, course content, and exercises. Initial results show that the proposed framework developed information literacy skills in a maturing stage. We believe that this experience and the design of the framework could be replicated or adapted to different Engineering Careers.

Coreference resolution is essential for automatic text understanding to facilitate high-level information retrieval tasks such as text summarisation or question answering. Previous work indicates that the performance of state-of-the-art approaches (e.g. based on BERT) noticeably declines when applied to scientific papers. In this paper, we investigate the task of coreference resolution in research papers and subsequent knowledge graph population. We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55,485 papers in 10 STM domains. Comprehensive experiments show the usefulness of the proposed approach. Our transfer learning approach considerably outperforms state-of-the-art baselines on our corpus with an F1 score of 61.4 (+11.0), while the evaluation against a gold standard KG shows that coreference resolution improves the quality of the populated KG significantly with an F1 score of 63.5 (+21.8).

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

ICT is fast-growing and changing field. A lot of researches are being done in various area of ICT, and results are presented in various platforms like conferences, journal and books. This is common observations in the publications from developing countries (especially in sub – Saharan africa) are not being published in reputed and established publishers even their technical/experiments are good. This is due to lack of several factors including professional presentation, the novelty of the topic, quality of literature review etc. This work guides final year bachelor’s students, PG students (masters and PhD) and young researchers, especially working in computing-related disciplines, on how to convert their project works into quality publications. The authors provide details on how these researchers can select suitable project topics, do a proper review, write up the key components of a paper and present their results in an appropriate form (that is, writing style starting from abstract to conclusion). This paper also presents and guides on how to write various types of review papers.

The final disposal of sludge from recycled paper mill effluent treatment plants is costly and demands large areas for landfilling. Environmentally sound alternative uses of the sludge, enabling economic valoration of this material appear to be advantageous to industry and to the environment. This paper evaluates the feasibility of the incorporation of paper mill sludge in manufacturing of ceramic bodies. The combination of different proportions of weak clay, strong clay and sludge from recycled paper effluent treatment plant for the production of ceramic bodies was determined by using the Network Simplex method planning of mixtures. Samples were prepared and burnt in oven at temperatures of 750, 850 and 950 °C. Physical, mechanical and environmental tests were carried out, in addition to chemical, microscopic and thermic analysis. The results demonstrated that the flexion resistance increased with the increase in temperature, while the absorption decreased at higher temperatures. The mixture design is efficient combining sludge addition at small fractions with large amount of clay, which demonstrate the viability of sludge incorporation for ceramic bodies manufacture, since the desired characteristics are observed.

The multifaceted hydrolytic enzyme xylanase has been found to play a pivotal role in the energy and green technology sectors. Extensive research is being carried out with the aim to produce xylanases that would fulfill industrial parameters, in the paper and pulp and fuel industries. Cellulase-free xylanase with good thermal stability finds promising use for biobleaching in paper manufacturing. In current scenario, it has been found that renewable energy is a burgeoning area, where xylanase finds a major role. Xylanase is synergistically involved with other cooperating enzymes for the deconstruction of lignocellulosic raw material, which ultimately paves way for the production of fuel ethanol. In other words, efficient utilization of lignocellulosic raw materials will positively boost the economics of alternate fuel industry.

We discuss a new approach to interactive exploration of high-dimensional data sets which is aimed at building human’s understanding of the data by iterative additions of recommended attributes and objects that can together represent a context in which it may be useful to analyze the data. We identify challenges and expected benefits that our methodology can bring to the users. We also show how our ideas got inspired by Formal Concept Analysis (FCA) and Rough Set Theory (RST). It is though worth emphasizing that this particular paper is not aimed at investigating relationships between FCA and RST. Instead, the goal is to discuss which algorithmic methods developed within FCA and RST could be reused for the purpose of our approach.

Machine learning is widely used in malware detection systems as a core component. However, machine learning algorithm is based on the assumption that the underlying malware concept is stable for training and testing. The assumption is vulnerable to well-crafted concept drift attacks, such as mimicry attacks, gradient descent attacks, poisoning attacks and so on. This paper proposes an ensemble learning system which combines vertical and horizontal correlation learning models. The significant diversity among vertical and horizontal correlation models increases the difficulty of concept drift attacks. And average p-value assessment is applied to fortify the system to be sensitive to hidden concept drift. The experiment results show that the hybrid system could actively recognize the concept drift among different Miuref variants.

Recently, high-speed IPv4 scanners, such as ZMap, have enabled rapid and timely collection of TLS certificates and other security-sensitive parameters. Such large datasets led to the development of the Censys search interface, facilitating comprehensive analysis of TLS deployments in the wild. Several recent studies analyzed TLS certificates as deployed in web servers. Beyond public web servers, TLS is deployed in many other Internet-connected devices, at home and enterprise environments, and at network backbones. In this paper, we report the results of a preliminary analysis using Censys on TLS deployments in such devices (e.g., routers, modems, NAS, printers, SCADA, and IoT devices in general). We compare certificates and TLS connection parameters from a security perspective, as found in common devices with Alexa 1M sites. Our results highlight significant weaknesses, and may serve as a catalyst to improve TLS security for these devices.

With the purpose of delivering more robust systems, this paper revisits the problem of Inverse Uncertainty Quantification that is related to the discrepancy between the measured data at runtime (while the system executes) and the formal specification (i.e., a mathematical model) of the system under consideration, and the value calibration of unknown parameters in the model. We foster an approach to quantify and mitigate system uncertainty during the development cycle by combining Bayesian reasoning and online Model-based testing.

While computer-based design tools are widely used in architecure during late design phases for creating final floor plans, early design phases usually still take place in a traditional manner, using pen, paper and scissors. At the beginning of these phases, there is often only a rough idea of how a building should look like. Viewing existing floorplans of similar buildings can help an architect in his/her creative work, but searching for those plans manually is very time-consuming. Automated tools for searching similar floor plans could help to lower the amount of time needed for such investigations tremendously. In order to employ such search mechanisms, proper user interfaces are needed that fit to the architect’s working process. These interfaces should be useable easily and naturally, requiring less initial training. They should be capable of creating search requests that can be processed by the attached search mechanism. In this article, we describe two different user interfaces to serve this purpose. We describe their structures and interaction principles. Afterwards we show their general usability and user acceptance by the means of a users study.

The goal of anti-phishing techniques is to reduce the delivery rate of phishemails, and anti-phishing training aims to decrease the phishing click-through rates. This paper presents the X-Platform Phishing Attack, a deceptive phishing attack with an alarmingly high delivery and click-through rates, and highlights a subclass of phishing attacks that existing anti-phishing methods do not seem to be able to address. The main characteristic of this attack is that an attacker is able to embed a malicious link within a legitimate message generated by service providers (e.g., Github, Google, Amazon) and sends it using their infrastructure to his targets. This technique results in the bypassing of existing anti-phishing filters because it utilizes reputable service providers to generate seemingly legitimate emails. This also makes it highly likely for the targets of the attack to click on the phishing link as the email id of a legitimate provider is being used. An X-Platform Phishing attack can use email-based messaging and notification mechanisms such as friend requests, membership invitations, status updates, and customizable gift cards to embed and deliver phishing links to their targets. We have tested the delivery and click-through rates of this attack experimentally, based on a customized phishing email tunneled through GitHub’s pull-request mechanism. We observed that 100% of X-Platform Phishing emails passed the anti-phishing systems and were delivered to the inbox of the target subjects. All of the participants clicked on phishing messages, and in some cases, forwarded the message to other project collaborators who also clicked on the phishing links.

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

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

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

Research workers engaged in scientific pursuits bring out new ideas and information continually on various aspects of science. However, such research results, if not made available to the widest possible audience within a reasonable time, are worthless. New research is often based on the work of others. One may start where another scientist stopped. Research workers, therefore, after the completion of research projects, must compile the results and publish them in scientific journals with possible interpretations and clear conclusions. Only then, one can say that the research project is completed. Apart from research papers, there are some other established means of research reporting. These include theses, dissertations, conference proceedings, electronic publications, progress reports, and completion reports. For obtaining Ph.D., M.Phil., or Master’s degree in professional subjects such as agriculture, medicine, and engineering one has to submit a thesis or dissertation—another form of research report.

Residency training programs are excellent environments for medical students to become successful clinical practitioners to develop skillsets in care delivery for our patients. Training for an academic career, however, is less organized, whereby trainees who enjoy and/or are talented with academic outputs (e.g., publishing papers) gravitate toward such a chosen path. To be successful in this pursuit, however, requires a different set of skills, which is lightly touched upon during residency programs, appropriately; but once young faculty starts down this path, the expectation to become successful quickens rapidly.

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

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

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

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

Co-citation analysis can be exploited as a bibliometric technique used for mining information on the relationships between scientific papers. Proposed methods rely, however, on co-citation counting techniques that slightly take the semantic aspect into consideration. The present study proposes a new technique based on the measure of Semantic Similarity (SS) between the titles of co-cited papers. Several computational measures rely on knowledge resources to quantify the semantic similarity, such as the WordNet «is a» taxonomy. Our proposal analyzes the SS between the titles of co-cited papers using word-based SS measures. Two major analytical experiments are performed: the first includes the benchmarks designed for testing word-based SS measures; the second exploits the dataset DBLP (DBLP: Digital Bibliography & Library Project.) citation network. As a result, we found the SS measures behave the same as human judgement for the lexical similarity and can be consequently used for the automatic assessment of similarity between co-cited papers. The analysis of highly repeated co-citations demonstrates that the different SS measures display almost similar behaviours, with slight differences due to the distribution of the provided SS values. Furthermore, we note a low percentage of similar referred papers into the co-citations.

Technological solutions have been playing an important role in health care provision and their implementation in social care area, though a bit slower, promises good outcomes in terms of the quality of care delivery. The study reported in this paper analysed the shifting process from a typical paper registration to a registration performed by a web-based platform (i.e., the Ankira® platform) in a Portuguese social care institution. In order to gather evidence and best practices, 13630 registries were analysed, 6390 in paper format and 7240 from the web-based platform. The results show that the shifting from paper to the web-based platform lead to a significant error rate decline in all type of registers, which demonstrates the significant impact of the introduction of information technologies to support social care provision.

This work shows the design and structural analysis of a tensile testing machine for paper used as a substrate for the manufacture of piezoresistive sensors similar to strain gauges. This article also discusses the manufacturing process and the performance analysis of a low cost machine to characterize electromechanical structures made of graphite on polymeric substrate. Graphite in thin paper films have been investigated for a wide range of applications due to their excellent electrical and mechanical properties. Graphite sensors on flexible polymer substrates can be applied to portable wearable devices, as well as sensors for the Internet of things, bioengineering and applications in various areas of science and technology.

Understanding the current research trends, problems, and their innovative solutions remains a bottleneck due to the ever-increasing volume of scientific articles. In this paper, we propose NLPExplorer, a completely automatic portal for indexing, searching, and visualizing Natural Language Processing (NLP) research volume. NLPExplorer presents interesting insights from papers, authors, venues, and topics. In contrast to previous topic modelling based approaches, we manually curate five course-grained non-exclusive topical categories namely Linguistic Target (Syntax, Discourse, etc.), Tasks (Tagging, Summarization, etc.), Approaches (unsupervised, supervised, etc.), Languages (English, Chinese, etc.) and Dataset types (news, clinical notes, etc.). Some of the novel features include a list of young popular authors, popular URLs and datasets, list of topically diverse papers and recent popular papers. Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers. To facilitate future research and system development, we make all the processed dataset accessible through API calls. The current system is available at http://nlpexplorer.org.

The International Conference on Agile Software Development (XP) was established almost sixteen years ago. Based on data from Scopus database, a total of 789 papers have been published in between years of 2002 and 2018. We employed bibliometrics analysis and topic modeling with R/RStudio to analyze these published papers from various dimensions, including the most active authors, collaboration of authorship, most cited papers, used keywords and trends of probable topics from the titles and abstracts of those papers. The results show that the first five years of XP conference cover nearly 40% of the papers published until now and almost 62% of the XP papers have been cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues and problems faced by the practitioners in the industry. The results highlight the most influential researchers and institutions, and the collaboration between the authors in the conference papers. The approach applied in this study can be extended to other software engineering venues and can be applied to large-scale studies.

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

This paper is the result of the collaboration between the professors of the Department of Mechanics and the Production Management of the University of Mondragon and the 4th year students of the Karelia University of Applied Sciences in Finland. The developed case is the optimization of the flight process of a paper propeller, based on the Six Sigma DMAIC methodology. The objective was to show how to train the students of the Master in Innovation and Project Management in Mondragon University, in the acquisition of the knowledge and skills needed in continuous improvement process (CIP) and quality engineering (QE). Student feedback reflect, that the application of simulation case, has been valid to understand the theory and acquiring skills related to the techniques of QE, specifically in standard methods of continuous improvement, statistical tools, and standardization systems.

The International Conference on Big Data Analytics and Knowledge Discovery (DaWaK) has become a key conduit to exchange experience and knowledge among researchers and practitioners in the field of data warehousing and knowledge discovery. This study has quantitatively analyzed the 775 papers published in DaWaK from 1999 to 2019. This study presents the knowledge structure of the DaWaK papers and identifies the evolution of research topics in this discipline. Several text mining techniques were applied to analyze the contents of the research fields and to structure the knowledge presented at DaWaK. Dirichlet Multinomial Regression (DMR) is used to examine the trend of the research topics. Research metrics were used to identify conference and paper performance in terms of citation counts, readers, and the number of downloads. The study shows that DaWaK research outcomes have been receiving consistent attention from the scholarly community in the past 21 years. The 775 papers were cited by 4,339 times, marking the average number of citations of each proceeding as 207 times, and the average number of citations per published paper as six times.

Model Based Systems Engineering (MBSE) thrived in the recent decades in order to overcome the increasing complexity within systems. MBSE envisioned replacing document centric systems engineering by model centric systems engineering. MBSE can handle systems complexity efficiently by representing each entity in the model only once and generating different viewpoints. Most commonly used MBSE language is the “Systems Modeling Language (SysML)”. Virtual Reality can play a bigger role on creating truly model centric systems engineering approaches. The core application of MBSE approach implies utilization of a single system model, which will act as “source of truth” to all embedded system analyzing tools. Thus, the system model can communicate back and forth with Virtual Reality Environment to analyze the system performance. Systems engineer can conduct interactive immersive simulation of a scenario described in the system model and return the results obtained from the Virtual Reality Environment to the system model. Requirements analysis can be performed more efficiently by ensuring participation of the customers early in the product lifecycle utilizing the VR environment. A customer can walk into VR environment and see how the system design would look like and interact with it. Based on the interaction results, the systems engineer can modify the product/service specifications and design. This paper reviewed the works been done to incorporate virtual reality environment with SysML. The paper also proposed a framework on how VR environment can be implemented with SysML to perform true MBSE practice.

This paper is a plea for developing possibilistic learning methods that would be consistent with if-then rule-based reasoning. The paper first recall the possibility theory-based handling of cascading sets of parallel if-then rules. This is illustrated by an example describing a classification problem. It is shown that the approach is both close to a possibilistic logic handling of the problem and can also be put under the form of a max-min-based matrix calculus describing a function underlying a structure somewhat similar to a max-min neural network. The second part of the paper discusses how possibility distributions can be obtained from precise or imprecise statistical data, and then surveys the few existing works on learning in a possibilistic setting. A final discussion emphasizes the interest of handling learning and reasoning in a consistent way.

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

The amount of data that produced is increased day after day especially data as a text, so with this massive production it would be difficult to analyze or extract information to discover the patterns from the unstructured text. Text mining is used for availing the massive amount of knowledge that is in the text and deriving high quality information from the text automatically. This Process would save effort and time. Text mining considered as a subset of data mining where data mining is more generic. This paper proposes a methodology of mining a text for a case study related to publication papers. Some of text mining approaches will be introduced for mining the publication papers using machine learning (ML) and natural language processing (NLP) techniques. Describing each phase as following: First phase is keywords extraction using natural language processing techniques, second phase named entity recognition and last phase is document classification. The last two phases are using the ML techniques. Then a case study is built to simulate the system phases, showing what is the input and the output in each phase.

Paper manufacturing is energy demanding and improved modelling of the pulp bleach process is the main non-invasive means of reducing energy costs. In this paper, time it takes to bleach paper pulp to desired brightness is examined. The model currently used is analysed and benchmarked against two machine learning models Random Forest and TreeBoost. Results suggests that the current model can be superseded by the machine learning models and it does not use the optimal compact subset of features. Despite the differences between the machine learning models, a feature ranking correlation has been observed for the new models. One novel, yet unused, feature that both machine learning models found to be important is the concentration of bleach agent.

Although the Domain Name System (DNS) was designed as a naming system, its features have made it appealing to repurpose it for the deployment of novel systems. One important class of such systems are security enhancements, and this work sheds light on their deployment. We show the characteristics of these solutions and measure reliability of DNS in these applications. We investigate the compatibility of these solutions with the Tor network, signal necessary changes, and report on surprising drawbacks in Tor’s DNS resolution.

Apps in the FINANCE category constitute approximately 2% of the 2,000,000 apps in the Google Play Store. These apps handle extremely sensitive data, such as online banking credentials, budgets, salaries, investments and the like. Although apps are automatically vetted for malicious activity before being admitted to the Google Play Store, it remains unclear whether app developers themselves check their apps for vulnerabilities before submitting them to be published. Additionally, it is not known how financial apps compare to other apps in terms of dangerous permission usage or how they evolve as they are updated. We analyse 10,400 apps to understand how apps in general and financial apps in particular have evolved over the past two years in terms of dangerous permission usage and the vulnerabilities they contain. Worryingly, we discover that both financial and non-financial apps are getting more vulnerable over time. Moreover, we discover that while financial apps tend to have less vulnerabilities, the rate of increase in vulnerabilities in financial apps is three times as much as that of other apps.

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

There are several frameworks for password-based authenticated key exchange (PAKE) protocols with common reference string following the work of Katz, Ostrovsky and Yung (Eurocrypt’01), and it seems that the IND-CCA secure encryption is inevitable when constructing PAKE in standard model.

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

Context and motivation. Collaborative AI systems aim at working together with humans in a shared space. Building these systems, which comply with quality requirements, domain specific standards and regulations is a challenging research direction. This challenge is even more exacerbated for new generation of systems that leverage on machine learning components rather than deductive (top-down programmed) AI.