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Thermal papers, commonly used for printed receipts or lottery tickets, are omnipresent in our everyday life. They are regarded as semi-porous substrates, and yet can be critical to analyze when looking for latent fingermarks due to their thermosensibility. The aim of this study was to investigate a development sequence that would better combine the adequate detection techniques in order to maximize the chances to develop latent fingermarks left on these substrates. Different methods of development have been compared on test substrates: black magnetic powder, Lumicyano™, thermal development, ninhydrin and 1,2-indanedione/ZnCl2. Whitening stages and thermal development have been focused on, tested and optimized. The results of these preliminary tests enabled the study of three development sequences. They have subsequently been compared to the one currently used in the Gendarmerie's laboratories and the best results have been provided during pseudo-operational comparative trials by one of these sequences, consisting in 6 stages.

One of the benefits of Internet of Vehicles (IoV) is improved traffic safety and efficiency, for example due to the capability to share vehicular messages in real-time. While most of the vehicular messages only need to be shared by nearby vehicles, some messages (e.g., announcement messages) may need to be more broadly distributed, for example to vehicles in a wider region. Finding a single trusted entity to store and distribute such messages can be challenging, and vehicles may not be inclined to participate (e.g., generation and distribution of announcement messages) unless they can benefit from such participation. In addition, achieving both security and privacy can be challenging. In this paper, we propose a blockchain based secure data sharing system to address the above challenges in an IoV setting. Specifically, in our system, announcement messages are stored using blockchain. To encourage/incentivize participation, vehicles that faithfully broadcast the announcement messages and/or contribute to the block generation will be rewarded by some cryptocurrency. Our system is also designed to be privacy-preserving and realizes both priori and posteriori countermeasures.

Background In Software Engineering (SE), conference publications have high importance both in effective communication and in academic careers. Researchers actively discuss how a paper should be organized to be accepted in mainstream conferences. Aiming This work tackles the problem of generalizing and characterizing the type of papers accepted at SE conferences. Method The paper offers a new perspective in the analysis of SE literature: a categorization scheme for SE papers is obtained by merging, extending and revising related proposals from a few existing studies. The categorization scheme is used to classify the papers accepted at three top-tier SE conferences during five years (2012–2016). Results While a broader experience is certainly needed for validation and fine-tuning, preliminary outcomes can be observed relative to what problems and topics are addressed, what types of contributions are presented and how they are validated. Conclusions The results provide insights to paper writers, paper reviewers and conference organizers in focusing their future efforts, without any intent to provide judgments or authoritative guidelines.

This study explored the relationship between syntactic complexity and rhetorical functions and the relationship between syntactic complexity and level of move/step realization in learner-generated research paper introductions. We created a corpus of 79 introductions written by undergraduate L2 students at intermediate to high-intermediate levels. We annotated the introductions for moves and steps, measured their syntactic complexity at the global, clausal, and phrasal levels, and rated their level of move/step realization across four levels (no, low, intermediate, and high). We conducted one-way MANOVA, one-way ANOVA, Pearson correlation, and stepwise multiple linear regression analyses. We found sentences realizing Reviewing items of previous research function to be significantly longer than other steps in Move1 and to contain significantly more clausal and phrasal complexity features. Sentences performing Claiming centrality function also demonstrated significantly more phrasal complexity features as measured by complex nominals per clause. We also found a higher level of phrasal complexity in introductions with a higher level of move/step realization, which can be attributed to introductions including Step1 and Step3, rather than containing all steps. Our findings imply that there is value in developing explicit instructions on how to use complex structures in performing rhetorical steps in research paper introductions. © 2021 Elsevier Ltd

Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

The Internet of Things (IoT) based remote monitoring of the environment gained tremendous attention from researchers. Wireless monitoring is offered through the IoT-enabled technologies under applications like Intelligent Transport System (ITS), healthcare, home automation, precision agriculture, etc. The working of IoT applications has been the basis on different kinds regards sensor nodes that were deployed for field data sensing & transmitting through a base station (BS). The Wireless Sensor Network (WSN)- assisted IoT networks to suffer from two challenges like energy-efficiency & various security threats such as sinkhole, black hole, Daniel of Service attacks, etc. This survey paper, studying the recent security measures to protect communications from such attackers. A systematic review conducted on lightweight security solutions like trust-based & probe route-based mechanisms under this research. Comparative analysis of such methods presented in terms of security technique, attacks, performance metrics, etc. The outputs of this paper have been critics of research observed through the comparative analysis of reviewed methods.

This White Paper summarizes the current state of discussion regarding the main technological challenges in cybersecurity and impact of those, including ways and approaches to addressing them, on key fundamental values. It provides an overview on current cybersecurity threads and countermeasures and focuses on ethical dilemmas that emerge when counteracting those threads. It also points to the fact that the cybersecurity community relies much more on interpersonal relations when sharing intelligence and data than in explicit national or supranational regulations. Furthermore, the White Paper presents advanced cryptographic techniques and data anonymization techniques that may help to solve or minimize some of the ethical dilemmas.

The top 1000 biomedical papers by number of citations are classified by method, type of method and non-methods by examination of citation contexts. Supervised machine learning is applied to the context data for a training sample of papers which is then used to classify the full list, revealing that words indicating utility are most important for the classification of methods. Further word analysis is carried out using corpus linguistics to uncover context words that characterize non-methods. Hedging words are found to play an important role for non-methods, and several are selected for further analysis with logistic regression. Other variables in the regression are a consensus variable based on the similarity of contexts for a paper and another variable based on whether citations come from “methods” sections of citing papers. Accuracy of predictions from logistic regression is comparable to machine learning. The results are interpreted in terms of the perceived certainty or uncertainty of the underlying knowledge, that is, methods and their outputs have higher certainty, and non-methods higher uncertainty. Evidence is found that hedging is inversely related to citation frequency. Implications of this work for the study of the development of science and the role of methods and tools in biomedical research are discussed.

This paper reviews several dimensions in terms of which electronic/Internet and paper voting can be compared (vote secrecy, verifiability, ballot box integrity, transparency and trust base). We conclude that, for many vulnerabilities of Internet voting systems, there exist related weakness in paper systems as well. The main reason why paper-based elections are perceived as more secure is historical experience. We argue that recent criticisms of Internet voting have unfairly concentrated on the associated risks and neglected the benefits. Remote electronic voting lowers the cost of election participation and provides the most secure means for absentee voting. The latter is something that is urgently required in the contemporary, increasingly mobile world. Hence, we need to give Internet voting a chance, even if it means risking unknown threats and learning by trial and error.

Paper cooperation network embodies expert topic similarity in an extent, thus, a novel method is proposed for expert topic analysis and extraction by combining paper cooperation network and topic model. In the method, we extract each paper’ author information and construct an expert cooperation network. At the same time, by means of LDA model, a probabilistic topic model is also built to analyze papers’ latent topics. Then, by making full use of the feature that adjacent nodes in the expert cooperation network share similar themes distribution, we makes a constraint on expert topic distribution in Gibbs sampling process of solving the probabilistic topic model. Experimental results on NIPS dataset show that the proposed method can effectively extract expert topics, and the expert paper cooperation network plays a very good supporting role on the extracting task.

We used the random forest classifier to predict Gross Motor Function Classification System (GMFCS) levels I-IV from patient reported abilities recorded on the Gillette Functional Assessment Questionnaire (FAQ). The classifier exhibited outstanding accuracy across GMFCS levels I-IV, with 83%-91% true positive rate (TPR), area under the receiver operation characteristic (ROC) curve greater than 0.96 for all levels, and misclassification by more than one level only occurring 1.2% of the time. This new approach to GMFCS level assignment overcomes several difficulties with the current method: (i) it is based on a broad spectrum of functional abilities, (ii) it resolves functional ability profiles that conflict with existing GMFCS level definitions, (iii) it is based entirely on self-reported abilities, and (iv) it removes complex age dependence. Further work is needed to examine inter-center differences in classifier performance-which would most likely reflect interpretive differences in GMFCS level definitions between centers.

Machine learning methods are being successfully applied in various domains of human activity, including optics. Can they become a universal instrument capable of improving user aspect of photonic devices? Is their application a necessary element of such improvement? Answers to fundamental questions arising from application of machine learning methods in ultrashort-pulsed lasers are discussed. Future prospects and current fundamental limitations of such methods are analysed.

This research is concerned with the detection of similar academic papers. Given a tested paper from a given corpus of 10,099 peer-reviewed scientific papers, a two-stage process was activated. During the first stage, most of the papers were filtered out using a fast filter method. In the second stage, in order to detect similar papers we applied 23 heuristic variants derived from 3 novel prototype methods using various parameter settings. The three novel prototype methods are: CT-TR Constant Number of randomized T fingerprints, compared to each one-third of R (first/middle/last) fingerprints, CT-AR: Constant Number of randomized T fingerprints, compared to all R fingerprints, and CDT-AR: Constant Number of divided randomized T fingerprints compared, to all R fingerprints. Results achieved by the new methods are superior to those of previous heuristic methods, which were approximations of the Full Fingerprint (FF) method, currently considered the best heuristic method. The order of this new methods' run-time, (n), is far more efficient than the order of the FF method run-time, (n2) (after removing short documents from the corpus).

Objective This article reviews the available evidence and guidance on methods to identify reports of quasi-experimental (QE) studies to inform systematic reviews of health care, public health, international development, education, crime and justice, and social welfare. Study Design and Setting Research, guidance, and examples of search strategies were identified by searching a range of databases, key guidance documents, selected reviews, conference proceedings, and personal communication. Current practice and research evidence were summarized. Results Four thousand nine hundred twenty-four records were retrieved by database searches, and additional documents were obtained by other searches. QE studies are challenging to identify efficiently because they have no standardized nomenclature and may be indexed in various ways. Reliable search filters are not available. There is a lack of specific resources devoted to collecting QE studies and little evidence on where best to search. Conclusion Searches to identify QE studies should search a range of resources and, until indexing improves, use strategies that focus on the topic rather than the study design. Better definitions, better indexing in databases, prospective registers, and reporting guidance are required to improve the retrieval of QE studies and promote systematic reviews of what works based on the evidence from such studies.

Architectures, networks, and systems and middleware technologies have been advancing.These advances lead to new concepts and platforms for computing, ranging from clusters and grids to clouds and datacenters.The CCGrid 2015 Conference discussed research and results on topics related to these concepts and platforms, and their applications.This special section presents five high-quality papers, extended from CCGrid 2015 papers.

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper’s ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.

The advancement of electric grid into smart grid has become possible by integrating communication network with the component of power system for free disturbance and effective power distribution. Advanced metering infra-structure (AMI) is used for measurement, collection, analysis and the electricity consumption of the customer. This AMI has variety of sensors for the consumption of electricity usage, smart meter, data concentrators, various server and different communication network. The implementation of net-work for making information available all the time introduces the cyber security concern on metering data of the customer. The modification or adulteration on metering data may lead to Financial loss during billing, false pre-diction of required energy and physical damage to the equipment present in the entire infrastructure. Hence it become essential to ensure confidentiality and integrity of metering data. In this paper various security algorithm has been surveyed to understand the factor for achieving the confidentiality and integrity of smart metering data. At last comparative work and future scope is discussed to identify factors and their importance for cyber security on metering data of the customer has been identified.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

Although software has helped researchers conduct research, little is known of the impact of software on science. To fill this gap, this article proposes an improved bootstrapping method to extract software entities from full-text papers and assess their impact on science. Evaluation results show that the proposed entity extraction system outperforms three baseline methods on extracting software entities from full-text papers. The proposed method is then used to learn software entities from all papers published in PLoS ONE in 2014. More than 2000 unique software entities are obtained which accounted for more than 20,000 mentions and more than 7000 citations. The paper finds that software is commonly used in the scientific community along with a substantial uncitedness.

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, where the subjects, methods, and results are more valuable from a physician's perspective. Based on previous studies on English literature section identification, we experiment with the effective features to use with classic machine learning algorithms to tackle the problem. It is found that Conditional Random Fields, which consider sentence interdependency, is more effective in combining different feature sets, such as bag-of-words, part-of-speech, and headings, for Chinese literature section identification. Moreover, we find that classic machine learning algorithms are more effective than generic deep learning models for this problem. Based on these observations, we design a novel deep learning model, the Structural Bidirectional Long Short-Term Memory (SLSTM) model, which models word and sentence interdependency together with the contextual information. Experiments on a human-curated asthma literature dataset show that our approach outperforms the traditional machine learning methods and other deep learning methods and achieves close to 90% precision and recall in the task. The model shows good potential for use in other text mining tasks. The research has significant methodological and practical implications.

There have been several cases in the news regarding the presence of synthetic cannabinoids on paper being smuggled into jails and prisons in the United States. The Allegheny County Office of the Medical Examiner has been involved in the analysis of multiple cases. For the first time the laboratory received an analysis request for not only identification of controlled substances, but also for the presence of latent fingerprints. It was unknown if the processing methods used by the latent print unit would interfere with the identification of the controlled substances. This paper shows that DFO and ninhydrin processing will remove some of the synthetic cannabinoids, possibly effecting concentration on the paper, but the substances were still able to be positively identified after processing.

Every product that exists, ranging from a toothbrush to a car, has first been conceived as a mental concept. Due to its efficacy in rapidly externalizing concepts, paper-based sketching is still extensively used by practising designers to gradually develop the three-dimensional (3D) geometric form of a concept. It is a common practice that form concepts are sketched on paper prior to generating 3D virtual models in commercial Computer-Aided Design (CAD) systems. However, the user-interface of such systems does not support automatic generation of 3D models from sketches. Furthermore, the inherent characteristics of form sketching (e.g. idiosyncrasy) pose a challenge to computer-based understanding of the form concept semantics expressed on paper. To address these issues, this paper is therefore concerned with the development of a visual language that is prescribed and to be used by product designers to annotate paper-based sketches such that the form geometry semantics can be formally represented; parsing the annotated sketch allows for the automatic generation of 3D virtual models in CAD. Inspired by re-usable 3D CAD modelling functions and the related environmental constraints and requirements, a prescribed sketching language, PSL, has been developed to annotate paper-based form sketches. The framework architecture which parses the annotated sketch and subsequently extracts the form concept semantics is described. Based on this framework, a prototype computer tool has been implemented and evaluated. Evaluation results provide a degree of evidence, first on the suitability of PSL in representing the semantics of a range of forms, and secondly on the designers? acceptance of taking up this annotated sketching approach in practice. The key contribution is a visual language to formally represent form geometry semantics on paper.Parsing the language allows for the automatic generation of 3D virtual models.A proof-of-concept prototype tool was implemented.The language is capable to roughly model forms with linear topological ordering.Evaluation results show that practising designers would use the language.

The calculation of various test characteristics may be required as part of a data analysis exercise. This paper explains how to set up these calculations in Microsoft Excel in order to obtain sensitivity, specificity, positive and negative predictive values, diagnostic accuracy and prevalence.

The specification of a concurrent program module is a difficult problem. The specifications must be strong enough to enable reasoning about the intended clients without reference to the underlying module implementation. We survey a range of verification techniques for specifying concurrent modules, in particular highlighting four key concepts: auxiliary state, interference abstraction, resource ownership and atomicity. We show how these concepts combine to provide powerful approaches to specifying concurrent modules.

Open source software (OSS) is a software product with the source code made public so that anyone can read, analyze, and change or improve the code. The use of this software is under a license, like Apache, GNU, MIT, Mozilla Public, and Eclipse Public License. Open source software development (OSSD) provides high quality assurance through user testing and peer reviews. The quality of these products depends on the size of the product community. This paper discusses the stakeholders of the OSS community, the quality assurance frameworks and models proposed in some studies, some statistics about OSS, the problems that affect the quality of OSSD, and the advantages and disadvantages of OSS compared to closed source software. This allows us to understand how we can achieve and improve the quality assurance and quality control of OSSD.

The laws of physics play a crucial role in the security of quantum key distribution (QKD). This fact has often been misunderstood as if the security of QKD would be based only on the laws of physics. As the experts know well, things are more subtle. We review the progresses in practical QKD focusing on (I) the elements of trust that are common to classical and quantum implementations of key distribution; and (II) some threats to security that have been highlighted recently, none of which is unredeemable (i.e., in principle QKD can be made secure). This leads us to guess that the field, similar to non-quantum modern cryptography, is going to split in two directions: those who pursue practical devices may have to moderate their security claims; those who pursue ultimate security may have to suspend their claims of usefulness., Small changes and clarifications. This paper still describes the situation of QKD in June 2009, when its first version was sent out; we have mentioned a few developments in a Note added in proof. Accepted in a special issue of "Theoretical Computer Science" (T. Mor and R. Renner, editors)

In a very recently presented paper, Turky and Abdullah 5 proposed a novel multi-population harmony search with external archive (MHSA-ExtArchive) for dynamic optimization problems. In the experimental results, the authors claimed that their approach could outperform several state-of-the-art algorithms. They also showed the superiority of their method by means of numerical experiments on Moving Peaks Benchmark (MPB). Despite the interesting idea of applying multi-population scheme on harmony search and using a new type of external archive for dealing with dynamic problems, we believe that there are two very important shortcomings in the result analysis, which we point out in this short note. The main motivation of the present note is to contribute toward preventing the same mistakes from happening by the other researchers.

Cloud computing is becoming increasingly fashionable in distributed computing environment. Processing and Data storage use cloud environment is becoming a movement universal. Software as a Service (SaaS) has on many business applications as well as in our day to day life, we can simply say that this disruptive technology. Cloud computing can be seen since Internetbased computing, in which shared resources, software, and information are made available to devices on demand. It allows resources towards leveraged on per-use basis. It diminishes cost and complexity of service providers by means of assets and operational costs. It allows users to access applications tenuously. On behalf of user, this construct directs cloud service provider to feel software updates and cost of servers etc. For both, cloud providers and consumers; availability, integrity, authenticity, confidentiality, and privacy are important concern. Infrastructure as a Service (IaaS) serves as base layer for many other release models and Platform-as-a-Service (PaaS) clouds. Security of PaaS clouds is considered from multiple perspective including access control, service continuity and privacy while protecting together the service provider and the user. Security problems of PaaS clouds are explored and classified. In this paper we are going to some major security issues of current cloud computing environments.

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security,etc.

Highlights • Alert fatigue is induced by amount of pop up and alert messages. • Vendors of technology underestimate human factor problems. • Readability of messages is a patient safety problem. • Characterizing the alerts for different linacs and treatment types is required., Radiotherapy software messages (sometimes called alerts, pop-up windows, alarms, or error messages) to the user appear continuously on computer screens. These software messages sometimes require decisions to be made as to the next appropriate action. However, mainly these messages are for information only. Dealing with software messages is a well-recognized problem in healthcare and has contributed to catastrophic events both outside and within radiotherapy. The purpose of this work is to highlight the prevalence and raise awareness within the radiotherapy community of such software messages related to external beam radiation therapy procedures at the linear accelerator. Radiation Therapists (RTTs) were asked to record the type and frequency of software message over 50 fractions and for 50 different patients. The data was collected at 6 institutions in the Netherlands using linear accelerators from Elekta, Ltd. and Varian Medical Systems, Inc. Results show that linear accelerator software messages (including record and verify) occur at a rate of about 8.9 messages per patient fraction. This number of software messages is potentially impacting on patient safety as these messages range in level of importance. The impact and potential reduction of these software messages should be the focus of future research and improved implementation.

Background Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment. Methods Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks to classify eardrum and external auditory canal features into six categories of ear diseases, covering most ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating several optimization schemes, two best-performing models were selected to compose an ensemble classifier, by combining classification scores of each classifier. Findings According to accuracy and training time, transfer learning models based on Inception-V3 and ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial data-size dependency of classifier performance in the transfer learning, evaluated in this study, the high accuracy in the current model is attributable to the large database. Interpretation The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trained with data in a various acquisition condition, which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. Fund This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( NRF-2017M3C7A1049051 ).

Competitive sporting environments demand reliable statistics on an athlete’s performance to measure an athlete’s actions during competition, and to differentiate between the fine-grained actions performed. This is especially true for combat sports such as boxing where the variations observed between the main punching actions are subtle, making automatic classification of movements extremely difficult. This paper presents a robust framework for the automatic classification of a boxer’s punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through multi-class Support Vector Machine (SVM) and Random Forest classifiers using combinations of features. A coarse-to-fine hierarchical SVM classifier is presented in this paper based on prior knowledge of boxing punches. This framework has been applied to boxing image sequences taken at the Australian Institute of Sport with 14 elite boxers. Results demonstrate the effectiveness of the action recognition method, with the hierarchical SVM classifier yielding a 97.3% accuracy improving on the recent state-of-the-art action recognition systems.

Shredded document recovery is an important research sub-field of information security. The paper document is broken into a large number of similar shreds by the shredder. Usually these shreds imply some important information. If they are restored, it will have an important impact on judicial investigation, military command, and archaeological research. Therefore, it has a great value in research of shredded document recovery. In consideration of large differences between languages and great demands for Chinese document recovery, our study will focus on the shredded Chinese document. In this paper, a method of shredded document recovery based on character stroke feature is proposed. We use horizontal strokes to match Chinese characters. Finally the shredded document is restored. In the test of actual sample, the method proposed in this paper has achieved good recovery result. The average accuracy is 13.96% higher than the traditional algorithm.

For successful management of construction projects and organizations, it is crucial to adopt effective management systems that can handle the various information and documents of project activities. Document Management System (DMS) is the system used to store, control, coordinate, process and/or retrieve documents whether it is in electronic or paper based format. This research aims at investigating existing electronic and paper based DMSs in a sample of small size contracting companies in Jordan. Interviews and questionnaire survey with contractors, contractors’ representatives and practitioners of DMSs in a number of small contracting companies were carried out to investigate and evaluate the components, processes, motivations and challenges of the existing and intended DMSs. Electronic formats of documents and files used in small contracting companies will be also investigated. The results of this research can help contracting companies to enhance their DMSs, and improve efficiency and performance of the processes of construction projects management.

Background To achieve imaging report standardization and improve the quality and efficiency of the intra-interdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. Methods We created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. Findings Our IILS is clinically applicable due to the consistency with nodules detected by IILS, with its highest consistency of 0·94 and an AUC of 90·6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76·5% and specificity of 89·1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14·45 ± 0·38 to 2, time consumed from 16·87 ± 0·38 s to 6·92 ± 0·10 s, number of invalid images from 7·06 ± 0·24 to 0, and missing lung nodules from 46·8% to 0%. Interpretation This IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. Fund The National Natural Science Foundation of China.

Background: Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks. Methods: From 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set. Findings: Promising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p

As the volume of medicinal information stored electronically grows, the necessity to boost the security increases too. Health care systems are now very common in the world. A healthcare system must maintain privacy while preventing patient information from being transmitted. Therefore, securing such systems is crucial for gaining public confidence in the use of network-based healthcare systems. Information hiding is particularly used for security applications to protect the secret message from an unauthorized person. Employing information hiding in healthcare systems, deals with storing, sharing, and managing medical data in a reliable and secure manner. This paper reviews the algorithms used to provide security in healthcare systems. Furthermore, these algorithms are categorized in this paper based on the application and the way of providing security. Finally, the challenges in implementing the security of healthcare systems are described.

When producing complex and highly customisable products in low volumes (or in ‘batch sizes of one’), automation of production systems is critical for competitiveness and profitability in high labour-cost economies. To facilitate batch-size-of-one production, ‘topology generation’, ‘realisability’, and ‘control’ algorithms have been developed as part of the Evolvable Assembly Systems (EAS) project. The topology generation algorithm computes all the possible sequences of parallel activities that assembly resources can perform on parts and is run offline whenever the layout of the production facility changes, whereas realisability checking and controller generation are performed at run-time to check whether a production facility with a given set of assembly resources can assemble a desired product, and how the product should be assembled, e.g., which resources to use, and when. Generated controllers are output in Business to Manufacturing Markup Language (B2MML). Taken together, the algorithms thus represent a step toward a complete path from the formal specification of an assembly system and the products to be assembled, to the automated synthesis of executable process plans. This paper presents each algorithm in sufficient detail to allow their reimplementation by other researchers. Topology generation is the most expensive step in the approach. A preliminary experimental evaluation of the scalability of topology generation is presented, which suggests that, for small to medium sized production facilities, the time required for recomputing the topology is sufficiently small not to preclude frequent factory transformations, e.g., the addition of new resources.

Novel organic forms of interactive displays are developed recently from flexible materials that can bend, fold or roll between the hands of its user giving him a wholly new sense of digital interactive media. In this paper, we hold a survey for different OUIs (Organic User Interfaces) and categorize them into four main categories: flat surfaces, non-flat surfaces, digital substrates and handheld devices. Each of which has its uses that suites certain contexts and does not fit others. We discuss each category thoroughly with its possible sub-categories, examples and applications. Each category is noticed to have a physical design that has a great deal of resemblance to the physical form or design of a pharaonic monumental style or type of artifacts from the history of ancient Egypt. The paper highlights the observed resemblance between organic digital displays and ancient monumental displays with respect to the similarities and differences in the sense that they are both used as information displays for targeted users in certain contexts. This unintended physical resemblance points out to where we might inspire novel designs for digital content on interactive displays.

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie., Highlights • This work presents the power of deep learning approaches for classifying pathology images in presence of extensive tumor heterogeneity. • A basic CNN architecture and several state-of-the-art image classification architectures are compared in an open source pipeline. • The pipeline is applicable to wide rang of histopathology images.

In this paper, an efficient beamforming technique at the aggregator node in the patient wireless body area network (WBAN) is proposed to improve the communication performance with the gateway node using C-shaped switched-beam antenna arrays. This proposed technique provides high gain beams towards the gateway node which may be located indoor or outdoor based on the medical quarantine or isolation topology and help medical staff monitor patients with less contact and infection probability. Also, a semi-blind switched-beam algorithm is developed to both reduce the processing time required for beam selection and reduce the power loss due to the off-mainlobe orientation of the selected beam with the signal direction. The proposed technique is analyzed at different wearing scenarios and simulation results show that the received power levels are improved with as less as 0.3 dBm standard deviation which helps in extending the communication range between the WBAN aggregator and gateway nodes in the health monitoring system and also extend the battery lifetime at the patient WBAN.

Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions., Highlights • Artificial intelligence leads to a paradigm shift in sleep research. • Sleep stage scoring with deep neural networks is robust and reliable. • Neural networks find embeddings of EEG raw data. • Probabilities of momentary sleep stages can be visualized as hypnodensity graphs. • Cross-correlations of sleep stage probability vectors provide an estimate of sleep cycle period length.

The date palm is one of the most valuable fruit trees in the world. Most methods used for date fruit inspection, harvesting, grading, and classification are manual, which makes them ineffective in terms of both time and economy. Research on automated date fruit harvesting is limited as there is no public dataset for date fruits to aid in this. In this work, we present a comprehensive dataset for date fruits that can be used by the research community for multiple tasks including automated harvesting, visual yield estimation, and classification tasks. The dataset contains images of date fruit bunches of different date varieties, captured at different pre-maturity and maturity stages. These images cover multiple sets of variations such as multi-scale images, variable illumination, and different bagging states. We also marked date bunches for selected palms and measured the weights of the bunches, captured their images on a graph paper, and recorded 360° video of the palms. This dataset can help in advancing research and automating date palm agricultural applications, including robotic harvesting, fruit detection and classification, maturity analysis, and weight/yield estimation. The dataset is freely and publicly available for the research community in the IEEE DataPort repository [1] (https://doi.org/10.21227/x46j-sk98). Keywords: Date fruits, Visual dataset, Robotic harvesting, Machine vision, Fruits classification, Visual yield estimation

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.