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Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates). Graphic abstract Electronic supplementary material The online version of this article (10.1007/s10928-020-09695-z) contains supplementary material, which is available to authorized users.

Input estimation is employed in cases where it is desirable to recover the form of an input function which cannot be directly observed and for which there is no model for the generating process. In pharmacokinetic and pharmacodynamic modelling, input estimation in linear systems (deconvolution) is well established, while the nonlinear case is largely unexplored. In this paper, a rigorous definition of the input-estimation problem is given, and the choices involved in terms of modelling assumptions and estimation algorithms are discussed. In particular, the paper covers Maximum a Posteriori estimates using techniques from optimal control theory, and full Bayesian estimation using Markov Chain Monte Carlo (MCMC) approaches. These techniques are implemented using the optimisation software CasADi, and applied to two example problems: one where the oral absorption rate and bioavailability of the drug eflornithine are estimated using pharmacokinetic data from rats, and one where energy intake is estimated from body-mass measurements of mice exposed to monoclonal antibodies targeting the fibroblast growth factor receptor (FGFR) 1c. The results from the analysis are used to highlight the strengths and weaknesses of the methods used when applied to sparsely sampled data. The presented methods for optimal control are fast and robust, and can be recommended for use in drug discovery. The MCMC-based methods can have long running times and require more expertise from the user. The rigorous definition together with the illustrative examples and suggestions for software serve as a highly promising starting point for application of input-estimation methods to problems in drug discovery. Electronic supplementary material The online version of this article (doi:10.1007/s10928-016-9467-z) contains supplementary material, which is available to authorized users.

Purpose To predict precision and other performance characteristics of chromatographic purity methods, which represent the most widely used form of analysis in the biopharmaceutical industry. Methods We have conducted a comprehensive survey of purity methods, and show that all performance characteristics fall within narrow measurement ranges. This observation was used to develop a model called Uncertainty Based on Current Information (UBCI), which expresses these performance characteristics as a function of the signal and noise levels, hardware specifications, and software settings. Results We applied the UCBI model to assess the uncertainty of purity measurements, and compared the results to those from conventional qualification. We demonstrated that the UBCI model is suitable to dynamically assess method performance characteristics, based on information extracted from individual chromatograms. Conclusions The model provides an opportunity for streamlining qualification and validation studies by implementing a “live validation” of test results utilizing UBCI as a concurrent assessment of measurement uncertainty. Therefore, UBCI can potentially mitigate the challenges associated with laborious conventional method validation and facilitates the introduction of more advanced analytical technologies during the method lifecycle. Electronic supplementary material The online version of this article (doi:10.1007/s11095-012-0836-z) contains supplementary material, which is available to authorized users.

Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voice-based modules, about the doctors visiting schedule, his/her body parameter details, etc. This paper presents an approach of a complete Autonomous Nursing Robot which supports all the aforementioned tasks. In this paper, we present a new Autonomous Nursing Robot system capable of operating in a smart hospital environment area. The objective of the system is to identify the patient room, perform robot path planning for the delivery of medicines to a patient, and measure the patient body parameters, through a wireless BLE (Bluetooth Low Energy) beacon receiver and the BLE beacon transmitter at the respective patient rooms. Assuming that a wireless beacon is kept at the patient room, the robot follows the beacon’s signal, identifies the respective room and delivers the needed medicine to the patient. A new fuzzy controller system which consists of three ultrasonic sensors and one camera is developed to detect the optimal robot path and to avoid the robot collision with stable and moving obstacles. The fuzzy controller effectively detects obstacles in the robot’s vicinity and makes proper decisions for avoiding them. The navigation of the robot is implemented on a BLE tag module by using the AOA (Angle of Arrival) method. The robot uses sensors to measure the patient body parameters and updates these data to the hospital patient database system in a private cloud mode. It also makes uses of a Google assistant to interact with the patients. The robotic system was implemented on the Raspberry Pi using Matlab 2018b. The system performance was evaluated on a PC with an Intel Core i5 processor, while the solar power was used to power the system. Several sensors, namely HC-SR04 ultrasonic sensor, Logitech HD 720p image sensor, a temperature sensor and a heart rate sensor are used together with a camera to generate datasets for testing the proposed system. In particular, the system was tested on operations taking place in the context of a private hospital in Tirunelveli, Tamilnadu, India. A detailed comparison is performed, through some performance metrics, such as Correlation, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), against the related works of Deepu et al., Huh and Seo, Chinmayi et al., Alli et al., Xu, Ran et al., and Lee et al. The experimental system validation showed that the fuzzy controller achieves very high accuracy in obstacle detection and avoidance, with a very low computational time for taking directional decisions. Moreover, the experimental results demonstrated that the robotic system achieves superior accuracy in detecting/avoiding obstacles compared to other systems of similar purposes presented in the related works.

Biometric technologies have become the main focus in the design of state-of-the-art border security solutions. While respective research in the field of multimedia vision has been centred around improving quality and accuracy of identity recognition, the impact of such technologies upon society and legal regulations still remains a topic unaddressed, specifically within the engineering community. Research in technology can and in some respect must include collaboration with social sciences and social practice. Building on participation in the EU funded research project PERSONA [18] (Privacy, Ethical, Regulatory and SOcial No-gate crossing point solutions Acceptance), authors of this paper look at the challenges associated with biometrics-based solutions in no-gate border crossing point scenarios. This included the procedures needed for the assessment of their social, ethical, privacy and regulatory acceptance, particularly in view of the impact on both, the passengers and border control authorities as well as the potential pitfalls of biometric technology due to fraudulent activities. In consultation with the collaborating border control authorities, the paper reports on the formal assessment of biometric technologies for real-world acceptance to cope with the increasing demand of global travellers crossing state borders.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on “mobile data science and intelligent apps” in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

As a type of coronavirus, COVID-19 has quickly spread around the majority of countries worldwide, and seriously threatens human health and security. This paper aims to depict cumulative numbers of COVID-19 infections in China using the growth model chosen by cross validation. The residual plot does not look like a null plot, so we can not find a distribution function for the disturbance term that is close enough to the true frequency. Therefore, the disturbance term can not be characterized as random variables, and stochastic regression analysis is invalid in this case. To better describe this pandemic automatically, this paper first employs uncertain growth models with the help of uncertain hypothesis tests to detect and modify outliers in data. The forecast value and confidence interval for the cumulative number of COVID-19 infections in China are provided.

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a conventional neural network and a recurrent neural network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

Context: Obfuscation is a common technique used to protect software against malicious reverse engineering. Obfuscators manipulate the source code to make it harder to analyze and more difficult to understand for the attacker. Although different obfuscation algorithms and implementations are available, they have never been directly compared in a large scale study.\ud \ud Aim: This paper aims at evaluating and quantifying the effect of several different obfuscation implementations (both open source and commercial), to help developers and project managers to decide which algorithms to use.\ud \ud Method: In this study we applied 44 obfuscations to 18 subject applications covering a total of 4 millions lines of code. The effectiveness of these source code obfuscations has been measured using 10 code metrics, considering modularity, size and complexity of code.\ud \ud Results: Results show that some of the considered obfuscations are effective in making code metrics change substantially from original to obfuscated code, although this change (called potency of the obfuscation) is different on different metrics. In the paper we recommend which obfuscations to select, given the security requirements of the software to be protected.

The problem of scheduling jobs on an unreliable single machine is considered in this paper. The scheduling problem is characterized by the following features: jobs are grouped into classes of equivalent jobs; the generalized due date model is adopted for each class of jobs; it is possible to reduce the processing time of a job, at the price of the payment of an extra cost; a costly setup is required when switching between jobs of different classes. The scheduling problem is solved from a perspective which is different from the traditional determination of an optimal sequence of jobs; in fact, the objective of the paper is to determine optimal control strategies (functions of the system state) which allow generating the optimal decisions during the evolution of the system, taking into account the actual system state. In this way, optimal decisions can be promptly taken also in the presence of perturbations which affect the single machine (such as breakdowns and slowdowns). To this aim, a specific optimal control problem is stated and solved in the paper.

Social media have become firmly established as a new publishing and communication tool with significant value for all the aspects of contemporary society, culture and science. Social media feature a dynamic and continuously evolving nature, fostering the creation and exchange of concepts and ideas in an unprecedented scale. The leading role of social media coupled with the inherent volatility and the ephemeral nature of this medium, compared with other forms of electronic communication, has rendered social media preservation as necessary. It is critical to preserve social media as they are an essential part of our heritage and they can prove valuable for current and future generations. Social media preservation is an advanced topic of digital preservation, encompassing the capture, management, preservation and availability of social media for future use and research. To achieve this goal, we need to advance the state of the art in a wide range of diverse topics such as social media modeling and analysis, classification, information retrieval, content migration, replication, emulation and web preservation strategies. Furthermore, we need to focus not only on theoretical advances but also on innovative applications and systems. In this special issue, we are aiming to exchange the latest fundamental advances in the state of the art and practice of social media preservation and important related areas. In this issue we have included 7 papers, which have been selected out of 17 papers that were submitted in response to our call. Some information about these papers follows. The paper by L. Akritidis and P. Bozanis entitled “Improving Opinionated Blog Retrieval Effectiveness with Quality Measures and Temporal Features” deals with the problem of retrieval of blog entries, which contain opinions about entries by paying special attention to the importance of such entries. The proposed ranking method takes into account the influence of the blogger who authored an opinion, the reputation of the specific blog site and the impact of the blog post itself. Experiments with TREC Blogs08 dataset shows enhancement of retrieval precision. The paper by G. Gkotsis, K. Stepanyan, A. Cristea and M. Joy entitled “Entropy-based Automated Wrapper Generation for Web Data Extraction” focus on the process of weblog data extraction. The approach includes a model for generating a wrapper that exploits web feeds for deriving probabilistically (based on entropy) a set of extraction rules in an automatic way. The World Wide Web (2014) 17:691–693 DOI 10.1007/s11280-014-0282-4

The special issue on “Machine Learning for Science and Society” showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perform repairs on critical infrastructure, how we predict severe weather and aviation turbulence, how we conduct tax audits, whether we can detect privacy breaches in access to healthcare data, and how we link individuals across census data sets for new insights into population changes. In this introduction, we discuss the need for such a special issue within the context of our field and its relationship to the broader world. In the era of “big data,” there is a need for machine learning to address important large-scale applied problems, yet it is difficult to find top venues in machine learning where such work is encouraged. We discuss the ramifications of this contradictory situation and encourage further discussion on the best strategy that we as a field may adopt. We also summarize key lessons learned from individual papers in the special issue so that the community as a whole can benefit., National Science Foundation (U.S.) (grant IIS-1053407), United States. National Aeronautics and Space Administration

The healthcare system aims to provide a reliable and organized solution to enhance the health of human society. Studying the history of patients can help physicians to consider patients’ needs in healthcare system designing and offering service, which leads to an increase in patient satisfaction. Therefore, healthcare is becoming a growing contesting market. With this significant growth in healthcare systems, such challenges as huge data volume, response time, latency, and security vulnerability are raised. Therefore, fog computing, as a well-known distributed architecture, could help to solve such challenges. In fog computing architecture, processing components are placed between the end devices and cloud components, and they execute applications. This architecture is suitable for such applications as healthcare systems that need a real-time response and low latency. In this paper, a systematic review of available approaches in the field of fog-based healthcare systems is proposed; the challenges of its application in healthcare are explored, classified, and discussed. First, the fog computing approaches in healthcare are categorized into three main classes: communication, application, and resource/service. Then, they are discussed and compared based on their tools, evaluation methods, and evaluation metrics. Finally, based on observations, some open issues and challenges are highlighted for further studies in fog-based healthcare.

Face age progression, goals to alter the individual's face from a given face image to predict the future appearance of that image. In today's world that demands more security and a touchless unique identification system, face aging attains tremendous attention. The existing face age progression approaches have the key problem of unnatural modifications of facial attributes due to insufficient prior knowledge of input images and nearly visual artifacts in the generated output. Research has been continuing in face aging to handle the challenge to generate aged faces accurately. So, to solve the issue, the proposed work focuses on the realistic face aging method using AttentionGAN and SRGAN. AttentionGAN uses two separate subnets in a generator. One subnet for generating multiple attention masks and the other for generating multiple content masks. Then attention mask is multiplied with the corresponding content mask along with an input image to finally achieve the desired results. Further, the regex filtering process is performed to separates the synthesized face images from the output of AttentionGAN. Then image sharpening with edge enhancement is done to give high-quality input to SRGAN, which further generates the super-resolution face aged images. Thus, presents more detailed information in an image because of its high quality. Moreover, the experimental results are obtained from five publicly available datasets: UTKFace, CACD, FGNET, IMDB-WIKI, and CelebA. The proposed work is evaluated with quantitative and qualitative methods, produces synthesized face aged images with a 0.001% error rate, and is also evaluated with the comparison to prior methods. The paper focuses on the various practical applications of super-resolution face aging using Generative Adversarial Networks (GANs).

Today’s digital era has undertaken most of the responsibilities of public and private sectors, not only the industries or big organizations dependent on the internet but individual’s household needs also lying on it. To make the data transmission/reception confidential and secure for both internet users and internet service providers, a large number of researches have been done in this field. It has proved that cryptography is the best solution for solving this purpose. Mostly, digital images are continuously transferring on the network rather than texts. Enciphering a digital image is a much bulkier and a complex task. It has been evident from many types of research that chaotic logistic map-based equations provide a great level of randomness. Hence Chaotic logistic maps-based image encryption techniques (also called chaos techniques) were implemented to obtain highly random cipher images. On the other hand, time consumption must be as low as it can be possible to sustain real-time communication. Presently, the advanced encryption schemes based on quantum technology have enhanced efficiency and security because of having a large key-space and less time complexity along with randomness. The quantum-chaos based encryption is done by utilizing uncertainty principles of quantum mechanics on logistic maps. This paper is an effort to compare chaos and quantum chaos-based image encryption schemes. MATLAB 2016a software is used for the execution and the comparison is made based on various security attack analyses. Based on the study and experimental results, the quantum chaos techniques used for bit plane scrambling provides better results in terms of effectiveness, efficiency, and trustworthy that can be adopted for highly secured image encryption.

The COVID 19 pandemic, fluctuating demand, market uncertainty and the emergence of new technologies explain the need for a more flexible and agile supply chain. In fact, several important factors should be taken into account in the process of building an adaptive and reconfigurable supply chain. Reconfigurability is used to measure quantitatively the capability of supply chain to change easily their structure and functions. The aim of this work is to evaluate the level of reconfigurability of a supply chain. Quantitative measures of six indicators that characterize reconfigurability are presented in this paper. Then, an index of reconfigurability in supply chain is developed based on The Multi-Attribute Utility Theory in order to choose the most reconfigurable configuration. An illustrative example is also given. From the discussion, it is deduced that the characteristics of the reconfigurable supply chain impacts positively on the degree of reconfigurability.

In this paper, a human-inspired optimization algorithm called stock exchange trading optimization (SETO) for solving numerical and engineering problems is introduced. The inspiration source of this optimizer is the behavior of traders and stock price changes in the stock market. Traders use various fundamental and technical analysis methods to gain maximum profit. SETO mathematically models the technical trading strategy of traders to perform optimization. It contains three main actuators including rising, falling, and exchange. These operators navigate the search agents toward the global optimum. The proposed algorithm is compared with seven popular meta-heuristic optimizers on forty single-objective unconstraint numerical functions and four engineering design problems. The statistical results obtained on test problems show that SETO is capable of providing competitive and promising performances compared with counterpart algorithms in solving optimization problems of different dimensions, especially 1000-dimension problems. Out of 40 numerical functions, the SETO algorithm has achieved the global optimum on 36 functions, and out of 4 engineering problems, it has obtained the best results on 3 problems.

In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

Traditional healthcare services have transitioned into modern healthcare services where doctors remotely diagnose the patients. Cloud computing plays a significant role in this change by providing easy access to patients’ medical records to all stakeholders, such as doctors, nurses, patients, life insurance agents, etc. Cloud services are scalable, cost-effective, and offer a broad range of mobile access to patients’ electronic health record (EHR). Despite the cloud’s enormous benefits like real-time data access, patients’ EHR security and privacy are major concerns. Since the information about patients’ health is highly sensitive and crucial, sharing it over the unsecured wireless medium brings many security challenges such as eavesdropping, modifications, etc. Considering the security needs of remote healthcare, this paper proposes a robust and lightweight, secure access scheme for cloud-based E-healthcare services. The proposed scheme addresses the potential threats to E-healthcare by providing a secure interface to stakeholders and prohibiting unauthorized users from accessing information stored in the cloud. The scheme makes use of multiple keys formed through the key derivation function (KDF) to ensure end-to-end ciphering of information for preventing misuse. The rights to access the cloud services are provided based on the identity and the association between stakeholders, thus ensuring privacy. Due to its simplicity and robustness, the proposed scheme is the best fit for protecting data security and privacy in cloud-based E-healthcare services.

Modern computer architectures are founded on multi-core processors. In order to efficiently process network traffic, it is necessary to dynamically split high-speed packet streams across cores based on the monitoring goal. Most network adapters are multi-core aware but offer limited facilities for assigning packets to processor cores. In this paper we introduce a hybrid traffic analysis framework that leverages flexible packet balancing mechanisms available on recent 10 Gbit commodity network adapters not yet exploited by operating systems. The main contribution of this paper is an open source hardware-assisted software layer for dynamically configuring packet balancing policies in order to fully exploit multi-core systems and enable 10 Gbit wire-speed network traffic analysis.

The healthcare industry requires the integration of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), to their full potential, particularly during this challenging time and the recent outbreak of the COVID-19 pandemic, which resulted in the disruptions in healthcare delivery, service operations, and shortage of healthcare personnel. However, every opportunity has barriers and bumps, and when it comes to IoT healthcare, data privacy is one of the main growing issues. Despite the recent advances in the development of IoT healthcare architectures, most of them are invasive for the data subjects. In this context, the broad applications of AI in the IoT domain have also been hindered by emerging strict legal and ethical requirements to protect individual privacy. Camera-based solutions that monitor human subjects in everyday settings, e.g., for Online Range of Motion (ROM) detection, are making this problem even worse. One actively practiced branch of such solutions is telerehabilitation, which provides remote solutions for the physically impaired to regain their strength and get back to their normal daily routines. The process usually involves transmitting video/images from the patient performing rehabilitation exercises and applying Machine Learning (ML) techniques to extract meaningful information to help therapists devise further treatment plans. Thereby, real-time measurement and assessment of rehabilitation exercises in a reliable, accurate, and Privacy-Preserving manner is imperative. To address the privacy issue of existing solutions, this paper proposes a holistic Privacy-Preserving (PP) hierarchical IoT solution that simultaneously addresses the utilization of AI-driven IoT and the demands for data protection. Furthermore, the efficiency of the proposed architecture is demonstrated by a novel machine learning-based system that allows immediate assessment and extraction of ROM as the critical information for analyzing the progress of patients.

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.

Twin support vector regression (TSVR) is generally employed with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \varepsilon $$\end{document}ε-insensitive loss function which is not well capable to handle the noises and outliers. According to the definition, Huber loss function performs as quadratic for small errors and linear for others and shows better performance in comparison to Gaussian loss hence it restrains easily for a different type of noises and outliers. Recently, TSVR with Huber loss (HN-TSVR) has been suggested to handle the noise and outliers. Like TSVR, it is also having the singularity problem which degrades the performance of the model. In this paper, regularized version of HN-TSVR is proposed as regularization based twin support vector regression (RHN-TSVR) to avoid the singularity problem of HN-TSVR by applying the structured risk minimization principle that leads to our model convex and well-posed. This proposed RHN-TSVR model is well capable to handle the noise as well as outliers and avoids the singularity issue. To show the validity and applicability of proposed RHN-TSVR, various experiments perform on several artificial generated datasets having uniform, Gaussian and Laplacian noise as well as on benchmark different real-world datasets and compare with support vector regression, TSVR, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \varepsilon $$\end{document}ε-asymmetric Huber SVR, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \varepsilon $$\end{document}ε-support vector quantile regression and HN-TSVR. Here, all benchmark real-world datasets are embedded with a different significant level of noise 0%, 5% and 10% on different reported algorithms with the proposed approach. The proposed algorithm RHN-TSVR is showing better prediction ability on artificial datasets as well as real-world datasets with a different significant level of noise compared to other reported models.

With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malware-infected apps that can steal vital information from these devices. The most critical problem is to detect malware-infected apps and keep them out of Google play store. The vulnerability lies in the underlying permission model of Android apps. Consequently, it has become the responsibility of the app developers to precisely specify the permissions which are going to be demanded by the apps during their installation and execution time. In this study, we examine the permission-induced risk which begins by giving unnecessary permissions to these Android apps. The experimental work done in this research paper includes the development of an effective malware detection system which helps to determine and investigate the detective influence of numerous well-known and broadly used set of features for malware detection. To select best features from our collected features data set we implement ten distinct feature selection approaches. Further, we developed the malware detection model by utilizing LSSVM (Least Square Support Vector Machine) learning approach connected through three distinct kernel functions i.e., linear, radial basis and polynomial. Experiments were performed by using 2,00,000 distinct Android apps. Empirical result reveals that the model build by utilizing LSSVM with RBF (i.e., radial basis kernel function) named as FSdroid is able to detect 98.8% of malware when compared to distinct anti-virus scanners and also achieved 3% higher detection rate when compared to different frameworks or approaches proposed in the literature.

Immersive virtual environments (IVEs) have been extensively investigated for applications in education and man-power training because of the benefits of immersion-driven experiences as immersion becomes a factor that can both accelerate and hamper learning depending on the user’s area of focus, which supports the importance of engagement. In this paper, two fundamental approaches to visual engagement in IVE are compared: discipline and guidance. The approaches aim to foster the learner’s engagement to predefined area to be focused by either subtracting visual stimuli (discipline) or appending visual indicators pointing to the area (guidance). The experimental results showed no significant improvement in memory recall accuracy and time. However, the guidance group showed superior performances in usability metrics. Interestingly, a significant difference was found in the objective measure of the participants’ gaze pattern revealing that the discipline makes the user’s gaze consistent and stable.

In this paper, methods and algorithms of suggesting correct words in a spell-checker for Farsi language have been developed, and complexity of them has been calculated. Some software covering these algorithms have been implemented and results of spell-checking and receiving suggestions has been discussed. We have concluded the paper by introducing some other techniques.

With the rapid growth of software size and complexity, how to understand and maintain a software system has become a great challenge in its life-cycle, especially from the global perspective. The paper mainly focuses on the comprehension problem of database-based software systems. A framework for visualizing and analyzing table dependency structure of a DB-based application is proposed, and its corresponding prototype tool named DBViewer is implemented. It mainly includes two main functions, i.e., table dependency visualization in database and structure feature metrics. The key methods of building this tool are proposed and some characters of table dependency relations in database are also found via metrics in this paper. Some experiments are performed on five DB-based software systems, and some preliminary rules have been found: (1) the degree distribution of table dependency graph roughly conforms to power law, (2) the average path length of the max connected subgraph is small, and (3) the clustering effect is not so evident and the coefficients are all less than 0.05 in the max connected sub-graph of all table dependency relations.

Many researchers and organizations are interested in creating a mechanism capable of automatically predicting software defects. In the last years, machine learning techniques have been used in several researches with this goal. Many recent researches use data originated from NASA (National Aeronautics and Space Administration) IV&V (Independent Verification & Validation) Facility Metrics Data Program (MDP). We have recently applied a constructive neural network (RBF-DDA) for this task, yet MLP neural networks were not investigated using these data. We have observed that these data sets contain inconsistent patterns, that is, patterns with the same input vector belonging to diAEerent classes. This paper has two main objectives, (i) to propose a modified version of RBF-DDA, named RBF-eDDA (RBF trained with enhanced Dynamic Decay Adjustment algorithm), which tackles inconsistent patterns, and (ii) to compare RBF-eDDA and MLP neural networks in software defects prediction. The simulations reported in this paper show that RBF-eDDA is able to correctly handle inconsistent patterns and that it obtains results comparable to those of MLP in the NASA data sets.

Although many governmental institutions have provided their costumers with access to electronic government documents there is still a lack of accessibility for handicapped citizens. In this paper we present an approach to improve access to governmental forms for handicapped citizens, in particular for people with visual impairments, elderly people, illiterates or immigrants. We describe a system where we combine scanned images of paper-based forms containing textual information and a text-to-speech synthesis to an audio-visual document representation. We exploit standard document formats based on XML and web service technology to ensure independence from software and hardware platforms.

Research in the area of the early-stage of innovation concentrates on non-linear innovation environments constituted by the nature of the “fuzzy front end” of innovations in which there are no well-defined problems or goals at that point in time [1,2]. Early-stage-innovation requirements are the general applicability and the support of iterations within the software tools to be developed within future collaborative working environments (CWE). The research presented in this paper focuses on innovators’ every day work and the related needs in todays and future work environments to provide a highly flexible software solution supportive to the early-stage-innovation. The adaptability of the software tools to — which depends on the fulfillment of the users requirements — will be achieved by supporting the real-life work routines of innovation workers and teams; be they co-located or dislocated. In the actor-network theory [3] early-stage-innovation is seen as a social process. Therefore the participation of individuals will be encouraged by the usage of game dynamics to supporting idea generation related workflows. To equalize the dependences of people working together in one place, time zone and personal relationship a database of knowledge and object representations will be implemented in the CWE. The CWE tools support and guide innovators to get connected to the right people, produce ideas based on explored knowledge and evaluate them to achieve the goal of developing successful innovations. The approach presented in the proposed paper is basing on the work carried out by the European funded research project Laboranova.

Enclosing a component within a software “wrapper” is a well-established way of adapting components for use in new environments. This paper presents an overview of an experimental evaluation of the use of a wrapper to protect against faults arising during the (simulated) operation of a practical and critical system; the specific context is a protective wrapper for an off-the-shelf software component at the heart of the control system of a steam raising boiler. Encouraged by the positive outcomes of this experimentation we seek to position protective wrappers as a basis for structuring the provision of fault tolerance in component-based open systems and networks. The paper addresses some key issues and developments relating wrappers to the provision of dependability in future computing systems.

It is fundamental for the kinematics simulation and mechanism design to apply the visualization in scientific computing on the simulation research of 3D mechanism. It is also the basis of independent development for 3D CAE software without any copyright limit. This paper offered a parameterized kinematics simulation template for 3D mechanism based on the visualization in scientific computing, in which the open graphics library is applied by MFC compiler. This paper illuminates the fundamental principle of the simulation development applying the OpenGL technology. The principle of OpenGL, the procedure structure, the working flow of OpenGL, the concept of program designing and the code implementation based on VC++ is demonstrated and an application approach for mechanism simulation is developed under the MFC environment.

The advent of multi-agent systems has brought together many disciplines and given us a new way to look at intelligent, distributed systems. However, traditional ways of thinking about and designing software do not fit the multiagent paradigm. Based on agent theory, technology research and multi-agent environment, this paper describes agent oriented programming software development method – MaSE and analyzed wheat growth model. Through requirement analysis, system design and system modeling, this paper builds manage agent, fertilize agent, irrigate agent, weather agent, soil agent, growth agent and simulation agent and develops wheat growth multi-agent system.

This paper reports on an investigation of challenges in enterprise resource planning systems (ERPs) development. The investigation, conducted as interviews with executives at a major ERP software vendor, identified six challenges when developing future ERPs. The challenges are then related to a question of knowledge sharing in ERP development. The question is, can downloads of open source ERPs be seen as a knowledge sharing activity with the potential to decrease the gap between ERP developers and users of ERPs? From identified challenges and by discussing reasons for the high attention and the high numbers of download of open source ERPs, the article presents some conclusions that could act as input for future research. The paper aims at building a foundation for the basic question: In what way could knowledge sharing in ERP development be improved? The main conclusion is that challenges for future development of ERPs addressed by proprietary ERP software vendors could be one reason for the high attention among developers of open source ERPs.

To facilitate the adoption of open-source software (OSS) in industry, it is important to provide potential users (i.e., those who could decide to adopt OSS) with the means for evaluating the trustworthiness of OS products. This paper presents part of the work done in the QualiPSo project for this purpose. A set of factors that are believed to affect the perception of trustworthiness are introduced. In order to test the feasibility of deriving a correct, complete and reliable evaluation of trustworthiness on the basis of these factors, a set of well-known OSS projects have been chosen. Then, the possibility to assess the proposed factors on each project was verified: not all the factors appear to be observable or measurable. The paper reports what information is available to support the evaluation and what is not. This knowledge is considered to be useful to users, who are warned that there are still dark areas in the characterization of OSS products, and to developers, who should provide more data and characteristics on their products in order to support their adoption.

Complex programs often contain multiple, interwoven strands of computation, each responsible for accomplishing a distinct goal. The individual strands responsible for each goal are typically delocalized and overlap rather than being composed in a simple linear sequence. We refer to these code fragments as being interleaved. Interleaving may be intentional-for example, in optimizing a program, a programmer might use some intermediate result for several purposes-or it may creep into a program unintentionally, due to patches, quick fixes, or other hasty maintenance practices. To understand this phenomenon, we have looked at a variety of instances of interleaving in actual programs and have distilled characteristic features. This paper presents our characterization of interleaving and the implications it has for tools that detect certain classes of interleaving and extract the individual strands of computation. Our exploration of interleaving has been done in the context of a case study of a corpus of production mathematical software, written in Fortran from the Jet Propulsion Laboratory. This paper also describes our experiences in developing tools to detect specific classes of interleaving in this software, driven by the need to enhance a formal description of this software library's components. The description, in turn aids in the automated component-based synthesis of software using the library.

This paper presents a new and open Web Service-Based DSS framework under distributed service-oriented architecture. In the past, DSS always refers to a software product. But in this new framework, DSS appears as a website, we called it as decision support website. This paper explores the details of the architecture of the decision support website and describes the business mode about this website. Then we give the whole implementation process of web service. The new DSS change the features of the traditional DSS completely by adopting web service application integration technology and introducing into new business mode.

This paper describes the evolution of organizational structures and collaborative behaviors of forty different software producing companies, in the state of Baden-Wiirttemberg, with a common aim of developing component based business software. The case shows that the role and cultural experience of network facilitators has a major input on further organizational evolution. To analyze the forces for diverse structural evolution and the exploration of suitable implementation processes for regional catalyst are the central intentions of the paper.

This empirical paper shows how free/libre open source software (FLOSS) contributes to mutual and collaborative learning in an educational environment. However, unlike proprietary software, FLOSS allows extensive customisation of software and supports the needs of local users better. In this paper, we observes how implementing FLOSS in an Italian high school challenges the conventional relationship between end users themselves (e.g. teachers and students) and that between users and developers. The findings will shed some light on the social aspects of FLOSS-based computerization -- including the roles of FLOSS in social and organizational change in educational environments and the ways that the social organization of FLOSS are influenced by social forces and social practices.

The effective use of knowledge results in better decision-making within projects. Moreover, the unpredictable nature of software projects and the need for effective communication within project teams requires a framework for social interaction and feedback that results in better decision-making. This paper analyses the creation and capture of knowledge within software development projects and discusses the central role of decision making in the development process, and how the effective use of knowledge helps improve decision-making. The paper views how the knowledge generated and decisions made within a software project can be provided greater visibility and communicated effectively, and to achieve this, presents a framework to facilitate social interaction and feedback during the development process

This paper proposes a generic human-computer software user interface design, called the Resource Allocation Planning System (RAPS), designed to support a person making resource allocation decisions. Although there are many algorithms for automatically solving resource allocation problems, it is often the case that human judgment is also required. Also, while there are software user interfaces to support decision-making for specific resource allocation problems, most of them serve more as organizational charts than as decision-support systems, and most of them become increasingly difficult to use as the size of the resource allocation problem increases. This paper discusses the design and rationale for RAPS and gives an example of how RAPS can be adapted to a specific resource allocation problem.

This paper was written based on experience with teaching subjects on control systems and industrial automation at technical universities. The paper presents proven methods on how to organize and moderate an educational process with an active participation of students during theoretical and practical work over the web. Such an approach does not limit the number nor the location of participants-students, teachers or consultant workers. The international and multicultural dimension is also the most challenging one, since the cooperation can be worldwide. The work with students requires a continuous development of syllabi, since the evolution and adaptation to the changing world is continuous too. The system is open to all the participants and the nature of all of the parties involved in the system can be corporate, scientific, social or educational. The software tools used for the area of supervisory control are presented in the paper, as well as ways on how to promote this work so that new members can use it.

The realization of Semantic Web vision is based on the creation and use of semantic web content which needs software tools both for semantic web developers and end users. Over the past few years, semantic web software tools like ontology editors and triple storage systems have emerged and are growing in maturity with time. While working on a large triple dataset during the course of a research aiming at a life-long “semantic” repository of personal information, besides other semantic web tools, we used several RDF browsing and visualization tools for analyzing our data. This analysis included ensuring the correctness of the data, conformance of instance data to the ontology, finding patterns and trails in the data, cross-checking and evaluating inferred data, etc. We found that many of the features needed by a metadata creator and analyst are missing from these tools. This paper presents an investigation of the tools that are used for browsing and visualizing RDF datasets. It first identifies the browsing and visualization features required by a semantic web developer and a metadata creator and analyst and then based on those features evaluates the most common RDF browsing and visualization tools available till date. We conclude this paper with recommendations for requirements to be fulfilled for future semantic web browsing and visualization.

Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts.

This paper will discuss the motivations and methods for collecting quantitative data about free, libre and open source (FLOSS) software projects. The paper also describes the current state of the art in collecting this data, and some of the problems with this process. Finally, the paper outlines the challenges data miners should look forward to when trying to improve the usefulness of their quantitative data streams.

This paper aims at presenting an overview of a European Integrated Project ATHENA to develop interoperability of enterprise applications and software. The first part of the paper tentatively maps the expected ATHENA solution components to ISO 15704 which is an important standard in the area of enterprise integration. This mapping allows categorising expected ATHENA research results according to ISO 15704 and evaluating the consistency and completeness of ATHENA solutions with respect to the ISO 15704 framework. The second part of the paper focuses on one solution component: enterprise modelling language. Possible use of UEML vl.0 in ATHENA Al project and related work to develop UEML 2.0 in INTEROP NoE will be discussed. Conclusions are given at the end of the paper.

Reconfigurable systems have the potential to combine the performance of ASICs with the flexibility of software. The architecture presented in this paper offers a new concept for reconfiguration by operating self-timed and self-controlling. Data is routed together with its control information in a so-called packet through the operator network to make local decisions concerning the behavior of the network. Therefore, we can realize different paths without a central control unit. In this paper, we describe the architecture from the aspect of reconfiguration. An example shows the architecture in practical operation.