Open AccessArticle
Developing Knowledge-Based Citizen Participation Platform to Support Smart City Decision Making: The Smarticipate Case Study
Information 2017, 8(2), 47; doi:10.3390/info8020047 -
Abstract
Citizen participation for social innovation and co-creating urban regeneration proposals can be greatly facilitated by innovative IT systems. Such systems can use Open Government Data, visualise urban proposals in 3D models and provide automated feedback on the feasibility of the proposals. Using such
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Citizen participation for social innovation and co-creating urban regeneration proposals can be greatly facilitated by innovative IT systems. Such systems can use Open Government Data, visualise urban proposals in 3D models and provide automated feedback on the feasibility of the proposals. Using such a system as a communication platform between citizens and city administrations provides an integrated top-down and bottom-up urban planning and decision-making approach to smart cities. However, generating automated feedback on citizens’ proposals requires modelling domain-specific knowledge i.e., vocabulary and rules, which can be applied on spatial and temporal 3D models. This paper presents the European Commission funded H2020 smarticipate project that aims to achieve the above challenge by applying it on three smart cities: Hamburg, Rome and RBKC-London. Whilst the proposed system architecture indicates various innovative features, a proof of concept of the automated feedback feature for the Hamburg use case ‘planting trees’ is demonstrated. Early results and lessons learned show that it is feasible to provide automated feedback on citizen-initiated proposals on specific topics. However, it is not straightforward to generalise this feature to cover more complex concepts and conditions which require specifying comprehensive domain languages, rules and appropriate tools to process them. This paper also highlights the strengths of the smarticipate platform, discusses challenges to realise its different features and suggests potential solutions. Full article
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Open AccessArticle
Assembling Deep Neural Networks for Medical Compound Figure Detection
Information 2017, 8(2), 48; doi:10.3390/info8020048 -
Abstract
Compound figure detection on figures and associated captions is the first step to making medical figures from biomedical literature available for further analysis. The performance of traditional methods is limited to the choice of hand-engineering features and prior domain knowledge. We train multiple
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Compound figure detection on figures and associated captions is the first step to making medical figures from biomedical literature available for further analysis. The performance of traditional methods is limited to the choice of hand-engineering features and prior domain knowledge. We train multiple convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) networks on top of pre-trained word vectors to learn textual features from captions and employ deep CNNs to learn visual features from figures. We then identify compound figures by combining textual and visual prediction. Our proposed architecture obtains remarkable performance in three run types—textual, visual and mixed—and achieves better performance in ImageCLEF2015 and ImageCLEF2016. Full article
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Open AccessArticle
A Framework for Systematic Refinement of Trustworthiness Requirements
Information 2017, 8(2), 46; doi:10.3390/info8020046 -
Abstract
The trustworthiness of systems that support complex collaborative business processes is an emergent property. In order to address users’ trust concerns, trustworthiness requirements of software systems must be elicited and satisfied. The aim of this paper is to address the gap that exists
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The trustworthiness of systems that support complex collaborative business processes is an emergent property. In order to address users’ trust concerns, trustworthiness requirements of software systems must be elicited and satisfied. The aim of this paper is to address the gap that exists between end-users’ trust concerns and the lack of implementation of proper trustworthiness requirements. New technologies like cloud computing bring new capabilities for hosting and offering complex collaborative business operations. However, these advances might bring undesirable side effects, e.g., introducing new vulnerabilities and threats caused by collaboration and data exchange over the Internet. Hence, users become more concerned about trust. Trust is subjective; trustworthiness requirements for addressing trust concerns are difficult to elicit, especially if there are different parties involved in the business process. We propose a user-centered trustworthiness requirement analysis and modeling framework. We integrate the subjective trust concerns into goal models and embed them into business process models as objective trustworthiness requirements. Business process model and notation is extended to enable modeling trustworthiness requirements. This paper focuses on the challenges of elicitation, refinement and modeling trustworthiness requirements. An application example from the healthcare domain is used to demonstrate our approach. Full article
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Open AccessArticle
BBDS: Blockchain-Based Data Sharing for Electronic Medical Records in Cloud Environments
Information 2017, 8(2), 44; doi:10.3390/info8020044 -
Abstract
Disseminating medical data beyond the protected cloud of institutions poses severe risks to patients’ privacy, as breaches push them to the point where they abstain from full disclosure of their condition. This situation negatively impacts the patient, scientific research, and all stakeholders. To
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Disseminating medical data beyond the protected cloud of institutions poses severe risks to patients’ privacy, as breaches push them to the point where they abstain from full disclosure of their condition. This situation negatively impacts the patient, scientific research, and all stakeholders. To address this challenge, we propose a blockchain-based data sharing framework that sufficiently addresses the access control challenges associated with sensitive data stored in the cloud using immutability and built-in autonomy properties of the blockchain. Our system is based on a permissioned blockchain which allows access to only invited, and hence verified users. As a result of this design, further accountability is guaranteed as all users are already known and a log of their actions is kept by the blockchain. The system permits users to request data from the shared pool after their identities and cryptographic keys are verified. The evidence from the system evaluation shows that our scheme is lightweight, scalable, and efficient. Full article
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Open AccessArticle
A Shallow Network with Combined Pooling for Fast Traffic Sign Recognition
Information 2017, 8(2), 45; doi:10.3390/info8020045 -
Abstract
Traffic sign recognition plays an important role in intelligent transportation systems. Motivated by the recent success of deep learning in the application of traffic sign recognition, we present a shallow network architecture based on convolutional neural networks (CNNs). The network consists of only
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Traffic sign recognition plays an important role in intelligent transportation systems. Motivated by the recent success of deep learning in the application of traffic sign recognition, we present a shallow network architecture based on convolutional neural networks (CNNs). The network consists of only three convolutional layers for feature extraction, and it learns in a backward optimization way. We propose the method of combining different pooling operations to improve sign recognition performance. In view of real-time performance, we use the activation function ReLU to improve computational efficiency. In addition, a linear layer with softmax-loss is taken as the classifier. We use the German traffic sign recognition benchmark (GTSRB) to evaluate the network on CPU, without expensive GPU acceleration hardware, under real-world recognition conditions. The experiment results indicate that the proposed method is effective and fast, and it achieves the highest recognition rate compared with other state-of-the-art algorithms. Full article
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Open AccessArticle
Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models
Information 2017, 8(2), 43; doi:10.3390/info8020043 -
Abstract
Object tracking is a challenging task in many computer vision applications due to occlusion, scale variation and background clutter, etc. In this paper, we propose a tracking algorithm by combining discriminative global and generative multi-scale local models. In the global model, we teach
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Object tracking is a challenging task in many computer vision applications due to occlusion, scale variation and background clutter, etc. In this paper, we propose a tracking algorithm by combining discriminative global and generative multi-scale local models. In the global model, we teach a classifier with sparse discriminative features to separate the target object from the background based on holistic templates. In the multi-scale local model, the object is represented by multi-scale local sparse representation histograms, which exploit the complementary partial and spatial information of an object across different scales. Finally, a collaborative similarity score of one candidate target is input into a Bayesian inference framework to estimate the target state sequentially during tracking. Experimental results on the various challenging video sequences show that the proposed method performs favorably compared to several state-of-the-art trackers. Full article
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Open AccessArticle
Security Awareness of the Digital Natives
Information 2017, 8(2), 42; doi:10.3390/info8020042 -
Abstract
Young generations make extensive use of mobile devices, such as smartphones, tablets and laptops, while a plethora of security risks associated with such devices are induced by vulnerabilities related to user behavior. Furthermore, the number of security breaches on or via portable devices
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Young generations make extensive use of mobile devices, such as smartphones, tablets and laptops, while a plethora of security risks associated with such devices are induced by vulnerabilities related to user behavior. Furthermore, the number of security breaches on or via portable devices increases exponentially. Thus, deploying suitable risk treatments requires the investigation of how the digital natives (young people, born and bred in the digital era) use their mobile devices and their level of security awareness, in order to identify common usage patterns with negative security impact. In this article, we present the results of a survey performed across a multinational sample of digital natives with distinct backgrounds and levels of competence in terms of security, to identify divergences in user behavior due to regional, educational and other factors. Our results highlight significant influences on the behavior of digital natives, arising from user confidence, educational background, and parameters related to usability and accessibility. The outcomes of this study justify the need for further analysis of the topic, in order to identify the influence of fine-grained semantics, but also the consolidation of wide and robust user-models. Full article
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Open AccessArticle
Correlation Coefficient between Dynamic Single Valued Neutrosophic Multisets and Its Multiple Attribute Decision-Making Method
Information 2017, 8(2), 41; doi:10.3390/info8020041 -
Abstract
Based on dynamic information collected from different time intervals in some real situations, this paper firstly proposes a dynamic single valued neutrosophic multiset (DSVNM) to express dynamic information and operational relations of DSVNMs. Then, a correlation coefficient between DSVNMs and a weighted correlation
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Based on dynamic information collected from different time intervals in some real situations, this paper firstly proposes a dynamic single valued neutrosophic multiset (DSVNM) to express dynamic information and operational relations of DSVNMs. Then, a correlation coefficient between DSVNMs and a weighted correlation coefficient between DSVNMs are presented to measure the correlation degrees between DSVNMs, and their properties are investigated. Based on the weighted correlation coefficient of DSVNMs, a multiple attribute decision-making method is established under a DSVNM environment, in which the evaluation values of alternatives with respect to attributes are collected from different time intervals and are represented by the form of DSVNMs. The ranking order of alternatives is performed through the weighted correlation coefficient between an alternative and the ideal alternative, which is considered by the attribute weights and the time weights, and thus the best one(s) can also be determined. Finally, a practical example shows the application of the proposed method. Full article
Open AccessArticle
HTCRL: A Range-Free Location Algorithm Based on Homothetic Triangle Cyclic Refinement in Wireless Sensor Networks
Information 2017, 8(2), 40; doi:10.3390/info8020040 -
Abstract
Wireless sensor networks (WSN) have become a significant technology in recent years. They can be widely used in many applications. WSNs consist of a large number of sensor nodes and each of them is energy-constrained and low-power dissipation. Most of the sensor nodes
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Wireless sensor networks (WSN) have become a significant technology in recent years. They can be widely used in many applications. WSNs consist of a large number of sensor nodes and each of them is energy-constrained and low-power dissipation. Most of the sensor nodes are tiny sensors with small memories and do not acquire their own locations. This means determining the locations of the unknown sensor nodes is one of the key issues in WSN. In this paper, an improved APIT algorithm HTCRL (Homothetic Triangle Cyclic Refinement Location) is proposed, which is based on the principle of the homothetic triangle. It adopts perpendicular median surface cutting to narrow down target area in order to decrease the average localization error rate. It reduces the probability of misjudgment by adding the conditions of judgment. It can get a relatively high accuracy compared with the typical APIT algorithm without any additional hardware equipment or increasing the communication overhead. Full article
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Open AccessArticle
Aesthetic Local Search of Wind Farm Layouts
Information 2017, 8(2), 39; doi:10.3390/info8020039 -
Abstract
The visual impact of wind farm layouts has seen little consideration in the literature on the wind farm layout optimisation problem to date. Most existing algorithms focus on optimising layouts for power or the cost of energy alone. In this paper, we consider
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The visual impact of wind farm layouts has seen little consideration in the literature on the wind farm layout optimisation problem to date. Most existing algorithms focus on optimising layouts for power or the cost of energy alone. In this paper, we consider the geometry of wind farm layouts and whether it is possible to bi-optimise a layout for both energy efficiency and the degree of visual impact that the layout exhibits. We develop a novel optimisation approach for solving the problem which measures mathematically the degree of visual impact of a layout. The approach draws inspiration from the field of architecture. To evaluate our ideas, we demonstrate them on three benchmark problems for the wind farm layout optimisation problem in conjunction with two recently-published stochastic local search algorithms. Optimal patterned layouts are shown to be very close in terms of energy efficiency to optimal non-patterned layouts. Full article
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Open AccessArticle
Continuous Leakage Resilient Lossy Trapdoor Functions
Information 2017, 8(2), 38; doi:10.3390/info8020038 -
Abstract
Lossy trapdoor functions (LTFs) were first introduced by Peikert and Waters (STOC’08). Since their introduction, lossy trapdoor functions have found numerous applications. They can be used as tools to construct important cryptographic primitives such as injective one-way trapdoor functions, chosen-ciphertext-secure public key encryptions,
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Lossy trapdoor functions (LTFs) were first introduced by Peikert and Waters (STOC’08). Since their introduction, lossy trapdoor functions have found numerous applications. They can be used as tools to construct important cryptographic primitives such as injective one-way trapdoor functions, chosen-ciphertext-secure public key encryptions, deterministic encryptions, et al. In this paper, we focus on the lossy trapdoor functions in the presence of continuous leakage. We introduce the new notion of updatable lossy trapdoor functions (ULTFs) and give their formal definition and security properties. Based on these, we extend the security model to the LTFs against continuous leakage when the evaluation algorithm is leakage resilient. Under the standard DDH assumption and DCR assumption, respectively, we show two explicit lossy trapdoor functions against continuous leakage in the standard model. In these schemes, using the technology of matrix kernel, the trapdoor can be refreshed at regular intervals and the adversaries can learn unbounded leakage information on the trapdoor along the whole system life. At the same time, we also show the performance of the proposed schemes compared with the known existing continuous leakage resilient lossy trapdoor functions. Full article
Open AccessArticle
TESMA: Requirements and Design of a Tool for Educational Programs
Information 2017, 8(1), 37; doi:10.3390/info8010037 -
Abstract
Defining and managing teaching programs at universities or other institutions is a complex task for which there is not much support in terms of methods and tools. This task becomes even more critical when the time comes to obtain certifications w.r.t. official standards.
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Defining and managing teaching programs at universities or other institutions is a complex task for which there is not much support in terms of methods and tools. This task becomes even more critical when the time comes to obtain certifications w.r.t. official standards. In this paper, we present an on-going project called TESMA, whose objective is to provide an open-source tool dedicated to the specification and management (including certification) of teaching programs. An in-depth market analysis regarding related tools and conceptual frameworks of the project is presented. This tool has been engineered using a development method called Messir for its requirements elicitation and introduces a domain-specific language dedicated to the teaching domain. This paper presents the current status of this project and the future activities planned. Full article
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Open AccessArticle
Automated Detection of Liver Histopathological Findings Based on Biopsy Image Processing
Information 2017, 8(1), 36; doi:10.3390/info8010036 -
Abstract
Hepatic steatosis is the accumulation of fat in the hepatic cells and the liver. Triglycerides and other kinds of molecules are included in the lipids. When there is some defect in the process, hepatic steatosis arise, during which the free fatty acids are
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Hepatic steatosis is the accumulation of fat in the hepatic cells and the liver. Triglycerides and other kinds of molecules are included in the lipids. When there is some defect in the process, hepatic steatosis arise, during which the free fatty acids are taken by the liver and exuded as lipoproteins. Alcohol is the main cause of steatosis when excessive amounts are consumed for a long period of time. In many cases, steatosis can lead to inflammation that is mentioned as steatohepatitis or non-alcoholic steatohepatitis (NASH), which can later lead to fibrosis and finally cirrhosis. For automated detection and quantification of hepatic steatosis, a novel two-stage methodology is developed in this study. Initially, the image is processed in order to become more suitable for the detection of fat regions and steatosis quantification. In the second stage, initial candidate image regions are detected, and then they are either validated or discarded based on a series of criteria. The methodology is based on liver biopsy image analysis, and has been tested using 40 liver biopsy images obtained from patients who suffer from hepatitis C. The obtained results indicate that the proposed methodology can accurately assess liver steatosis. Full article
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Open AccessArticle
Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
Information 2017, 8(1), 34; doi:10.3390/info8010034 -
Abstract
Recently, Wireless Body Area Networks (WBANs) have become an emerging technology in healthcare, where patients are equipped withwearable and implantable body sensor nodes to gather sensory information for remote monitoring. The increasing development of coordinator devices on patients enables the internetworking of WBANs
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Recently, Wireless Body Area Networks (WBANs) have become an emerging technology in healthcare, where patients are equipped withwearable and implantable body sensor nodes to gather sensory information for remote monitoring. The increasing development of coordinator devices on patients enables the internetworking of WBANs in heterogeneous wireless networks to deliver physiological information that is collected at remote terminals in a timely fashion. However, in this type of network, providing a seamless handover with a guaranteed Quality of Service (QoS), especially emergency services, is a challenging task. In this paper, we proposed an effective Multi-Attribute Decision-Making (MADM) handover algorithm that guarantees seamless connectivity. A patient’s mobile devices automatically connect to the best network that fulfills the QoS requirements of different types of applications. Additionally, we integrated a Content-Centric Networking (CCN) processing module into different wireless networks to reduce packet loss, enhance QoS and avoid unnecessary handovers by leveraging in-network caching to achieve efficient content dissemination for ubiquitous healthcare. Simulation results proved that our proposed approach forthe model with CCN outperforms the model without CCN and Received Signal Strength Vertical Handoff (RSS-VHD) in terms of the number of handovers, enhancing QoS, packet loss, and energy efficiency. Full article
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Open AccessEditorial
Information and Symmetry: Adumbrating the Abstract Core of Complex Systems
Information 2017, 8(1), 35; doi:10.3390/info8010035 -
Abstract
Information and symmetry are essential theoretical concepts that underlie the scientific explanation of a variety of complex systems. In spite of clear-cut developments around both concepts, their intersection is really problematic, either in fields related to mathematics, physics, and chemistry, or even more
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Information and symmetry are essential theoretical concepts that underlie the scientific explanation of a variety of complex systems. In spite of clear-cut developments around both concepts, their intersection is really problematic, either in fields related to mathematics, physics, and chemistry, or even more in those pertaining to biology, neurosciences, and social sciences. The present Special Issue explores recent developments, both theoretical and applied, in most of these disciplines. Full article
Open AccessArticle
Analysis and Modeling for China’s Electricity Demand Forecasting Based on a New Mathematical Hybrid Method
Information 2017, 8(1), 33; doi:10.3390/info8010033 -
Abstract
Electricity demand forecasting can provide the scientific basis for the country to formulate the power industry development strategy and the power-generating target, which further promotes the sustainable, healthy and rapid development of the national economy. In this paper, a new mathematical hybrid method
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Electricity demand forecasting can provide the scientific basis for the country to formulate the power industry development strategy and the power-generating target, which further promotes the sustainable, healthy and rapid development of the national economy. In this paper, a new mathematical hybrid method is proposed to forecast electricity demand. In line with electricity demand feature, the framework of joint-forecasting model is established and divided into two procedures: firstly, the modified GM(1,1) model and the Logistic model are used to make single forecasting. Then, the induced ordered weighted harmonic averaging operator (IOWHA) is applied to combine these two single models and make joint-forecasting. Forecasting results demonstrate that this new hybrid model is superior to both single-forecasting approaches and traditional joint-forecasting methods, thus verifying the high prediction validity and accuracy of mentioned joint-forecasting model. Finally, detailed forecasting-outcomes on electricity demand of China in 2016–2020 are discussed and displayed a slow-growth smoothly over the next five years. Full article
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Open AccessArticle
Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms
Information 2017, 8(1), 31; doi:10.3390/info8010031 -
Abstract
Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm
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Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA) and Cuckoo Optimization Algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods. Full article
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Open AccessArticle
The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
Information 2017, 8(1), 32; doi:10.3390/info8010032 -
Abstract
In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difficult since it is complex to handle the interference caused by simultaneous transmission.
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In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difficult since it is complex to handle the interference caused by simultaneous transmission. To solve this problem, we, according to the inhomogeneity of topology, divide the transmission into intra-cluster transmission and inter-cluster transmission. For the intra-cluster transmission, a spheroidal percolation model is constructed. The spheroidal percolation model guarantees a constant rate when a power control strategy is adopted. We also propose a cube percolation mode for the inter-cluster transmission. Different from the spheroidal percolation model, a constant transmission rate can be achieved without power control. For both transmissions, we propose a routing scheme with five phases. By comparing the achievable rate of each phase, we get the rate bottleneck, which is the throughput capacity of the network. Full article
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Open AccessArticle
Computer-Aided Identification and Validation of Intervenability Requirements
Information 2017, 8(1), 30; doi:10.3390/info8010030 -
Abstract
Privacy as a software quality is becoming more important these days and should not be underestimated during the development of software that processes personal data. The privacy goal of intervenability, in contrast to unlinkability (including anonymity and pseudonymity), has so far received little
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Privacy as a software quality is becoming more important these days and should not be underestimated during the development of software that processes personal data. The privacy goal of intervenability, in contrast to unlinkability (including anonymity and pseudonymity), has so far received little attention in research. Intervenability aims for the empowerment of end-users by keeping their personal data and how it is processed by the software system under their control. Several surveys have pointed out that the lack of intervenability options is a central privacy concern of end-users. In this paper, we systematically assess the privacy goal of intervenability and set up a software requirements taxonomy that relates the identified intervenability requirements with a taxonomy of transparency requirements. Furthermore, we provide a tool-supported method to identify intervenability requirements from the functional requirements of a software system. This tool-supported method provides the means to elicit and validate intervenability requirements in a computer-aided way. Our combined taxonomy of intervenability and transparency requirements gives a detailed view on the privacy goal of intervenability and its relation to transparency. We validated the completeness of our taxonomy by comparing it to the relevant literature that we derived based on a systematic literature review. The proposed method for the identification of intervenability requirements shall support requirements engineers to elicit and document intervenability requirements in compliance with the EU General Data Protection Regulation. Full article
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Open AccessArticle
Structural and Functional Modeling of Artificial Bioactive Proteins
Information 2017, 8(1), 29; doi:10.3390/info8010029 -
Abstract
A total of 32 synthetic proteins designed by Michael Hecht and co-workers was investigated using standard bioinformatics tools for the structure and function modeling. The dataset consisted of 15 artificial α-proteins (Hecht_α) designed to fold into 102-residue four-helix bundles and 17 artificial six-stranded
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A total of 32 synthetic proteins designed by Michael Hecht and co-workers was investigated using standard bioinformatics tools for the structure and function modeling. The dataset consisted of 15 artificial α-proteins (Hecht_α) designed to fold into 102-residue four-helix bundles and 17 artificial six-stranded β-sheet proteins (Hecht_β). We compared the experimentally-determined properties of the sequences investigated with the results of computational methods for protein structure and bioactivity prediction. The conclusion reached is that the dataset of Michael Hecht and co-workers could be successfully used both to test current methods and to develop new ones for the characterization of artificially-designed molecules based on the specific binary patterns of amino acid polarity. The comparative investigations of the bioinformatics methods on the datasets of both de novo proteins and natural ones may lead to: (1) improvement of the existing tools for protein structure and function analysis; (2) new algorithms for the construction of de novo protein subsets; and (3) additional information on the complex natural sequence space and its relation to the individual subspaces of de novo sequences. Additional investigations on different and varied datasets are needed to confirm the general applicability of this concept. Full article
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