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Information (ISSN 2078-2489; CODEN: INFOGG) is a scientific peer-reviewed open access journal of information science and technology, data, knowledge, and communication, and is published monthly online by MDPI. The International Society for Information Studies (IS4SI) is affiliated with Information and their members receive a discount on the article processing charge.
- Open Access free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed by Ei Compendex, Scopus (Elsevier), Emerging Sources Citation Index (ESCI - Web of Science) and other databases.
- Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 20 days after submission; acceptance to publication is undertaken in 4.3 days (median values for papers published in the first six months of 2018).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification
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Information 2018, 9(10), 252; https://doi.org/10.3390/info9100252 - 12 October 2018
Abstract
The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular
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The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision.
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What Smart Campuses Can Teach Us about Smart Cities: User Experiences and Open Data
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Information 2018, 9(10), 251; https://doi.org/10.3390/info9100251 - 12 October 2018
Abstract
Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How
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Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scaled up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom, employing stakeholder interviews and user surveys, which aimed to gain insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges, such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, then open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities.
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Robotic Choreography Inspired by the Method of Human Dance Creation
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Information 2018, 9(10), 250; https://doi.org/10.3390/info9100250 - 10 October 2018
Abstract
In general, human dance is created by the imagination and innovativeness of human dancers, which in turn provides an inspiration for robotic choreography generation. This paper proposes a novel mechanism for a humanoid robot to create good choreography autonomously with the imagination of
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In general, human dance is created by the imagination and innovativeness of human dancers, which in turn provides an inspiration for robotic choreography generation. This paper proposes a novel mechanism for a humanoid robot to create good choreography autonomously with the imagination of human dance. Such a mechanism combines innovativeness with the characteristic preservation of human dance, and enables a humanoid robot to present the characteristics of “imitation, memory, imagination, process and combination”. The proposed mechanism has been implemented on a real humanoid robot, NAO, to verify its feasibility and performance. Experimental results are presented to demonstrate good performance of the proposed mechanism.
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A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
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Information 2018, 9(10), 246; https://doi.org/10.3390/info9100246 - 1 October 2018
Abstract
The filter bank multicarrier employing offset quadrature amplitude modulation (FBMC/OQAM) is a candidate transmission scheme for 5G wireless communication systems. However, it has a high peak-to-average power ratio (PAPR). Due to the nature of overlapped signal structure of FBMC/OQAM, conventional PAPR reduction schemes
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The filter bank multicarrier employing offset quadrature amplitude modulation (FBMC/OQAM) is a candidate transmission scheme for 5G wireless communication systems. However, it has a high peak-to-average power ratio (PAPR). Due to the nature of overlapped signal structure of FBMC/OQAM, conventional PAPR reduction schemes cannot work effectively. A hybrid PAPR reduction scheme based on selective mapping (SLM) and multi data block partial transmit sequence (M-PTS) methods is proposed for FBMC/OQAM signals in this paper. Different from the simple SLM-PTS method, the proposed hybrid algorithm takes into account the overlapping effect of multiple adjacent data blocks on its PTS process. From simulation results, it can be obtained that the proposed method can offer a significant PAPR reduction performance improvement compared with the SLM, PTS and SLM-PTS methods. The proposed method can effectively reduce the PAPR in FBMC/OQAM systems.
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Performance Analysis of Honeypot with Petri Nets
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Information 2018, 9(10), 245; https://doi.org/10.3390/info9100245 - 30 September 2018
Abstract
As one of the active defense technologies, the honeypot deceives the latent intruders to interact with the imitated systems or networks deployed with security mechanisms. Its modeling and performance analysis have not been well studied. In this paper, we propose a honeypot performance
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As one of the active defense technologies, the honeypot deceives the latent intruders to interact with the imitated systems or networks deployed with security mechanisms. Its modeling and performance analysis have not been well studied. In this paper, we propose a honeypot performance evaluation scheme based on Stochastic Petri Nets (SPN). We firstly set up performance evaluation models for three types of defense scenarios (i.e., firewall; firewall and Intrusion Detection System (IDS); firewall, IDS and honeypot) based on SPN. We then theoretically analyze the SPN models by constructing Markov Chains (MC), which are isomorphic to the models. With the steady state probabilities based on the MC, the system performance evaluation is done with theoretical inference. Finally, we implement the proposed three SPN models on the PIPE platform. Five parameters are applied to compare and evaluate the performance of the proposed SPN models. The analysis of the probability and delay of three scenarios shows that the simulation results validate the effectiveness in security enhancement of the honeypot under the SPN models.
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PIF and ReCiF: Efficient Interest-Packet Forwarding Mechanisms for Named-Data Wireless Mesh Networks
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Information 2018, 9(10), 243; https://doi.org/10.3390/info9100243 - 29 September 2018
Abstract
In this paper, we propose three mechanisms to reduce the broadcast storm problem in wireless mesh networks based on the Named-Data Network (NDN) architecture. The goal of our mechanisms is to reduce the number of content requests forwarded by nodes and consequently, increase
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In this paper, we propose three mechanisms to reduce the broadcast storm problem in wireless mesh networks based on the Named-Data Network (NDN) architecture. The goal of our mechanisms is to reduce the number of content requests forwarded by nodes and consequently, increase the network efficiency. The first proposed mechanism, called Probabilistic Interest Forwarding (PIF), randomly forwards content requests. The second mechanism, called Retransmission-Counter-based Forwarding (ReCIF), decides to forward content requests based on the number of retransmissions by adding a counter to the header of requests. The third mechanism, called ReCIF+PIF, combines the features of PIF and ReCIF to suppress content requests. We compare the performance of our mechanisms with both the NDN default forwarding mechanism and the Listen First Broadcast Later (LFBL) mechanism. Our proposals outperform the default NDN forwarding mechanism by up to 21% regarding the data delivery rate in dense networks and provide a 25% lower delivery delay than the default NDN. Our mechanisms accomplish this performance by only reducing the number of content requests forwarded by nodes. One of our mechanisms, PIF, outperforms LFBL regarding the data delivery rate and delivery delay by up to 263% and 55%, respectively, for high network contention levels.
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Multi-User Searchable Symmetric Encryption with Dynamic Updates for Cloud Computing
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Information 2018, 9(10), 242; https://doi.org/10.3390/info9100242 - 28 September 2018
Abstract
With the advent of cloud computing, more and more users begin to outsource encrypted files to cloud servers to provide convenient access and obtain security guarantees. Searchable encryption (SE) allows a user to search the encrypted files without leaking information related to the
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With the advent of cloud computing, more and more users begin to outsource encrypted files to cloud servers to provide convenient access and obtain security guarantees. Searchable encryption (SE) allows a user to search the encrypted files without leaking information related to the contents of the files. Searchable symmetric encryption (SSE) is an important branch of SE. Most of the existing SSE schemes considered single-user settings, which cannot meet the requirements for data sharing. In this work, we propose a multi-user searchable symmetric encryption scheme with dynamic updates. This scheme is applicable to the usage scenario where one data owner encrypts sensitive files and shares them among multiple users, and it allows secure and efficient searches/updates. We use key distribution and re-encryption to achieve multi-user access while avoiding a series of issues caused by key sharing. Our scheme is constructed based on the index structure where a bit matrix is combined with two static hash tables, pseudorandom functions and hash functions. Our scheme is proven secure in the random oracle model.
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Correlation Tracking via Self-Adaptive Fusion of Multiple Features
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Information 2018, 9(10), 241; https://doi.org/10.3390/info9100241 - 27 September 2018
Abstract
Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It
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Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.
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Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses
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Information 2018, 9(10), 240; https://doi.org/10.3390/info9100240 - 22 September 2018
Abstract
Queuing systems (QS) represent everyday life in all business and economic systems. On the one hand, and there is a tendency for their time and cost optimization, but on the other hand, they have not been sufficiently explored. This especially applies to logistics
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Queuing systems (QS) represent everyday life in all business and economic systems. On the one hand, and there is a tendency for their time and cost optimization, but on the other hand, they have not been sufficiently explored. This especially applies to logistics systems, where a large number of transportation and storage units appear. Therefore, the aim of this paper is to develop an ANFIS (Adaptive neuro-fuzzy inference system) model in a warehouse system with two servers for defining QS optimization parameters. The research was conducted in a company for the manufacturing of brown paper located in the territory of Bosnia and Herzegovina, which represents a significant share of the total export production of the country. In this paper, the optimization criterion is the time spent in the system, which is important both from the aspect of all customers of the system, and from that of the owner of the company. The time criterion directly affects the efficiency of the system, but also the overall costs that this system causes. The developed ANFIS model was compared with a mathematical model through a sensitivity analysis. The mathematical model showed outstanding results, which justifies its development and application.
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Visual Saliency Model-Based Image Watermarking with Laplacian Distribution
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Information 2018, 9(9), 239; https://doi.org/10.3390/info9090239 - 19 September 2018
Abstract
To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the
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To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.
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Imbalanced Learning Based on Data-Partition and SMOTE
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Information 2018, 9(9), 238; https://doi.org/10.3390/info9090238 - 19 September 2018
Abstract
Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method based on data-partition and SMOTE for imbalanced learning. The proposed method
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Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method based on data-partition and SMOTE for imbalanced learning. The proposed method differs from conventional ones in both the learning and prediction stages. For the learning stage, the proposed method uses the following three steps to learn a class-imbalance oriented model: (1) partitioning the majority class into several clusters using data partition methods such as K-Means, (2) constructing a novel training set using SMOTE on each data set obtained by merging each cluster with the minority class, and (3) learning a classification model on each training set using convention classification learning methods including decision tree, SVM and neural network. Therefore, a classifier repository consisting of several classification models is constructed. With respect to the prediction stage, for a given example to be classified, the proposed method uses the partition model constructed in the learning stage to select a model from the classifier repository to predict the example. Comprehensive experiments on KEEL data sets show that the proposed method outperforms some other existing methods on evaluation measures of recall, g-mean, f-measure and AUC.
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Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks
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Information 2018, 9(9), 236; https://doi.org/10.3390/info9090236 - 18 September 2018
Abstract
A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a
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A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%.
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A Large Effective Touchscreen Using a Head-Mounted Projector
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Information 2018, 9(9), 235; https://doi.org/10.3390/info9090235 - 18 September 2018
Abstract
In our previous work, we proposed a user interface in which a user wears a projector and a depth camera on his or her head and performs touch operations on an image projected on a flat surface. By using the head-mounted projector, images
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In our previous work, we proposed a user interface in which a user wears a projector and a depth camera on his or her head and performs touch operations on an image projected on a flat surface. By using the head-mounted projector, images are always projected in front of the user in the direction of the user’s gaze. The image to be projected is changed according to the user’s head pose so as to fix the superimposed image on the surface, which realizes a large effective screen size. In this paper, we conducted an experiment for evaluating the accuracy of registration by measuring the positional and rotational errors between the real world and the superimposed image using our experimental system. As a result, the mean absolute errors of translation were about 10 mm when the user stopped his head, and the delay was estimated to be about 0.2 s. We also discuss the limitations of our prototype and show the direction of future development.
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A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance
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Information 2018, 9(9), 234; https://doi.org/10.3390/info9090234 - 14 September 2018
Abstract
The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing
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The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each class are found which are used to find k different local mean vectors, and then employed to compute their harmonic mean distance to the query sample. Lastly, the query sample is assigned to the class with minimum harmonic mean distance. The experimental results based on twenty-six real-world datasets shows that the proposed LMKHNCN classifier achieves lower error rates, particularly in small sample-size situations, and that it is less sensitive to parameter k when compared to therelated four KNN-based classifiers.
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News
21 September 2018
Meet Us at the 2018 IEEE Information Theory Workshop in Guangzhou, China, 25–29 November 2018
Meet Us at the 2018 IEEE Information Theory Workshop in Guangzhou, China, 25–29 November 2018
Conferences
Special Issues
Special Issue in
Information
AI AND THE SINGULARITY: A FALLACY OR A GREAT OPPORTUNITY?
Guest Editors: Robert K. Logan, Adriana BragaDeadline: 30 October 2018
Special Issue in
Information
Non-orthogonal Multiple Access in Cognitive 5G Systems
Guest Editor: Kaigui Bian Deadline: 15 November 2018
Special Issue in
Information
ICSTCC 2018: Advances in Control and Computers
Guest Editors: Marian Barbu, Răzvan ŞoleaDeadline: 1 December 2018
Special Issue in
Information
The Security and Digital Forensics of Cloud Computing
Guest Editors: Gary Wills, Ahmed AleneziDeadline: 15 December 2018
Topical Collections
Topical Collection in
Information
Pervasive Intelligent Data Systems
Collection Editors: Carlos Filipe da Silva Portela, Manuel Filipe Vieira Torres dos Santos, Kostas Kolomvatsos