Computers — Open Access Journal

Location services for ad-hoc networks are of indispensable value for a wide range of applications, such as the Internet of Things (IoT) and vehicular ad-hoc networks (VANETs). Each context requires a solution that addresses the specific needs of the application. For instance, IoT sensor nodes have resource constraints (i.e., computational capabilities), and so a localization service should be highly efficient to conserve the lifespan of these nodes. We propose an optimized energy-aware and low computational solution, requiring 3-GPS equipped nodes (anchor nodes) in the network. Moreover, the computations are lightweight and can be implemented distributively among nodes. Knowing the maximum range of communication for all nodes and distances between 1-hop neighbors, each node localizes itself and shares its location with the network in an efficient manner. We simulate our proposed algorithm in a NS-3 simulator, and compare our solution with state-of-the-art methods. Our method is capable of localizing more nodes (≈90% of nodes in a network with an average degree ≈10). Full article

Accurate speech recognition can provide a natural interface for human–computer interaction. Recognition rates of the modern speech recognition systems are highly dependent on background noise levels and a choice of acoustic feature extraction method can have a significant impact on system performance. This paper presents a robust speech recognition system based on a front-end motivated by human cochlear processing of audio signals. In the proposed front-end, cochlear behavior is first emulated by the filtering operations of the gammatone filterbank and subsequently by the Inner Hair cell (IHC) processing stage. Experimental results using a continuous density Hidden Markov Model (HMM) recognizer with the proposed Gammatone Hair Cell (GHC) coefficients are lower for clean speech conditions, but demonstrate significant improvement in performance in noisy conditions compared to standard Mel-Frequency Cepstral Coefficients (MFCC) baseline. Full article

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets. Full article

Biometric verification systems have become prevalent in the modern world with the wide usage of smartphones. These systems heavily rely on storing the sensitive biometric data on the cloud. Due to the fact that biometric data like fingerprint and iris cannot be changed, storing them on the cloud creates vulnerability and can potentially have catastrophic consequences if these data are leaked. In the recent years, in order to preserve the privacy of the users, homomorphic encryption has been used to enable computation on the encrypted data and to eliminate the need for decryption. This work presents DeepZeroID: a privacy-preserving cloud-based and multiple-party biometric verification system that uses homomorphic encryption. Via transfer learning, training on sensitive biometric data is eliminated and one pre-trained deep neural network is used as feature extractor. By developing an exhaustive search algorithm, this feature extractor is applied on the tasks of biometric verification and liveness detection. By eliminating the need for training on and decrypting the sensitive biometric data, this system preserves privacy, requires zero knowledge of the sensitive data distribution, and is highly scalable. Our experimental results show that DeepZeroID can deliver 95.47% F1 score in the verification of combined iris and fingerprint feature vectors with zero true positives and with a 100% accuracy in liveness detection. Full article

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer. Full article

We live in a heavily technologized global society. It is therefore not surprising that efforts are being made to integrate current information technology into the treatment of diabetes mellitus. This paper is dedicated to improving the treatment of this disease through the use of well-designed mobile applications. Our analysis of relevant literature sources and existing solutions has revealed that the current state of mobile applications for diabetics is unsatisfactory. These limitations relate both to the content and the Graphical User Interface (GUI) of existing applications. Following the analysis of relevant studies, there are four key elements that a diabetes mobile application should contain. These elements are: (1) blood glucose levels monitoring; (2) effective treatment; (3) proper eating habits; and (4) physical activity. As the next step in this study, three prototypes of new mobile applications were designed. Each of the prototypes represents one group of applications according to a set of given rules. The most optimal solution based on the users’ preferences was determined by using a questionnaire survey conducted with a sample of 30 respondents participating in a questionnaire after providing their informed consent. The age of participants was from 15 until 30 years old, where gender was split to 13 males and 17 females. As a result of this study, the specifications of the proposed application were identified, which aims to respond to the findings discovered in the analytical part of the study, and to eliminate the limitations of the current solutions. All of the respondents expressed preference for an application that includes not only the key functions, but a number of additional functions, namely synchronization with one of the external devices for measuring blood glucose levels, while five-sixths of them found suggested additional functions as being sufficient. Full article

This paper analyzes the most relevant spatial-temporal stochastic properties of benchmark synthetic mobility models. Each pattern suffers from various mobility flaws, as will be shown by the models’ validation. A set of metrics is used to describe mobility features, such as the speed decay problem, the density wave phenomenon, the spatial node distribution, and the average neighbor percentage. These metrics have already been validated for the random waypoint mobility model (RWPMM), but they have not yet been verified for other mobility patterns that are most frequently used. For this reason, this investigation attempts to deeply validate those metrics for other mobility models, namely the Manhattan Grid mobility, the Reference Point Group mobility, the Nomadic Community mobility, the Self-Similar Least Action Walk, and SMOOTH models. Moreover, we propose a novel mobility metric named the “node neighbors range”. The relevance of this new metric is that it proves at once the set of outcomes of previous metrics. It offers a global view of the overall range of mobile neighbors during the experimental time. The current research aims to more rigorously understand mobility features in order to conduct a precise assessment of each mobility flaw, given that this fact further impacts the performance of the whole network. These validations aim to summarize several parameters into 18,126 different scenarios with an average of 486 validated files. An exhaustive analysis with details like those found in this paper leads to a good understanding of the accurate behaviors of mobility models by displaying the ability of every pattern to deal with certain topology changes, as well as to ensure network performances. Validation results confirm the effectiveness and robustness of our novel metric. Full article

User reviews, blogs, and social media data are widely used for various types of decision-making. In this connection, Machine Learning and Natural Language Processing techniques are employed to automate the process of opinion extraction and summarization. We have studied different techniques of opinion mining and found that the extraction of opinion target and opinion words and the relation identification between them are the main tasks of state-of-the-art techniques. Furthermore, domain-independent features extraction is still a challenging task, since it is costly to manually create an extensive list of features for every domain. In this study, we tested different syntactic patterns and semantic rules for the identification of evaluative expressions containing relevant target features and opinion. We have proposed a domain-independent framework that consists of two phases. First, we extract Best Fit Examples (BFE) consisting of short sentences and candidate phrases and in the second phase, pruning is employed to filter the candidate opinion targets and opinion words. The results of the proposed model are significant. Full article

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing. Full article

Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach. Full article

This paper proposes two heuristic approaches to solve the Multi-Service Capacitated Facility Location Problem. This problem covers assigning equipment to access points, offering multiple services in a Smart City context. The access points should offer the services to the customer and fulfil their demand, given the coverage of the service and their capacity constraints. Both the heuristic approaches solve the assignment problem for the services separately and combine the solutions of the step together. One of them, however, updates the cost parameters between consecutive steps and produce near optimal solutions in reasonable time compared to the solution obtained from solving an integer linear programming problem exactly. Full article

The paper presents a Non-Uniform Memory Access (NUMA)-aware compiler optimization for task-level parallel code. The optimization is based on Non-Uniform Memory Access—Balanced Task and Loop Parallelism (NUMA-BTLP) algorithm Ştirb, 2018. The algorithm gets the type of each thread in the source code based on a static analysis of the code. After assigning a type to each thread, NUMA-BTLP Ştirb, 2018 calls NUMA-BTDM mapping algorithm Ştirb, 2016 which uses PThreads routine pthread_setaffinity_np to set the CPU affinities of the threads (i.e., thread-to-core associations) based on their type. The algorithms perform an improve thread mapping for NUMA systems by mapping threads that share data on the same core(s), allowing fast access to L1 cache data. The paper proves that PThreads based task-level parallel code which is optimized by NUMA-BTLP Ştirb, 2018 and NUMA-BTDM Ştirb, 2016 at compile-time, is running time and energy efficiently on NUMA systems. The results show that the energy is optimized with up to 5% at the same execution time for one of the tested real benchmarks and up to 15% for another benchmark running in infinite loop. The algorithms can be used on real-time control systems such as client/server based applications which require efficient access to shared resources. Most often, task parallelism is used in the implementation of the server and loop parallelism is used for the client. Full article

Distributed systems, such as the Internet of Things (IoT) and cloud computing, are becoming popular. This requires modeling that reflects the natural characteristics of such systems: the locality of independent components, the autonomy of their decisions, and asynchronous communication. Automated verification of deadlocks and distributed termination supports rapid development. Existing techniques do not reflect some features of distribution. Most formalisms are synchronous and/or use some kind of global state, both of which are unrealistic. No model supports the communication duality that allows the integration of a remote procedure call and client-server paradigm into a single, uniform model. The majority of model checkers refer to total deadlocks. Usually, they do not distinguish between communication deadlocks from resource deadlocks and deadlocks from distributed termination. Some verification mechanisms check partial deadlocks at the expense of restricting the structure of the system being verified. The paper presents an original formalism for the modeling and verification of distributed systems. The Integrated Model of Distributed Systems (IMDS) defines a distributed system as two sets: states and messages, and the relationship of the “actions” between these sets. Communication duality provides projections on servers and on traveling agents, but the uniform specification of the verified system is preserved. General temporal formulas over IMDS, independent of the structure of the verified system, allow automated verification. These formulas distinguish between deadlocks and distributed termination, and between communication deadlocks and resource deadlocks. Partial deadlocks and partial termination can be checked. The Dedan tool was developed using IMDS formalism. Full article

Providing Internet of Things (IoT) environments with service level guarantee is a challenging task for improving IoT application usage experience. We specify in this paper an IoT architecture enabling an IoT Service Level Agreement (iSLA) achievement between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). In order to guarantee the IoT applications’ requirements, Quality of Service (QoS) mechanisms should be implemented within all the layers of the IoT architecture. Thus, we propose a specific mechanism for the lowest layer of our service level based IoT architecture (i.e., sensing layer). It is an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism enabling to take into consideration the requirements of real-time IoT services. Our access method called QBAIoT (QoS based Access for IoT) extends IEEE 802.15.4 systems by creating a new contention access period for each specified traffic class in the iSLA. Furthermore, due to the huge number of IoT connected devices, self-configuring capability provisioning is necessary for limiting human intervention and total cost of ownership (TCO). Thus, we integrate a self-configuring capability to the QBAIoT access method by implementing the MAPE-K closed control loop within the IoT High Level Gateway (HL-Gw) of our proposed QoS based IoT architecture. Full article

Wireless sensor network is an emerging technology, and the collaboration of wireless sensors becomes one of the active research areas for utilizing sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if any radical change occurs. For the accuracy improvement, the calibration of sensors has been discussed, and sensor data analytics are becoming popular in research and development. However, they are not satisfactorily efficient for the situations where sensor devices are dynamically moving, abruptly appearing, or disappearing. If the abrupt appearance of sensors is a zero-day attack, and the disappearance of sensors is an ill-functioning comrade, then sensor data analytics of untrusted sensors will result in an indecisive artifact. The predefined sensor requirements or meta-data-based sensor verification is not adaptive to identify dynamically moving sensors. This paper describes a deep-learning approach to verify the trustworthiness of sensors by considering the sensor data only. The proposed verification on sensors can be done without having to use meta-data about sensors or to request consultation from a cloud server. The contribution of this paper includes (1) quality preservation of sensor data for mining analytics. The sensor data are trained to identify their characteristics of outliers: whether they are attack outliers, or outlier-like abrupt changes in environments; and (2) authenticity verification of dynamically moving sensors, which was possible. Previous unknown sensors are also identified by deep-learning approach. Full article

Sensor-driven systems provide data and information that facilitate real-time decision-making and autonomous actuation, as well as enable informed policy choices. However, can we be sure that these systems work as expected? Can we model them in a way that captures all the key issues? We define two concepts: frames of reference and frames of function that help us organise models of sensor-based systems and their purpose. Examples from a smart water distribution network illustrate how frames offer a lens through which to organise and balance multiple views of the system. Frames aid communication between modellers, analysts and stakeholders, and distinguish the purpose of each model, which contributes towards our trust that the system fulfils its purpose. Full article

Machinima is a computer imaging technology typically used in games and animation. It prints all movie cast properties into a virtual environment by means of a camera positioning. Since cinematography is complementary to Machinima, it is possible to simulate a director’s style via various camera placements in this environment. In a gaming application, the director’s style is one of the most impressive cinematic factors, where a whole different gaming experience can be obtained using different styles applied to the same scene. This paper describes a system capable of automatically profile a director’s style using fuzzy logic. We employed 19 output variables and 15 other calculated variables from the animation extraction data to profile two different directors’ styles from five scenes. Area plots and histograms were generated, and, by analyzing the histograms, different director’s styles could be subsequently classified. Full article

The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted model can successfully be generalized for different fuel cells and varying operating conditions, and therefore be readily applicable to fuel cell system simulations. Full article