Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

Abstract Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi‐level re‐ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi‐level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets.

Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

Stochastic behavior of ion channels, neurotransmitter release mechanisms and synaptic connections in neurons emerge as a source of variability and noise in neuronal circuits, causing uncertainty in the computations performed by the brain. One can gain insight into this important aspect of brain mechanism via computational modeling. Stochastic behavior in neurons is usually modeled with fine-grained, discrete-state, continuous-time Markov Chains (MCs). Although these models are considered as the golden standard, they become computationally prohibitive in analyzing multi-neuron circuits. Thus, several approximate models, where the random behavior is captured by coarse-grained, continuous-state, continuous-time Stochastic Differential Equations (SDEs), were proposed. In this paper, we first present a general, fine-grained modeling framework based on MC models of ion channels and synaptic processes. We then develop a formalism for automatically generating the corresponding SDE models, based on representing generic/abstract MCs as a set of chemical reactions and by utilizing techniques from stochastic chemical kinetics. With this formalism, we can exploit the sparsity and special structure in the MC models and arrive at compact SDE models. We present results obtained by our neuronal circuit simulator based on the proposed methodology in analyzing stochasticity in neurons and neuronal circuits. We employ numerical simulation techniques that were previously developed for noise in electronic circuits. We point to the use of non Monte Carlo noise analysis techniques for large-scale analysis of noise in the nervous system.

Nanonetworks constructed by interconnecting nanodevices using wireless communication allow the nanodevices to perform more complex functions by means of cooperation between them. For the first time in the literature, a novel and physically realizable nanoscale communication technique is introduced: Nanoscale Heat Communication (NHC) in which the heat transfer is used for communication at the nanoscale. The transmitted information is encoded in temperature signals using Magneto-Caloric Effect (MCE) which is the change in temperature of a magnetic material exposed to a varying magnetic field. Thermal energy emitted or absorbed by a transmitter nanodevice is subject to the laws of thermal diffusion which changes the temperature of the communication medium. The transmitted information is decoded by a receiver nanodevice that senses the temperature variations. Using information theoretical analysis, a closed-form expression for the channel capacity is obtained. According to the performance evaluation of the channel capacity, NHC provides a significantly higher capacity communication compared with the existing molecular communication techniques. Therefore, NHC stands as a promising solution to nanoscale communication between nanomachines based on its channel capacity performance, advantages, and possible applications for the emerging field of nanonetworks.

The integration of wireless communication and control systems revealed wireless networked control systems (WNCSs). One fundamental problem in WNCSs is to have a wide coverage area. For the first time in the literature, we address this problem and we obtain the maximum coverage area by solving an optimization problem. In this paper, we consider a WNCS where the output sensor measurements are transmitted over separate multi-hop wireless ad-hoc subnetworks. The system state is estimated using the Kalman filter. We present the critical arrival probability for a sensor measurement packet such that if the packet arrival probability is larger than the critical value, it is guaranteed that the expected state estimation error covariance is bounded, and hence the WNCS is stable. We find the optimum hop-diameter of a multi-hop wireless ad-hoc subnetwork under the constraints of both the stability of the WNCS and the cost-efficiency of the multi-hop wireless network. Furthermore, under these constraints, we derive the maximum total coverage area of the wireless subnetworks. The numerical analyses show that the maximum total coverage area can be increased by appropriately adjusting the number of sensors, the successful packet transmission probability between relay nodes, and the eigenvalues of the system matrix.

The traffic congestion problem is a common issue for the residents of metropolises. Although expanding the capacity of transportation systems and stimulating the public transportation may decrease the traffic congestion, they cannot completely solve the traffic congestion problem. As a solution for the worsening traffic congestion problem in urban areas, road pricing systems have been employed. In this paper, we propose a radically different road pricing scheme to prevent and decrease the traffic congestion in metropolises. Unlike designating a small congestion charge zone in a city, we propose to employ a road pricing system over the entire city. Thus, our road pricing system can control the traffic flow in the entire traffic network of the city. Furthermore, the road prices are adjusted dynamically based on the instantaneous traffic densities of each road in the city in order to rapidly and efficiently control the traffic flow and to prevent the traffic congestion. Moreover, we propose to change the road prices according to the past usage statistics of the road by predicting a possible congestion. The simulation results of our road pricing algorithm show that traffic congestion is prevented over the entire traffic network and the traffic densities of the roads are homogenized.

In the Molecular Communication (MC), molecules are utilized to encode, transmit, and receive information. Transmission of the information is achieved by means of diffusion of molecules and the information is recovered based on the molecule concentration variations at the receiver location. The MC is very prone to intersymbol interference (ISI) due to residual molecules emitted previously. Furthermore, the stochastic nature of the molecule movements adds noise to the MC. For the first time, we propose four methods for a receiver in the MC to recover the transmitted information distorted by both ISI and noise. We introduce sequence detection methods based on maximum a posteriori (MAP) and maximum likelihood (ML) criterions, a linear equalizer based on minimum mean-square error (MMSE) criterion, and a decision-feedback equalizer (DFE) which is a nonlinear equalizer. We present a channel estimator to estimate time varying MC channel at the receiver. The performances of the proposed methods based on bit error rates are evaluated. The sequence detection methods reveal the best performance at the expense of computational complexity. However, the MMSE equalizer has the lowest performance with the lowest computational complexity. The results show that using these methods significantly increases the information transmission rate in the MC.