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Cloud computing has emerged as an attractive platform for individuals and businesses to augment their basic processing capabilities. Mobile devices with access to Internet are also turning towards clouds for resource-intensive tasks by working out a trade-off between resources required for performing computation on-device against those required for off-loading task to the cloud. However, as with desktop clients, mobile clients face significant concerns related to confidentiality and integrity of data and applications moved to and from the cloud. Cloud-related security solutions proposed for desktop clients could not be readily ported to mobile clients owing to the obvious limitation in their processing capabilities and restrained battery life. We address this problem by proposing architecture for secure exchange and trusted execution between mobile devices and cloud hosts. We establish a symmetric-key-based secure communication channel between mobile and cloud, backed by a trusted coordinator. We also employee a Trusted Platform Module- (TPM-) based attestation of the cloud nodes on which the data and applications of mobile device will be hosted. This gives a comprehensive solution for end-to-end secure and trusted interaction of the mobile device with cloud hosts.

Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.

Prostate cancer (PCa) is a severe type of cancer and causes major deaths among men due to its poor diagnostic system. The images obtained from patients with carcinoma consist of complex and necessary features that cannot be extracted readily by traditional diagnostic techniques. This research employed deep learning long short-term memory $(LSTM)$ and Residual Net $(ResNet-101)$ , independent of hand-crafted features, and is fine-tuned. The results were compared with hand-crafted features such as texture, morphology, and gray level co-occurrence matrix $(GLCM)$ using non-deep learning classifiers such as support vector machine $(SVM)$ Gaussian Kernel, k-nearest neighbor-Cosine $(KNN-Cosine)$ , kernel naive Bayes, decision tree $(DT)$ and RUSBoost tree. This study reduces the features of carcinoma images, employed machine learning and deep learning approaches. For validation of training and testing data, a jack-knife ten-fold cross-validation method was used. The performance was measured using a confusion matrix such as sensitivity, specificity, positive predictive value $(PPV)$ , negative predictive value $(NPV)$ , accuracy $(AC)$ , Mathews Correlation Coefficient ( $MCC$ ), and area under the curve $(AUC)$ . The most remarkable performance was obtained using non-deep learning methods with $GLCM$ features using KNN-Cosine with sensitivity (98.00%), specificity (99.25%), PPV (98.99%), NPV (99.11%), accuracy (99.07%), and AUC (0.998). The LSTM deep learning method yields performance with sensitivity (98.33%), specificity (100%), PPV (100%), NPV (99.26%), accuracy (99.48%), MCC (0.9879) and AUC (0.9999), where using Deep learning method $ResNet-101$ , we obtained (100%) Accuracy and AUC (1) for Kernel Naive Bayes, SVM Gaussian and RUSBoost Tree. The results show that $ResNet-101$ deep learning outperformed than non-deep learning methods and $LSTM$ . Thus, the deep learning method $ResNet-101$ could be used as a better predictor for the detection of prostate cancer.

Psychometric measures reflecting people’s knowledge, ability, attitudes, and personality traits are critical for many real-world applications, such as e-commerce, health care, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this article, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multitask learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated, and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing 11 psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions.

With greater impetus on broad postmarket surveillance, the Voice of the Customer (VoC) has emerged as an important source of information for understanding consumer experiences and identifying potential issues. In organizations, risk management groups are increasingly interested in working with their information technology teams to develop robust VoC listening platforms. Two key challenges have impeded success. First, prior work has leveraged diverse sets of channels, adverse event types, and modeling methods, resulting in diverging conclusions regarding the viability and efficacy of various user-generated channels and accompanying modeling methods. Second, many existing detection methods rely on “mention models” that have low detection rates, have high false positives, and lack timeliness.

We propose a Discussion Logic-based Text Analytics (DiLTA) framework, which combines theories developed in social science and text mining fields. The framework extracts features that uncover discussion logic and uses these features in analyzing online discussions. A series of models are proposed including conversation disentanglement, coherence analysis, and visualization. Validation experiments showed that DiLTA achieved significantly superior performance over existing text analytics methods in reconstructing internal structure. A case study using DiLTA-enabled visualization on a healthcare forum illustrates the great potential of DiLTA in assisting comprehension of the internal linkage, structure, and logic of online group discussions.

Biosensor is a means to transmit some physical phenomena, like body temperature, pulse, respiratory rate, electroencephalogram (EEG), electrocardiogram (ECG), and blood pressure. Such transmission is performed via Wireless Medical Sensor Network (WMSN) while diagnosing patients remotely through Internet-of-Medical-Things (IoMT). The sensitive data transmitted through WMSN from IoMT over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. In contrast to addressing the security of all associated entities involving patient monitoring in the healthcare system or ensuring the integrity, authorization, and nonrepudiation of information over the communication line, no one can guarantee its security without a robust authentication protocol. Therefore, we have proposed a lightweight and robust authentication scheme for the network-enabled healthcare devices (IoMT) that mitigate all the identified weaknesses posed in the recent literature. The proposed protocol’s security has been analyzed formally using BAN logic and ProVerif2.02 and informally using pragmatic illustration. Simultaneously, at the end of the paper, the performance analysis result shows a delicate balance of security with performance that is often missing in the current protocols.

Nowadays, an Internet of Things (IoT) device consists of algorithms, datasets, and models. Due to good performance of deep learning methods, many devices integrated well-trained models in them. IoT empowers users to communicate and control physical devices to achieve vital information. However, these models are vulnerable to adversarial attacks, which largely bring potential risks to the normal application of deep learning methods. For instance, very little changes even one point in the IoT time-series data could lead to unreliable or wrong decisions. Moreover, these changes could be deliberately generated by following an adversarial attack strategy. We propose a robust IoT data classification model based on an encode-decode joint training model. Furthermore, thermometer encoding is taken as a nonlinear transformation to the original training examples that are used to reconstruct original time series examples through the encode-decode model. The trained ResNet model based on reconstruction examples is more robust to the adversarial attack. Experiments show that the trained model can successfully resist to fast gradient sign method attack to some extent and improve the security of the time series data classification model.

Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic nature, the phenomena of conception drift happened. Therefore, it is a challenging task to choose a suitable anomaly detection service (ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to detect appropriate ADS dynamically at run-time. These methods help to choose suitable service and their respective configuration based on the patterns of stream data. The features used to describe and reflect time-series data’s intrinsic characteristics are the main success factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm. Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various advanced approaches and can choose appropriate service in different scenarios efficiently.

Objectives: To compare significant position-based routing protocols based on their underlying techniques such as junction selection mechanisms that provide vehicle-to-vehicle communications in city scenarios. Background: Vehicular Adhoc Network is the most significant offshoot of Mobile Adhoc Networks which is capable of organizing itself in an infrastructure-less environment. The network builds smart transportation which facilitates deriving in-terms of traffic safety by exchanging timely information in a proficient manner. Findings: The main features of vehicular adhoc networks pertaining to the city environment like high mobility, network segmentation, sporadic interconnections, and impediments are the key challenges for the development of an effective routing protocol. These features of the urban environment have a great impact on the performance of a routing protocol. This study presents a brief survey on the most substantial position-based routing schemes premeditated for urban inter-vehicular communication scenarios. These protocols are provided with their operational techniques for exchanging messages between vehicles. A comparative analysis is also provided, which is based on various important factors such as the mechanisms of intersection selection, forwarding strategies, vehicular traffic density, local maximum conquering methods, mobility of vehicular nodes, and secure message exchange. Application/Improvements: the outcomes observed from this paper motivate us to improve routing protocol in terms of security, accuracy, and reliability in vehicular adhoc networks. Furthermore, it can be employed as a foundation of references in determining literature that are worth mentioning to the routing in vehicular communications.

Phishing is a significant, ongoing cybersecurity threat facing both individuals and organizations, and the consequences of falling victim to phishing can be dire, particularly in terms of financial loss. While much research has focused on understanding or predicting user susceptibility to phishing with the aim of preventing it, little of this research has focused on modeling the process of being phished holistically. Specifically, while the outcome of interacting with a phishing website may be thought of as binary (e.g., "Were you phished or notƒ"), the actual process typically involves a sequence of stages spanning from the choice to visit (or not visit) a link all the way to transacting with a website and giving away valuable personal or financial information. To better understand the variables that influence phishing website traversal, we conducted a controlled lab experiment with a large sample of 908 participants. In this experiment, each participant was given a task such as opening an online checking account and presented with a series of simulated search results that included both legitimate and phishing websites. By monitoring participants’ interactions with these websites and collecting additional information via surveys, we evaluated which variables were the most likely to result in behavior that could lead to greater phishing exposure using a multi-model comparison approach. The results of our analyses shed light on the key variables that can lead to a greater propensity for being phished and may prove invaluable to researchers interested in designing new interventions.

Phishing attacks remain pervasive and continue to be a source of significant monetary loss, identity theft, and malware. One of the challenges is that in most organizational settings, the detection paradigm is inherently about identifying and reacting to threats in real-time, as they are unfolding. As a way to complement these efforts with greater foresight, we introduce the idea of phishcasting — forecasting of phishing threat levels weeks or months into the future. Given that phishing attack volume time series data is noisy and devoid of traditional seasonal and cyclical trends, we extend the time series forecasting framework to utilize multiple time series, auxiliary information and alternate representations. We also introduce CoT-Net, a flexible, end-to-end CNN-LSTM based deep learning method for forecasting of complex phishing attack volume time series. CoT-Net uses time series embeddings to uncover correlations between organizational attack patterns within and across industry sectors. Using a publicly available test bed featuring multiple organizations’ attack volume over time, we find CoT-Net to outperform most state-of-the-art time series forecasting methods. By showing that phishcasting might be possible and practical, our work has important proactive implications for cybersecurity.

Time series predictions are important for various application domains. However, effective forecasting can be challenging in noisy contexts devoid of time series data encompassing stationarity, cyclicality, completeness, and non-sparseness. Cyber-security is a good example of such context. In organizational security settings, predicting time series related to emerging attacks could enhance cyber threat intelligence, resulting in timely and actionable insights at the operational, tactical, and strategic levels. In order to explore this gap, we propose a deep generative model-based framework for time series forecasting in noisy data environments. The proposed framework incorporates a novel ensembling strategy where generative adversarial networks and recurrent variational autoencoders are leveraged in unison with base predictors for enhanced regularization of time series predictive models. The framework is extensible, supporting different model combinations and analytical or iterative model fusion strategies. Using a test bed encompassing 10 years of weekly phishing attack volume data from 5 organizations in the technology, financial services, and social networking sectors, we show that the framework can boost predictive power for various standard time series models. Additional results reveal that the framework outperforms generative data augmentation approaches designed to enrich the input time series data matrices. Collectively, our findings suggest that utilizing generative models in more robust end-to-end setup can improve prediction in cyber threat intelligence contexts, as well as related problems involving challenging time series data.

Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.

Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by Twitter sentiment analysis and review the literature to determine how the devised approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of the next generation of approaches.

Keeping in view the importance of small and medium enterprises (SMEs) for the growth of a nation, we must also keep an eye on the challenges faced by those SMEs. There are various kinds of financing and supply chain options available for SMEs but they still face lot of hindrances. This paper would help us to understand why SMEs are important for the development of any country and how could we help the SMEs from facing challenges related to financing and supply chain. This study further highlights the key financing issues faced by SMEs and also focuses on major supply chain challenges confronted by the SMEs. This study put emphasis on the concept of supply chain finance (SCF) and that how SCF could help SMEs to overcome those challenges. In addition, this paper also points out the benefits and prospects of SMEs. Even though the concept of SCF is still in developing phase but it has shown significant assistance to SMEs in order to grow further.

Social media and online communities provide organizations with new opportunities to support their business-related functions. Despite their various benefits, social media technologies present two important challenges for sense-making. First, online discourse is plagued by incoherent, intertwined conversations that are often difficult to comprehend. Moreover, organizations are increasingly interested in understanding social media participants’ actions and intentions; however, existing text analytics tools mostly focus on the semantic dimension of language. The language-action perspective (LAP) emphasizes pragmatics; not what people say but, rather, what they do with language. Adopting the design science paradigm, we propose a LAP-based text analytics framework to support sense-making in online discourse. The proposed framework is specifically intended to address the two aforementioned challenges associated with sense-making in online discourse: the need for greater coherence and better understanding of actions. We rigorously evaluate a system that is developed based on the framework in a series of experiments using a test bed encompassing social media data from multiple channels and industries. The results demonstrate the utility of each individual component of the system, and its underlying framework, in comparison with existing benchmark methods. Furthermore, the results of a user experiment involving hundreds of practitioners, and a four-month field experiment in a large organization, underscore the enhanced sense-making capabilities afforded by text analytics grounded in LAP principles. The results have important implications for online sense-making and social media analytics.

Exploring and understanding outputs from agent-based models is challenging due to a relatively higher number of parameters and multidimensionality of the generated data. We use a combination of exploratory and data mining techniques to understand data generated from an existing agent-based model to understand the model’s behavior and its sensitivity to initial configurations of its parameters. This is a step in the direction of an ongoing research in the social simulation community to incorporate more sophisticated techniques to better understand how different parameters and internal processes influence outcomes of agent-based models.

This paper focuses on the proposed generic framework of Cyber-Physical Social Systems (CPSS) for Multidimensional Urban Mobility (MUM). The introduction of air mobility will increase the complexity of urban mobility management due to the increase of data (real-time information exchange), services, and infrastructure. This paper first summarizes the shift of the smart mobility and associated concerns and emphasizes the smart technologies to be introduced for efficient deployment of the MUM. Afterwards, the state-of-the-art on CPSS present the details of the research work in the context of CPSS data management process and challenges related with data. In conclusion, a framework to address the various challenges associated with the data paradigm is proposed.

For sustainable development construction, recycle or reuse of waste materials is utilized. Many researchers conducted tried to create an innovative green concrete, utilizing waste materials. The aim of this research is to contribute and promote the use of plastic waste in concrete. The concrete’s flexural and workability were investigated by using different percentages of 0%, 0.2%, 0.4%, 0.6%, 0.8% and 1% of plastic fibers in concrete. In this study, M15 grade concrete beams were casted and cured for 7 and 28 days to analyze the flexural performance and workability. The outcomes demonstrated that the workability was slightly reduced by the utilizing plastic fibers where flexural strength improved by 16.5% at 0.6% addition of plastic fibers in concrete.

VANET (Vehicular Ad-hoc Network) is an emerging offshoot of MANETs (Mobile Ad-hoc Networks) with highly mobile nodes. It is envisioned to play a vital role in providing safety communications and commercial applications to the on-road public. Establishing an optimal route for vehicles to send packets to their respective destinations in VANETs is challenging because of quick speed of vehicles, dynamic nature of the network, and intermittent connectivity among nodes. This paper presents a novel position based routing technique called Dynamic Multiple Junction Selection based Routing (DMJSR) for the city environment. The novelty of DMJSR as compared to existing approaches comes from its novel dynamic multiple junction selection mechanism and an improved greedy forwarding mechanism based on one-hop neighbors between the junctions. To the best of our knowledge, it is the first ever attempt to study the impact of multiple junction selection mechanism on routing in VANETs. We present a detailed depiction of our protocol and the improvements it brings as compared to existing routing strategies. The simulation study exhibits that our proposed protocol outperforms the existing protocols like Geographic Source Routing Protocol (GSR), Enhanced Greedy Traffic Aware Routing Protocol (E-GyTAR) and Traffic Flow Oriented Routing Protocol (TFOR) in terms of packet delivery ratio, end-to-end delay, and routing overhead.

Robotic arms in industries encompassing long assembly lines has increased in vein of achieving automated and increased production. Material selection for utilization of robotic arms in such environments entails significant importance. Material selected has to interact with everyday job atmosphere and must withstand the forces applied at the end effector. Two commonly available materials Aluminum and Poly-methyl methacrylate were considered for designing of 5R robotic arm. Static force and torque analysis depicted the torques required in case of Aluminum slightly more as compared to those required in case of Poly-methyl methacrylate. Static structural analysis showed the total deformation experienced by Aluminum to be between 0.0148 to 0.1480 mm when end effector was subject to applied load of 5, 10, 20, 30, 40 and 50 N respectively, however, poly-methyl methacrylate reached the yield point when subjected to 40 N force. Therefore, based on reliable machineability, serviceability, static torque and structural analysis, aluminum was selected over Poly-methyl methacrylate, and was used for fabrication of 5R robotic arm.

We propose a method to evaluate adverse drug event (ADE) narratives using biomedical semantic similarity measures. Automated drug surveillance systems have used social media as a prime resource to detect ADEs. However, the problem of language usage over social media has been a challenge in evaluating the performance of such systems. We address this key issue by using semantic similarity measures and the biomedical vocabularies from the Unified Medical Language System. This is important in comparing results of social media driven approaches against standard reference documents from regulatory agencies.

Phishing website-based attacks remain pervasive, with high user susceptibility continuing to be a major factor. In this study we use cluster analysis coupled with an elaborate controlled experiment involving hundreds of participants to identify and examine high susceptibility user segments in terms of their perceptions, demographics, and phishing website traversal behavior. The results reveal three sets of users, including two sets that exhibit highly problematic behavior. The results have important implications for training programs, usability of anti-phishing tools, and security policies.

The number of active, online phishing websites continues to grow unabated in recent years. This has created an ever-increasing security risk for both individual and enterprise users in terms of identity theft, malware, financial loss, etc. Although resources exist for tracking, cataloguing, and blacklisting these types of sites (e.g., PhishTank.com), the ephemeral nature of phishing websites makes in-depth analysis exceptionally difficult. In order to better understand how these phishing sites exploit user and system weaknesses, we have crafted a platform named PhishMonger for capturing live phishing websites in real-time on an ever-present, rolling basis, which we outline in this paper. Moreover, we present details regarding our growing database of verified phishing websites, which currently encompasses over 88,754 sites, spanning 10,956,415 files and folders, utilizing 108GB of compressed storage. We offer recommendations on how this corpus can be leveraged by the cybersecurity and security informatics research communities to examine several important research problems.

A dual band sleeve dipole antenna for WLAN application is presented. The antenna covers both the WLAN bands. The proposed antenna exhibits excellent omni-directional characteristics and reflection properties S11 values which are less than −10db. The dipole is constructed from two concentric hallow cylinders having different radius and different lengths. The complicated balun network is avoided by feeding the antenna at the center with the help of coaxial cable. The scattering parameters and radiation patterns are simulated using CST microwave studio. These simulation results are validated by the measured results. The antenna is best suitable for airborne and outdoor applications.

Recently; AI based methods are frequently used in healthcare industry to unfold historical hindsight to explore the insight and envisage the foresight. For example, identification of epidemiological patterns of thyroid disease in targeted area(s) supports healthcare industry stakeholders (government agencies, health organizations, NGOs, policy makers and so on) in formulating proper policies to combat such kind of fatal diseases. Also, predictive Future Visualization (FV) of prevalence patterns of the thyroid disease is really helpful for these stakeholders to properly focus on specific area(s). This paper offers a system so called TDV: Intelligent System for Thyroid Disease Visualization, which offers a potential surveillance pattern of thyroid disease to policy makers for next ten years (2013–2022) by presenting thyroid disease prevalence facts of past ten year (2002–2012). The methodology of our system comprises upon three main steps, in first step, we apply data preprocessing techniques. In second step; we construct the decision model using Time Series Regression (TSR) in R software, finally we visualized the results by using a geographic map plotted in Q-GIS. As per results of our approach, we conclude that thyroid disease may increase more than 15% for next ten years in age group 21–30 and female gender is more prone to be affected from thyroid disease.

Regulators, analysts, policy-makers, and advocacy groups are increasingly interested in utilizing the abundance of available online Health 2.0 content to support key decision-making tasks. However, existing systems are ill-suited to deal with the plethora of medical spam and variety of relevant online channels. We present a prototype system for collecting, analyzing, aggregating, and presenting key topics and sentiments encompassed in online medical content. By incorporating modules for examining credibility, relevance, and context, the system is able to present users with information that is markedly better with respect to credibility, coverage, precision, and timeliness.

The accumulated literature base in the behavioral sciences represents a great source of knowledge on human behaviors, and yet the same literature has grown beyond human comprehension. We address this information overload problem by proposing a novel IT artifact --- TheoryOn. Based on the design science paradigm, we identify five design requirements. We first adapt the ontology learning layer cake framework to develop a four-step process --- hypothesis extraction, construct extraction, construct relationship extraction and theory extraction --- to automatically extract integral "parts" of behavioral theories. We then design four functionalities allowing researchers to quickly access synonymous constructs, construct relationships and theoretically related constructs e.g. antecedents and consequents, as well as integrate related theories. To illustrate the applicability and usefulness, we use a dataset of all the relevant behavioral studies from three top journals in Information Systems and Psychology and conduct an A/B test between the prototype TheoryOn system and the EBSCOhost full-text search engine.

Vehicular Ad-hoc Networks (VANETs) have been gaining significant attention from the research community due to their increasing importance for building an intelligent transportation system. The characteristics of VANETs, such as high mobility, network partitioning, intermittent connectivity and obstacles in city environments, make routing a challenging task. Due to these characteristics of VANETs, the performance of a routing protocol is degraded. The position-based routing is considered to be the most significant approach in VANETs. In this paper, we present a brief review of most significant position based unicast routing protocols designed for vehicle to vehicle communications in the urban environment. We provide them with their working features for exchanging information between vehicular nodes. We describe their pros and cons. This study also provides a comparison of the vehicle to vehicle communication based routing protocols. The comparative study is based on some significant factors such as mobility, traffic density, forwarding techniques and method of junction selection mechanism, and strategy used to handle a local optimum situation. It also provides the simulation based study of existing dynamic junction selection routing protocols and a static junction selection routing protocol. It provides a profound insight into the routing techniques suggested in this area and the most valuable solutions to advance VANETs. More importantly, it can be used as a source of references to other researchers in finding literature that is relevant to routing in VANETs.

Methods and tools to conduct authorship analysis of web contents is of growing interest to researchers and practitioners in various security-focused disciplines, including cybersecurity, counter-terrorism, and other fields in which authorship of text may at times be uncertain or obfuscated. Here we demonstrate an automated approach for authorship analysis of web contents. Analysis is conducted through the use of machine learning methodologies, an expansive stylometric feature set, and a series of visualizations intended to help facilitate authorship analysis at the author, message, and feature levels. To operationalize this, we utilize a testbed containing 506,554 forum messages in English and Arabic, source from 14,901 authors that participated in an online web forum. A prototype portal system providing authorship comparisons and visualizations was then designed and constructed in order to support feasibility analysis and real world value of the automated authorship analysis approach. A preliminary user evaluation was performed to assess the efficacy of visualizations, with evaluation results demonstrating task performance accuracy and efficiency was improved through use of the portal.