Showing papers by "Concordia University published in 2018"

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Abstract: Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report.

Abstract: Following a review of extant reporting standards for scientific publication, and reviewing 10 years of experience since publication of the first set of reporting standards by the American Psychological Association (APA; APA Publications and Communications Board Working Group on Journal Article Reporting Standards, 2008), the APA Working Group on Quantitative Research Reporting Standards recommended some modifications to the original standards. Examples of modifications include division of hypotheses, analyses, and conclusions into 3 groupings (primary, secondary, and exploratory) and some changes to the section on meta-analysis. Several new modules are included that report standards for observational studies, clinical trials, longitudinal studies, replication studies, and N-of-1 studies. In addition, standards for analytic methods with unique characteristics and output (structural equation modeling and Bayesian analysis) are included. These proposals were accepted by the Publications and Communications Board of APA and supersede the standards included in the 6th edition of the Publication Manual of the American Psychological Association (APA, 2010). (PsycINFO Database Record

Many analysts, one dataset: Making transparent how variations in analytical choices affect results

Abstract: Twenty-nine teams involving 61 analysts used the same data set to address the same research question: whether soccer referees are more likely to give red cards to dark-skin-toned players than to light-skin-toned players. Analytic approaches varied widely across the teams, and the estimated effect sizes ranged from 0.89 to 2.93 (Mdn = 1.31) in odds-ratio units. Twenty teams (69%) found a statistically significant positive effect, and 9 teams (31%) did not observe a significant relationship. Overall, the 29 different analyses used 21 unique combinations of covariates. Neither analysts’ prior beliefs about the effect of interest nor their level of expertise readily explained the variation in the outcomes of the analyses. Peer ratings of the quality of the analyses also did not account for the variability. These findings suggest that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy in which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective, analytic choices influence research results.

Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients

Abstract: In this paper, a novel computationally intelligent-based electrocardiogram (ECG) signal classification methodology using a deep learning (DL) machine is developed. The focus is on patient screening and identifying patients with paroxysmal atrial fibrillation (PAF), which represents a life threatening cardiac arrhythmia. The proposed approach operates with a large volume of raw ECG time-series data as inputs to a deep convolutional neural networks (CNN). It autonomously learns representative and key features of the PAF to be used by a classification module. The features are therefore learned directly from the large time domain ECG signals by using a CNN with one fully connected layer. The learned features can effectively replace the traditional ad hoc and time-consuming user’s hand-crafted features. Our experimental results verify and validate the effectiveness and capabilities of the learned features for PAF patient screening. The main advantages of our proposed approach are to simplify the feature extraction process corresponding to different cardiac arrhythmias and to remove the need for using a human expert to define appropriate and critical features working with a large time-series data set. The extensive simulations and case studies conducted indicate that combining the learned features with other classifiers will significantly improve the performance of the patient screening system as compared to an end-to-end CNN classifier. The effectiveness and capabilities of our proposed ECG DL classification machine is demonstrated and quantitative comparisons with several conventional machine learning classifiers are also provided.

Brain Tumor Type Classification via Capsule Networks

Abstract: Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images; (iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and; (iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.

Environmental challenges for the belt and road initiative

Abstract: The Belt and Road Initiative will greatly influence the future of global trade. However, it may also promote permanent environmental degradation. We call for rigorous strategic environmental and social assessments, raising the bar for environmental protection worldwide.

Corporate Social Responsibility and Investment Efficiency

Abstract: Using a sample of 21,030 US firm-year observations that represents more than 3000 individual firms over the 1998–2012 period, we investigate the relationship between Corporate Social Responsibility (CSR) and investment efficiency. We provide strong and robust evidence that high CSR involvement decreases investment inefficiency and consequently increases investment efficiency. This result is consistent with our expectations that high CSR firms enjoy low information asymmetry and high stakeholder solidarity (stakeholder theory). Moreover, our findings suggest that CSR components that are directly related to firms’ primary stakeholders (e.g. employee relations, product characteristics, environment, and diversity) are more relevant in reducing investment inefficiency compared with those related to secondary stakeholders (e.g. human rights and community involvement). Finally, additional results show that the effect of CSR on investment efficiency is more pronounced during the subprime crisis. Taken together, our results highlight the important role that CSR plays in shaping firms’ investment behaviour and efficiency.

Accurate and efficient refactoring detection in commit history

Abstract: Refactoring detection algorithms have been crucial to a variety of applications: (i) empirical studies about the evolution of code, tests, and faults, (ii) tools for library API migration, (iii) improving the comprehension of changes and code reviews, etc. However, recent research has questioned the accuracy of the state-of-the-art refactoring detection tools, which poses threats to the reliability of their application. Moreover, previous refactoring detection tools are very sensitive to user-provided similarity thresholds, which further reduces their practical accuracy. In addition, their requirement to build the project versions/revisions under analysis makes them inapplicable in many real-world scenarios. To reinvigorate a previously fruitful line of research that has stifled, we designed, implemented, and evaluated RMiner, a technique that overcomes the above limitations. At the heart of RMiner is an AST-based statement matching algorithm that determines refactoring candidates without requiring user-defined thresholds. To empirically evaluate RMiner, we created the most comprehensive oracle to date that uses triangulation to create a dataset with considerably reduced bias, representing 3,188 refactorings from 185 open-source projects. Using this oracle, we found that RMiner has a precision of 98% and recall of 87%, which is a significant improvement over the previous state-of-the-art.

Demand-Side Management by Regulating Charging and Discharging of the EV, ESS, and Utilizing Renewable Energy

Abstract: The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs), and renewable energy sources will all play a significant role in balancing the planned generation of electricity and its real-time use We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a preplanned day-ahead energy generation by the microgrid, based on predicted customers’ aggregate load A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers To develop our system, we formulate a game with mixed strategy which in the first phase (ie, prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand Then, in the second stage (ie, allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one To achieve this, customers exploit renewable energy and ESSs and decide optimal strategies for their charging/discharging, taking into account their operational constraints RDCDSM will help the microgrid operator to better deal with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control noncooperative game method both of which rely on a day ahead demand prediction without any refinement We run simulations with various microgrid configurations, by varying the load and generated power, and compare the outcomes

The Design of Fixed-Time Observer and Finite-Time Fault-Tolerant Control for Hypersonic Gliding Vehicles

Abstract: This paper proposes a fault-tolerant control (FTC) scheme for a hypersonic gliding vehicle to counteract actuator faults and model uncertainties. Starting from the kinematic and aerodynamic models of the hypersonic vehicle, the control-oriented model subject to actuator faults is built. The observers are designed to estimate the information of actuator faults and model uncertainties, and to guarantee the estimation errors for converging to zero in fixed settling time. Subsequently, the finite-time multivariable terminal sliding mode control and composite-loop design are pursued to enable integration into the FTC, which can ensure the safety of the postfault vehicle in a timely manner. Simulation studies of a six degree-of-freedom nonlinear model of the hypersonic gliding vehicle are carried out to manifest the effectiveness of the investigated FTC system.

Brain Tumor Type Classification via Capsule Networks

Abstract: Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images; (iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and; (iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.

A review of power management strategies and component sizing methods for hybrid vehicles

Abstract: This paper comprehensively reviews power management strategies (PMS) and component sizing (CS) methods for hybrid vehicles with more than one energy storage system (ESS). The PMS aims to coordinate the power flow among different ESSs while meeting the drive commands and other constraints; whereas, CS is used to optimize the combination of the components to compose a cost-effective powertrain. However, these two aspects are usually coupled such that it is not reasonable to discuss them separately from a system-level design perspective. Therefore, this review briefly discusses the popular PMS, followed by a detailed CS review in different aspects, including the classic and optimization-based with their own subtypes. In addition, several case studies belonging to different optimization structures are also reviewed in detail to demonstrate the features and the main conclusions of each method. As the comparison results show that with the proper CS methods, the hybrid powertrain witness a large fuel saving compared to their conventional counterparts. Furthermore, factors or issues that affect the performance of the PMS and CS methods are discussed. Meanwhile, the future research trends in PMS and CS are elaborated. This study intends to help researchers to have an overview of the state-of-the-art in PMS and CS methods and more importantly, to provide directions for optimal powertrain and power management controller designs for cost-effective and environmental-friendly vehicles.

A DEMATEL based approach for investigating barriers in green supply chain management in Canadian manufacturing firms

Abstract: Green supply chain management (GSCM) involves consideration of environmental impacts of all the processes involved in a typical supply chain to minimise their negative consequences. In this paper, ...

Broadband mm-Wave Microstrip Array Antenna With Improved Radiation Characteristics for Different 5G Applications

Abstract: A Ka-band inset-fed microstrip patches linear antenna array is presented for the fifth generation (5G) applications in different countries. The bandwidth is enhanced by stacking parasitic patches on top of each inset-fed patch. The array employs 16 elements in an H-plane new configuration. The radiating patches and their feed lines are arranged in an alternating out-of-phase 180° rotating sequence to decrease the mutual coupling and improve the radiation pattern symmetry. A (24.4%) measured bandwidth (24.35–31.13 GHz) is achieved with −15 dB reflection coefficients and 20 dB mutual coupling between the elements. With uniform amplitude distribution, a maximum broadside gain of 19.88 dBi is achieved. Scanning the main beam to 49.5° from the broadside achieved 18.7 dBi gain with −12.1 dB sidelobe level. These characteristics are in good agreement with the simulations, rendering the antenna to be a good candidate for 5G applications.

UAV-Aided Cooperation for FSO Communication Systems

Abstract: Relay-assisted FSO systems were proposed as a means for remedying the effects of the various atmospheric impairments on the quality of the FSO signal. Conventional relay-assisted FSO systems, however, are designed around two basic assumptions: relays are buffer-free, and relays are stationary. This article proposes to improve the performance of the existing relay-assisted FSO systems by relaxing both of these highly restrictive assumptions through the integration of UAVs as buffer-aided moving relays into the conventional relay-assisted FSO systems. Specifically, two possible simple integration scenarios are proposed and analyzed through simulation. The obtained simulation results demonstrate the great potential associated with the proposed highly promising, innovative, hybrid FSO architecture. Given that high performance gains are observed under small buffer sizes, it becomes conceivable to employ buffer-aided moving relaying UAVs to serve a variety of other purposes. This includes, for instance, having these UAVs oversee the operation of amateur drones for potential misbehavior or wrongdoing within the area of their deployment.

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Abstract: We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

Abstract: The Internet-of-Things (IoT) is rapidly becoming ubiquitous. However the heterogeneous nature of devices and protocols in use, the sensitivity of the data contained within, as well as the legal and privacy issues, make security for the IoT a growing research priority and industry concern. With many security practices being unsuitable due to their resource intensive nature, it is deemed important to include second line defences into IoT networks. These systems will also need to be assessed for their efficacy in a variety of different network types and protocols. To shed light on these issues, this paper is concerned with advancements in intrusion detection practices in IoT. It provides a comprehensive review of current intrusion detection systems (IDSs) for IoT technologies, focusing on architecture types. A proposal for future directions in IoT based IDS are then presented and evaluated. We show how traditional practices are unsuitable due to their inherent features providing poor coverage of the IoT domain. In order to develop a secure, robust and optimized solution for these networks, the current research for intrusion detection in IoT will need to move in a different direction. An example of which is proposed in order to illustrate how malicious nodes might be passively detected.

Advances in the Treatment of Context in Organizational Research

Abstract: Although scholars in the field of organizational behavior have raised concerns about a lack of contextual appreciation, there has been a recent embrace of contextual thinking in the organizational sciences. In this review, I discuss several recent theories and measures of context. The added value of a contextual approach is illustrated by how context can shape personality, how it affects the emergence of work designs, and how it benefits the study of organizational demography. Future research topics include context cue sensitivity, the way context is shaped, the mediators of context effects, and the breadth and limits of contextual impact. A recurrent theme is that although context enables a demarcation of what is distinctive about situations, it also permits integration across research areas and levels of analysis, identifying what they have in common as settings for organizational behavior.

Investigation of inter- and intraspecies variation through genome sequencing of Aspergillus section Nigri

Abstract: Aspergillus section Nigri comprises filamentous fungi relevant to biomedicine, bioenergy, health, and biotechnology. To learn more about what genetically sets these species apart, as well as about potential applications in biotechnology and biomedicine, we sequenced 23 genomes de novo, forming a full genome compendium for the section (26 species), as well as 6 Aspergillus niger isolates. This allowed us to quantify both inter- and intraspecies genomic variation. We further predicted 17,903 carbohydrate-active enzymes and 2,717 secondary metabolite gene clusters, which we condensed into 455 distinct families corresponding to compound classes, 49% of which are only found in single species. We performed metabolomics and genetic engineering to correlate genotypes to phenotypes, as demonstrated for the metabolite aurasperone, and by heterologous transfer of citrate production to Aspergillus nidulans. Experimental and computational analyses showed that both secondary metabolism and regulation are key factors that are significant in the delineation of Aspergillus species.

Social Performance and Firm Risk: Impact of the Financial Crisis

Abstract: This paper examines the impact of the recent financial crisis (2008–2009) on the relation between a firm’s risk and social performance (SP) using a sample of non-financial U.S. firms covering the period 1991–2012. We find that the relation between SP and risk is significantly different in the crisis period (post-crisis period) compared to the pre-crisis period. SP reduces volatility during the financial crisis. The risk reduction potential of SP is mainly due to the strengths component of SP. Since the relation of risk is stronger with SP strengths than SP concerns, this implies an asymmetric relation between these SP components and a firm’s risk. Specifically, strengths act as a risk reduction tool during an adverse economic environment.

Are Blockchain Crowdsales the New 'Gold Rush'? Success Determinants of Initial Coin Offerings

Abstract: Initial Coin Offerings (ICOs) are a new and unregulated form of crowdfunding that raises funds through a blockchain by selling venture-related tokens or coins in exchange for legal tender or cryptocurrencies. In this paper, we establish token or coin tradability as the primary ICO success measure, and we develop a theoretical framework for how venture uncertainty, venture quality, and investor opportunity set interrelate. We use the largest available dataset to date, consisting of 1,009 ICOs from 2015 to March 2018. Our data highlights that venture uncertainty (not being on Github and Telegram, shorter whitepapers, higher percentage of tokens distributed) is negatively correlated, while higher venture quality (better connected CEOs and larger team size) is positively correlated, with ICO success. Moreover, providing a hard cap in a pre-ICO can help investors measure success in the pre-sale. This is another positive signal of funding success.

Cognitive Involvement in Balance, Gait and Dual-Tasking in Aging: A Focused Review From a Neuroscience of Aging Perspective.

Abstract: A substantial corpus of evidence suggests that the cognitive involvement in postural control and gait increases with aging. A large portion of such studies were based on dual-task experimental designs, which typically use the simultaneous performance of a motor task (e.g., static or dynamic balancing, walking) and a continuous cognitive task (e.g., mental arithmetic, tone detection). This focused review takes a cognitive neuroscience of aging perspective in interpreting cognitive motor dual-task findings. Specifically, we consider the importance of identifying the neural circuits that are engaged by the cognitive task in relation to those that are engaged during motor task performance. Following the principle of neural overlap, dual-task interference should be greatest when the cognitive and motor tasks engage the same neural circuits. Moreover, the literature on brain aging in general, and models of dedifferentiation and compensation, in particular, suggest that in cognitive motor dual-task performance, the cognitive task engages different neural substrates in young as compared to older adults. Also considered is the concept of multisensory aging, and the degree to which the age-related decline of other systems (e.g., vision, hearing) contribute to cognitive load. Finally, we discuss recent work on focused cognitive training, exercise and multimodal training of older adults and their effects on postural and gait outcomes. In keeping with the principle of neural overlap, the available cognitive training research suggests that targeting processes such as dividing attention and inhibition lead to improved balance and gait in older adults. However, more studies are needed that include functional neuroimaging during actual, upright performance of gait and balance tasks, in order to directly test the principle of neural overlap, and to better optimize the design of intervention studies to improve gait and posture.

Guidelines and recommendations on yeast cell death nomenclature

Abstract: Elucidating the biology of yeast in its full complexity has major implications for science, medicine and industry. One of the most critical processes determining yeast life and physiology is cel-lular demise. However, the investigation of yeast cell death is a relatively young field, and a widely accepted set of concepts and terms is still missing. Here, we propose unified criteria for the defi-nition of accidental, regulated, and programmed forms of cell death in yeast based on a series of morphological and biochemical criteria. Specifically, we provide consensus guidelines on the differ-ential definition of terms including apoptosis, regulated necrosis, and autophagic cell death, as we refer to additional cell death rou-tines that are relevant for the biology of (at least some species of) yeast. As this area of investigation advances rapidly, changes and extensions to this set of recommendations will be implemented in the years to come. Nonetheless, we strongly encourage the au-thors, reviewers and editors of scientific articles to adopt these collective standards in order to establish an accurate framework for yeast cell death research and, ultimately, to accelerate the pro-gress of this vibrant field of research.