Showing papers by "S. M. Riazul Islam published in 2021"

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

Abstract: Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Abstract: Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer’s disease (AD), brain tumor, epilepsy, and Parkinson’s disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

Machine learning in the prediction of cancer therapy.

Abstract: Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.

Secrecy Performance Analysis of Mixed α − μ and Exponentiated Weibull RF-FSO Cooperative Relaying System

Abstract: Increasing concerns regarding wireless systems’ security are leading researchers to exploit the physical properties of a medium while designing any secured wireless network. The secrecy performance of a mixed radio frequency-free space optical (RF-FSO) system with a variable gain relaying scheme is investigated in this paper under the attempt of wiretapping by an eavesdropper. We assume that the eavesdropper can intrude the target data from the RF link only. Both the RF links (main and eavesdropper) undergo the $\alpha -\mu $ fading statistics and the FSO link experiences the exponentiated Weibull fading statistics. Exploiting the amplify-and-forward (AF) relaying scheme while considering two detection techniques (i.e. heterodyne detection and intensity modulation/direct detection) with pointing error impairments, the mathematical formulations of the unified probability density function and cumulative distribution function are performed for the equivalent signal-to-noise ratio of the considered dual-hop RF-FSO link. Closed-form analytical expressions for average secrecy capacity, secrecy outage probability, and the probability of non-zero secrecy capacity are derived in terms of Meijer’s $G$ and Fox’s $H$ functions to quantify the system performance. Capitalizing on these expressions, the secrecy performance is further analyzed for various channel parameters of RF links, aperture sizes of the receiver, pointing errors, and atmospheric turbulence severity. The results reveal that aperture averaging can improve the secrecy performance remarkably by suppressing the effects of turbulence. Monte Carlo simulations are provided to justify the accuracy of the proposed model.

Impact of Correlation and Pointing Error on Secure Outage Performance Over Arbitrary Correlated Nakagami- $m$ and $\mathcal {M}$ -Turbulent Fading Mixed RF-FSO Channel

Abstract: In Radio Frequency (RF)-Free Space Optical (FSO) mixed links, secrecy capacity (SC) can be improved by exploiting spatial diversity (i.e., antenna diversity) in the RF path. In addition to atmospheric turbulence and point error of the FSO link, antenna correlation in the RF link can significantly deteriorate the secrecy performance. In this paper, a secrecy rate of wiretap channels with a single source, relay, destination, and eavesdropper is analyzed under practical environments with the aforementioned impairments. The RF hop (source-to-relay) and the FSO hop (relay-to-destination) are modeled utilizing arbitrarily correlated Nakagami- $m$ and Malaga ( $\mathcal {M}$ ) distributions, respectively. The correlated signal branches of the RF hop are combined at the relay exploiting equal gain combining reception technique. We assume that the eavesdropper is capable of wiretapping via RF and FSO links separately. We derive novel closed-form expressions for secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) considering heterodyne detection (HD) and intensity modulation with direct detection (IM/DD) techniques in order to examine the impact of atmospheric scintillation, pointing error, fading, and correlation on the system's secrecy performance. It is shown that the HD technique exhibits a better performance than an IM/DD technique. In addition, similar to the pointing error and turbulent fading, the correlation imposes a detrimental impact on SC. Finally, Monte-Carlo simulation results are provided for validation of the derived expressions.

Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm

Abstract: This paper exploits the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes. DR, a leading cause of blindness worldwide, is a sort of swelling leakage due to excessive blood sugar in the retina vessels. An early-stage diagnosis is therefore beneficial to prevent diabetes patients from losing their sight. This study introduced a novel method to detect DR for binary class and multiclass classification based on the APTOS-2019 blindness detection and Messidor-2 datasets. First, DR images have been pre-processed using Ben Graham’s approach. After that, contrast limited adaptive histogram equalization (CLAHE) has been used to get contrast-enhanced images with lower noise and more distinguishing features. Then a novel hybrid convolutional neural network-singular value decomposition model has been developed to reduce input features for classifiers. Finally, the proposed method uses an ELM algorithm as the classifier that minimizes the training time cost. The experiments focus on accuracy, precision, recall, and F1-score and demonstrate the feasibility of adopting the proposed scheme for DR diagnosis. The method outperforms the existing techniques and shows an optimistic accuracy and recall of 99.73% and 100%, respectively, for binary class. For five stages of DR classification, the proposed model achieved an accuracy of 98.09% and 96.26% for APTOS-2019 and Messidor-2 datasets, respectively, which outperformed the existing state-of-art models.

Security at the Physical Layer Over GG Fading and mEGG Turbulence Induced RF-UOWC Mixed System

Abstract: With the rapid evolution of communication technologies, high-speed optical wireless applications under the water surface as a replacement or complementary to the conventional radio frequency (RF) and acoustic technologies are attracting significant attention from the researchers. Since underwater turbulence (UWT) is an inevitable impediment for a long distance underwater optical wireless communication (UOWC) link, mixed RF-UOWC is being considered as a more feasible solution by the research community. This article deals with the secrecy performance of a variable gain relay-based mixed dual-hop RF-UOWC framework under the intercepting attempt of a potential eavesdropper. The RF link undergoes Generalized Gamma (GG) fading distribution, whereas the UOWC link is subjected to mixture Exponential Generalized Gamma (mEGG) distribution. The eavesdropper is capable of wiretapping via a RF link that also experiences the GG fading. The secrecy analysis incorporates the derivations of closed-form expressions for strictly positive secrecy capacity, average secrecy capacity, and exact as well as lower bound of secrecy outage probability in terms of univariate and bivariate Meijer’s $G$ and Fox’s $H$ functions. Based on these expressions, impacts of heterodyne and intensity modulation/direct detection techniques along with weak, moderate, and severe UWT conditions due to air bubbles, temperature, and salinity gradients are quantified. To the best of authors’ knowledge, the proposed model is the first of its kind that addresses the secrecy analysis of a temperature gradient RF-UOWC system along with air bubbles, as opposed to the existing models that considered thermally uniform scenarios only. Finally, the derived expressions are verified via Monte-Carlo simulations.

A highly sensitive quadruple D-shaped open channel photonic crystal fiber plasmonic sensor: A comparative study on materials effect

Abstract: A highly sensitive dual-polarized 'X' oriented quadruple D-shaped open channel photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor for various analyte detection is proposed in this paper. Gold is taken as a plasmonic material for its stability and compatibility. Silicon nitride (Si3N4) and titanium oxide (TiO2) has been used separately as an adhesive layer of gold to elevate the sustainability of the evanescent field. This paper shows a comparative study and inspects the effect of sensing performance between Si3N4 and TiO2 as an adhesive layer of gold. Numerical investigations have been followed up using the finite element method (FEM). For practical feasibility, analyte and plasmonic materials have been placed at the outer surface of the sensor. After watchful investigation, the maximum wavelength sensitivities of 21,000 nm/RIU (Refractive Index Unit) and 18,000 nm/RIU have been found for the y-polarization when using TiO2 and Si3N4, respectively. The highest amplitude sensitivities are of 914RIU−1 and 625RIU−1 for TiO2 and Si3N4, respectively. Furthermore, minimum wavelength resolutions of 4.76 × 10−6 RIU and 5.55 × 10−6 RIU have been observed in y-polarization for TiO2 and Si3N4, respectively. The sensor evinces a maximum figure of merit (FOM) of 236RIU−1 for TiO2. This sensor has the analyte sensing range of 1.31–1.38RI (Refractive Index) for TiO2 and 1.32–1.39RI for Si3N4. The sensor also delivers low confinement loss for Si3N4 and TiO2, which certifies viability in fabricating the design. Recognizing this sensor’s wavelength sensitivity, amplitude sensitivity, and sensing RI range, it could be a promising candidate for detecting different liquid analytes with excellent accuracy.

Milled Microchannel-Assisted Open D-Channel Photonic Crystal Fiber Plasmonic Biosensor

Abstract: A surface plasmon resonance (SPR) based photonic crystal fiber (PCF) sensor having a milled microchannel, and an open D-channel has been proposed in this paper. The sensor shows good functionality in the wide sensing range of 1.14-1.36 Refractive Index Units (RIU) of the analyte, having the capability to detect low refractive index (RI). The Finite Element Method (FEM) based numerical investigations dictate that the proposed sensor has been able to gain a maximum wavelength sensitivity of 53,800 nm/RIU according to the wavelength interrogation technique. The amplitude interrogations show that the sensor has the highest amplitude sensitivity of 328 RIU−1. The highest FOM (Figure of Merit) has been found to be 105 RIU−1. The sensor evinces a minimum wavelength resolution of $1.86\times 10 ^{-6}$ RIU, which secures high detection accuracy. A circular perfectly matched layer (PML) is implemented in the sensor’s outermost layer as a boundary condition to absorb surface radiations. Gold is the plasmonic metal, while TiO2 acts as the adhesive layer for gold attachment on silica. Due to the high sensitivity with a broad range of analyte detection, the sensor is well suited for practical biochemical detection purposes.

Internet of Things (IoT)-Based Wireless Health: Enabling Technologies and Applications

Abstract: Wireless health is transforming health care by integrating wireless technologies into conventional medicine, including the diagnosis, monitoring, and treatment of illness [...]

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges.

Abstract: Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

Prognostic role of EGR1 in breast cancer: a systematic review.

Abstract: EGR1 (early growth response 1) is dysregulated in many cancers and exhibits both tumor suppressor and promoter activities, making it an appealing target for cancer therapy. Here, we used a systematic multi-omics analysis to review the expression of EGR1 and its role in regulating clinical outcomes in breast cancer (BC). EGR1 expression, its promoter methylation, and protein expression pattern were assessed using various publicly available tools. COSMIC-based somatic mutations and cBioPortal-based copy number alterations were analyzed, and the prognostic roles of EGR1 in BC were determined using Prognoscan and Kaplan-Meier Plotter. We also used bc-GenEx- Miner to investigate the EGR1 co-expression profile. EGR1 was more often downregulated in BC tissues than in normal breast tissue, and its knockdown was positively correlated with poor survival. Low EGR1 expression levels were also associated with increased risk of ER+, PR+, and HER2- BCs. High positive correlations were observed among EGR1, DUSP1, FOS, FOSB, CYR61, and JUN mRNA expression in BC tissue. This systematic review suggested that EGR1 expression may serve as a prognostic marker for BC patients and that clinicopathological parameters influence its prognostic utility. In addition to EGR1, DUSP1, FOS, FOSB, CYR61, and JUN can jointly be considered prognostic indicators for BC. [BMB Reports 2021; 54(10): 497-504].

On the Intercept Probability and Secure Outage Analysis of Mixed ( α – κ – μ )-Shadowed and Málaga Turbulent Models

Abstract: This work deals with the secrecy performance analysis of a dual-hop RF-FSO DF relaying network composed of a source, a relay, a destination, and an eavesdropper. We assume the eavesdropper is located close to the destination and overhears the relay’s transmitted optical signal. The RF and FSO links undergo ( $\alpha -\kappa -\mu $ )-shadowed fading and unified Malaga turbulence with pointing error. The secrecy performance of the mixed system is studied by deriving closed-form analytical expressions of secure outage probability (SOP), strictly positive secrecy capacity (SPSC), and intercept probability (IP). Besides, we also derive the asymptotic SOP, SPSC, and IP upon utilizing the unfolding of Meijer’s $G$ function where the electrical SNR of the FSO link tends to infinity. Finally, the Monte-Carlo simulation is performed to corroborate the analytical expressions. Our results illustrate that fading, shadowing, detection techniques (i.e. heterodyne detection (HD) and intensity modulation and direct detection (IM/DD)), atmospheric turbulence, and pointing error significantly affect the secrecy performance. In addition, better performance is obtained exploiting the HD technique at the destination relative to IM/DD technique.

Exploiting Secrecy Performance of Uplink NOMA in Cellular Networks

Abstract: We study the secrecy transmission of uplink non-orthogonal multiple access (NOMA) with single antenna and multi-antenna users in presence of an eavesdropper. Two phases are required for communications during each time frame between the users and the base station in cellular networks. We study the case where an eavesdropper overhears the relay and direct links from the users to the base stations. In terms of the secure performance analysis, we focus on two main metrics including secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) with the assumption that the eavesdropper is able to detect the signals. Analytical closed-form expressions for the SOP and SPSC are derived to evaluate the system secure performance achieved by the proposed schemes. Furthermore, the asymptotic analysis is presented to gain further insights. The analytical and numerical results indicate that the proposed schemes can realize better secrecy performance once we improve the channel condition and signal-to-noise ratio (SNR) at the base station. Our results confirms that the secrecy performance gaps exist among the two users since different power allocation factors are assigned to these users.

Numerical Design and Investigation of Circularly Segmented Air Holes-Assisted Hollow-Core Terahertz Waveguide as Optical Chemical Sensor

Abstract: In this paper,a polarization-maintaining single-mode rectangular-shaped hollow-core waveguide with four segmented air cladding in the terahertz (THz) regime is presented for detecting various toxic industrial chemicals. A new type of injection moldable cyclic olefin copolymer, commercially named as TOPAS is used as the base fiber material for its high optical transmission and high resistance to other chemicals. The finite element method with a perfectly matched layer as the boundary condition is employed for numerical explorations. The proposed sensor exhibits ultra-high relative sensitivity of 99.73% and ultra-low effective material loss of 0.007 cm−1 at 1.6 THz frequency for Toluene in y polarization. This sensor also evinces a high birefringence of $4.16\times 10^{-3}$ at 1.6 THz frequency. A maximum V parameter of 2.224 has been found at 2.2 THz which ensures the single-mode propagation of light. The sensor shows a very low confinement loss of $3.2\times 10^{-11}$ dB/m and a high numerical aperture of 0.3574 at 1.6 THz frequency for Hydrogen Sulfide. This paper also concentrates on other important design parameters such as bending loss, mode field radius, beam divergence and effective area for serviceability of the sensor in the THz region. This sensor can be a very good candidate for various chemical detection as well as other applications in the terahertz regime.

Kinematic Measurements of Novel Chaotic Micromixers to Enhance Mixing Performances at Low Reynolds Numbers: Comparative Study.

Abstract: In this work, a comparative investigation of chaotic flow behavior inside multi-layer crossing channels was numerically carried out to select suitable micromixers. New micromixers were proposed and compared with an efficient passive mixer called a Two-Layer Crossing Channel Micromixer (TLCCM), which was investigated recently. The computational evaluation was a concern to the mixing enhancement and kinematic measurements, such as vorticity, deformation, stretching, and folding rates for various low Reynolds number regimes. The 3D continuity, momentum, and species transport equations were solved by a Fluent ANSYS CFD code. For various cases of fluid regimes (0.1 to 25 values of Reynolds number), the new configuration displayed a mixing enhancement of 40%–60% relative to that obtained in the older TLCCM in terms of kinematic measurement, which was studied recently. The results revealed that all proposed micromixers have a strong secondary flow, which significantly enhances the fluid kinematic performances at low Reynolds numbers. The visualization of mass fraction and path-lines presents that the TLCCM configuration is inefficient at low Reynolds numbers, while the new designs exhibit rapid mixing with lower pressure losses. Thus, it can be used to enhance the homogenization in several microfluidic systems.

Simultaneous Cellular and D2D Communications Exploiting Cooperative Uplink NOMA

Abstract: A novel device-to-device (D2D) aided uplink transmission scheme exploiting non-orthogonal multiple access (termed as D2D-UNOMA) is proposed and investigated. In D2D-UNOMA, two similar gain near users (NUs) and a far user (FU) can transmit their information to the base station (BS) in two time slots. The NUs can directly communicate with the BS, whereas the FU requires assistance of one of the NUs to facilitate the communication. In addition, as part of D2D communications, one of the NUs can transmit to the other NU in the first phase, whereas the FU can transmit to a NU in the second phase. The performance of D2D-UNOMA is studied in terms of ergodic rate along with analytical formulation. Through numerical results, it is demonstrated that the proposed D2D-UNOMA can ameliorate the ergodic sum rate significantly compared to the conventional orthogonal multiple access.

Blockchain-Enabled EHR Framework for Internet of Medical Things

Abstract: The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for health services. Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health services. The patients can use their smart devices to create, store and share their electronic health records (EHR) with a variety of medical personnel including medical doctors and nurses. However, unless the underlying combination within IoMT is secured, malicious users can intercept, modify and even delete the sensitive EHR data of patients. Patients also lose full control of their EHR since most health services within IoMT are constructed under a centralized platform outsourced in the cloud. Therefore, it is appealing to design a decentralized, auditable and secure EHR system that guarantees absolute access control for the patients while ensuring privacy and security. Using the features of blockchain including decentralization, auditability and immutability, we propose a secure EHR framework which is mainly maintained by the medical centers. In this framework, the patients' EHR data are encrypted and stored in the servers of medical institutions while the corresponding hash values are kept on the blockchain. We make use of security primitives to offer authentication, integrity and confidentiality of EHR data while access control and immutability is guaranteed by the blockchain technology. The security analysis and performance evaluation of the proposed framework confirms its efficiency.

A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks

Abstract: Resistance versus temperature characteristics of superconducting films have been studied for decades, and are still considered an important subject of condensed matter physics. They have recently received increased attention, primarily motivated by electromagnetic metamaterial strategy, which has been used in the implementation of one-dimensional microwave transmission lines with high-temperature superconducting films. In some of the recent works, it has been argued that the physical measurement of these curves is a strenuous and costly process, which becomes tedious when incessantly performed for a wide range of parameters. Contemplating on their significance, in this work, we propose a resistance–temperature curves approximation framework using three different artificial neural networks architectures, and carry out a detailed comparison between the variants in terms of the accuracy they achieve. We demonstrate that the mean-squared error, between the approximated and the physically measured curves, is negligible, which justifies extrapolation of these curves over a wide range of parameters using the proposed framework.

Development of Duplex Eye Contact Framework for Human-Robot Inter Communication

Abstract: Establishing eye contact is the fundamental key to begin any interaction between human-human and robot-human. Two approaches are available to develop an eye contact mechanism for robot-human interaction, such as simplex and duplex. The two most critical tasks: gaze crossing and gaze awareness, are prerequisite to implementing an active eye contact mechanism in any approach. However, most past robot-human interaction studies implemented a gaze crossing function to develop eye contact in the simplex mode where a robot holds for the human to initiate the communication. However, implementing gaze crossing alone is inadequate to create an active eye contact episode; the gaze awareness function also essential to achieve. This paper aims to develop a mechanism of duplex eye contact for robot-human inter-communication satisfying both functions. This work proposes a conceptual model of a duplex eye contact mechanism considering two cases: human initiative (where the human starts communication with the robot) and robot initiative (where the robot starts the communication with the participant) to achieve a duplex eye contact mechanism. Moreover, a simple robotic system is developed consisting of four software constituents: face detection module, gaze detection and tracking module, gaze awareness module, and robot response and control module to implement the conceptual model of duplex eye contact. Several preliminary experiments are performed to extract necessary cues for designing the duplex eye contact mechanism’s behavioural protocol and present their results to show the usefulness of extracted cues. Moreover, the robotic framework results in a scenario ( e.g., reading the book ) with the proposed duplex eye contact mechanism are presented. The results show that the proposed scheme achieved 92% and 86% accuracy for human initiative case and robot initiative case, respectively in making eye contact.

Towards Prevention of Sportsmen Burnout: Formal Analysis of Sub-Optimal Tournament Scheduling

Abstract: Scheduling a sports tournament is a complex optimization problem, which requires a large number of hard constraints to satisfy. Despite the availability of several such constraints in the literature, there remains a gap since most of the new sports events pose their own unique set of requirements, and demand novel constraints. Specifically talking of the strictly time bound events, ensuring fairness between the different teams in terms of their rest days, traveling, and the number of successive games they play, becomes a difficult task to resolve, and demands attention. In this work, we present a similar situation with a recently played sports event, where a suboptimal schedule favored some of the sides more than the others. We introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the others. Furthermore, we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire schedules. The latter is crucial, since successively playing a large number of games may lead to sportsmen burnout, which must be prevented.

SUPnP: Secure Access and Service Registration for UPnP-Enabled Internet of Things

Abstract: The service-oriented nature of the Universal Plug-and-Play (UPnP) protocol supports the creation of flexible, open, and dynamic systems. As such, it is widely used in Internet-of-Things (IoT) deployments. However, the protocol’s service access mechanism does not consider security from the first principles and is therefore vulnerable to various attacks. In this article, we present an in-depth analysis of the service advertisement, discovery, and access methods of the UPnP protocol stack and identify security issues in an IoT network. Our analysis shows that adversaries can perform resource exhaustion, buffer overflow, reflection, and amplification attacks by exploiting the vulnerabilities of the UPnP protocol. To address these issues, we propose a capability-based security model for UPnP to ensure secure discovery, advertisement, and access of the UPnP services that considers the resource limitations of IoT devices. Our analysis shows the effectiveness of the proposed model against potential attacks, and our experimental evaluation highlights the feasibility of implementing our Secure UPnP (SUPnP) protocol in a network of IoT devices, incurring minimal network and performance overhead.

Impact of Aperture Averaging and Antenna Correlation on the Secrecy Outage Performance over Mixed RF-FSO Cooperative System under Simultaneous RF and FSO Eavesdropping Attempts

Abstract: The upcoming generations of communications demand high-speed data transfer while concentrating on a human-centric system that demands complete security of the transmitted data as well as the user. So, the focus of this research is on the security of a hybrid radio frequency (RF)-free-space optical (FSO) model with a decode-and-forward (DF) relay between the transmitter and the destination. The RF and FSO links are designed to follow the correlated η−µ and exponentiated Weibull fading distribution, respectively. Here, we consider simultaneous eavesdropping with eavesdroppers at both RF and FSO links trying to steal the transmitted data. Maximal ratio combining (MRC) receiver and selection combining (SC) receiver has been used in the RF links and the FSO links, respectively to improve the secrecy performance of the system. Using the well-known Meijer’s-G function, the security of this proposed method is investigated by deriving closed-form formulas for secrecy outage probability and probability of non-zero secrecy capacity. Numerical results along with Monte-Carlo (MC) simulations are also provided to demonstrate the effects of simultaneous eavesdrop-ping, correlation, aperture averaging, fading parameters, etc. on the secrecy performance of the system.