Internet of Things-based environments pose various challenges due to their anytime/anywhere computing, and the efficient cryptographic based key management is one of the major challenges in Internet of Things. The key management life cycle consists of initialization, key generation, key registration, key backup, key update, key recovery, and key revocation. Our contribution in this paper is to summarize the state-of-the-art key management schemes and techniques in different scenarios, such as mobile ad hoc networks, wireless sensor networks, and the Internet of (...) Things environments. Further different issues related specifically to the Internet of Things environment are discussed and the causes and effects pertaining to the security breach for Internet of Things are identified. Furthermore, in this research work, we develop a novel permutation of threshold and identity-based key management schemes for the Internet of Things environment and have proposed future directions to counteract the attacks on confidentiality, integrity, authentication, and availability of security services in the Internet of Things environment and identified the two key management schemes, that is, identity and threshold schemes for Internet of Things, to resolve Internet of Things key management issues and maximum possible security services effective implementation. We evaluate our scheme with the help of mathematical and statistical techniques. (shrink)

This study aims to examine the underlying mechanism of the relationship between perceived green human resource management and perceived employee green behavior. By drawing on attitude and social exchange theories, we examined green commitment as a mediator and green knowledge sharing as a moderator of the GHRM–EGB relationship. The study employs partial least square structural equation modeling to analyze 329 responses. Data were collected in two time lags. The empirical results confirmed that GC mediates the relationship between GHRM and EGB. (...) However, the study results found that GKS moderated the indirect influence of GHRM on green behavior via GC. This research signifies the effect of GHRM, GKS, GC, and green behavior on organizations’ sustainability and environmental management. Despite the emerging literature on the significance of green practices in organizations for environmental management, no study has examined the moderating role of GKS on the indirect effect of GHRM on green behavior via mediating role of GC. This study offers valuable insight into environmental management in organizations through green practices and green behavior. (shrink)

Drawing on social cognitive theory and social information processing theory, this study investigated how organizations' efforts to embody environmental corporate social responsibility (ECSR) shape consumer perception of a green corporate image through employees' environmental passion and pro-environmental behavior (PEB). To test our hypotheses, we collected multisource time-lagged data from 214 employee–customer dyads from hotel and banking sector organizations in Pakistan. The findings show that organizations' green corporate image is a function of their efforts to engage in ECSR activities as demonstrated (...) through employees' environmental passion and PEB. These findings have important implications in the environment and management domains because it can help organizations draft useful strategies to counter environmental degradation and generate a green corporate image in customers' minds through employees' green behavior. (shrink)

Nature-inspired optimization techniques are useful tools in electrical engineering problems to minimize or maximize an objective function. In this paper, we use the firefly algorithm to improve the optimal solution for the problem of directional overcurrent relays (DOCRs). It is a complex and highly nonlinear constrained optimization problem. In this problem, we have two types of design variables, which are variables for plug settings (PSs) and the time dial settings (TDSs) for each relay in the circuit. The objective function is (...) to minimize the total operating time of all the basic relays to avoid unnecessary delays. We have considered four models in this paper which are IEEE (3-bus, 4-bus, 6-bus, and 8-bus) models. From the numerical results, it is obvious that the firefly algorithm with certain parameter settings performs better than the other state-of-the-art algorithms. (shrink)

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam (...) product reviews is a challenging issue in NLP. Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron, K-Nearest Neighbour, and Random Forest and predicts the outcome of the review as spam or real, based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers and state-of-the-art boosting approaches like Generalized Boost Regression Model, Extreme Gradient Boost, and AdaBoost Regression Model in terms of classification accuracy. (shrink)

This work identifies the influence of chaos theory on fractional calculus by providing a theorem for the existence and stability of solution in fractional-order gyrostat model with the help of a fixed-point theorem. We modified an integer order gyrostat model consisting of three rotors into fractional order by attaching rotatory fuel-filled tank and provided an iterative scheme for our proposed model as a working rule of obtained analytical results. Moreover, this iterative scheme is injected into algorithms for a system of (...) integer order dynamical systems to observe Lyapunov exponents and a bifurcation diagram for our proposed fractional-order dynamical model. Furthermore, we obtained five equilibrium points, including four unstable spirals and one saddle node, using local dynamical analysis which acted as self-exciting attractors and a separatrix in a global domain. (shrink)

This study aims to investigate the impact of stakeholders’ views on the practices of green innovation, consequent effect on environmental and organizational performance, and moderating influence of innovation orientation. A quantitative method was employed for the sample size of 515 responses. To accumulate the data from the respondents, convenient random sampling was used. Data were collected from manufacturing and services firms through a field survey by using a closed-ended questionnaire based in the Punjab province of Pakistan. The analysis was done (...) using the structural equation model of the partial least square analysis method. Our findings proved a positive and significant link between stakeholders’ views on GI practices. A significant association has been found between GI practices and environmental and OP. The moderating effect was found to be negative but statistically significant. This research offers numerous contributions and provides decision-making insinuations. (shrink)

Using a time-lagged design, we tested the main effects of Islamic Work Ethic (IWE) and perceived organizational justice on turnover intentions, job satisfaction, and job involvement. We also investigated the moderating influence of IWE in justice–outcomes relationship. Analyses using data collected from 182 employees revealed that IWE was positively related to satisfaction and involvement and negatively related to turnover intentions. Distributive fairness was negatively related to turnover intentions, whereas procedural justice was positively related to satisfaction. In addition, procedural justice was (...) positively related to involvement and satisfaction for individuals high on IWE however it was negatively related to both outcomes for individuals low on IWE. For low IWE, procedural justice was positively related to turnover intentions, however it was negatively related to turnover intentions for high IWE. In contrast, distributive justice was negatively related to turnover intentions for low IWE and it was positively related to turnover intentions for high IWE. (shrink)

The current study investigates whether tournament incentives motivate chief executive officer to be socially responsible. Furthermore, it explores the role of sub-national institutional contingencies [i.e., state-owned enterprises vs. non-SOEs, foreign-owned entities vs. non-FOEs, cross-listed vs. non-cross-listed, developed region] in CEO tournament incentives and the corporate social responsibility performance relationship. Data were collected from all A-shared companies listed in the stock exchanges of China from 2014 to 2019. The study uses the baseline methodology of ordinary least squares and cluster OLS regression. (...) Moreover, firm-fixed effects regression, two-stage least squares regression, and propensity score matching deal with the endogeneity problem and check the robustness of the results. The results provide reliable evidence that tournament incentives motivate CEOs to be more socially responsible. On the other hand, sub-national institutional contingencies positively affect the association between CEO tournament incentives and CSRP. The findings have important implications for companies and regulators who wish to enhance CSP by providing incentives to top managers. (shrink)

This study examined the influence of ethical leadership on knowledge sharing, the mediating role of psychological ownership, and the moderating effect of professional commitment between ethical leadership and knowledge sharing. Data were collected from 307 public listed Pakistani companies’ employees. Statistical analyses were performed by using SPSS Version 25 and AMOS version 22. The findings indicate a positive relationship between EL and KS behavior. Additionally, the impact of EL on KS was partially mediated by psychological ownership. Furthermore, professional commitment buffers (...) the link between EL and KS. This study contributes to the body of knowledge in the field of leadership by confirming the role of ethics. The results show that ethical leaders develop employee attitudes that contribute to employee KS behavior. Ethical leaders create and encourage a learning culture to enhance organizational performance. This study adds to the little data on the positive impact of EL on listed company’s employees and addresses the gaps in previous studies on the role of EL in changing environments. In addition, professional commitment as a moderator has not been previously investigated with ethical leadership antecedents. (shrink)

Synthesizing theories of ethical leadership, psychological climate, pro-environmental behavior, and gender, first, we proposed and tested a model linking supervisors’ ethical leadership and organizational environmental citizenship behavior via green psychological climate. Then we tested the moderating effect of gender on the indirect (via green psychological environment) relationship between supervisors’ ethical leadership and organizational environmental citizenship behavior. Time-lagged (three waves, two months apart) survey data were collected from 447 employees in various manufacturing and service sector firms operating in China. Data were (...) analyzed using structural equation modeling, bootstrapping, and multi-group technique to test the hypothesized relationships. The results showed a positive relationship between employee ratings of supervisors’ ethical leadership and organizational environmental citizenship behavior. Moreover, green psychological climate mediates the relationship between supervisors’ ethical leadership and organizational environmental citizenship behavior. Importantly, the multi-group analysis revealed that gender moderates the indirect relationship (via green psychological climate) between supervisors’ ethical leadership and organizational environmental citizenship behavior. The study carries useful practical implications for policymakers and managers concerned about environmental sustainability. (shrink)

This study explores the impact of CEO ability on corporate environmental sustainability information disclosure. We take samples from Chinese A-share listed companies from 2010 to 2019 and use the ordinary least squares as a baseline regression model to check the relationship between CEO ability and corporate environmental sustainability information disclosure. Our findings are robust to different corporate environmental sustainability information disclosure measures and CEO ability. We found a positive association between CEO ability and corporate environmental sustainability information disclosure; thus, firms (...) with a more able CEO are more likely to disclose more corporate environmental sustainability information. Our results also found a significant negative interaction coefficient between CEO ability and CEO career concerns. As the CEO grows older, his/her ability decreases; therefore, corporate environmental sustainability information disclosure reduces. Furthermore, we document that a female CEO strengthens the relationship between CEO ability and corporate environmental sustainability information disclosure. The study results are important for understanding corporate environmental responsibility development and implementation, particularly in China. (shrink)

The aim of the study is to reveal the underlying structure of issues of university students taking online classes during the COVID-19 pandemic period. The overall design of the study includes a review of contemporary literature and field survey for data collection and analysis. Discourse of literature coupled with expert opinion has been employed for identification of issues. Interpretive Structural Modeling is used for the determination of intra-issue relationships and analyzing the underlying structure. Cross impact matrix multiplication applied to classification (...) is used as a technique for classifying issues on the basis of driving–dependence power. Results of the literature show that there are 21 major issues faced by the students taking online classes. ISM shows that lack of institutional guidelines, lack of regulators’ guidelines, stress of pandemic situation, and abrupt start of online classes are the most critical issues. MICMAC analysis reveals that there is no autonomous issue, 4 are independent, 6 other issues are dependent, and the remaining 11 are linking. This is a valuable study having practical implications for regulators, students, parents, and society to understand the current problem. It is an original attempt that contributes toward literature in the form of a structural model and a diagram of classification of issues. (shrink)

During the COVID-19 outbreak, educational institutions were closed, and students worldwide were confined to their homes. In an educational environment, students depend on collaborative learning to improve their learning performance. This study aimed to increase the understanding of social media adoption among students during the COVID-19 pandemic for the purpose of CL. Social media provides a learning platform that enables students to easily communicate with their peers and subject specialists, and is conducive to students' CL. This study addresses the key (...) concept of CL during the COVID-19 pandemic by assessing social media use among students in higher education. The relationship between social media use and students' performance is crucial to understanding the role of social media during a pandemic. This study is based on constructivism theory and the technology acceptance model. Structural equation modeling was used to analyze the conceptual model using SmartPLS. The research findings indicate that social media plays an important role during the pandemic because it provides opportunities for students to enhance CL under the aforementioned situations. This study makes noteworthy theoretical and practical contributions. (shrink)

Recently the World Health Organization has highlighted the need to strengthen mental health systems following emergencies, including natural and manmade disasters. Mental health services need to be informed by culturally attuned evidence that is developed through research. Therefore, there is an urgent need to establish rigorous ethical research practice to underpin the evidence-base for mental health services delivered during and following emergencies.

By incorporating the conservation of resource theory, this study examines how high-performance work practices (HPWPs) affect the employee's in-role performance (EIRP) and employee's task performance (ETP) during the coronavirus disease 2019 (COVID-19) pandemic. Furthermore, this study investigates how organization-based self-esteem (OBSE) and positive psychological capital (PPC) affect the relationship between HPWPs and outcomes of employees such as EIRP and ETP. A quantitative technique based on the survey method was used to gather the primary data of the investigation. Two hundred and (...) eleven bank employees working in different banks of Punjab and Pakistan participated in the survey process. The PROCESS-macro was used to test the relationship among the study constructs. Our results supported all the study hypotheses, however positive psychological capital did not moderate the indirect effect of high performance work practices on employee's task performance via organization based self-esteem. This study is the earliest of its kind that focuses on HPWPs and outcomes of employees amid the COVID-19 pandemic in a developing country like Pakistan. The findings of this study provide practical implications in the post and continuing pandemic situation for organizations to human resource management to redesign workforce strategies to increase their contribution and responses to realize organizational priorities. Although researchers have explored the topic in different sectors, scant studies have investigated the potential impact, barriers, and enabling mechanisms that function as a catalyst in HPWPs during the pandemic situation. (shrink)

Our objective is to examine the effects of Big Five personality traits on ethical ideologies using a time-lagged design of 406 employees of higher education institutions in Pakistan. Based on low/high idealism versus relativism, we investigate the conceptual linkage between each of the personality traits and moral philosophy. The results illustrate that extraversion and openness to experience believed on subjectivism moral philosophy, agreeableness believed on situationism, and neuroticism believed on absolutism moral philosophies. In addition, contentiousness believed on exceptionism moral philosophy. (...) Furthermore, managerial implications and future research directions are suggested. (shrink)

Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of (...) ECG. Single Shoot Detection MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder. (shrink)

We examined a largely ignored but imperative dimension of safety literature by testing the impact of ethical leadership style on organizational safety performance. We also tested dual mediating paths of safety culture and safety consciousness in the relationship between ethical leadership style and organizational safety consciousness. Data were collected from a large public sector telecom company in Pakistan. Confirmatory factor analysis was used to assess the reliability and validity of the study scales and model fit. Preacher and Hayes’s macro of (...) mediation was employed to test the direct and indirect paths proposed in the study. The findings suggest that ethical leadership has a positive impact on organizational safety performance. Partial mediating roles of safety culture and safety consciousness were also found between the dependent and independent variables. Theoretical and practical implications are discussed. (shrink)

Generally toxic employees are under performers, yet some get better salaries and excel at workplace, getting positioned at higher ranks. This research assesses the relationship between the dark triad (Narcissism, Machiavellianism, and Psychopathy) and perceived victimization with a focus on the mediating effect of abusive supervision and the moderating effect of mindfulness. The data were gathered in three waves. Both the structural equation model with partial least square and Process were used to analyze the data. The study findings suggest that (...) abusive supervision plays a mediating role in the association between the dark triad and perceived victimization. However, the results did not support the mediating role of abusive supervision in the association between narcissism and perceived victimization. Moreover, the findings suggest that mindfulness weakens the effect of abusive supervision on perceived victimization. Furthermore, our findings support the mediated-moderation model. Theoretical and practical implications of the results are also discussed. (shrink)

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a (...) dataset of images by applying a convolutional neural network. The extraction of leaf gas enables identification of the actual level of chlorophyll and nitrogen which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color-based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf. (shrink)

Jensen’s inequality is one of the fundamental inequalities which has several applications in almost every field of science. In 2003, Mercer gave a variant of Jensen’s inequality which is known as Jensen–Mercer’s inequality. The purpose of this article is to propose new bounds for Csiszár and related divergences by means of Jensen–Mercer’s inequality. Also, we investigate several new bounds for Zipf–Mandelbrot entropy. The idea of this article may further stimulate research on information theory with the help of Jensen–Mercer’s inequality.

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored (...) putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD. (shrink)

This study sheds light on strategies for retaining skilled pharmacists in Pakistan's pharmaceutical sector, offering valuable insights for both academia and industry stakeholders by investigating the impact of human resource management practices, including training and development, compensation and rewards, job performance, and job satisfaction, on employee retention. It also examines the moderating role of career growth in this context. Theoretical foundations are grounded in international migration theories and social exchange theory, providing a comprehensive framework for the study. A cross-sectional survey (...) design was used to collect data from 254 pharmacists in southern Punjab, Pakistan. Structural equation modeling (SEM) was employed for data analysis. Findings reveal that training and development, compensation and rewards, job performance, and job satisfaction all have significant direct effects on employee retention. Additionally, career growth acts as a crucial moderator, strengthening the relationship between training and development and job performance, as well as compensation and rewards and job satisfaction in relation to employee retention. Theoretical implications emphasize the importance of tailored training programs, fair compensation practices, and career growth opportunities. Practical recommendations include creating a positive work environment, rewarding high-performing employees, and fostering career development. Future research directions encompass longitudinal studies, the influence of organizational culture and technology, regional variations, and additional factors affecting retention. (shrink)

Social media plays a significant role in modern life, but excessive use of it during the COVID-19 pandemic has become a source of concern. Supported by the conservation of resources theory, the current study extends the literature on problematic social media usage during COVID-19 by investigating its association with emotional and mental health outcomes. In a moderated mediation model, this study proposes that problematic social media use by workers during COVID-19 is linked to fear of COVID-19, which is further associated (...) with depression. The current study tested trait mindfulness as an important personal resource that may be associated with reduced fear of COVID-19 despite problematic social media use. The study collected temporally separate data to avoid common method bias. Pakistani employees working in different organizations completed a series of survey questionnaires. The results supported the moderated mediation model, showing that problematic social media use during the current pandemic is linked to fear of COVID-19 and depression among employees. Furthermore, trait mindfulness was found to be an important buffer, reducing the negative indirect association between problematic social media use and depression through fear of COVID-19. These results offer implications for practitioners. The limitations of this study and future research directions are also discussed. (shrink)

The purpose of this research is to highlight the relationship between green product attributes and consumer trust that influence consumers’ decision to purchase green products in the context of Pakistan. This study contributes to determining quantitatively how green product attributes such as physical, perceptual, and reflexive attributes influence consumers’ trust to purchase a green product and investigates the mediating role of green marketing. Data was collected from different industrial sectors through a survey questionnaire. We employed Structural Equation Modeling using the (...) SMART-PLS software to check the reliability and validity of the constructs, and to test the hypotheses. This study reveals variations in terms of shaping the sustainable consumers’ buying behavior by modifying product attributes and green marketing strategies that are in congruence with the proposed hypotheses of this study. In the end, the findings and interpretations of the results are given which can guide the managers to develop effective green marketing campaigns in reshaping the purchase intentions of consumers toward their green products. (shrink)

Healthcare workers in Pakistan are still fighting at the frontline to control the spread of severe acute respiratory syndrome coronavirus-2 and have been identified as the earliest beneficiaries for COVID-19 vaccination by the health authorities of the country. Besides, the high vaccination rates of frontline healthcare workers are essential to overcome the ongoing pandemic and reduce the vaccines hesitancy among the general population. The current research employed the theory of planned behavior to investigate the COVID-19 vaccination behavior among FHWs in (...) Pakistan as well as the predictors of such behavior. Following the epidemic control and prevention policies, a sample of 680 FHWs were accessed to fill in the questionnaire evaluating the components of the TPB. Moreover, the potential role of anticipated regret and perceived susceptibility on COVID-19 vaccination behavior was also assessed. The partial least square structural equation modeling results revealed that the TPB components, as well as the AR, have positive associations with the COVID-19 vaccination behavior. The results further confirmed that PS positively affects the anticipated regret, attitude, and subjective norm to vaccinate against SARS-CoV-2. The perceived susceptibility also has a positive association with COVID-19 vaccination behavior through the mediation of anticipated regret, ATT, and SN. Our findings highlighted the importance of COVID-19 vaccination among healthcare workers, which can be applied to reduce vaccine hesitancy among the general public. (shrink)

This research explores the effect of servant leadership on prosocial rule-breaking (PSRB) and the mediating mechanism of public service motivation (PSM) between the association of servant leadership and PSRB. The said phenomenon is examined in the civil service context of Pakistan during the continuing crises of the COVID-19 pandemic, a situation where the traditional civil service policy and rule system has become highly complicated for passionate employees’ service performance and efficiency, and where servant leadership has received greater attention for inspiring (...) the attitudinal and behavioral outcomes of frontline workers during the pandemic. Data were collected from 546 frontline workers of the corona relief tiger force. The findings of the study revealed that servant leadership has a significant effect on PSRB and PSM, and that PSM significantly promotes PSRB. The results also revealed that servant leadership has a significant impact on PSRBviaengendering PSM. (shrink)

Brain-computer interface multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system—achieved through a fusion of concurrently recorded electroencephalography and functional near-infrared spectroscopy signals—is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational (...) approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy. (shrink)

Web search engines usually keep users’ profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users’ privacy since a user’s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval protocols that use peer nodes to preserve privacy. Previously, (...) we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN-QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network based model to associate queries with their original users. The results show that the NN-QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data. (shrink)

Current development in a deep neural network has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial network (...) holography framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms. The experimental results have demonstrated that the proposed method outperforms the existing methods. (shrink)

Energy poverty is a problem that affects developed and developing economies, and its mitigation is of great significance to social welfare. EP affects Latin American countries, and policymakers have recently attempted to address this issue, particularly in the aftermath of the recent economic crisis. It is essential to measure and evaluate EP to implement strategies and policies effectively. Using a panel quantile regression approach, we investigate the heterogeneous impact of green finance, renewable energy, and energy efficiency on EP for 33 (...) Latin American countries from 2000 to 2018. Furthermore, certain associated control variables are incorporated into our model to avoid an omitted variable bias. According to empirical results, the impact of independent variables on EP is heterogeneous. Specifically, green finance is an essential source of alleviating EP, and it has a significant positive effect across all quantiles, but it is especially strong in the middle quantiles. RE and EE significantly mitigate EP, with the strongest effects occurring at higher quantiles. By including green finance, RE, and EE as the main explanatory determinants of EP, the findings urge policymakers in Latin American countries to design a comprehensive energy conservation policy to minimize the effects of massive EP. (shrink)

In this paper, we utilized triangular conorms. The essence of using S-norm is that the similarity order does not change using different norms. In fact, we are investigating for a new conception for calculating the similarity of two Fermatean fuzzy sets. For this purpose, utilizing an S-norm, we first present a formula for calculating the similarity of two Fermatean fuzzy values, so that they are truthful in similarity properties. Following that, we generalize a formula for calculating the similarity of the (...) two Fermatean fuzzy sets which prove truthful in similarity conditions. Finally, various numerical examples have been presented to elaborate this method. (shrink)

Caricatures can help to understand the perception of a face. The prominent facial feature of a subject can be exaggerated, so the subject can be easily identified by humans. Recently, significant progress has been made to face detection and recognition from images. However, the matching of caricature with photographs is a difficult task. This is due to exaggerated features, representation of modalities, and different styles adopted by artists. This study proposed a cross-domain qualitative feature-based approach to match caricature with a (...) mugshot. The proposed approach uses Haar-like features for the detection of the face and other facial attributes. A point distribution measure is used to locate the exaggerated features. Furthermore, the ratio between different facial features was computed using different vertical and horizontal distances. These ratios were used to calculate the difference vector which is used as input to different machine and deep learning models. In order to attain better performance, stratified k-fold cross-validation with hyperparameter tuning is used. Convolution neural network-based implementation outperformed the machine learning-based models. (shrink)

Landslide is a critical natural geological hazard that causes severe damage to property, infrastructure, and humans. In general, some location-specific factors trigger a landslide. Wireless sensor network is an enabling technology to monitor most of the parameters associated with these factors. A challenge in landslide monitoring through WSN is that each sensed data item might be critical whereas the underlying wireless communication is often unreliable. In case of landslides, the terrains have irregular shapes, providing harsh conditions for wireless communication thereby (...) more data loss may be expected. This study focuses on the effect of lossy communication in WSN on the efficiency and accuracy of landslide monitoring systems. To this end, collaborative local data analysis is used to enable each node to decide locally whether its sensed data corresponds to a potential event-of-interest. Through extensive simulations, the performance of various landslide prediction and detection models has been evaluated. By and large, the study lets a significant insight into landslide monitoring before implementing a parameterized application for real-world deployment. (shrink)

Business Ethics, the Environment & Responsibility, EarlyView.

Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from (...) discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets. (shrink)

Keyword extraction refers to the process of selecting most significant, relevant, and descriptive terms as keywords, which are present inside a single document. Keyword extraction has major applications in the information retrieval domain, such as analysis, summarization, indexing, and search, of documents. In this paper, we present a novel supervised technique for extraction of keywords from medium-sized documents, namely Corpus-based Contextual Semantic Smoothing. CCSS extends the concept of Contextual Semantic Smoothing, which considers term usage patterns in similar texts to improve (...) term relevance information. We introduce four more features beyond CSS as our novel contributions in this work. We systematically compare the performance of CCSS with other techniques, when implemented over INSPEC dataset, where CCSS outperforms all state-of-the-art keyphrase extraction techniques presented in the literature. (shrink)

Since the creation of Pakistan the foundation of the Idea of Pakistan has been a matter of academic and political debate. There is a difference between the two spheres. The academic debate allows a room to discuss popular and unpopular ideas and respect the contrary views. The ideology of Pakistan, political system of Pakistan and the constitution of Pakistan have always been discussed and debated amongst scholars. The purpose of this research is to explore the famous presidential address of Allama (...) Iqbal at the annual session of Muslim League in 1930. There are many perceptions about the address delivered by Iqbal, such as he demanded a separate homeland for Muslims of India or not. This research will explore the facts of Iqbal’s speech of Allahabad and will attempt to do justice to his words which have been misinterpreted by some spheres. (shrink)

The COVID-19 outbreak wreaked havoc on the hospitality business, resulting in significant layoffs, salary cuts, and unpaid leaves globally. This study uses the sensemaking theory to investigate how COVID-19 induced unfavorable human resource practices affect the link between perceived corporate social responsibility and employee identification and commitment. We tested this model using the data collected from 392 hospitality sector employees in Pakistan. The results reveal that “cut in salaries” and “work from home” positively moderate CSR’s impact on employees’ identification and (...) commitment. On the other hand, employee layoff and leave without pay do not impact the positive relationship between CSR and employees’ attitudes. Furthermore, the study finds that CSR during this pandemic has a significant positive impact on employees’ attitudes. However, this relationship becomes insignificant for employees who reported unfavorable HR practices in their organizations. The finding further reveals that CSR’s impact during COVID-19 on employees’ attitudes is moderated by the different levels of CSR importance in employees’ minds. This evidence is significant since HR practices implemented during this crisis need to be identified and framed to understand the effects of CSR on employee commitment and identification. CSR involvement in the pandemic can help managers keep their employees committed to organizations; only if this charity begins from their internal stakeholders first. (shrink)