Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author’s research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher’s scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author’s profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The selfcitation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect selfcitation must be included

Effect of location in a cylinder wake on dynamics of a flexible energy harvesting plate

© 2019 Penerbit Akademia Baru. Flexible plate in the wake of a bluff body may be exploited to harvest energy, for example by attaching piezoelectric sheets on both surfaces of the plate. A computational investigation on flow-induced vibration of a flexible plate in the wake of a cylinder is undertaken to understand the effects of plate location on their vibrations and hence, energy harvesting potential. Based on cylinder diameter D (0.1m), flow at a sub-critical Reynolds number of 10000 was considered in the present study. The fluid-structure interaction was implemented via a closely-coupled partitioned scheme that employs a Scale-Adaptive Simulation (SAS) of the Shear Stress Transport (SST) method to model flow turbulence. A flexible plate was placed at several locations (streamwise: x/D = 0.5, 1.0, 1.5, 2.0; crossflow: y/D = 0, 0.25, 0.5) downstream of the cylinder and their flow-induced response were compiled and analysed. Benchmarking of present model showed good agreement with previous experimental investigations. Results suggest that maximum deflection may be found if flexible plate is placed in the region between cylinder surface and x/D < 1.0. Oscillation of flexible plate placed at y/D = 0.25 shows similar amplitude, if not slightly higher, than if plate is placed at wake centerline. Present findings suggest that energy output may be optimised by positioning flexible energy harvesting plates at favourable locations in the wake region

Single image defocus estimation by modified gaussian function

© 2019 John Wiley & Sons, Ltd. This article presents an algorithm to estimate the defocus blur from a single image. Most of the existing methods estimate the defocus blur at edge locations, which further involves the reblurring process. For this purpose, existing methods use the traditional Gaussian function in the phase of reblurring but it is found that the traditional Gaussian kernel is sensitive to the edges and can cause loss of edges information. Hence, there are more chances of missing spatially varying blur at edge locations. We offer the repeated averaging filters as an alternative to the traditional Gaussian function, which is more effective, and estimate the spatially varying defocus blur at edge locations. By using repeated averaging filters, a blur sparse map is computed. The obtained sparse map is propagated by integration of superpixels segmentation and transductive inference to estimate full defocus blur map. Our adopted method of repeated averaging filters has less computational time of defocus blur map estimation and has better visual estimates of the final defocus recovered map. Moreover, it has surpassed many previous state-of-the-art proposed systems in terms of quantative analysis

Robust Multimodal Representation Learning with Evolutionary Adversarial Attention Networks

Multimodal representation learning is beneficial for many multimedia-oriented applications such as social image recognition and visual question answering. The different modalities of the same instance (e.g., a social image and its corresponding description) are usually correlational and complementary. Most existing approaches for multimodal representation learning are not effective to model the deep correlation between different modalities. Moreover, it is difficult for these approaches to deal with the noise within social images. In this paper, we propose a deep learning-based approach named Evolutionary Adversarial Attention Networks (EAAN), which combines the attention mechanism with adversarial networks through evolutionary training, for robust multimodal representation learning. Specifically, a two-branch visual-textual attention model is proposed to correlate visual and textual content for joint representation. Then adversarial networks are employed to impose regularization upon the representation by matching its posterior distribution to the given priors. Finally, the attention model and adversarial networks are integrated into an evolutionary training framework for robust multimodal representation learning. Extensive experiments have been conducted on four real-world datasets, including PASCAL, MIR, CLEF, and NUS-WIDE. Substantial performance improvements on the tasks of image classification and tag recommendation demonstrate the superiority of the proposed approach

Abstraction Layer Based Virtual Data Center Architecture for Network Function Chaining

© 2016 IEEE. Network virtualization is one of the most promising technology for the data centers. It was innovated to use the network resources efficiently to evaluate new protocols and services on the same hardware. This paper discusses a virtual distributed data center network architecture in the network function virtualization environment. This architecture virtualize physical resources into multiple virtual clusters where each cluster consists of a group of machines (physical/virtual) and an abstraction layer. Optical technologies are used to construct the core of the network. Our abstraction layer construction algorithm selects the minimum optical switches that provide connectivity to all the machines of the group. One of the main use case of this architecture is orchestration of network function chains, where each chain corresponds to one cluster and network functions can be deployed over the optical switches of the abstraction layer. We also showed that this deployment can also save expensive optical/electronic/optical conversion cost

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers’ payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers’ implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies

Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter

The COVID-19 pandemic has disrupted people’s lives and caused significant economic damage around the world, but its impact on people’s mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user’s PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model’s effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern

WLAN aware cognitive medium access control protocol for IoT applications

© 2020 by the authors. Internet of Things (IoT)-based devices consist of wireless sensor nodes that are battery-powered; thus, energy efficiency is a major issue. IEEE 802.15.4-compliant IoT devices operate in the unlicensed Industrial, Scientific, and Medical (ISM) band of 2.4 GHz and are subject to interference caused by high-powered IEEE 802.11-compliant Wireless Local Area Network (WLAN) users. This interference causes frequent packet drop and energy loss for IoT users. In this work, we propose a WLAN Aware Cognitive Medium Access Control (WAC-MAC) protocol for IoT users that uses techniques, such as energy detection based sensing, adaptive wake-up scheduling, and adaptive backoff, to reduce interference with the WSN and improve network lifetime of the IoT users. Results show that the proposedWAC-MAC achieves a higher packet reception rate and reduces the energy consumption of IoT nodes

On the Design and Implementation of a Blockchain Enabled E-Voting Application within IoT-Oriented Smart Cities

A smart city refers to an intelligent environment obtained by deploying all available resources and recent technologies in a coordinated and smart manner. Intelligent sensors (Internet of Things (IoT) devices) along with 5G technology working mutually are steadily becoming more pervasive and accomplish users' desires more effectively. Among a variety of IoT use cases, e-voting is a considerable application of IoT that relegates it to the next phase in the growth of technologies related to smart cities. In conventional applications, all the devices are often assumed to be cooperative and trusted. However, in practice, devices may be disrupted by the intruders to behave maliciously with the aim of degradation of the network services. Therefore, the privacy and security flaws in the e-voting systems in particular lead to a huge problem where intruders may perform a number of frauds for rigging the polls. Thus, the potential challenge is to distinguish the legitimate IoT devices from the malicious ones by computing their trust values through social optimizer in order to establish a legitimate communication environment. Further, in order to prevent from future modifications of data captured by smart devices, a Blockchain is maintained where blocks of all legitimate IoT devices are recorded. This article has introduced a secure and transparent e-voting mechanism through IoT devices using Blockchain technology with the aim of detecting and resolving the various threats caused by an intruder at various levels. Further, in order to validate the proposed mechanism, it is analyzed against various security parameters such as message alteration, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack and authentication delay

Reinforcement-Learning-Enabled Massive Internet of Things for 6G Wireless Communications

Recently, extensive research efforts have been devoted to developing beyond fifth generation (B5G), also referred to as sixth generation (6G) wireless networks aimed at bringing ultra-reli-able low-latency communication services. 6G is expected to extend 5G capabilities to higher communication levels where numerous connected devices and sensors can operate seamlessly. One of the major research focuses of 6G is to enable massive Internet of Things (mIoT) applications. Like Wi-Fi 6 (IEEE 802.11ax), forthcoming wireless communication networks are likely to meet massively deployed devices and extremely new smart applications such as smart cities for mIoT. However, channel scarcity is still present due to a massive number of connected devices accessing the common spectrum resources. With this expectation, next-generation Wi-Fi 6 and beyond for mIoT are anticipated to have inherent machine intelligence capabilities to access the optimum channel resources for their performance optimization. Unfortunately, current wireless communication network standards do not support the ensuing needs of machine learning (ML)-aware frameworks in terms of resource allocation optimization. Keeping such an issue in mind, we propose a reinforcement-learning-based, one of the ML techniques, a framework for a wireless channel access mechanism for IEEE 802.11 standards (i.e., Wi-Fi) in mIoT. The proposed mechanism suggests exploiting a practically measured channel collision probability as a collected dataset from the wireless environment to select optimal resource allocation in mIoT for upcoming 6G wireless communications