Network Effects Research Articles

Self-adaptive equation embedded neural networks for traffic flow state estimation with sparse data

The data-driven approach in intelligent traffic systems has achieved successive breakthroughs, thanks to the ever-increasing volume of traffic data. Nonetheless, in practical scenarios, the collected data often contain some issues, e.g., missing values, significantly impacting the accuracy and efficiency of the algorithms. To enhance the precision of traffic estimation utilizing the sparse data, we have developed a physics-informed neural network (PINN) based algorithm in the line with the traffic flow theory and deep learning principles. In contrast to the conventional PINNs, our approach uniquely incorporates a self-adaptive macro model for mixed flow into the network's architecture, serving as an embedded source of physics information. With this algorithm, we can capture the dynamic behavior of an entire traffic flow including its spatiotemporal evolution with sparse traffic data such as initial and boundary value information. To realize the model's adaptability, we have revised the macro model by inverting its parameters and incorporating a data-driven nonlinear element, which simplifies the intricate macro model structure. The network's effectiveness has been validated through the experiments conducted on a mixed traffic flow system experiencing local agglomeration and real-world data, demonstrating its capability for precise traffic simulation, efficient traffic flow prediction, and interpretability. Our study offers a novel insight for data-driven traffic flow state estimation.

Energy Optimization Approach in Wireless Sensor Network

In recent years, the importance of routing protocols in wireless sensor networks and the Internet of Things has been demonstrated by the numerous studies that have been presented in this area. because it is crucial to the network's functionality and effectiveness. Among routing's main goals are lowering latency, enhancing reliability, and averting network collisions. Additionally, it can be claimed that the type of network routing affects service quality, network longevity, and network traffic load control. Thus, in order to lower routing error and boost network performance and efficiency, the firefly is employed in this study to identify the best information transmission path within the network. It is also employed to reduce energy consumption. Taking into account the ability of evolutionary algorithms to optimize specified objective functions, we attempted to lower the network's energy consumption. The network can enhance the number of sending packets with the beginning energy of 3 joules from 1480 rounds to 1798 rounds, based on the proposed algorithm's results. Additionally, by contrasting the approaches described in the same article, it was discovered that the suggested approach can send roughly 40.11% more packets than the RM-LB algorithm and 1.9573% more than the OMS-LB technique. Additionally, it was discovered that by raising the starting energy to 150 joules, the suggested technique outperformed the other two algorithms by 51.23% and 45.17%, respectively, and was able to lengthen the longevity. This was in comparison to the RM-LB method and the OMS-LB method.

Revolutionizing WBAN communication for public health: Effective Optimization strategy-driven health assessment

This study explores the optimization procedures that are critical to enhancing WBAN communication efficacy in an environment of public health initiatives. In order to observe someone's physiological characteristics continuously and non-invasively, wireless body area networks (WBANs), are an essential piece of innovation for distant health observation. The WBAN individual nodes, yet, might relocate and lose reception from a distant base station over time. Thus, an effective inter-WBAN interaction approach is required to address this issue. In order to lower cost associated with networks and maximize cluster lifespan towards the network's effectiveness, this research develops a clustering-driven routing approach that uses cluster head serving as a gateway. For efficient cluster head selection, a novel customized chicken swarm optimization (CCSO) technique was introduced. Various parameter adjustments are made in multiple trials to verify the suggested approach effectiveness. The research conduct comprehensive comparison analysis of suggested and existing algorithms, several tests are conducted. The results show that the approach technique can increase the effectiveness of WBAN engagement and create clusters more quickly than the other methods in the framework of public health initiatives.

Updated delegated proof of stake and Nash equilibrium: A mining pool game model for blockchain network

SummaryIn the present era, blockchain technology has an extensive variety of uses in the financial, marketing, and so forth. The performance and security of the blockchain system are directly affected by the consensus algorithm. Since there are 30 consensus techniques like proof of stake, delegated proof of stake, and proof of work, and so forth in the previous years, the operating efficiency, safety, and stability still lag behind the basic requirements. To this concern, this research manuscript introduces a novel consensus mechanism‐based game model using upgraded delegated proof of stake (UDPoS) and Nash equilibrium (NE) that is UDPoS‐NE. In a blockchain network powered by UDPoS, blockchain miners provide computing power to publish blocks. Due to a single miner's limited Computing Power (CP), miners frequently join mining pools and divide the pool's profits in accordance with their respective contributions. However, certain miners launch block‐withholding attacks that lead to loss the computer power and endanger the blockchain network's effectiveness. Therefore, NE is used to calculate cooperative game solutions and optimize policy decisions of the miners to frustrate the block withholding attack by the influence of stakes and computing power on producing blocks. Using this mechanism, efficiency, and stability in the consensus process is achieved through the combined influence of computing resources and stakes on block generation. Finally, the suggested model's applicability and validity are confirmed with a throughput (600 TPS), delay (3 s), and energy consumption (<100 KWh) for 10 nodes and processing time (0.9 s) for 200 nodes.

Can access to urban networks promote urban development? Evidence from the Yangtze River Delta region of China.

The regional networking strategy is widely implemented in China as a normative policy aimed at fostering cohesion and enhancing competitiveness. However, the empirical basis for this strategy remains relatively weak due to limitations in measurement methods and data availability. This paper establishes the urban networks by the enterprise investment data, and then accurately measures the network's external effects of each city by the method of MGWR model. The results show that: (1) Regional networking plays a significant role in urban development, although it is not the dominant factor. (2) The benefits of network connections may vary depending on the location and level of cities. (3) The major cities assume a pivotal role in the urban network. Based upon the aforementioned research conclusions, this paper presents strategic measures to enhance the network's external impacts, aiming to offer insights for other regions in formulating regional development strategies and establishing regional urban networks.

Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities

The emergence of smart cities is an example of how new technologies, such as the Internet of Things (IoT), have facilitated the creation of extensive interconnected and intelligent ecosystems. The widespread deployment of IoT devices has enabled the provision of constant environmental feedback, thereby facilitating the automated adaptation of associated systems. This has brought about a fundamental transformation in the way contemporary society functions. The security of emerging technologies such as IoT has become a significant challenge due to the added complexities, misconfigurations, and conflicts between modern and legacy systems. This challenge has a notable impact on the reliability and accessibility of existing infrastructure. Edge computing (EC) is a collaborative computing system that brings data processing and analysis closer to the edge of the network, where the data is generated, rather than in a centralized cloud environment. The utilization of the IoT has become more prevalent in both everyday life and the manufacturing sector, with a particular emphasis on critical infrastructure. The IoT is presently being utilized across diverse domains, including but not limited to industrial, agricultural, healthcare, and logistical sectors. The security of IoT networks has implications for the safety of individuals, the security of the nation, and economic development. Notwithstanding, conventional intrusion detection techniques that rely on centralized cloud-based systems that have been suggested in previous studies for IoT network security are insufficient to meet the requirements for data confidentiality, network capacity, and prompt responsiveness. In addition, the integration of IoT applications into smart devices has been shown to augment their functionalities. However, it is important to note that this integration also brings about potential security vulnerabilities. Furthermore, a significant number of contemporary IoT devices exhibit restricted security capabilities, rendering them vulnerable to intricate attacks and impeding the extensive integration of IoT technologies. Also, a lot of IoT network devices have been put in place that don't have hardware security measures. This means that traditional intrusion detection systems (IDS) aren't enough to protect the IoT network ecosystem. To address these issues, this research suggests the IoT-Defender framework, which combines a Modified Genetic Algorithm (MGA) model with a deep Long Short-Term Memory (LSTM) network to find cyberattacks in IoT networks. This research represents a pioneering attempt to employ the MGA for feature selection and the GA for fine-tuning the LSTM parameters within an EC framework. The parameters of the LSTM model were fine-tuned through the manipulation of the number of hidden layers, utilizing the GA fitness function. The customization of the MGA aimed to enhance its performance in selecting relevant features, optimizing the use of limited resources on IoT devices and edge nodes. The fine-tuning process involved optimizing hyperparameters, architecture, and training strategies to maximize the LSTM network's effectiveness in learning and detecting patterns in IoT network traffic. The synergy between the MGA and LSTM aimed at creating a comprehensive and efficient IDS. The feature selection by the MGA contributes to improving the LSTM’s performance by providing it with more relevant and discriminating features. In order to solve the issue of class imbalance, we utilize the focal loss function, which provides greater weights to minority classes, hence improving the model’s capacity to learn from those particular classes. The performance of the IoT-Defender model was assessed on the BoT-IoT, UNSW-NB15, and N-BaIoT datasets utilizing a Raspberry Pi IoT device. The results of our study show that the IoT-Defender model works better than other methods. This is shown by its accuracy score of 99.41%, detection rate of 99.78%, precision score of 98.50%, false alarm rate of 2.56%, mean intersection over union (mIoU) of 0.68, and training time of 81.3 seconds on BoT-IoT. The proposed IoT model is designed to be lightweight and can be installed on edge servers to detect cyber-attacks in real-time, specifically in the context of IoT security.

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

Cluster Head Selection and Secured Routing Using Glowworm Swarm Algorithm and Hybrid Security Algorithm for Over IoT-WSNs

WSNs (Wireless Sensor Networks) have received a lot of attention in a number of industries due to its self-configurability, simplicity in maintenance, and scaling capacities. WSN is configured with extra nodes in order to transport data around the network. The usual characteristics of sensor networks are low bandwidth, low energy, constrained power supply, and little memory space, which makes security particularly difficult in these networks. CH (Cluster heads) in a cluster experience heavier traffic loads than other cluster nodes. This results in the hotspot problems. Therefore, in a cluster-oriented routing scheme, picking the right CH is crucial. The two-level security is recommended in this research to safeguard data transmission via the WSN. A novel CH selection methodology is presented in order to increase the network's lifespan and energy effectiveness. This work also combines the fruit fly algorithm (FGF) with the GSA (firefly swarm algorithm) based on fitness to choose the optimal CH. To defend the network from assaults, it employs a hybrid security method that combines the Diffie-Hellman key exchange mechanism and the ECC (elliptic curve cryptography) algorithm. The suggested GSA-HSA protocol consistently produces good performance results in terms of MSR (Message Success Rate), E2E (End-to-End) Latency, NT (Network Throughput), and network throughput. average AEE (energy efficiency). The suggested approach outperforms all prior research by giving the wireless sensor network combination security, optimised coverage, and energy savings.

Identification of banana leaf disease based on KVA and GR-ARNet

Banana is a significant crop, and three banana leaf diseases, including Sigatoka, Cordana and Pestalotiopsis, have the potential to have a serious impact on banana production. Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases. Therefore, this paper proposes a novel method to identify banana leaf diseases. First, a new algorithm called K-scale VisuShrink algorithm (KVA) is proposed to denoise banana leaf images. The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds, the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image. Then, this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net (GR-ARNet) based on Resnet50. In this, the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed; the ResNeSt Module adjusts the weight of each channel, increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification; the model's computational speed is increased using the hybrid activation function of RReLU and Swish. Our model achieves an average accuracy of 96.98% and a precision of 89.31% applied to 13,021 images, demonstrating that the proposed method can effectively identify banana leaf diseases.

CDK1-SRC Interaction-Dependent Transcriptional Activation of HSP90AB1 Promotes Antitumor Immunity in Hepatocellular Carcinoma.

This study aimed to analyze multiomics data and construct a regulatory network involving kinases, transcription factors, and immune genes in hepatocellular carcinoma (HCC) prognosis. The researchers used transcriptomic, proteomic, and clinical data from TCGA and GEO databases to identify immune genes associated with HCC. Statistical analysis, meta-analysis, and protein-protein interaction analyses were performed to identify key immune genes and their relationships. In vitro and in vivo experiments validated the CDK1-SRC-HSP90AB1 network's effects on HCC progression and antitumor immunity. A prognostic risk model was developed using clinicopathological features and immune infiltration. The immune genes LPA, BIRC5, HSP90AB1, ROBO1, and CCL20 were identified as the key prognostic factors. The CDK1-SRC-HSP90AB1 network promoted HCC cell proliferation and migration, with HSP90AB1 being transcriptionally activated by the CDK1-SRC interaction. Manipulating SRC or HSP90AB1 reversed the effects of CDK1 and SRC on HCC. The CDK1-SRC-HSP90AB1 network also influenced HCC tumor formation and antitumor immunity. Overall, this study highlights the importance of the CDK1-SRC-HSP90AB1 network as a crucial immune-regulatory network in the HCC prognosis.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.

Enhancing the Lifetime of WSN Using a Modified Ant Colony Optimization Algorithm

Wireless sensor networks (WSNs) have been extensively used in various fields, such as health, defense, education, and industrial applications, to collect and transmit environmental data to the base station. However, energy efficiency is a significant challenge in WSNs, as data transmission is typically limited to a single route, leading to excessive energy consumption by the nodes along that route. This can lead to a decrease in the network's overall efficiency and effectiveness. To address this issue, this study aims to extend the lifespan of WSNs by optimizing route selection based on three variables: residual node energy, distance to the base station, and number of shared neighbors. In this paper, the authors propose three systematic approaches, namely Energy-Aware ACO Routing (EACO), Cost-Effective ACO Routing (CEACO), and Cost-Efficient Node Replacement Strategies ACO (CERACO), to enhance the lifetime of WSNs. These approaches consider various factors such as cost, energy consumption, replacement, and reliability. The paper provides a practical guide for researchers and practitioners to overcome the challenges related to energy efficiency and cost-effectiveness in WSNs. Experimental results demonstrate that the first dead node occurs later with the proposed methods than with the traditional Ant Colony Optimization (ACO) algorithm.

The cold-start problem in nascent AI strategy: Kickstarting data network effects

While many artificial intelligence (AI) strategies are successful, countless others fail. Why do some strategies succeed while others fail? We adopt a network effects (NEs) perspective to conceptualize AI strategies, highlighting the AI context’s specifics. We argue that nascent AI strategies’ success depends on data NEs: companies establishing a functional “running system” to capitalize on these effects. However, this presents a challenge known as the cold-start problem (CSP), which involves initiating and accelerating a virtuous cycle: more data benefits the AI system, enhancing performance, which then attracts more data. In this paper, we examine the CSP in nascent AI strategy, exploring how it can be understood in terms of its technological and business dimensions and ultimately be overcome to kick-start a virtuous cycle of data NEs. By drawing insights from existing literature and practitioner interviews, we present a research agenda to encourage further investigation into overcoming the CSP.

Complexity synchronization: a measure of interaction between the brain, heart and lungs

Herein we address the measurable consequences of the network effect (NE) on time series generated by different parts of the brain, heart, and lung organ-networks (ONs), which are directly related to their inter-network and intra-network interactions. Moreover, these same physiologic ONs have been shown to generate crucial event (CE) time series, and herein are shown, using modified diffusion entropy analysis (MDEA) to have scaling indices with quasiperiodic changes in complexity, as measured by scaling indices, over time. Such time series are generated by different parts of the brain, heart, and lung ONs, and the results do not depend on the underlying coherence properties of the associated time series but demonstrate a generalized synchronization of complexity. This high-order synchrony among the scaling indices of EEG (brain), ECG (heart), and respiratory time series is governed by the quantitative interdependence of the multifractal behavior of the various physiological ONs’ dynamics. This consequence of the NE opens the door for an entirely general characterization of the dynamics of complex networks in terms of complexity synchronization (CS) independently of the scientific, engineering, or technological context. CS is truly a transdisciplinary effect.

Metaheuristic algorithm for energy efficient clustering scheme in wireless sensor networks

The utilization of wireless sensor networks (WSNs), which combine sensing, communication, and computation in an intelligent environment. The use of energy becomes the primary obstacle for sensor nodes to overcome. Clustering and routing algorithms are promising ways to fix the issue and prolong the network's effectiveness. The availability of restricted energy resources is the most significant challenge in WSNs. In this paper, we propose an energy-efficient clustering scheme integrated with a particle swarm optimization method called PSO-EECS for wireless sensor networks to raise energy efficiency while simultaneously increasing the lifetime of sensor nodes. The predominant concern being addressed in this paper is the selection of a Cluster Head (CH), which helps gather, aggregate and forward the data from the cluster-based routing paradigm. PSO-EECS algorithm is used to optimize the fitness parameters for CH selection, which include the energy ratio, distance considerations, node density, load balancing, and the Network's average energy, among other things. The simulation results reveal that the proposed technique offers a considerable improvement in network stability and operation time compared to the currently used approaches. Compared to the PSOGSA, GAPSOH, and ABE-DE protocol, simulation findings demonstrate that PSO-EECS enhances stability by 95.4%, 34.8%, and 29.2%, respectively.

Low-dose CT denoising with a high-level feature refinement and dynamic convolution network.

Since the potential health risks of the radiation generated by computer tomography (CT), concerns have been expressed on reducing the radiation dose. However, low-dose CT (LDCT) images contain complex noise and artifacts, bringing uncertainty to medical diagnosis. Existing deep learning (DL)-based denoising methods are difficult to fully exploit hierarchical features of different levels, limiting the effect of denoising. Moreover, the standard convolution kernel is parameter sharing and cannot be adjusted dynamically with input change. This paper proposes an LDCT denoising network using high-level feature refinement and multiscale dynamic convolution to mitigate theseproblems. The dual network structure proposed in this paper consists of the feature refinement network (FRN) and the dynamic perception network (DPN). The FDN extracts features of different levels through residual dense connections. The high-level hierarchical information is transmitted to DPN to improve the low-level representations. In DPN, the two networks' features are fused by local channel attention (LCA) to assign weights in different regions and handle CT images' delicate tissues better. Then, the dynamic dilated convolution (DDC) with multibranch and multiscale receptive fields is proposed to enhance the expression and processing ability of the denoising network. The experiments were trained and tested on the dataset "NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge," consisting of 10 anonymous patients with normal-dose abdominal CT and LDCT at 25% dose. In addition, external validation was performed on the dataset "Low Dose CT Image and Projection Data," which included 300 chest CT images at 10% dose and 300 head CT images at 25%dose. Proposed method compared with seven mainstream LDCT denoising algorithms. On the Mayo dataset, achieved peak signal-to-noise ratio (PSNR): 46.3526 dB (95% CI: 46.0121-46.6931 dB) and structural similarity (SSIM): 0.9844 (95% CI: 0.9834-0.9854). Compared with LDCT, the average increase was 3.4159 dB and 0.0239, respectively. The results are relatively optimal and statistically significant compared with other methods. In external verification, our algorithm can cope well with ultra-low-dose chest CT images at 10% dose and obtain PSNR: 28.6130 (95% CI: 28.1680-29.0580 dB) and SSIM: 0.7201 (95% CI: 0.7101-0.7301). Compared with LDCT, PSNR/SSIM is increased by 3.6536dB and 0.2132, respectively. In addition, the quality of LDCT can also be improved in head CT denoising. This paper proposes a DL-based LDCT denoising algorithm, which utilizes high-level features and multiscale dynamic convolution to optimize the network's denoising effect. This method can realize speedy denoising and performs well in noise suppression and detail preservation, which can be helpful for the diagnosis ofLDCT.

Semi-Supervised Authentically Distorted Image Quality Assessment With Consistency-Preserving Dual-Branch Convolutional Neural Network

Recently, convolutional neural networks (CNNs) have provided a favoured prospect for authentically distorted image quality assessment (IQA). For good performance, most existing CNN-based methods rely on a large amount of labeled data for training, which is time-consuming and cumbersome to collect. By simultaneously exploiting few labeled data and many unlabeled data, we make a pioneering attempt to propose a semi-supervised framework (termed SSLIQA) with consistency-preserving dual-branch CNN for authentically distorted IQA in this paper. The proposed SSLIQA introduces a consistency-preserving strategy and transfers two kinds of consistency knowledge from the teacher branch to the student branch. Concretely, SSLIQA utilizes the sample prediction consistency to train the student to mimic output activations of individual examples represented by the teacher. Considering that subjects often refer to previous analogous cases to make scoring decisions, SSLIQA computes the semantic relation among different samples in a batch and encourages the consistency of sample semantic relation between two branches to explore extra quality-related information. Benefiting from the consistency-preserving strategy, we can exploit numerous unlabeled data to improve network's effectiveness and generalization. Experimental results on three authentically distorted IQA databases show that the proposed SSLIQA is stably effective under different student-teacher combinations and different labeled-to-unlabeled data ratios. In addition, it points out a new way on how to achieve higher performance with a smaller network.

Robust goal programming approach for healthcare network management for perishable products under disruption

In this paper, a robust goal programming model for a healthcare network design problem under disruption is developed. The objective of the proposed multi-period, multi-echelon model is to simultaneously optimize the total deviation from the considered three goals, including minimizing network cost, maximizing network coverage, and maximizing network reliability. Distribution uncertainty of healthcare demand and facility capacity is captured. The perishability of healthcare products is regarded with limited shelf-life. Using an option contract ensures appropriate coordination with suppliers during pre and post-disaster periods. Via handling the tractable robust budget-based counterpart model, suitable strategies are proposed to present the designed network's efficiency and effectiveness. Risk mitigation strategies deal with disrupted demand and supply in the healthcare network with specific impacts on coverage, cost, and reliability of the services. The proposed cooperate coverage mechanism provides appropriate service coverage under disruption propagation. Finally, the option contract has achieved cost-efficient coordination under demand uncertainty. Furthermore, practical managerial insights are demonstrated by investigating a real-life case study and conducting extensive sensitivity analyses.

Co-Design of Dielectric Resonator Antenna and T-Shaped Microstrip Feeding Network for Back-Radiation Suppression

In practice, the broadband feeding network on the back side of an antenna can have a nonnegligible effect on the antenna's back radiation due to the feeding network's radiation contribution. Thus, a back-radiation cancellation scheme is proposed by co-designing the antenna and its feeding network to solve this problem. The antenna and its feeding network can be viewed as a two-element array. Their radiation toward the antenna's backward direction can cancel each other when both have equal amplitude and opposite phases along the back direction. A differential fed dielectric resonator antenna (DRA) through a rat-race coupler ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${180}^\circ $</tex-math></inline-formula> hybrid coupler) and a T-shaped broadband differential feeding network are compared and analyzed to verify the proposed scheme. In addition, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1\!\times\!4$</tex-math></inline-formula> DRA array with a T-shaped broadband differential feeding network in a previous work (where the feeding network's radiation effect on the back side was overlooked) is used for experimental validation of the proposed scheme. Simulations and measurements demonstrate significant back-radiation suppression than the ideal differential feeding network.

Revisiting Group Fairness Metrics: The Effect of Networks

An increasing amount of work studies fairness in socio-technical settings from a computational perspective. This work has introduced a variety of metrics to measure fairness in different settings. Most of these metrics, however, do not account for the interactions between individuals or evaluate any underlying network's effect on the outcomes measured. While a wide body of work studies the organization of individuals into a network structure and how individuals access resources in networks, the impact of network structure on fairness has been largely unexplored. We introduce templates for group fairness metrics that account for network structure. More specifically, we present two types of group fairness metrics that measure distinct yet complementary forms of bias in networks. The first type of metric evaluates how access to others in the network is distributed across groups. The second type of metric evaluates how groups distribute their interactions across other groups, and hence captures inter-group biases. We find that ignoring the network can lead to spurious fairness evaluations by either not capturing imbalances in influence and reach illuminated by the first type of metric, or by overlooking interaction biases as evaluated by the second type of metric. Our empirical study illustrates these pronounced differences between network and non-network evaluations of fairness.