Reviewerly/exHarmony huggingface dataset:
The Relationships among Authentic Leadership, Social Exchange Relationships, and Trust in Organizations during COVID-19 Pandemic

Purpose: Trust is essential for organizations' cooperative relationships during COVID-19. Authentic leadership and social exchange relationships are required to increase trust during a crisis. However, leader behaviors during COVID-19 have effects on the relationships among authentic leadership, social exchange relationships, and trust in organizations to understand their effects. The moderating effects of leader behaviors during COVID-19 on the relationships among authentic leadership, social exchange relationships, and trust in organizations are investigated. Design/ Methodology/Approach: Hierarchical multiple regression with interaction terms is used to examine interactions of leader behaviors during COVID-19 on the relationships between variables. Moreover, bootstrapping is performed to explore the mediating role of social exchange relationships between authentic leadership and trust in organizations. Findings: The findings indicate that authentic leadership positively affects social exchange relationships and trust, whereas social exchange relationships positively affect trust. Moreover, a social exchange relationship has a mediating effect between authentic leadership and trust. Both leader behaviors are regrading (a) managerial ability with ethics and (b) a positive working relationship based on work from home as moderators, positively influencing authentic leadership, social exchange relationships, and trust. Practical implications: This paper suggests that authentic leadership increases the quality of social exchange relationships and trust. Leader behavior qualities and leader-follower relationships based on social exchange theory are essential for improving trust even after post-COVID-19. Finally, based on the social exchange theory, leader behavior qualities and leader-follower relationships can motivate positive relationships in organizations. The chief executive officers (CEOs) and the organizational management teams benefit from the outcomes of this paper to comprehend which factors affect relationships and trust changes in organizations. Keywords: authentic leadership (AL); trust; social exchange relationships (SERs); Leader behaviors during COVID-19 JEL: D23, D91, M14
Reduction of satellite images size in 5G networks using machine learning algorithms

The high data volume of multispectral satellite images is compressed for better visual perception without loss of image and statistical properties of the local or global image to provide superior information for obtaining effective results using 5G networks. This compression is a technique applied to remote sensing applications to analyse the data for prediction or forecasting the real-time applications by remote sensing applications like IoT and data transmission over 5G wireless networks. The extensive data images have multiple bands, which contain earth surface/object information with various frequencies. It is difficult to handle this extensive data for processing data. The compression is mandatory to avoid this complexity by removing redundancy data, unnecessary pixel information and non-visual redundancy data between bands. There are various standard compression techniques are available like JPEG 2000, Wavelet and DCT methods. The proposed method is implemented with a combination of intra coding and machine learning algorithm. The standard compression technique does not give better results due to degradation of pixels, lack of spatial and spectral information. This paper enriches progressive results by reduced satellite images for transmission of data in IoT and 5G wireless networks, in which qualitative results are compared by standard compression technique with suitable parameters.
Novel eye-based features for head pose-free gaze estimation with web camera: New model and low-cost device

In this paper, we propose a new set of efficient features for the gaze estimation system that successfully works with a simple and cheap eye gazing system. These features are composed of Pupil – Glint vectors, Pupil – inner-eye-Corner (canthus) vectors, Glint – inner-eye-Corner vectors, distance vector that connects 2 inner-eye-corners, angles between Pupil – Glint vectors and the Glint – inner-eye-Corner vectors, and deviation angle. These features together can capture the head movement and positions of the eyes relative to the position of the screen effectively. From the investigating results, ANN with 2 hidden layers provided the best classification in 15 regions of interest under the proposed features which has an accuracy of 97.71%, delivering better performance than the other techniques. Our proposed features are applicable for real-time applications and fit with low cost and simple system, therefore, it can be widely used for disabled and special need persons in various HCI applications.
RSMT: A Remote Sensing Image-to-Map Translation Model via Adversarial Deep Transfer Learning

Maps can help governments in infrastructure development and emergency rescue operations around the world. Using adversarial learning to generate maps from remote sensing images is an emerging field. As we now know, the urban construction styles of different cities are diverse. The current translation methods for remote sensing image-to-map tasks only work on the specific regions with similar styles and structures to the training set and perform poorly on previously unseen areas. We argue that this greatly limits their use. In this work, we intend to seek a remote sensing image-to-map translation model that approaches the challenge of generating maps for the remote sensing images of unseen areas. Our remote sensing image-to-map translation model (RSMT) achieves universal and general applicability to generate maps over multiple regions by combining adversarial deep transfer training schemes with novel attention-based network designs. Extracting the content and style latent features from remote sensing images and a series of maps, respectively, RSMT generalizes a pattern applied to the remote sensing images of new areas. Meanwhile, we introduce feature map loss and map consistency loss to reinforce generated maps’ precision and geometry similarity. We critically analyze qualitative and quantitative results using widely adopted evaluation metrics through extensive validation and comparisons with previous remote sensing image-to-map approaches. The results of experiment indicate that RSMT can translate remote sensing images to maps better than several state-of-the-art methods.
All-type optical logic gates using plasmonic coding metamaterials and multi-objective optimization

In this article, we propose a multiport plasmonic system (MPS) for implementing all-type logic gates based on coding metamaterials and inverse design technology. Compared to traditional plasmonic logic gates, the coding metamaterials based on metal-dielectric-metal (MDM) structures provide powerful programmability for manipulating electromagnetic (EM) waves and have a compact footprint (0.8 µm × 1.1 µm) for integration. To improve the performance of logic gates, the nondominated sorting genetic algorithm version II (NSGA-II) are used to optimize the distributions of coding metamaterials. After the optimization, the simulation results show that all types of logic gates (AND, OR, NOT, NAND, NOR, XNOR, and XOR) can be obtained with an operating wavelength of 1.31 µm. The maximum extinction ratios between logic states "1" and "0" reach 10.15 dB, 57.54 dB, 43.25 dB, 20.76 dB, 10.42 dB, 24.04 dB, and 27.74 dB for the AND, OR, NOT, NAND, NOR, XNOR, and XOR gates, respectively. Moreover, wavelength-tunable logic operations are also demonstrated to work within a wide spectrum. Our proposed plasmonic system not only provides a universal scheme for implementing all-type compact logic gates for optical processing and computing but also demonstrates effective applications of inverse design in nanophotonic devices.
Bayesian Framework for Multi-Timescale State Estimation in Low-Observable Distribution Systems

To support the smart grid paradigm, there has been a significant increase in sensor deployments and metering infrastructure in distribution systems. However, the measurements provided by these sensors and metering devices are typically sampled at different rates and could suffer from losses during the aggregation process. It is crucial to effectively reconcile the time-series measurements for a reliable state estimation. While weighted least squares has been the traditional approach for state estimation, sparsity-based approaches like matrix completion have become popular due to their superior performance in low-observability conditions. This paper proposes a Bayesian framework for both multi-timescale data aggregation and matrix completion based state estimation. Specifically, the multi-scale time-series data aggregated from heterogenous sources are reconciled using a multitask Gaussian process that exploits the spatio-temporal correlations. The resulting consistent time-series alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints to accurately estimate the states in low-observability conditions. Results on three phase unbalanced IEEE 37 and IEEE 123 bus test systems reveal the superior performance of the proposed Bayesian framework. The computational complexity for the proposed Bayesian framework is also quantified.
Lightweight Privacy-Preserving GAN Framework for Model Training and Image Synthesis

Generative adversarial network (GAN) has excellent performance for data generation and is widely used in image synthesis. Outsourcing GAN to cloud platform is a popular way to save local computation resources and improve the efficiency, but it still faces the privacy leakage concerns: (1) the sensitive information of the training dataset may be disclosed in the cloud; (2) the trained model may reveal the privacy of training samples since it extracts the characteristics from the data. In this paper, we propose a lightweight privacy-preserving GAN framework (LP-GAN) for model training and image synthesis based on secret sharing scheme. Specifically, we design a series of efficient secure interactive protocols for different layers (convolution, batch normalization, ReLU, Sigmoid) of neural network (NN) used in GAN. Our protocols are scalable to build secure training or inference tasks for NN-based applications. We utilize edge computing to reduce the latency and all the protocols are executed on two edge servers collaboratively. Compared with the existing schemes, the proposed solution greatly improves efficiency, reduces communication overhead, and guarantees the privacy. We prove the correctness and security of LP-GAN by theoretical analysis. Extensive experiments on different real-world datasets demonstrate the effectiveness, accuracy, and efficiency of our scheme.
Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm

In Self-Organizing Maps (SOM) are unsupervised neural networks that cluster high dimensional data and transform complex inputs into easily understandable inputs. To find the closest distance and weight factor, it maps high dimensional input space to low dimensional input space. The Closest node to data point is denoted as a neuron. It classifies the input data based on these neurons. The reduction of dimensionality and grid clustering using neurons makes to observe similarities between the data. In our proposed Mutated Self Organizing Maps (MSOM) approach, we have two intentions. One is to eliminate the learning rate and to decrease the neighborhood size and the next one is to find out the outliers in the network. The first one is by calculating the median distance (MD) between each node with its neighbor nodes. Then those median values are compared with one another. In case, if any of the MD values significantly varies from the rest then it is declared as anomaly nodes. In the second phase, we find out the quantization error (QE) in each instance from the cluster center.
IMG-CenterNet: An optimized algorithm based on CenterNet for pedestrian detection

With the development of deep learning in the field of target detection, there are more and more types of algorithms with higher accuracy. Different from the traditional anchor_based algorithm, CenterNet proposed in recent years is an anchor_based algorithm which has the advantages of high accuracy, simple network and fast detection speed. Although CenterNet is already light and its accuracy can be maintained at a relatively high degree, there is still a space for further improvement. In order to get a better detection effect, IBN_Net normalization method and group convolution are introduced into the residual module of CenterNet to improve the accuracy of network detection and reduce parameters. In addition, Mosaic data enhancement method is also used to optimize the training mode of the algorithm to enrich the detection background and optimize the detection performance. Compared with the original network, the MAP has been improved by nearly 3% and the number of parameters is also 30% lower.
Deep reinforcement learning with emergent communication for coalitional negotiation games

For tasks intractable for a single agent, agents must cooperate to accomplish complex goals. A good example is coalitional games, where a group of individuals forms coalitions to produce jointly and share surpluses. In such coalitional negotiation games, how to strategically negotiate to reach agreements on gain allocation is however a key challenge, when the agents are independent and selfish. This work therefore employs deep reinforcement learning (DRL) to build autonomous agent called DALSL that can deal with arbitrary coalitional games without human input. Furthermore, DALSL agent is equipped with the ability to exchange information between them through emergent communication. We have proved that the agent can successfully form a team, distribute the team's benefits fairly, and can effectively use the language channel to exchange specific information, thereby promoting the establishment of small coalition and shortening the negotiation process. The experimental results shows that the DALSL agent obtains higher payoff when negotiating with handcrafted agents and other RL-based agents; moreover, it outperforms other competitors with a larger margin when the language channel is allowed.
An efficient spatial transformation-based entropy retained reversible data hiding scheme in encrypted images

A critical issue with the current communication revolution is data security and privacy, which is an inevitable part of trustworthiness in the communication system. Hence, the applicability of the reversible data hiding schemes (RDH) in this scenario is encouraging and critical. Like medical image communication, satellite image transmission, etc. Earlier, we explored Arnold transform in one of our previous works to hide the secret data that uses the convolutional neural network (CNN) model to design a complete RDH scheme. The proposed scheme follows a statistical approach to support recovering the cover image and the embedded information. This approach proves advantageous over the previous work following its computational capability. The scheme designed can retain the entropy of the encrypted images even after embedding the additional information, complementing the security of the encryption algorithm.
IQ Symbols Processing Schemes With LSTMs in OFDM System

IQ modulation enjoys great popularity in wireless communication. Its main advantage is the symmetric convenience of combining independent signal components into a single composite signals in the transmitter. And it splits the composite signal into its independent components in the receiver. However, due to the limitations of communication facilities and imperfect channel environments, the symbols will be distorted. In this paper, we propose several signal processing schemes based on deep learning (DL) neural networks (i.e., long short-term memory (LSTM)) to process signal in an end-to-end manner, known as DLA, EDLA and PDNet. These schemes combine with advanced DL architectures and data-driven models for complex-valued signal processing. Enlightened by IQ demodulation ideas, we adopt LSTMs to develop several schemes to implement different functions (i.e., signal detection and peak to average power ratio (PAPR) reduction). The simulation results show that the schemes have significant performance improvement in bit error rate (BER) while reducing the probability of high PAPR.
HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation

An efficient solution to the large-scale recommender system is to represent users and items as binary hash codes in the Hamming space. Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work. Despite of their efficiency, these methods suffer from the suboptimal representative capacity, since they forgo the correlation established by connecting multiple first-order similarities, i.e., the relation among the indirect instances, which could be defined as the high-order similarity. To tackle this drawback, we propose to model both the first- and the high-order similarities in the Hamming space through the user-item bipartite graph. Therefore, we develop a novel learning to hash framework, namely Hamming Spatial Graph Convolutional Networks (HS-GCN), which explicitly models the Hamming similarity and embeds it into the codes of users and items. Extensive experiments on three public benchmark datasets demonstrate that our proposed model significantly outperforms several state-of-the-art hashing models, and obtains performance comparable with the real-valued recommendation models.
Image Alignment in Pose Variations of Human Faces by Using Corner Detection Method and Its Application for PIFR System

Major challenge faced by the recent face recognition techniques treat with pose variation during matching. When comparing different person images, the change in facial image caused by motion in the image or due to because of rotation in image is very considerable. Research into Pose Invariant Face Recognition is still an open area in front of developers today. In this paper, we concentrated on PIFR techniques and combined them with other algorithms to achieve better results. Here we are using the Harris Corner Detection model. Image alignment and Image tagging also used to get front face images. We also went into more detail about PIFR and its interrelated operations for future implementation. By generalization different tricks to handle the pose on face images and minimize the pose variation evaluating performance of the system, We are also going to calculate the Euler angle and their position change, and fixing the pose variation based on it for future research,' said the researchers.
Constraint-handling techniques used with evolutionary algorithms

tutorial Constraint-handling techniques used with evolutionary algorithms Author: Carlos A. Coello Coello CINVESTAV-IPN, Mexico City, México CINVESTAV-IPN, Mexico City, MéxicoView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 692–714https://doi.org/10.1145/3449726.3461400Published:08 July 2021Publication History 5citation234DownloadsMetricsTotal Citations5Total Downloads234Last 12 Months74Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Blockchain for the Security of Internet of Things: A Smart Home use Case using Ethereum

Internet of Things (IoT) that has been developed owing toward the merging of various technologies such as instantaneous analytics, machine learning, artificial neural network, product sensors and implanted systems, etc. It also stages a significant part in multiple applications which are a smart city, smart home, agricultural, health monitoring, tourism, transportation, communication, business, education, etc. The security concern associating IoT has to be situated in the direction to attract the research community due to its immensely growing application in our daily life. Since it is light-weight nature, the security mechanism needs changes apart from comprehensive web security. PKI based certificate driven techniques, which in some instances seem not appropriate on the way toward encounter IoT challenges, e.g., as real-time effectiveness, costs, and performance from the security perspective. However, Blockchain has mind-blowing potentials to peer with the IoT aiming to build trust, transparency, and security. Its distinct properties, such as distributed behavior, immutability, and consensus mechanism, can stimulate and improve the rapidly growing IoT system through meaningful integration. We propose to address a practical smart home use case scenario using Ethereum Blockchain to improve IoT security. Public type and smart contract oriented Ethereum Blockchain are adaptable to enhance IoT security remains individual of the contributions that we claim throughout this research work.
A hybrid collaborative filtering recommendation algorithm for requirements elicitation

Requirements elicitation is one of the most critical and difficult tasks in software development. Requirements reuse has shown to be an effective and efficient elicitation technique that can enhance the quality of the requirements elicitation process and, as a result, lead to a project's success. However, the information overload problem, which is caused by the rapidly growing number of reusable software requirements in large requirements repositories, hinders the effectiveness of the requirements reuse process. Recommender systems proved to be a well-known solution to such problems. This paper focuses on the adoption of recommender systems to mitigate the problem of information overload that is inherent in the requirements elicitation process, specifically by assisting requirement engineers in retrieving relevant reusable requirements from large-scale requirements repositories. The validation results on the RALIC dataset illustrate that the proposed algorithm outperforms and mitigates the drawbacks of the benchmark collaborative filtering-based recommendation approaches.
Construction of a sharing model for network digital teaching resources oriented to big data

Aiming at the problems of low resource utilisation rate, low model reliability and poor feasibility of current network resource sharing model, this paper proposes and constructs a sharing model for network digital teaching resource based on semantic web. The game model is used to analyse the behaviour evolution of teaching resource sharing model. Based on ontology relationship, a general framework of metadata for teaching resources is constructed to realise interoperability between metadata. Taking XML as the basic grammar, according to the details of the application of teaching resources, the related application based on XML is created, and the sharing of network digital teaching resources is realised by defining the pattern layer by layer. The experimental results show that the utilisation rate of teaching resources under this model is high, and the fitting degree between the model and the actual situation is high.