Reviewerly/exHarmony huggingface dataset:
Employee loyalty during slowdown of Covid-19: Do satisfaction and trust matter?

Purpose This research aims to test employee loyalty in the hotel industry, particularly during the Covid-19 pandemic. This study examines the relationship between leader support in building job satisfaction, trust and employee loyalty. Also, this research aims to test and explain the role of satisfaction and trust as mediator variables. Design/methodology/approach This research used a quantitative design by distributing questionnaires to 206 employees of the 97 hotels in Bali, Indonesia, particularly during pandemic Covid-19. The research data were then analyzed by using WarpPLS software. Findings The results revealed that leader support did not have a significant effect on loyalty. Satisfaction and trust act as double mediators in leaders’ support and loyalty relationships. Research limitations/implications Employees need leaders’ support to remain loyal to their organization in a slowdown situation due to the Covid-19 pandemic and its various challenges. Originality/value Research on the effects of the Covid-19 pandemic on various sectors has been comprehensive. However, the research that invests in employee loyalty in the hospitality industry is still rare. This study analyzes the loyalty of hotel employees, particularly when the tourism sector is experiencing a slowdown. This study also examines the role of trust and satisfaction as mediating relationships between leaders’ support and loyalty, which have not been widely analyzed in previous studies.
Deep Multi-view Depth Estimation with Predicted Uncertainty

In this paper, we address the problem of estimating dense depth from a sequence of images using deep neural networks. Specifically, we employ a dense-optical-flow network to compute correspondences and then triangulate the point cloud to obtain an initial depth map. Parts of the point cloud, however, may be less accurate than others due to lack of common observations or small parallax. To further increase the triangulation accuracy, we introduce a depth-refinement network (DRN) that optimizes the initial depth map based on the image’s contextual cues. In particular, the DRN contains an iterative refinement module (IRM) that improves the depth accuracy over iterations by refining the deep features. Lastly, the DRN also predicts the uncertainty in the refined depths, which is desirable in applications such as measurement selection for scene reconstruction. We show experimentally that our algorithm outperforms state-of-the-art approaches in terms of depth accuracy, and verify that our predicted uncertainty is highly correlated to the actual depth error.
SARR: A Cybersecurity Metrics and Quantification Framework (Keynote)

Cybersecurity Metrics and Quantification is a fundamental but notoriously hard problem and is undoubtedly one of the pillars underlying the emerging Science of Cybersecurity. In this paper, we present an novel approach to addressing this problem by unifying Security, Agility, Resilience and Risk (SARR) metrics into a single framework. The SARR approach and the resulting framework are unique because: (i) it is driven by the assumptions that are made when modeling, designing, implementing, operating, and defending systems, which are broadly defined to include infrastructures and enterprise networks; and (ii) it embraces the uncertainty inherent to the cybersecurity domain. We will review the status quo by looking into existing metrics and quantification research through the SARR lens and discuss a range of open problems.
Mithra: Anomaly Detection as an Oracle for Cyberphysical Systems

Testing plays an essential role in ensuring the safety and quality of cyberphysical systems (CPSs). One of the main challenges in automated and software-in-the-loop simulation testing of CPSs is defining effective oracles that can check that a given system conforms to expectations of desired behavior. Manually specifying such oracles can be tedious, complex, and error-prone, and so techniques for automatically learning oracles are attractive. Characteristics of CPSs, such as limited or no access to source code, behavior that is non-deterministic and sensitive to noise, and that the system may respond differently to input based on its context introduce considerable challenges for automated oracle learning. We present Mithra, a novel, unsupervised oracle learning technique for CPSs that operates on existing telemetry data. It uses a three-step multivariate time series clustering to discover the set of unique, correct behaviors for a CPS, which it uses to construct robust oracles. We instantiate our proposed technique for ArduPilot, a popular, open-source autopilot software. On a set of 24 bugs, we show that Mithra effectively identifies buggy executions with few false positives and outperforms AR-SI, a state-of-the-art CPS oracle learning technique. We demonstrate Mithra’s wider applicability by applying it to an autonomous racer built for the Robot Operating System.
Shared Autonomy for Teleoperated Driving: A Real-Time Interactive Path Planning Approach

Teleoperation deals with extraordinary situations where an external operator takes over the control of an autonomous vehicle. Especially in complex urban scenarios, this may cause a too high workload for the human operator, resulting in suboptimal solutions. This contribution presents a teleoperation paradigm to raise the autonomy level of teleoperated driving, while the operator still remains the main decision-maker in all driving tasks. The introduced approach generates collision-free paths using LiDAR sensor information and suggests them to the operator. Therefore, a new hybrid path planning method has been developed, which searches and clusters in the first phase all feasible paths in the environment using a modified Rapidly-Exploring-Random Tree (RRT). In the second phase, the path selected by the operator is optimized online by a modified CHOMP algorithm. Real driving experiments confirm the effectiveness of the approach and highlight both the achieved driving safety and real time capability.
Security enhancement of image encryption method based on Fresnel diffraction with chaotic phase

In the traditional image encryption methods based on Fresnel diffraction (FRT), the Fresnel diffraction distance and the wavelength of light are considered as two essential secret keys, named distance key and wavelength key, respectively. However, these two keys are both vulnerable to be attacked due to their low sensitivities, resulting in an inadequate intensity of secret key for several high-security applications. This is a common and serious problem in image encryption methods based on FRT. In this paper, a security-enhanced image encryption method based on Fresnel diffraction with chaotic phase (CPFRT) is proposed and verified, where CPFRT is an alternative algorithm of diffraction calculation that combines ordinary FRT with special phase generated by chaotic system. In such a way, the sensitivities of distance key and wavelength key could be substantially improved thanks to the nonlinear, pseudo-random, high sensitivity of initial value of chaotic phase, which effectively solves the previous common problem, contributing to an extremely high security of image encryption method meanwhile the number of secret keys is not increased. Moreover, computational simulations are performed to demonstrate the validity of the proposed security-enhanced image encryption method, and the results confirm that the proposed method has a substantially enhanced security in distance key and wavelength key, and the high quality of correct decrypted image could be successfully maintained. Furthermore, the proposed method based on CPFRT can also be applied to double-phase image encryption by replacing FRT with CPFRT. Analogously, the sensitivities of distance key and wavelength key in double-phase image encryption are also greatly improved, which leads to an outstanding anti-attack capability. In addition, the effectiveness of the proposed double-phase image encryption method based on CPFRT is also verified by rigorous computational simulation, and the proposed method based on CPFRT shows a promising potential for other image encryption methods in order to substantially enhance their securities.
A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

Nowadays, due to the increase in information resources, the number of parameters and complexity of feature vectors increases.Optimization methods offer more practical solutions instead of exact solutions for the solution of this problem.The Emperor Penguin Optimizer (EPO) is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins.It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features.Although traditional EPO overcomes the optimization problems in continuous search space, many problems today shift to the binary search space.Therefore, in this study, using the power of traditional EPO, binary EPO (BEPO) is presented for the effective solution of binary-nature problems.BEPO algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search space.For this purpose, the sigmoidal functions are preferred in determining the emperor positions.In addition, the boundaries of the search space remain constant by choosing binary operators.BEPO's performance is evaluated over twenty-nine benchmarking functions.Statistical evaluations are made to reveal the superiority of the BEPO algorithm.In addition, the performance of the BEPO algorithm was evaluated for the binary feature selection problem.The experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.
Behavior‐based POI recommendation for small groups in location‐based social networks

Abstract In point‐of‐interest (POI) group recommender systems, suggesting the most suitable visit places for a heterogeneous group in which members are far from each other would be difficult in terms of finding a consensus among group members. In this article, we argue that the geographical proximity of POIs to users' locations has a notable influence on group decisions to visit the POI and their check‐in behavior. We develop a new geographical model based on the check‐in behavior of the group in location‐based social networks to improve group recommendations by introducing a new concept called users' spatial ratio in a group vectors and using a 2D kernel density estimation method. To evaluate the performance of the approach, experimental studies were conducted on a real dataset of users and group check‐in activities collected from Foursquare Swarm. In our experiments, the proposed method makes better recommendations compared with other existing approaches. Besides, analyzing group check‐ins shows that in a group visit, the distance between the most visited place by a group member alone and the places visited by the group follows a generalized gamma distribution. Also, the ratio of this distance for the group member to the distance of another member depends on the group size.
Federated Anomaly Analytics for Local Model Poisoning Attack

The local model poisoning attack is an attack to manipulate the shared local models during the process of distributed learning. Existing defense methods are passive in the sense that they try to mitigate the negative impact of the poisoned local models instead of eliminating them. In this paper, we leverage the new federated analytics paradigm, to develop a proactive defense method. More specifically, federated analytics is to collectively carry out analytics tasks without disclosing local data of the edge devices. We propose a Federated Anomaly Analytics enhanced Distributed Learning (FAA-DL) framework, where the clients and the server collaboratively analyze the anomalies. FAA-DL firstly detects all the uploaded local models and splits out the potential malicious ones. Then, it verifies each potential malicious local model with functional encryption. Finally, it removes the verified anomalies and aggregates the remaining to produce the global model. We analyze the FAA-DL framework and show that it is accurate, robust, and efficient. We evaluate FAA-DL by training classifiers on MNIST and Fashion-MNIST under various local model poisoning attacks. Our experiment results show FAA-DL improves the accuracy of the learned global model under strong attacks up to 6.90 times and outperforms the state-of-the-art defense methods with a robustness guarantee.
Targetless Multiple Camera-LiDAR Extrinsic Calibration using Object Pose Estimation

We propose a targetless method for calibrating the extrinsic parameters among multiple cameras and a LiDAR sensor using object pose estimation. Contrast to previous targetless methods requiring certain geometric features, the proposed method exploits any objects of unspecified shapes in the scene to estimate the calibration parameters in single-scan configuration. Semantic objects in the scene are initially segmented from each modal measurement. Using multiple images, a 3D point cloud is reconstructed up-to-scale. By registering the up-to-scale point cloud to the LiDAR point cloud, we achieve an initial calibration and find correspondences between point cloud segments and image object segments. For each point cloud segment, a 3D mesh model is reconstructed. Based on the correspondence information, the color appearance model for the mesh can be elaborately generated with corresponding object instance segment within the images. Starting from the initial calibration, the calibration is gradually refined by using an object pose estimation technique with the appearance models associated with the 3D mesh models. The experimental results confirmed that the proposed framework achieves multimodal calibrations successfully in a single shot. The proposed method can be effectively applied for extrinsic calibration for plenoptic imaging systems of dozens of cameras in single-scan configuration without specific targets.
Novel Hybrid Public/Private Key Cryptography Based on Perfect Gaussian Integer Sequences

This paper proposes a novel hybrid public/private key cryptography scheme based on perfect Gaussian integer sequences (PGISs) of period N = pq.First, a review study of construction degree-4 PGIS is addressed.We show that circular convolution over PGISs is a trapdoor one-way permutation function that enables simultaneous cipher encryption and digital signatures.To implement the proposed cipher encryption scheme, a private PGIS is assigned as the encryption key sequence for circular convolution with the plaintext to generate the ciphertext.The reverse decryption key sequence involves the time reflection and complex conjugation of the encryption sequence, which can be regenerated using a pair of public and private keys.The security level of the proposed scheme is the same as that of the Rivest-Shamir-Adleman (RSA) system; however, the capacity of a cryptosystem based on PGISs may outperform that of based on RSA, because the abundant PGISs are available.Simulation results show that the approximation error when finite digits are used to represent the irrational coefficients of a normalized PGIS can be relatively small compared with the noise.This contributes to the simplicity of this scheme's implementation.With the fast development of IoT (internet of things), the adaptation and applicability of the proposed scheme to IoT platforms are also addressed, where lightweight cryptographic functions are preferable due to the limited resources of IoT devices.
An Anonymous Authentication Protocol With Delegation and Revocation for Content Delivery Networks

By redirecting a user’s request to the nearest cache server, content delivery networks (CDNs) enable users to obtain services in time and reduce the burden of the origin server. Nevertheless, the origin server suffers from the private key leakage problem when delegating the authentication capability to untrusted CDN nodes. To handle this challenge, this article proposes an anonymous authentication protocol with delegation and revocation for CDNs depending on the proxy resignature (PRS) cryptography. Specifically, we first present an efficient revocable PRS (R-PRS) scheme, which is proved to be secure under the extended Computational Diffie-Hellman assumption. Then, based on R-PRS scheme, the proposed protocol enables the origin server to delegate and revoke the authentication capability to the CDN nodes without leaking its private key. Furthermore, our protocol not only provides anonymity, mutual authentication, and session key establishment, but also achieves forward security. Eventually, theoretical analysis and implementation demonstrate that the proposed protocol has an excellent performance in efficiency and practicality.
Learning and Planning for Temporally Extended Tasks in Unknown Environments

We propose a novel planning technique for satisfying tasks specified in temporal logic in partially revealed environments. We define high-level actions derived from the environment and the given task itself, and estimate how each action contributes to progress towards completing the task. As the map is revealed, we estimate the cost and probability of success of each action from images and an encoding of that action using a trained neural network. These estimates guide search for the minimum-expected-cost plan within our model. Our learned model is structured to generalize across environments and task specifications without requiring retraining. We demonstrate an improvement in total cost in both simulated and real-world experiments compared to a heuristic-driven baseline.
Policy Transfer via Kinematic Domain Randomization and Adaptation

Transferring reinforcement learning policies trained in physics simulation to the real hardware remains a challenge, known as the "sim-to-real" gap. Domain randomization is a simple yet effective technique to address dynamics discrepancies across source and target domains, but its success generally depends on heuristics and trial-and-error. In this work we investigate the impact of randomized parameter selection on policy transferability across different types of domain discrepancies. Contrary to common practice in which kinematic parameters are carefully measured while dynamic parameters are randomized, we found that virtually randomizing kinematic parameters (e.g., link lengths) during training in simulation generally outperforms dynamic randomization. Based on this finding, we introduce a new domain adaptation algorithm that utilizes simulated kinematic parameters variation. Our algorithm, Multi-Policy Bayesian Optimization, trains an ensemble of universal policies conditioned on virtual kinematic parameters and efficiently adapts to the target environment using a limited number of target domain rollouts. We showcase our findings on a simulated quadruped robot in five different target environments covering different aspects of domain discrepancies.
A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs

In this study, an end-to-end person-to-job post data matching model is constructed, and the experiments for matching people with the actual recruitment data are conducted. First, the representation of the constructed knowledge in the low-dimensional space is described. Then, it is explained in the Bidirectional Encoder Representations from Transformers (BERT) pretraining language model, which is introduced as the encoding model for textual information. The structure of the person-post matching model is explained in terms of the attention mechanism and its computational layers. Finally, the experiments based on the person-post matching model are compared with a variety of person-post matching methods in the actual recruitment dataset, and the experimental results are analyzed.
Federated learning for AI to improve patient care using wearable and IoMT sensors

This paper presents a brief overview of ongoing research on creating a data-driven health monitoring and diagnostics ecosystem based on Artificial Intelligence (AI) and Internet of Medical Things (IoMT) sensors and wearables at the point where data is generated using Federated Learning (FL).
DFENet for Domain Adaptation-Based Remote Sensing Scene Classification

Domain adaptation scene classification refers to the task of scene classification where the training set (called source domain) has different distributions from the test set (called target domain). Although remarkable results have been reported, the misalignment of source and target domain features still remains a big challenge when the large intraclass variances of remote sensing images encounter the insufficient exploration of discriminative feature representations for both domains. To address this challenge, a novel domain feature enhancement network (DFENet) is proposed to adaptively enhance the discriminative ability of the learned features for dealing with the domain variances of scene classification. Specifically, an adaptive context-aware feature refinement (CAFR) module is first designed to automatically recalibrate global and local features by explicitly modeling interdependencies between the channel and spatial for each domain. Then, a multilevel adversarial dropout (MAD) module is further designed to strengthen the generalization capability of our network by adaptively reconfiguring the sparsity of the feature level and decision level in the target domain. The cooperation of CAFR module and MAD module formulates a unique DFENet that can be learned in an end-to-end manner. Comprehensive experiments show that our proposed method is better than state-of-the-art methods on Merced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> RSSCN7, AID <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> RSSCN7, NWPU <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> RSSCN7, RSSCN7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Merced, RSSCN7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> AID, and RSSCN7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> NWPU datasets.
Learning Human Motion Prediction via Stochastic Differential Equations

Human motion understanding and prediction is an integral aspect in our pursuit of machine intelligence and human-machine interaction systems. Current methods typically pursue a kinematics modeling approach, relying heavily upon prior anatomical knowledge and constraints. However, such an approach is hard to generalize to different skeletal model representations, and also tends to be inadequate in accounting for the dynamic range and complexity of motion, thus hindering predictive accuracy. In this work, we propose a novel approach in modeling the motion prediction problem based on stochastic differential equations and path integrals. The motion profile of each skeletal joint is formulated as a basic stochastic variable and modeled with the Langevin equation. We develop a strategy of employing GANs to simulate path integrals that amounts to optimizing over possible future paths. We conduct experiments in two large benchmark datasets, Human 3.6M and CMU MoCap. It is highlighted that our approach achieves a 12.48% accuracy improvement over current state-of-the-art methods in average.