Reviewerly/exHarmony huggingface dataset:
Multi-Modal 3D Object Detection in Autonomous Driving: A Survey

The past decade has witnessed the rapid development of autonomous driving systems. However, it remains a daunting task to achieve full autonomy, especially when it comes to understanding the ever-changing, complex driving scenes. To alleviate the difficulty of perception, self-driving vehicles are usually equipped with a suite of sensors (e.g., cameras, LiDARs), hoping to capture the scenes with overlapping perspectives to minimize blind spots. Fusing these data streams and exploiting their complementary properties is thus rapidly becoming the current trend. Nonetheless, combining data that are captured by different sensors with drastically different ranging/ima-ging mechanisms is not a trivial task; instead, many factors need to be considered and optimized. If not careful, data from one sensor may act as noises to data from another sensor, with even poorer results by fusing them. Thus far, there has been no in-depth guidelines to designing the multi-modal fusion based 3D perception algorithms. To fill in the void and motivate further investigation, this survey conducts a thorough study of tens of recent deep learning based multi-modal 3D detection networks (with a special emphasis on LiDAR-camera fusion), focusing on their fusion stage (i.e., when to fuse), fusion inputs (i.e., what to fuse), and fusion granularity (i.e., how to fuse). These important design choices play a critical role in determining the performance of the fusion algorithm. In this survey, we first introduce the background of popular sensors used for self-driving, their data properties, and the corresponding object detection algorithms. Next, we discuss existing datasets that can be used for evaluating multi-modal 3D object detection algorithms. Then we present a review of multi-modal fusion based 3D detection networks, taking a close look at their fusion stage, fusion input and fusion granularity, and how these design choices evolve with time and technology. After the review, we discuss open challenges as well as possible solutions. We hope that this survey can help researchers to get familiar with the field and embark on investigations in the area of multi-modal 3D object detection.
Resource-efficient quantum key distribution with integrated silicon photonics

Integrated photonics provides a promising platform for quantum key distribution (QKD) system in terms of miniaturization, robustness, and scalability. Tremendous QKD works based on integrated photonics have been reported. Nonetheless, most current chip-based QKD implementations require additional off-chip hardware to demodulate quantum states or perform auxiliary tasks such as time synchronization and polarization basis tracking. Here, we report a demonstration of resource-efficient chip-based BB84 QKD with a silicon-based encoder and a decoder. In our scheme, the time synchronization and polarization compensation are implemented relying on the preparation and measurement of the quantum states generated by on-chip devices; thus, we need no additional hardware. The experimental tests show that our scheme is highly stable with a low intrinsic quantum bit error rate of 0.50 % ±0.02 % in a 6 h continuous run. Furthermore, over a commercial fiber channel up to 150 km, the system enables the realization of secure key distribution at a rate of 866 bit/s. Our demonstration paves the way for a low-cost, wafer-scale manufactured QKD system.
GIS-based multicriteria decision analysis for spatial planning of solar photovoltaic power plants in Çanakkale province, Turkey

A comprehensive site selection analysis is essential for implementing cost-effective and efficient SPPs. We selected optimal sites for SPPs by considering different criteria and restrictions using the GIS-based MCDA. Highly suitable and most suitable sites were determined by GIS and AHP for establishing PV SPP in the Çanakkale province. Furthermore, the potential of these sites to meet the energy demand was examined. Next, the potential power capacity and payback period of each of the five alternative SPP project sites selected from the most suitable sites were analysed. The results demonstrated that 96% of the study area was restricted, 3‰ was most suitable and 3% was highly suitable for PV SPPs. The most suitable sites exhibited the potential to meet all electrical energy needs of the province of Çanakkale during installation. The potential power capacities of the five selected alternatives were between approximately 0.9 and 1.6 MWp, whereas potential payback periods were between 12.1 and 14.7 years.
Perturbation theory with quantum signal processing

Perturbation theory is an important technique for reducing computational cost and providing physical insights in simulating quantum systems with classical computers. Here, we provide a quantum algorithm to obtain perturbative energies on quantum computers. The benefit of using quantum computers is that we can start the perturbation from a Hamiltonian that is classically hard to solve. The proposed algorithm uses quantum signal processing (QSP) to achieve this goal. Along with the perturbation theory, we construct a technique for ground state preparation with detailed computational cost analysis, which can be of independent interest. We also estimate a rough computational cost of the algorithm for simple chemical systems such as water clusters and polyacene molecules. To the best of our knowledge, this is the first of such estimates for practical applications of QSP. Unfortunately, we find that the proposed algorithm, at least in its current form, does not exhibit practical numbers despite of the efficiency of QSP compared to conventional quantum algorithms. However, perturbation theory itself is an attractive direction to explore because of its physical interpretability; it provides us insights about what interaction gives an important contribution to the properties of systems. This is in sharp contrast to the conventional approaches based on the quantum phase estimation algorithm, where we can only obtain values of energy. From this aspect, this work is a first step towards ``explainable'' quantum simulation on fault-tolerant quantum computers.
Programmable Tanh-, ELU-, Sigmoid-, and Sin-Based Nonlinear Activation Functions for Neuromorphic Photonics

We demonstrate a programmable analog opto-electronic (OE) circuit that can be configured to provide a range of nonlinear activation functions for incoherent neuromorphic photonic circuits at up to 10 Gbaud line-rates. We present a set of well-known activation functions that are typically used to train DL models including tanh-, sigmoid-, ReLU- and inverted ReLU-like activations, introducing also a series of novel photonic nonlinear functions that are referred to as Rectified Sine Squared (ReSin), Sine Squared with Exponential tail (ExpSin) and Double Sine Squared. Experimental validation for all these activation functions is performed at 10 Gbaud operation. The ability of the mathematically modelled photonic activation functions to train Deep Neural Networks (DNNs) has been verified through their employment in Deep Learning (DL) models for MNIST and CIFAR10 classification purposes, comparing their performance against corresponding NNs that utilize an ideal ReLU activation function.
The Role of Internet of Things and Blockchain in the Development of Agile and Sustainable Supply Chains

Achieving customer satisfaction requires seamless collaboration and coordination across the value chain of suppliers, manufacturers, regulators, logistics service providers, and retailers. At the same time, disruption in the supply chain causes increased costs, and, more importantly, lost revenue. Therefore, organizations are looking for transparency, flexibility, and agility in their supply chains. The internet of things and blockchain, as a part of efficient technologies in supply chains of organizations, build trust, transparency, and consensus among all stakeholders, creating benefits for every actor and ensuring flexible business outcomes .Due to the increasing importance of these technologies in today's world and the integration of these tools in all business processes, in this research, the dimensions, components, and vital indicators affecting the creation of a smart and efficient supply chain with high reliability and has been assessed as having strong resilience. Finally, the structure of a smart supply chain is shown by the interaction of these transformative technologies.
Multi-modal policy fusion for end-to-end autonomous driving

Multi-modal learning has made impressive progress in autonomous driving by leveraging information from multiple sensors. Existing feature fusion methods make decisions by integrating perceptions from different sensors. However, autonomous driving systems could be risky since the fused feature are unreliable when one of the sensors fails. Moreover, these methods require either sophisticated geometric designs to align features or complex neural networks to effectively fuse features, significantly increasing the training cost. In this paper, we propose PolicyFuser, a policy fusion method for end-to-end autonomous driving to address these issues. PolicyFuser retains an independent decision for each sensor, and no feature alignment or complex neural networks are required. To focus on the best policy, we use reinforcement learning to select the action with the highest Q-value as the primary decision, and the remaining actions as the secondary decisions. Then the secondary decisions are used to fine-tune the primary decision through a primary and secondary policy fusion (PSF) module. To bridge the gap between the decisions from different sensors and improve the stability of policy fusion, we use a conditional variational autoencoder (CVAE) to generate pseudo-expert decisions. We demonstrate the effectiveness of our method in CARLA, and our method achieves the highest driving scores and handles sensor failures with excellence.
More than meets the eye: In-store retail experiences with augmented reality smart glasses

Augmented reality smart glasses (ARSGs) promise to enhance consumer experiences and decision-making when deployed as in-store retail technologies. However, research to date has not studied in-store use cases; instead, it has focused primarily on consumers' potential adoption of these devices for everyday use. Nor have prior studies compared ARSG uses with the now-common use of AR on touchscreen devices. The current research addresses these knowledge gaps by examining whether ARSGs outperform AR on touchscreen devices in the context of in-store retail experiences. Testing with an actual retail application (n = 308) shows that ARSGs are superior to AR on touchscreen devices for evoking consumers’ perceptions of immersion and mental intangibility. Furthermore, this superiority leads consumers to evaluate their shopping experiences more positively, in terms of their decision comfort, satisfaction, and ease of evaluation, with significantly positive effects on their purchase intentions. These results highlight the relevance of implementing ARSGs in-store and provide retailers with recommendations for effective ARSG strategies.
Constructive replication, reproducibility, and generalizability: Getting theory testing for <i>JOMSR</i> right

In this editorial, we provide guidance regarding the design and execution of replication, reproducibility, and generalizability studies and explain the unique purpose and empirical contribution of each research design to the overall knowledge generation and consolidation cycle. We evaluate these research designs within the confines of JOMSR's mission to avoid novel theorizing, but instead to engage only in testing of existing theory or of combinations of existing theory. We identify two important dimensions on which these research designs can vary, independence and constructiveness, and highlight the particular utility of independent studies that are high in constructiveness. We offer suggestions for the identification of suitable targets for reproducibility and replication as well as concrete examples demonstrating high-quality theory testing.
CrowdFormer: Weakly-supervised crowd counting with improved generalizability

Convolutional neural networks (CNNs) have dominated the field of computer vision for nearly a decade. However, due to their limited receptive field, CNNs fail to model the global context. On the other hand, transformers, an attention-based architecture, can model the global context easily. Despite this, there are limited studies that investigate the effectiveness of transformers in crowd counting. In addition, the majority of the existing crowd-counting methods are based on the regression of density maps which requires point-level annotation of each person present in the scene. This annotation task is laborious and also error-prone. This has led to an increased focus on weakly-supervised crowd-counting methods, which require only count-level annotations. In this paper, we propose a weakly-supervised method for crowd counting using a pyramid vision transformer. We have conducted extensive evaluations to validate the effectiveness of the proposed method. Our method achieves state-of-the-art performance. More importantly, it shows remarkable generalizability.
Hotel AI service: Are employees still needed?

This research adopts the artificial intelligence (AI) device usage acceptance (AIDUA) model and draws on cognitive appraisal theory. It articulates a mediated relationship leading from social influence, hedonic motivation, and anthropomorphism to acceptance of or objection to AI device usage through the mediation of performance expectancy, effort expectancy, and customer emotion in the context of smart hotels. The study further draws on two-factor theory of emotion to identify the moderation of employee presence. Based on a survey design, our research adds to the literature by underscoring why human staff is still critical even in the smart service encounter, especially when customers are looking for superior performance with low effort when using AI devices. However, the presence of employees may be a hindrance in the low performance and high effort expectancies conditions. This research pinpoints a more precise mechanism of the role of employees and technology in delivering intended emotional valence.
Atmospheric turbulence removal with complex-valued convolutional neural network

Atmospheric turbulence distorts visual imagery and is always problematic for information interpretation by both human and machine. Most well-developed approaches to remove atmospheric turbulence distortion are model-based. However, these methods require high computation and large memory making real-time operation infeasible. Deep learning-based approaches have hence gained more attention but currently work efficiently only on static scenes. This paper presents a novel learning-based framework offering short temporal spanning to support dynamic scenes. We exploit complex-valued convolutions as phase information, altered by atmospheric turbulence, is captured better than using ordinary real-valued convolutions. Two concatenated modules are proposed. The first module aims to remove geometric distortions and, if enough memory, the second module is applied to refine micro details of the videos. Experimental results show that our proposed framework efficiently mitigates the atmospheric turbulence distortion and significantly outperforms existing methods.
Chatbots and Voice Assistants: Digital Transformers of the Company–Customer Interface—A Systematic Review of the Business Research Literature

Chatbots and voice assistants are digital transformers of the interface between companies and customers. They have become part of the current practice of companies and represent a distinct domain of business research. This trend is significant in the broad business context marked by the digital transformation of companies, the fast development of e-commerce and the omnichannel behavior of customers. This article is a systematic review of the high-quality business research literature on chatbots and voice assistants. The purpose of this review is to critically analyze the current status of this literature from the perspective of the theories, contexts, characteristics and methodologies applied. The final aim of this review is to support the domain of study by suggesting a relevant agenda for future research. This review brings several contributions to the research domain, including the following: the identification of the main streams of high-quality business research in function of the theories in which the studies are grounded; the development of a conceptual framework of the investigated variables (antecedents, mediators, moderators and consequences); the creation of a conceptual framework of the humanlikeness of chatbots and voice assistants; the development of a conceptual framework of the consumer experience with chatbots and voice assistants and the presentation of insights for business practice.
Crowd counting in smart city via lightweight Ghost Attention Pyramid Network

Crowd counting targets for determining the number of pedestrians in an image, which is of crucial importance for smart city construction. The problem of scale variation is an ingrained and drastic challenge in crowd counting, and it severely degrades the performance of counting. To address this problem, many powerful models with complex network structures and tricks are built, but the constrained resources of embedded systems prevent the direct deployment of these models into an edge device. Thus, it is on high demand to design favourable lightweight models that require fewer parameters and a fast inference speed, while maintaining competitive counting performance. To this aim, we devise a lightweight network, termed as Ghost Attention Pyramid Network (GAPNet). Specifically, a lightweight GhostNet is adopted as the backbone to encode low-level features. Subsequently, a zero-parameter channel attention module is designed to select the discriminative crowd region efficiently. In addition, an efficient pyramid fusion module is built with a four-branch architecture to obtain multiscale hierarchy representations while reducing the parameters. Finally, a decoder generates the prediction by exploiting a series of transposed convolution blocks. Extensive experiments on crowd counting benchmarks have proved the superiority of the GAPNet in both accuracy and efficiency.
Blockchain-Based Personalized Federated Learning for Internet of Medical Things

The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing services have enabled the Internet of Medical Things (IoMT) to provide various healthcare services to patients, including neural network-based disease diagnosis, heart rate monitoring, and fall detection. Generally, end devices should transmit the collected patient data to a centralized server for further model training, but at the same time, the patient's privacy may be at risk. In addition, due to the diversity of patient conditions, a one-size-fits-all model cannot meet personalized healthcare needs. To address the above challenges, we propose a blockchain-based personalized federated learning (FL) system that enables clients to participate in personalized model training without directly uploading private data. We further realize the decentralized FL by combining blockchain technology, which improves the security level of the system. Finally, we verify the reliable performance of our system on different datasets through simulation experiments.
Batch generating keyed strong S-Boxes with high nonlinearity using 2D hyper chaotic map

Cryptanalysis result of the S-Box construction in AES revealed that, (1) the number of irreducible polynomials can be extend to 30, (2) if we ignore the existence of fixed point(s) and reverse fixed point(s) in an S-Box, the affine transformation constant c can select from [01, FF], and (3) the S-Box in AES is fixed, which causes potential security risks to the AES algorithm. Based on the above analysis, first we constructed a non-degenerate 2D enhanced quadratic map (2D-EQM) with ergodicity and randomness, and then used it to generate affine transformation matrices and affine transformation constants which were applied to seed S-Boxes to generate a batch of keyed strong S-Boxes with high nonlinearity. Furthermore, we counted that of all the generated 4.0913 × 1022 S-Boxes, about 5.4414 × 1020 of them can still keep the nonlinearity of 112. Finally, we used six criteria to evaluate the performance of constructed S-Boxes. Security and statistical analysis demonstrated the effectiveness and practicability of the proposed S-Box batch generating algorithm.
A novel chaos-based permutation for image encryption

Image encryption is an essential method for ensuring confidentiality during transmission on open channels. Digital images have a high correlation with large redundant data, requiring alternative spatial handling during encryption. Digital chaos offers shared properties of randomness and sensitivity that can provide secure image encryption. The security and performance of chaotic image ciphers depend on the underlying digital chaotic map. Therefore, classical chaotic maps must improve security while maintaining implementation time. The main objective of this paper is to propose a new image encryption algorithm based on an enhanced chaotic map that offers high security and low time consumption. A new perturbed logistic chaotic map based on hybridizing backward and forward perturbation methods is introduced, which exhibits better chaotic features than other existing chaotic systems such as a large chaotic range, higher sensitivity, and randomness. Based on this, a new image encryption algorithm is proposed, employing a novel permutation operation and two substitution operations to achieve superior encryption speed and efficiency. The novel permutation operation based on a chaotic data sequence utilizes all chaotic states to generate 8-bit numbers. Each index of the resulting 8-bit number is compiled into one block. The plain image is then scanned in accordance with the randomized indices of each block. The two substitution operations involve an XORing operation for each pixel and a hashing process for each block. The newly proposed encryption algorithm can effectively encrypt images of varying sizes and types, transforming them into indecipherable cipher images that successfully pass through rigorous security tests, including differential attack analysis with a score of 28/28 and local Shannon entropy with a score of 25/28. The experimental findings showcase the superior performance of the proposed cipher over existing chaotic image ciphers, creating a cipher image that resembles noise and has the ability to resist a diverse range of cyber-attacks.