Reviewerly/exHarmony huggingface dataset:
ASL-DWA: An Improved A-Star Algorithm for Indoor Cleaning Robots

The traditional A-star algorithm has many search nodes, and the obtained path has polylines and cannot avoid local unknown obstacles. In response to these problems, this paper proposes a new improved A-star algorithm suitable for indoor cleaning robots, called ASL-DWA (A Star Leading Dynamic Window Approach). First, to solve the problem of many search nodes in the A-star algorithm, a new hybrid heuristic function that combines Euclidean distance and point-to-line distance is proposed, thereby reducing the number of search nodes. Then, to solve the problem that the A-star algorithm has a polyline path and cannot avoid local unknown obstacles, this paper designs the global path yaw angle according to the relationship between the real-time position of the robot and the global path, which is added as a score item to the traditional score function. A decay coefficient with prediction function is also added to the score function to reduce the risk of the algorithm falling into local optima. Finally, a mechanism to adaptively adjust the coefficient according to the distance between the robot and the target point is designed, thereby realizing ASL-DWA. The ASL-DWA algorithm is tested in three indoor environments and compared with traditional algorithms. The experimental results show that ASL-DWA can meet the path planning requirements of mobile robots in indoor environments, and has obvious advantages over traditional algorithms.
An Efficient DAG Blockchain Architecture for IoT

Directed acyclic graph (DAG) blockchain is a new paradigm of blockchain that solves the poor scalability and low throughput caused by a single-chain structure for IoT applications in the blockchain. However, for DAG-structured blockchains, it is difficult to determine the order of blocks. Additionally, there is also lack of secure and efficient ways to generate blocks in parallel. To address these problems, we propose an efficient DAG blockchain architecture. First, a novel heaviest chain rule based on the block weight is proposed to guide the selection of the parent and uncle blocks for a new block, which realize that appending is ordering. Second, a tree-based gossip protocol (TBGP) is proposed to improve consensus efficiency by reducing message redundancy. Furthermore, federated learning is used to select nodes constructing the tree-based gossip network (TBGN). Finally, compared to the random gossip protocol (RGP), the simulation results indicate TBGP can effectively reduce communication redundancy and improve consensus efficiency.
Safe consensus control of cooperative-competitive multi-agent systems via differential privacy

This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach.Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents.We then prove that the proposed algorithm can achieve the mean square bipartite consensus and (p, r)accuracy.Furthermore, a differential privacy analysis is conducted, which shows that the performance of privacy protection is positively correlated with the number of neighbors.Thus, a practical method is established for the agents to select their own privacy levels.Finally, the simulation results are presented to demonstrate the validity of the proposed safe consensus algorithm.
A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis

Smart home IoT devices lack proper security, raising safety and privacy concerns.One-sizefits-all network administration is ineffective because of the diverse QoS requirements of IoT devices.Device classification can improve IoT administration and security.It identifies vulnerable and rogue items and automates network administration by device type or function.Considering this, a promising research topic focusing on Machine Learning (ML)-based traffic analysis has emerged in order to demystify hidden patterns in IoT traffic and enable automatic device classification.This study analyzes these approaches to understand their potential and limitations.It starts by describing a generic workflow for IoT device classification.It then looks at the methods and solutions for each stage of the workflow.This mainly consists of i) an analysis of IoT traffic data acquisition methodologies and scenarios, as well as a classification of public datasets, ii) a literature evaluation of IoT traffic feature extraction, categorizing and comparing popular features, as well as describing open-source feature extraction tools, and iii) a comparison of ML approaches for IoT device classification and how they have been evaluated.The findings of the analysis are presented in taxonomies with statistics showing literature trends.This study also explores and suggests undiscovered or understudied research directions.
Resize Me! Exploring the user experience of embodied realistic modulatable avatars for body image intervention in virtual reality

Obesity is a serious disease that can affect both physical and psychological well-being. Due to weight stigmatization, many affected individuals suffer from body image disturbances whereby they perceive their body in a distorted way, evaluate it negatively, or neglect it. Beyond established interventions such as mirror exposure, recent advancements aim to complement body image treatments by the embodiment of visually altered virtual bodies in virtual reality (VR). We present a high-fidelity prototype of an advanced VR system that allows users to embody a rapidly generated personalized, photorealistic avatar and to realistically modulate its body weight in real-time within a carefully designed virtual environment. In a formative multi-method approach, a total of 12 participants rated the general user experience (UX) of our system during body scan and VR experience using semi-structured qualitative interviews and multiple quantitative UX measures. Using body weight modification tasks, we further compared three different interaction methods for real-time body weight modification and measured our system’s impact on the body image relevant measures body awareness and body weight perception. From the feedback received, demonstrating an already solid UX of our overall system and providing constructive input for further improvement, we derived a set of design guidelines to guide future development and evaluation processes of systems supporting body image interventions.
Semi-Supervised Unpaired Medical Image Segmentation Through Task-Affinity Consistency

Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-supervised learning (SSL) algorithms treat the labelled images and unlabelled images separately and ignore the explicit connection between them; this disregards essential shared information and thus hinders further performance improvements. To mine the shared information between the labelled and unlabelled images, we introduce a class-specific representation extraction approach, in which a task-affinity module is specifically designed for representation extraction. We further cast the representation into two different views of feature maps; one is focusing on low-level context, while the other concentrates on structural information. The two views of feature maps are incorporated into the task-affinity module, which then extracts the class-specific representations to aid the knowledge transfer from the labelled images to the unlabelled images. In particular, a task-affinity consistency loss between the labelled images and unlabelled images based on the multi-scale class-specific representations is formulated, leading to a significant performance improvement. Experimental results on three datasets show that our method consistently outperforms existing state-of-the-art methods. Our findings highlight the potential of consistency between class-specific knowledge for semi-supervised medical image segmentation. The code and models are to be made publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jingkunchen/TAC</uri> .
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to evaluate XAI hinders the advancement of the field. We highlight that XAI is not a monolithic set of technologies---researchers and practitioners have begun to leverage XAI algorithms to build XAI systems that serve different usage contexts, such as model debugging and decision-support. Algorithmic research of XAI, however, often does not account for these diverse downstream usage contexts, resulting in limited effectiveness or even unintended consequences for actual users, as well as difficulties for practitioners to make technical choices. We argue that one way to close the gap is to develop evaluation methods that account for different user requirements in these usage contexts. Towards this goal, we introduce a perspective of contextualized XAI evaluation by considering the relative importance of XAI evaluation criteria for prototypical usage contexts of XAI. To explore the context dependency of XAI evaluation criteria, we conduct two survey studies, one with XAI topical experts and another with crowd workers. Our results urge for responsible AI research with usage-informed evaluation practices, and provide a nuanced understanding of user requirements for XAI in different usage contexts.
Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study

Real-world optimization problems require some advanced metaheuristic algorithms, which functionally sustain a variety of solutions and technically explore the tracking space to find the global optimal solution or optimizer. One such algorithm is the newly developed COOT algorithm that is used to solve complex optimization problems. However, like other swarm intelligence algorithms, the COOT algorithm also faces the issues of low diversity, slow iteration speed, and stagnation in local optimization. In order to ameliorate these deficiencies, an improved population-initialized COOT algorithm named COBHCOOT is developed by integrating chaos map, opposition-based learning strategy and hunting strategy, which are used to accelerate the global convergence speed and boost the exploration efficiency and solution quality of the algorithm. To validate the dominance of the proposed COBHCOOT, it is compared with the original COOT algorithm and the well-known natural heuristic optimization algorithm on the recognized CEC2017 and CEC2019 benchmark suites, respectively. For the 29 CEC2017 problems, COBHCOOT performed the best in 15 (51.72%, 30-Dim), 14 (48.28%, 50-Dim) and 11 (37.93%, 100-Dim) respectively, and for the 10 CEC2019 benchmark functions, COBHCOOT performed the best in 7 of them. Furthermore, the practicability and potential of COBHCOOT are also highlighted by solving two engineering optimization problems and four truss structure optimization problems. Eventually, to examine the validity and performance of COBHCOOT for medical feature selection, eight medical datasets are used as benchmarks to compare with other superior methods in terms of average accuracy and number of features. Particularly, COBHCOOT is applied to the feature selection of cervical cancer behavior risk dataset. The findings testified that COBHCOOT achieves better accuracy with a minimal number of features compared with the comparison methods.
Lineament Extraction from Digital Terrain Derivate Model: A Case Study in the Girón–Santa Isabel Basin, South Ecuador

Geological lineaments are linear or curvilinear surfaces that are considered a superficial expression of discontinuities on the earth’s surface. The extraction of lineaments from remotely sensed satellite data is one of the most frequently used applications of remote sensing in geology. This study focuses on the semi-automatic extraction of lineaments in the Girón–Santa Isabel basin using a Topographic Position Index (TPI). The lineaments were extracted in the PCI Geomatics 2016 software and analyzed in the ArcGIS and Rockworks software. Statistical and density map analyses of the lineaments were performed; then, these results were interpreted to obtain the geological lineaments. Finally, a bibliographic verification was carried out, and structures such as faults and folds were defined. The total number of geological lineaments was 76, and 71 of them were defined as faults, the longest with a length of 33 km. It was determined that the preferential orientation of the lineaments is NE–SW, which is consistent with the axis of the Girón–Santa Isabel basin. This methodology can be useful to optimize time and reduce costs when gathering the structural information of the study area in the first stage of geological and mining prospecting or the educational field.
Retail Central Bank Digital Currencies (CBDC), Disintermediation and Financial Privacy: The Case of the Bahamian Sand Dollar

The fast-growing, market-driven demand for cryptocurrencies worries central banks, as their monetary policy could be completely undermined. Central bank digital currencies (CBDCs) could offer a solution, yet our understanding of their design and consequences is in its infancy. This non-technical paper examines how The Bahamas has designed the Sand Dollar, the first real-world instance of a retail CBDC. It contrasts the Sand Dollar with definition-based specifications. The author then develops a scenario analysis to illustrate commercial bank risks. In this process, the central bank becomes a deposit monopolist, leading to high funding risks, disintermediation risks, and solvency risks for the commercial banking sector. This paper argues that restrictions and caps will be the new specifications of a regulatory framework for CBDCs if disintermediation in the banking sector is to be prevented. The anonymity of CBDCs is identified as a comparative disadvantage that will affect their adoption. These findings provide insight into governance problems facing central banks and coherently lead to the design of the Sand Dollar. This paper concludes by suggesting that combating cryptocurrencies is a task that cannot be solved by a CBDC.
An Introduction to Convolutional Neural Networks

Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous forms of artificial intelligence in common machine-learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve difficult image-driven pattern recognition tasks and with their precise yet simple architecture, offer a simplified method of getting started with ANNs. This document briefly introduces CNNs, discussing recently published papers and newly formed techniques in developing this bill- leniently fantastic image recognition models. This introduction assumes you are familiar with ANNs and machine learning fundamentals.
High‐Fidelity and Low‐Cost Hyperparallel Quantum Gates for Photon Systems via Λ‐Type Systems

Abstract Quantum gates designed with minimized resources overhead have a crucial role in quantum information processing. Here, based on the degrees of freedom (DoFs) of photons and Λ‐type atom systems, two high‐fidelity and low‐cost protocols are presented for realizing polarization‐spatial hyperparallel controlled‐not (CNOT) and Toffoli gates on photon systems with only two and four two‐qubit polarization–polarization swap (P‐P‐SWAP) gates in each DoF, respectively. Moreover, the quantum gates can be extended feasibly to construct 2 m ‐target‐qubit hyperparallel CNOT and 2 n ‐control‐qubit Toffoli gates required only 4 m and 4 n P‐P‐SWAP gates on ‐ and ‐photon systems, respectively, which dramatically lower the costs and bridge the divide between the theoretical lower bounds and the current optimal syntheses for the photonic quantum computing. Further, the unique auxiliary atom of these quantum gates can be regarded as a temporary quantum memory that requires no initialization and measurement, and is reused within the coherence time, as the state of the atom remains unchanged after the hyperparallel quantum computing.
The Metaverse and Beyond: Implementing Advanced Multiverse Realms With Smart Wearables

With the online-everything transformation accelerated by a global Covid-19 pandemic, we may finally find ourselves on the verge of the next potentially paradigm-shifting step after the mobile Internet: The Metaverse. Among others, the Metaverse will utilize head-mounted devices (HMDs) and extended reality (XR), including but not limited to virtual and augmented reality (VR/AR), as the medium to connect avatars and users in the real world. In addition, the Metaverse is supposed to provide gamified experiences around emerging Web 3.0 technologies and is anticipated to be the precursor of the so-called Multiverse, which will serve as an architecture of advanced XR experience realms. In this paper, we focus on the anticipated 6G post-smartphone era, where smart wearables such as VR/AR HMDs are increasingly replacing the functionalities of smartphones. Our contributions are threefold: ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) we first extend Metaverse’s primary focus on VR/AR to Multiverse’s advanced XR realms of experience. Next, we gamify and implement all eight Multiverse realms of experience using Oculus Quest 2 and Microsoft HoloLens 2 as state-of-the-art VR/AR HMDs, experimentally investigating and comparing the performance of a ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) single-player origami game and ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) multi-player maze game across our proposed integrated VR/AR HMD and Amazon Mechanical Turk crowd-of-Oz (CoZ) platform.
Blockchain Technology - The Use Of Cryptocurrencies In Digital Revolution

?Cryptocurrency is a blockchain-based technology that is often used as a digital currency. Digital money is different from conventional money, this type of money does not have a physical form but only a block of data bound by a hash as validation. The data is spread to every cryptocurrency user who is in the environment. So that when a user makes a transaction, data mining will be carried out. Cryptocurrencies have several advantages but also have disadvantages when used as currency, from a legal point of view there is still no law governing the circulation of currency in digital form (cryptocurrency). The government must recognize digital currency so that it can be accepted by the general public as a means of payment. For now, the government has not recognized digital money (cryptocurrency) as a currency, because Cryptocurrency is a new phenomenon by some people in Indonesia. The rapid development of technology in the 4.0 revolution era, in the next few years money in physical form will be replaced by digital money seeing the many conveniences provided by using digital money.
Theoretical error performance analysis for variational quantum circuit based functional regression

Abstract The noisy intermediate-scale quantum devices enable the implementation of the variational quantum circuit (VQC) for quantum neural networks (QNN). Although the VQC-based QNN has succeeded in many machine learning tasks, the representation and generalization powers of VQC still require further investigation, particularly when the dimensionality of classical inputs is concerned. In this work, we first put forth an end-to-end QNN, TTN-VQC, which consists of a quantum tensor network based on a tensor-train network (TTN) for dimensionality reduction and a VQC for functional regression. Then, we aim at the error performance analysis for the TTN-VQC in terms of representation and generalization powers. We also characterize the optimization properties of TTN-VQC by leveraging the Polyak-Lojasiewicz condition. Moreover, we conduct the experiments of functional regression on a handwritten digit classification dataset to justify our theoretical analysis.
Towards deep learning-aided wireless channel estimation and channel state information feedback for 6G

Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.
Analog nanophotonic computing going practical: silicon photonic deep learning engines for tiled optical matrix multiplication with dynamic precision

Abstract Analog photonic computing comprises a promising candidate for accelerating the linear operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low footprint and low power consumption computing capabilities. However, the confined photonic hardware size, along with the limited bit precision of high-speed electro-optical components, impose stringent requirements towards surpassing the performance levels of current digital processors. Herein, we propose and experimentally demonstrate a speed-optimized dynamic precision neural network (NN) inference via tiled matrix multiplication (TMM) on a low-radix silicon photonic processor. We introduce a theoretical model that relates the noise figure of a photonic neuron with the bit precision requirements per neural layer. The inference evaluation of an NN trained for the classification of the IRIS dataset is, then, experimentally performed over a silicon coherent photonic neuron that can support optical TMM up to 50 GHz, allowing, simultaneously, for dynamic-precision calculations. Targeting on a high-accuracy and speed-optimized classification performance, we experimentally applied the model-extracted mixed-precision NN inference scheme via the respective alteration of the operational compute rates per neural layer. This dynamic-precision NN inference revealed a 55% decrease in the execution time of the linear operations compared to a fixed-precision scheme, without degrading its accuracy.