Reviewerly/exHarmony huggingface dataset:
Fundamental Limits of Thermal-noise Lossy Bosonic Multiple Access Channel

Bosonic channels describe quantum-mechanically many practical communication links such as optical, microwave, and radiofrequency. We investigate the maximum rates for the bosonic multiple access channel (MAC) in the presence of thermal noise added by the environment and when the transmitters utilize Gaussian state inputs. We develop an outer bound for the capacity region for the thermal-noise lossy bosonic MAC. We additionally find that the use of coherent states at the transmitters is capacity-achieving in the limits of high and low mean input photon numbers. Furthermore, we verify that coherent states are capacity-achieving for the sum rate of the channel. In the non-asymptotic regime, when a global mean photon-number constraint is imposed on the transmitters, coherent states are the optimal Gaussian state. Surprisingly however, the use of single-mode squeezed states can increase the capacity over that afforded by coherent state encoding when each transmitter is photon number constrained individually.
Adaptive Low-Light Image Enhancement with Decomposition Denoising

Noise in low-light image enhancement seriously affects human observation and computer vision algorithms, while existing methods inevitably introduce under- or over-contrast problems. We proposed an adaptive low-light image enhancement with decomposition denoising to perform a good visual effect. Firstly, the low-rank denoising algorithm is designed to decrease the noise of an input low-light image. And then, adaptive luminance enhancement is developed by generating a nonlinear function to improve the luminance of the denoise image. Finally, a color restoration method based on the relationship between spectral bands and input image into a color image. Subjective and objective experiments show that compared to several state-of-the-art methods, the proposed method can reasonably enhance image luminance and contrast while removing heavy noise.
Unsupervised Concept Drift Detection Using Dynamic Crucial Feature Distribution Test in Data Streams

Distribution of data often changes over time and leads to the unpredictable changes in the implicit information behind data streams. This phenomenon is referred to as Concept Drift. The accuracy of conventional models reduces as time goes by, and old models are rendered impractical. In this paper, we propose a novel approach for solving the concept drift detection problem using the unsupervised method and focusing on the dynamic crucial feature distribution test. Extensive experiments have been done to evaluate the performance of the proposed method against classic and state-of-the-art methods. Experimental results demonstrate the efficacy of the proposed model when applied to synthetic as well as real-world datasets.
Federated Learning Operations (FLOps): Challenges, Lifecycle and Approaches

Federated Learning has witnessed a rapid growth in research and industry applications as it offers the benefits of privacy preserving while contributing to the global model training. Cross-silo federated learning systems which are usually geographically distributed and cross-organizational are becoming a reality. Although DevOps and MLOps methodologies may help improving traditional machine learning systems' development efficiency and productivity, it is still challenging for them to develop cross-silo federated learning systems in a productive way. In this paper, we propose FLOps (Federated Learning Operations), a new methodology for developing cross-silo federated learning systems efficiently and continuously. By elaborating the challenges that FLOps is facing, we construct the lifecycle of FLOps, and propose approaches to FLOps. Finally, we highlight potential research directions of FLOps.
Image Based Visual Servo Control with a Two-Axis Inertially Stabilized Platform Using Deep Deterministic Policy Gradient

This paper introduces a visual tracking control scheme for the two axes Inertially Stabilized Platform. An adaptive PID controller based on the Deep Deterministic Policy Gradient (DDPG) algorithm is used where it is treated as a Reinforcement Learning Policy. The optimal PID parameters are obtained at each time step via a training process. To effectively train the parameters, we address the problem of handling time delay and uncertainty of the system by presenting an observer system. Simulation results show that our method achieves good performance for various cases of time delay.
RRT Based Frontier Point Detection for 2D Autonomous Exploration

The ability to explore an unknown environment and create a representation is a must-have for a fully autonomous robot. Autonomous exploration is a multifaceted problem and requires solutions to a combination of sub-problems such as Simultaneous Localization and Mapping (SLAM), path planning and following, detecting potential navigation target points and evaluating them to select a target. This study implements a frontier point based approach to identify potential navigation targets. Permanent and temporary RRT-based tree structures are used to search the existing map and detect frontier points. Permanent tree is rooted from the initial position of the robot and is never reset. Temporary trees are reset when they hit a frontier point or reach a certain number of branches. Two types of temporary trees are used, starting from the robot's current location and starting from the frontier points that lose their frontier point characteristics as the region they lie is mapped. A revenue function considering path cost, heading cost, and information gain is used to evaluate the frontier points and select a target among them.
Improving Clustering Uncertainty-weighted Embeddings for Active Domain Adaptation

Domain adaptation generalizes deep neural networks to new target domains under domain shift. Active domain adaptation (ADA) does so efficiently by allowing the learning model to strategically ask data annotation questions. The state-of-the-art active domain adaptation via clustering uncertainty-weighted embeddings (ADA-CLUE) uses uncertainty-weighted clustering to identify target instances for labeling. In this work, we carefully study how ADA-CLUE balances uncertainty and diversity during active learning. We compare the original ADA-CLUE with a variant that weights clusters by a constant instead of by the uncertainty, and confirm that constant-weighted clustering sampling outperforms ADA-CLUE at early stages due to its stability. We then merge constant-weighted sampling and uncertainty-weighted sampling with a threshold to get the best of the two worlds. The merged solution, called CLUE with a loop threshold, is shown to be an empirically better choice than the original ADA-CLUE.
Interactive Hashtag Recommendation System

With the progressive advance of Internet technologies, more and more users share their lives by posting tweets on social media platforms like Twitter. These tweets use hashtags as links to constitute discussion topics on social media. However, since most users are not used to using hashtags, a large number of tweets cannot be related to corresponding topics. To solve this problem, we propose an interactive hashtag recommendation system, which predicts the topic of an input tweet and interactively recommends relevant hashtags in different phases of writing tweets. When users use recommended hashtags, the input tweet can be related to the corresponding topic by using hashtags. Tweets on social media can form many discussion topics through hashtags. As such, the system can help to build consensus about hashtags on social media. We conducted user experiments to verify the usability of the implemented recommendation system. The experimental results and user feedback reveal that this interactive hashtag recommendation system can provide accurate hashtags relevant to the corresponding topic.
Personalized Federated Learning for Automotive Intrusion Detection Systems

In connected cars, the Controller Area Network (CAN) bus communication is the central connectivity and communication system for electronic control units (ECUs). Although the CAN bus is the central communication system for most cars, it lacks basic security features, i.e., authentication and encryption. Consequently, an attacker may compromise the CAN bus system effortlessly with even free attacking tools. In case of an attacker succeeds in compromising the ECUs, they can take control and stop the engine, disable the brakes, turn the lights on/off, etc., which makes the questions concerning the transformation of modern cars and safe driving. In this study, we propose a Personalized Federated learning-based Intrusion Detection System that ensures effective, secure training procedures without sharing any sort of data. In our research, we contemplate Supervised and Unsupervised Federated Learning to observe the behavior of CAN bus intrusion data. Our experiment result demonstrates that the Federated Learning-based supervised classifier effectively detects the CAN bus attacks, with accuracy of 99.98%.
TinyDRaGon: Lightweight Radio Channel Estimation for 6G Pervasive Intelligence

Due to the emerging challenges with future 6G networks such as high data rates and the need for remarkably low latency, future wireless communication systems must be planned extremely carefully and with respect to 6G's various applications. Our previous work DRaGon has shown the potential of leveraging Machine Learning (ML)-based channel models, but its high model complexity lacks embedded system application. In this work, we present TinyDRaGon as a novel signal strength prediction method that combines expert knowledge from the mobile communications domain with lightweight ML methods to achieve accurate and computationally efficient predictions. In a comprehensive performance evaluation, the performance of TinyDRaGon is compared to both real-world measurements and a vast range of state-of-the-art channel modeling methods. It is found that TinyDRaGon achieves similar or even slightly better accuracy than its deep learning predecessor while also being ten times less time-consuming during training, less computationally expensive, and less energy-consuming. This makes TinyDRaGon a promising channel prediction candidate for pervasive intelligence within future 6G communication networks.
A Learning-Based Zero-Trust Architecture for 6G and Future Networks

In the evolution of 6G and Future Networks, a dynamic, flexible, learning-based security architecture will be essential with the ability to handle both current and evolving cybersecurity threats. This is specially critical with future networks' increased reliance on distributed learning-based approaches for operation. To address this challenge, a distributed learning framework must provide security and trust in an integrated fashion. In contrast to existing approach such as federated learning (FL), that update parameters of a shared model, this work proposes an architecture that is capable of integrating advanced learning with real-time digital forensics, e.g. monitoring compute and storage resources. With real-time monitoring, it is possible to develop a learning-based, real-time Zero-Trust Architecture (ZTA) to achieve the high levels of security. The proposed architecture, serves as a framework to enable and spur innovation, where new machine learning based techniques can be developed for enhanced real-time, adaptive and proactive security, thus, embedding future networks' security with learning-based ZTA elements.
A Lightweight Hash-Chain-Based Multi-Node Mutual Authentication Algorithm for IoT Networks

As an emerging technology, IoT is rapidly revolutionizing the global communication network with billions of new devices deployed and connected with each other. Many of these devices collect and transfer a large amount of sensitive or mission critical data, making security a top priority. Compared to traditional Internet, IoT networks often operate in open and harsh environment, and may experience frequent delays, traffic loss and attacks; Meanwhile, IoT devices are often severally constrained in computational power, storage space, network bandwidth, and power supply, which prevent them from deploying traditional security schemes. Authentication is an important security mechanism that can be used to identify devices or users. Due to resource constrains of IoT networks, it is highly desirable for the authentication scheme to be lightweight while also being highly effective. In this paper, we developed and evaluated a hash-chain-based multi-node mutual authentication algorithm. Nodes on a network all share a common secret key and broadcast to other nodes in range. Each node may also add to the hash chain and rebroadcast, which will be used to authenticate all nodes in the network. This algorithm has a linear running time and complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n)$</tex> , a significant improvement from the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n^{2})$</tex> running time and complexity of the traditional pairwise multi-node mutual authentication.
Evaluation Method of English Online and Offline Mixed Teaching Quality Based on Three-Dimensional Teaching

The lack of process evaluation indicators in teaching quality evaluation will affect the evaluation effect. In view of the problems of low accuracy and efficiency of evaluation results in traditional methods, an online and offline mixed teaching quality evaluation method of English based on three-dimensional teaching is proposed. Establish a three-dimensional English teaching model, which is divided into three stages: teaching preparation, teaching implementation and teaching extension, and analyze the characteristics of English online and offline mixed teaching. According to the characteristics of three-dimensional teaching, the process evaluation index is introduced, and the index system is designed. Calculate the internal weight of indicators, rank them in order according to the degree of importance, and establish a teaching quality evaluation model to test the teaching quality. The experimental results show that compared with the evaluation method based on grey correlation analysis, neural network and decision tree classification algorithm, this method can still maintain high accuracy when the amount of data is large, so it is effective and scientific.
Online Interactive Teaching System of Sanda Course Based on P2P Network

In order to improve the online teaching effect of sanda, this study designed an online interactive teaching system of sanda based on P2P network. The microcontroller is taken as the core of the hardware part of the system, and the processor, CPU sub-board and compatible motherboard are designed. After debugging, the normal transceiver of the hardware environment is realized. Then, on the basis of designing system function modules and database structure based on P2P network, the index function of the system is designed to complete the construction of system software environment. Experimental results show that the system improves the interactive efficiency, teaching performance and content collection accuracy, and effectively improves the teaching effect.
Assessment and Evaluation System of Preschool Education Curriculum Based on Big Data

The current pre-school education curriculum assessment and evaluation system is easily affected by other factors in the implementation process, resulting in poor evaluation results and affecting the evaluation efficiency. Therefore, this paper proposes a pre-school education curriculum assessment and evaluation system based on big data, optimizes the pre-school education curriculum objective system structure, constructs a pre-school education curriculum assessment and evaluation algorithm in combination with big data technology, constructs a standardized pre-school education curriculum management system, supervision and evaluation system, and verifies through experiments that the pre-school education curriculum assessment and evaluation system based on big data has high practical value and fully meets the research requirements.
Design of Online Open Wushu Sanda Teaching Platform Based on Hybrid Reality Technology

IN view of the lack of interaction in Wushu Sanda Teaching, an online open Wushu Sanda Teaching Platform Based on hybrid reality technology is designed. In the hardware part, the serializer packs the image data into FPD link signal and outputs it to the deserializer circuit board through coaxial cable. The deserializer is responsible for unpacking the received FPD link signal, restoring it to mipi-csi signal and transmitting it to the image receiving and processing chip. In the software part, the logic layer includes two components: knowledge agent and interaction agent, which constitutes the architecture of learning and management system for online open platform. The collaborative interaction model is designed based on hybrid reality technology to realize the interaction behavior between users and the environment. According to the overall requirements of the platform, each functional module is designed to ensure teaching interaction. The test results show that when the number of concurrent users is 5000, the response time of the online open Wushu Sanda Teaching Platform Based on hybrid reality technology is 0.9 s, which is 0.5 s and 0.49 s shorter than that based on virtual technology and big data analysis technology, respectively. Therefore, the platform realizes each functional module and has good performance.
Design of Online Teaching Assistant Platform for Technical Courses of Physical Education Specialty

In order to increase the number of online users that can be accommodated by the physical education teaching auxiliary platform, so as to improve the actual teaching ability of physical education technology courses, an online teaching auxiliary platform for physical education professional technology courses is designed. The B/S three-tier architecture is used to provide the necessary conditions for the connection of database application structure and teaching process management module, and complete the overall design of physical education online teaching auxiliary platform. On this basis, set the key session interface, calculate the specific value of the weight of the evaluation index through the way of curriculum standardization, complete the online evaluation of the technical courses of physical education, and then combine the relevant hardware equipment structure to realize the design and application of the online teaching auxiliary platform of the technical courses of physical education. The experimental results show that, compared with the traditional online teaching system, with the application of teaching assistance platform, the number of logged in online users has increased significantly, which can play a strong role in promoting the actual teaching ability of physical education technology courses.
Application of Artificial Intelligence in Pre-school Education Professional Talent Training in the Era of Big Data

Aiming at the problem that there are few preschool teachers with scientific and technological literacy and the training of preschool education professionals does not conform to the natural trend, in order to make the training of talents more convenient and more conducive to the formation of personalized education programs, the application of artificial intelligence in the training of preschool talents is studied. What the society needs now is preschool teachers with scientific and technological literacy. These preschool teachers can cultivate children’s awareness of innovation and technology. As future kindergarten teachers, they should be compound talents with “content + technology + education” and “pre-school education + artificial intelligence”. The training of pre-school education professionals with “pre-school education + artificial intelligence” should pay attention to the integration of artificial intelligence and training programs, and adhere to the educational vision of artificial intelligence education; The integration of artificial intelligence and personality teaching, in-depth excavation of the “embodiment” of the current situation of artificial intelligence education; Combine artificial intelligence with teaching evaluation to highlight the era value of artificial intelligence education; The integration of artificial intelligence and labor spirit will promote the spirit of Chinese model workers and craftsmen.
Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling

We consider the approximation of unknown or intractable integrals using quadrature when the evaluation of the integrand is considered very costly. This is a central problem both within and without machine learning, including model averaging, (hyper-)parameter marginalization, and computing posterior predictive distributions. Recently, Batch Bayesian Quadrature has successfully combined the probabilistic integration techniques of Bayesian Quadrature with the parallelization techniques of Batch Bayesian Optimization, resulting in improved performance when compared to state-of-the-art Markov Chain Monte Carlo techniques, especially when parallelization is increased. While the selection of batches in Batch Bayesian Quadrature mitigates costs associated with individual point selection, every point within every batch is nevertheless chosen serially, which impedes the realization of the full potential of batch selection. We resolve this shortcoming. We have developed a novel Batch Bayesian Quadrature method that allows for the updating of points within a batch without incurring the costs traditionally associated with non-serial point selection. We show that our method efficiently reduces uncertainty, leads to lower error estimates of the integrand, and therefore results in more numerically robust estimates of the integral. We demonstrate our method and support our findings using a synthetic test function from the Batch Bayesian Quadrature literature.
Neural Network Based Drift Detection

The unprecedented growth in machine learning has shed light on its unique set of challenges. One such challenge is apparent changes in the input data distribution over time known as Concept Drifts. In such cases, the model’s performance degrades according to the changes in the data distribution. The remedy for concept drifts is retraining the model with the most recent data to improve the model’s performance. The significant issue is identifying the precise point at which the model must be updated for maximum performance benefits with minimum retraining effort. This problem is challenging to address in unsupervised detection methods with no access to label data to identify the changing distributions for the targets of the input data. Here, we present our unsupervised method based on a Generative Adversarial Network and a feed forward neural network for detecting concept drifts without the need for target labels. We demonstrate that our method is better at identifying concept drifts and outperforms the baseline and other comparable methods.
MicroRacer: A Didactic Environment for Deep Reinforcement Learning

MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many different methods, networks and hyperparameters settings without requiring sophisticated software or exceedingly long training times. Baseline agents for major learning algorithms such as DDPG, PPO, SAC, TD3 and DSAC are provided too, along with a preliminary comparison in terms of training time and performance.