亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

Wireless short-packet communications pose challenges to the security and reliability of the transmission. Besides, the proactive warder compounds these challenges, who detects and interferes with the potential transmission. An extra jamming channel is introduced by the proactive warder compared with the passive one, resulting in the inapplicability of analytical methods and results in exsiting works. Thus, effective system design schemes are required for short-packet communications against the proactive warder. To address this issue, we consider the analysis and design of covert and reliable transmissions for above systems. Specifically, to investigate the reliable and covert performance of the system, detection error probability at the warder and decoding error probability at the receiver are derived, which is affected by both the transmit power and the jamming power. Furthermore, to maximize the effective throughput, an optimization framework is proposed under reliability and covertness constraints. Numerical results verify the accuracy of analytical results and the feasibility of the optimization framework. It is shown that the tradeoff between transmission reliability and covertness is changed by the proactive warder compared with the passive one. Besides, it is shown that longer blocklength is always beneficial to improve the throughput for systems with optimized transmission rates. But when transmission rates are fixed, the blocklength should be carefully designed since the maximum one is not optimal in this case.

Secure Aggregation (SA) is a key component of privacy-friendly federated learning applications, where the server learns the sum of many user-supplied gradients, while individual gradients are kept private. State-of-the-art SA protocols protect individual inputs with zero-sum random shares that are distributed across users, have a per-user overhead that is logarithmic in the number of users, and take more than 5 rounds of interaction. In this paper, we introduce LISA, an SA protocol that leverages a source of public randomness to minimize per-user overhead and the number of rounds. In particular, LISA requires only two rounds and has a communication overhead that is asymptotically equal to that of a non-private protocol -- one where inputs are provided to the server in the clear -- for most of the users. In a nutshell, LISA uses public randomness to select a subset of the users -- a committee -- that aid the server to recover the aggregated input. Users blind their individual contributions with randomness shared with each of the committee members; each committee member provides the server with an aggregate of the randomness shared with each user. Hence, as long as one committee member is honest, the server cannot learn individual inputs but only the sum of threshold-many inputs. We compare LISA with state-of-the-art SA protocols both theoretically and by means of simulations and present results of our experiments. We also integrate LISA in a Federated Learning pipeline and compare its performance with a non-private protocol.

The future Six-Generation (6G) envisions massive access of wireless devices in the network, leading to more serious interference from concurrent transmissions between wireless devices in the same frequency band. Existing interference mitigation approaches takes the interference signals as Gaussian white noise, which cannot precisely estimate the non-Gaussian interference signals from other devices. In this paper, we present IntLearner, a new interference mitigation technique that estimates and mitigates the impact of interference signals with only physical-layer (PHY) information available in base-station (BS) and user-equipment (UE), including channel estimator and constellations. More specifically, IntLearner utilizes the power of AI to estimate the features in interference signals, and removes the interference from the interfered received signal with neural network (NN). IntLearner's NN adopts a modular NN design, which takes the domain knowledge of BS and UE PHY as the guidance to NN design for minimizing training confusion and NN complexity. Simulation results show IntLearner increases Uplink (UL) channel estimation accuracy up to 7.4x, and reduces the Downlink (DL) Signal to Interference Ratio plus Noise Ratio (SINR) requirement to achieve the same Block Error Rate (BLER) by 1.5dB in a conventional multi-cell scenario.

In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the control plane are responsible for programming data plane forwarding devices, while the top layer, the application plane, enforces policies and programs the network. The different levels of the SDN use interfaces for communication. However, SDN faces challenges with traffic distribution, such as load imbalance, which can negatively affect the network performance. Consequently, developers have developed various SDN load-balancing solutions to enhance SDN effectiveness. In addition, researchers are considering the potential of implementing some artificial intelligence (AI) approaches into SDN to improve network resource usage and overall performance due to the fast growth of the AI field. This survey focuses on the following: Firstly, analyzing the SDN architecture and investigating the problem of load balancing in SDN. Secondly, categorizing AI-based load balancing methods and thoroughly assessing these mechanisms from various perspectives, such as the algorithm/technique employed, the tackled problem, and their strengths and weaknesses. Thirdly, summarizing the metrics utilized to measure the effectiveness of these techniques. Finally, identifying the trends and challenges of AI-based load balancing for future research.

This paper contributes to building a standard process of research and development (R&D) for new user experiences (UX) in metaverse services. We tested this R&D process on a new UX proof of concept (PoC) for Meta Quest head-mounted display (HMDs) consisting of a school-life karaoke experience with the hypothesis that it is possible to design the avatars with only the necessary functions and rendering costs. The school life metaverse is a relevant subject for discovering issues and problems in this type of simultaneous connection. To qualitatively evaluate the potential of a multi-person metaverse experience, this study investigated subjects where each avatar requires expressive skills. While avatar play experiences feature artistic expressions, such as dancing, playing musical instruments, and drawing, and these can be used to evaluate their operability and expressive capabilities qualitatively, the Quest's tracking capabilities are insufficient for full-body performance and graphical art expression. Considering such hardware limitations, this study evaluated the Quest, focusing primarily on UX simplicity using AI Fusion techniques and expressiveness in instrumental scenes played by approximately four avatars. This research reported methods for multiuser metaverse communication and its supporting technologies, such as head-mounted devices and their graphics performance, special interaction techniques, and complementary tools and the importance of PoC development, its evaluation, and its iterations. The result is remarkable for further research; these expressive technologies in a multi-user context are directly related to the quality of communication within the metaverse and the value of the user-generated content (UGC) produced there.

Emerging technologies in the domain of extended reality offer rich, new possibilities for the study and practice of joint music performance. Apart from the technological challenges, bringing music players together in extended reality raises important questions on their performance and embodied coordination. In this study, we designed an extended reality platform to assess a remote, bidirectional polyrhythmic interaction between two players, mediated in real time by their three-dimensional embodied avatars and a shared, virtual drum circle. We leveraged a multi-layered analysis framework to assess their performance quality, embodied co-regulation and first-person interaction experience, using statistical techniques for time-series analysis and mixed-effect regression and focusing on contrasts of visual coupling (not seeing / seeing as avatars / seeing as real) and auditory context (metronome / music). Results reveal that an auditory context with music improved the performance output as measured by a prediction error, increased movement energy and levels of experienced agency. Visual coupling impacted experiential qualities and induced prosocial effects with increased levels of partner realism resulting in increased levels of shared agency and self-other merging. Embodied co-regulation between players was impacted by auditory context, visual coupling, and task complexity, suggesting prediction-based compensatory mechanisms to deal with the novelty, difficulty, and expressivity in the musical interaction. This study contributes to the understanding of music performance in extended reality by using a methodological approach to demonstrate how co-regulation between players is impacted by visual coupling and auditory context and provides a basis and future directions for further action-oriented research.

Recently, significant advancements have been made in time-series forecasting research, with an increasing focus on analyzing the nature of time-series data, e.g, channel-independence (CI) and channel-dependence (CD), rather than solely focusing on designing sophisticated forecasting models. However, current research has primarily focused on either CI or CD in isolation, and the challenge of effectively combining these two opposing properties to achieve a synergistic effect remains an unresolved issue. In this paper, we carefully examine the opposing properties of CI and CD, and raise a practical question that has not been effectively answered, e.g.,"How to effectively mix the CI and CD properties of time series to achieve better predictive performance?" To answer this question, we propose Mlinear (MIX-Linear), a simple yet effective method based mainly on linear layers. The design philosophy of Mlinear mainly includes two aspects:(1) dynamically tuning the CI and CD properties based on the time semantics of different input time series, and (2) providing deep supervision to adjust the individual performance of the "CI predictor" and "CD predictor". In addition, empirically, we introduce a new loss function that significantly outperforms the widely used mean squared error (MSE) on multiple datasets. Experiments on time-series datasets covering multiple fields and widely used have demonstrated the superiority of our method over PatchTST which is the lateset Transformer-based method in terms of the MSE and MAE metrics on 7 datasets with identical sequence inputs (336 or 512). Specifically, our method significantly outperforms PatchTST with a ratio of 21:3 at 336 sequence length input and 29:10 at 512 sequence length input. Additionally, our approach has a 10 $\times$ efficiency advantage at the unit level, taking into account both training and inference times.

In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained models be applied to multi-modal tasks? Researchers have explored this problem and made significant progress. This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and video-text pre-training. To give readers a better overall grasp of VLP, we first review its recent advances from five aspects: feature extraction, model architecture, pre-training objectives, pre-training datasets, and downstream tasks. Then, we summarize the specific VLP models in detail. Finally, we discuss the new frontiers in VLP. To the best of our knowledge, this is the first survey on VLP. We hope that this survey can shed light on future research in the VLP field.

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.