Reviewerly/exHarmony huggingface dataset:
EAVS: Edge-assisted Adaptive Video Streaming with Fine-grained Serverless Pipelines

Recent years have witnessed video streaming gradually evolve into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the serverless computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel computing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimization scheme to address video bitrate adaptation. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Our results show that EAVS significantly improves QoE and reduces the video stall rate, achieving over 9.1% QoE improvement and 60.2% latency reduction compared to state-of-the-art solutions.
ICARUS: Learning on IQ and Cycle Frequencies for Detecting Anomalous RF Underlay Signals

The RF environment in a secure space can be compromised by intentional transmissions of hard-to-detect underlay signals that overlap with a high-power baseline transmission. Specifically, we consider the case where a direct sequence spread spectrum (DSSS) signal is the underlay signal hiding within a baseline 4G Long-Term Evolution (LTE) signal. As compared to overt actions like jamming, the DSSS signal allows the LTE signal to be decodable, which makes it hard to detect. ICARUS presents a machine learning based framework that offers choices at the physical layer for inference with inputs of (i) in-phase and quadrature (IQ) samples only, (ii) cycle-frequency features obtained via cyclostationary signal processing (CSP), and (iii) fusion of both, to detect the underlay DSSS signal and its modulation type within LTE frames. ICARUS chooses the best inference method considering both the expected accuracy and the computational overhead. ICARUS is rigorously validated on multiple real-world datasets that include signals captured in cellular bands in the wild and the NSF POWDER testbed for advanced wireless research (PAWR). Results reveal that ICARUS can detect DSSS anomalies and its modulation scheme with 98-100% and 67 − 99% accuracy, respectively, while completing inference within 3 − 40 milliseconds on an NVIDIA A100 GPU platform.
CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes (<2/3) while securing the system, thus reducing the shard size safely. We implement CoChain based on Harmony and conduct extensive experiments. Compared with Harmony, CoChain achieves 35x throughput gain with 6,000+ nodes.
Boosting quantum amplitude exponentially in variational quantum algorithms

Abstract We introduce a family of variational quantum algorithms, which we coin as quantum iterative power algorithms (QIPAs), and demonstrate their capabilities as applied to global-optimization numerical experiments. Specifically, we demonstrate the QIPA based on a double exponential oracle as applied to ground state optimization of the H 2 molecule, search for the transmon qubit ground-state, and biprime factorization. Our results indicate that QIPA outperforms quantum imaginary time evolution (QITE) and requires a polynomial number of queries to reach convergence even with exponentially small overlap between an initial quantum state and the final desired quantum state, under some circumstances. We analytically show that there exists an exponential amplitude amplification at every step of the variational quantum algorithm, provided the initial wavefunction has non-vanishing probability with the desired state and that the unique maximum of the oracle is given by <?CDATA $\lambda_1\gt0$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>&gt;</mml:mo> <mml:mn>0</mml:mn> </mml:math> , while all other values are given by the same value <?CDATA $0\lt\lambda_2\lt\lambda_1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0</mml:mn> <mml:mo>&lt;</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>&lt;</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> (here λ can be taken as eigenvalues of the problem Hamiltonian). The generality of the global-optimization method presented here invites further application to other problems that currently have not been explored with QITE-based near-term quantum computing algorithms. Such approaches could facilitate identification of reaction pathways and transition states in chemical physics, as well as optimization in a broad range of machine learning applications. The method also provides a general framework for adaptation of a class of classical optimization algorithms to quantum computers to further broaden the range of algorithms amenable to implementation on current noisy intermediate-scale quantum computers.
A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning

Federated learning (FL) enables distributed clients to collaboratively learn a shared model while keeping their raw data private. To mitigate the system heterogeneity issues of FL and overcome the resource constraints of clients, we investigate a novel paradigm in which heterogeneous clients learn uniquely designed models with different architectures, and transfer knowledge to the server to train a larger server model that in turn helps to enhance client models. For efficient knowledge transfer between client models and server model, we propose FedHKT, a Hierarchical Knowledge Transfer framework for FL. The main idea of FedHKT is to allow clients with similar data distributions to collaboratively learn to specialize in certain classes, then the specialized knowledge of clients is aggregated to a super knowledge covering all specialties to train the server model, and finally the server model knowledge is distilled to client models. Specifically, we tailor a hybrid knowledge transfer mechanism for FedHKT, where the model parameters based and knowledge distillation (KD) based methods are respectively used for client-edge and edge-cloud knowledge transfer, which can harness the pros and evade the cons of these two approaches in learning performance and resource efficiency. Besides, to efficiently aggregate knowledge for conducive server model training, we propose a weighted ensemble distillation scheme with server-assisted knowledge selection, which aggregates knowledge by its prediction confidence, selects qualified knowledge during server model training, and uses selected knowledge to help improve client models. Extensive experiments demonstrate the superior performance of FedHKT compared to state-of-the-art baselines.
AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics

The quality of the video stream is key to neural network-based video analytics. However, low-quality video is inevitably collected by existing surveillance systems because of poor quality cameras or over-compressed/pruned video streaming protocols, e.g., as a result of upstream bandwidth limit. To address this issue, existing studies use quality enhancers (e.g., neural super-resolution) to improve the quality of videos (e.g., resolution) and eventually ensure inference accuracy. Nevertheless, directly applying quality enhancers does not work in practice because it will introduce unacceptable latency. In this paper, we present AccDecoder, a novel accelerated decoder for real-time and neural-enhanced video analytics. AccDecoder can select a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality by neural super-resolution and then up-scale the unselected frames that reference them, which leads to 6-21% accuracy improvement. AccDecoder provides efficient inference capability via filtering important frames using DRL for DNN-based inference and reusing the results for the other frames via extracting the reference relationship among frames and blocks, which results in a latency reduction of 20-80% than baselines.
De-anonymization Attacks on Metaverse

Virtual reality (VR) can provide users with an immersive experience in the metaverse. One of the most promising properties of VR is that users’ identities can be protected by changing their physical world appearances into arbitrary virtual avatars. However, recent proposed de-anonymization attacks demonstrate the feasibility of recognizing the user’s identity behind the VR avatar’s masking. In this paper, we propose AvatarHunter, a non-intrusive and user-unconscious de-anonymization attack based on victims’ inherent movement signatures. AvatarHunter imperceptibly collects the victim avatar’s gait information via recording videos from multiple views in the VR scenario without requiring any permission. A Unity-based feature extractor is designed that preserves the avatar’s movement signature while immune to the avatar’s appearance changes. Real-world experiments are conducted in VRChat, one of the most popular VR applications. The experimental results demonstrate that AvatarHunter can achieve attack success rates of 92.1% and 66.9% in closed-world and open-world avatar settings, respectively, which are much better than existing works.
Background-Aware Classification Activation Map for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations for object localization by using image-level annotation to supervise the learning process. However, most WSOL methods only focus on forcing the object classifier to produce high activation score on object parts without considering the influence of background locations, causing excessive background activations and ill-pose background score searching. Based on this point, our work proposes a novel mechanism called the background-aware classification activation map (B-CAM) to add background awareness for WSOL training. Besides aggregating an object image-level feature for supervision, our B-CAM produces an additional background image-level feature to represent the pure-background sample. This additional feature can provide background cues for the object classifier to suppress the background activations on object localization maps. Moreover, our B-CAM also trained a background classifier with image-level annotation to produce adaptive background scores when determining the binary localization mask. Experiments indicate the effectiveness of the proposed B-CAM on four different types of WSOL benchmarks, including CUB-200, ILSVRC, OpenImages, and VOC2012 datasets.
A Quantum Overlay Network for Efficient Entanglement Distribution

Distributing quantum entanglements over long distances is essential for the realization of a global scale quantum Internet. Most of the prior work and proposals assume an on-demand distribution of entanglements which may result in significant network resource under-utilization. In this work, we introduce Quantum Overlay Networks (QONs) for efficient entanglement distribution in quantum networks. When the demand to create end-to-end user entanglements is low, QONs can generate and store maximally entangled Bell pairs (EPR pairs) at specific overlay storage nodes of the network. Later, during peak demands, requests can be served by performing entanglement swaps either over a direct path from the network or over a path using the storage nodes. We solve the link entanglement and storage resource allocation problem in such a QON using a centralized optimization framework. We evaluate the performance of our proposed QON architecture over a wide number of network topologies under various settings using extensive simulation experiments. Our results demonstrate that QONs fare well by a factor of 40% with respect to meeting surge and changing demands compared to traditional non-overlay proposals. QONs also show significant improvement in terms of average entanglement request service delay over non-overlay approaches.
FPattNet: A Multi-Scale Feature Fusion Network with Occlusion Awareness for Depth Estimation of Light Field Images

A light field camera can capture light information from various directions within a scene, allowing for the reconstruction of the scene. The light field image inherently contains the depth information of the scene, and depth estimations of light field images have become a popular research topic. This paper proposes a depth estimation network of light field images with occlusion awareness. Since light field images contain many views from different viewpoints, identifying the combinations that contribute the most to the depth estimation of the center view is critical to improving the depth estimation accuracy. Current methods typically rely on a fixed set of views, such as vertical, horizontal, and diagonal, which may not be optimal for all scenes. To address this limitation, we propose a novel approach that considers all available views during depth estimation while leveraging an attention mechanism to assign weights to each view dynamically. By inputting all views into the network and employing the attention mechanism, we enable the model to adaptively determine the most informative views for each scene, thus achieving more accurate depth estimation. Furthermore, we introduce a multi-scale feature fusion strategy that amalgamates contextual information and expands the receptive field to enhance the network's performance in handling challenging scenarios, such as textureless and occluded regions.
Manipulation Guidance Field for Collaborative Object Manipulation in VR

AbstractObject manipulation is a fundamental interaction in virtual reality (VR). Efficient and accurate manipulation is important for many VR applications, especially collaborative VR applications. We introduce a collaborative method based on the manipulation guidance field (MGF) to improve manipulation accuracy and efficiency. MGF aims to guide users of different manipulation types to different manipulation viewpoints to efficiently and collaboratively manipulate objects. First, we introduce the concept of MGF and its construction method. Two strategies are offered to accelerate the MGF updating process. A collaborative manipulation method to manipulate objects using the guidance of MGF is then proposed. Finally, a user study (n = 36 participants) was conducted to evaluate the efficiency and accuracy of our MGF-based collaborative object manipulation method in three scenes: (1) Livingroom scene; (2) WaveHouse scene; (3) Pipe scene. Compared to a control method without MGF, the results show that our MGF-based method has significantly reduced task completion time, position error, rotation error, and task load.Keywords: Virtual realitycollaborationobject manipulationguiding fieldhuman-computer interaction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by National Key R&D plan [2019YFC1521102]; the National Natural Science Foundation of China through Projects [61932003]; Beijing Science and Technology Plan Project [Z221100007722004].Notes on contributorsXiaolong LiuXiaolong Liu is a PhD student in the School of Computer Science and Engineering of Beihang University, China. His current research focuses on virtual reality, augmented reality, and HCI.Lili WangLili Wang received her PhD degree from Beihang University, Beijing, China. She is a professor with the School of Computer Science and Engineering of Beihang University and a researcher with the State Key Laboratory of Virtual Reality Technology and Systems. Her interests include virtual reality, real-time rendering and HCI.Shuai LuanShuai Luan is a master student in the School of Computer Science and Engineering of Beihang University, China. His current research focuses on virtual reality, augmented reality, and HCI.
SmartLog: A Smart TV-Based Lifelogging System for Capturing, Storing, and Visualizing Watching Behavior

AbstractThe modern smart TV can record viewers’ daily life-watching experiences called digital logs. These logs can be used for various purposes, including but not limited to recommendations, monitoring viewers, watching behavior, memory augmentation, e-learning, viewers’ privacy, and designing and developing adaptive User Interfaces (UI). Much work is available on lifelogging technologies related to smartphones, fitness gadgets, and wearable and non-wearable devices. However, little attention has been given to smart TV-based lifelogging systems. In this paper, we proposed a TV-based SmartLog framework for logging specific information about watching experiences. The proposed work is implemented and installed on Android-based Smart TV. We logged various activities of individual viewers and other household members and their environmental contexts. We empirically evaluated the proposed solution by analyzing data from 75 household viewers of different age groups. These groups include teenagers, homemakers, senior citizens, literate, and educated. We carried out various tests on the collected data, ie, descriptive tabulation, Kendall’s tau-b, Cronbach alpha, and Principal Component Factor Analysis (PCFA). The findings suggest that the SmartLog app received a positive perception regarding attitude, user satisfaction, ease of use, and intention to use. The proposed solution can be used to assist household viewers in terms of personalization, usability, accessibility, user experience, learnability, and reducing cognitive overload while interacting with smart TV.Keywords: Smart TVlifelogging systemsmartphonesmemory augmentation AcknowledgementsWe thank and acknowledge the participants who took part in the evaluation of SmartLog for their time and interview.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://github.com/rocketjob/semantic_logger2 https://activitywatch.net/3 https://www.manictime.com/4 https://wakatime.com/5 https://www.cambridgebrainsciences.com/6 https://www.rescuetime.com/7 https://toggl.com/8 https://devpost.com/software/hyfy-screen-recorder9 https://screenshotmonitor.com/10 https://www.timecamp.com/11 https://www.remouse.com/Additional informationFundingThis article receives no funding from any source.Notes on contributorsMumtaz KhanMumtaz Khan is a PhD candidate at the Department of Computer Science, University of Peshawar, Pakistan. His research interest includes Smart TV Lifelogging Systems, Smart TV User Interfaces, Adaptive User Interfaces, User Experience (UX), User-cantered Design, Dynamic User Models, Human Factors, Recommender systems, Web Semantics, Web Engineering.Shah KhusroShah Khusro is a Professor of Computer Science at the Department of Computer Science, University of Peshawar, Peshawar, Pakistan. His research interests include Web Semantics, Web Engineering, Smrt TVs, HCI, Lifelogging, Web Mining, and Technology for People with Special Needs. He is a member of IEEE, IEICE, and ACM.
Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications.
Enhancing Dynamic Parameter Adaptation in the Bird Swarm Algorithm Using General Type-2 Fuzzy Analysis and Mathematical Functions

The pursuit of continuous improvement across diverse processes presents a pressing challenge. Precision in manufacturing, efficient delivery route planning, and accurate diagnostics are imperative, prompting the exploration of innovative solutions. Nature-inspired algorithms offer a pathway for enhancing these processes. In this study, we address this challenge by dynamically adapting parameters in the Bird Swarm Algorithm using General Type-2 Fuzzy Systems, encompassing a range of rules and membership functions. Two complex case studies validate the effectiveness of our approach. The first evaluates Congress of Evolutionary Competition 2017 functions, while the second tackles the intricacies of Congress of Evolutionary Competition 2019 functions. Our methodology achieves an 97% improvement for Congress of Evolutionary Competition 2017 functions and a significant 70% enhancement for Congress of Evolutionary Competition 2019 functions. Notably, our results are benchmarked against the original method. Crucially, rigorous statistical analysis underscores the significant advancements facilitated by our proposed method. The comparison demonstrates clear and statistically significant improvements over the original approach. This study proves the marked impact of integrating General Type-2 Fuzzy Systems into the Bird Swarm Algorithm, presenting a promising avenue for addressing intricate optimization challenges in diverse domains.