Fine-grained Control Research Articles

DemePro: DEcouple packet Marking from Enqueuing for multiple services with PROactive congestion control

Most of current Data Center Network (DCN) protocols leverage Explicit Congestion Notification (ECN) for congestion control to maintain low latency and high throughput. However, the majority of them only consider single-queue scenario in each switch port, making their performance inferior in multiple-service multiple-queue scenario. To this end, we propose DemePro, a DCN scheme for multiple-service multiple-queue scenario. In face of congestion signal, DemePro also leverages ECN for congestion notification, while decouples packet marking from enqueuing, in order to ensure fairness among multiple services. We also question the effectiveness of the congestion signal derived from the single threshold for port buffer queue length, via a set of experiments. Then, DemePro utilizes multiple thresholds for proactive congestion control, and packets are encapsulated to carry congestion extent information, in order to notify TCP senders for precise and fine-grained congestion control. Experiments show that DemePro has better performance than MQ-ECN [1] which is currently the best DCN scheme for multiple-service multiple-queue scenario, and DemePro can also well guarantee the fairness.

MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images.

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. Algorithmic comparative assessments and blind evaluations conducted by 10 board-certified radiologists indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines and airways. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.

A Large-Scale Study of Cookie Banner Interaction Tools and their Impact on Users' Privacy

Cookie notices (or cookie banners) are a popular mechanism for websites to provide (European) Internet users a tool to choose which cookies the site may set. Banner implementations range from merely providing information that a site uses cookies over offering the choice to accepting or denying all cookies to allowing fine-grained control of cookie usage. Users frequently get annoyed by the banner's pervasiveness as they interrupt ''natural'' browsing on the Web. As a remedy, different browser extensions have been developed to automate the interaction with cookie banners. In this work, we perform a large-scale measurement study comparing the effectiveness of extensions for ''cookie banner interaction.'' We configured the extensions to express different privacy choices (e.g., accepting all cookies, accepting functional cookies, or rejecting all cookies) to understand their capabilities to execute a user's preferences. The results show statistically significant differences in which cookies are set, how many of them are set, and which types are set---even for extensions that aim to implement the same cookie choice. Extensions for ''cookie banner interaction'' can effectively reduce the number of set cookies compared to no interaction with the banners. However, all extensions increase the tracking requests significantly except when rejecting all cookies.

Learning cooperative strategies in StarCraft through role-based monotonic value function factorization

<abstract><p>StarCraft is a popular real-time strategy game that has been widely used as a research platform for artificial intelligence. Micromanagement refers to the process of making each unit perform appropriate actions separately, depending on the current state in the the multi-agent system comprising all of the units, i.e., the fine-grained control of individual units for common benefit. Therefore, cooperation between different units is crucially important to improve the joint strategy. We have selected multi-agent deep reinforcement learning to tackle the problem of micromanagement. In this paper, we propose a method for learning cooperative strategies in StarCraft based on role-based montonic value function factorization (RoMIX). RoMIX learns roles based on the potential impact of each agent on the multi-agent task; it then represents the action value of a role in a mixed way based on monotonic value function factorization. The final value is calculated by accumulating the action value of all roles. The role-based learning improves the cooperation between agents on the team, allowing them to learn the joint strategy more quickly and efficiently. In addition, RoMIX can also reduce storage resources to a certain extent. Experiments show that RoMIX can not only solve easy tasks, but it can also learn better cooperation strategies for more complex and difficult tasks.</p></abstract>

Attribute identification based IoT fog data security control and forwarding.

As Internet of Things (IoT) applications continue to proliferate, traditional cloud computing is increasingly unable to meet the low-latency demands of these applications. The IoT fog architecture solves this limitation by introducing fog servers in the fog layer that are closer to the IoT devices. However, this architecture lacks authentication mechanisms for information sources, security verification for information transmission, and reasonable allocation of fog nodes. To ensure the secure transmission of end-to-end information in the IoT fog architecture, an attribute identification based security control and forwarding method for IoT fog data (AISCF) is proposed. AISCF applies attribute signatures to the IoT fog architecture and uses software defined network (SDN) to control and forward fog layer data flows. Firstly, IoT devices add attribute identifiers to the data they send based on attribute features. The ingress switch then performs fine-grained access control on the data based on these attribute identifiers. Secondly, SDN uses attribute features as flow table matching items to achieve fine-grained control and forwarding of fog layer data flows based on attribute identifiers. Lastly, the egress switch dynamically samples data flows and verifies the attribute signatures of the sampled data packets at the controller end. Experimental validation has demonstrated that AISCF can effectively detect attacks such as data tampering and forged matching items. Moreover, AISCF imposes minimal overhead on network throughput, CPU utilization and packet forwarding latency, and has practicality in IoT fog architecture.

Fine-Grained Redactable Blockchain Using Trapdoor-Hash

The blockchains immutability permits widespread application in untrusted contexts such as digital currency, the Internet of Things, and anti-counterfeiting. This functionality, however, presents difficulties for regulators, developers, and users. For instance, unsuitable data may be permanently retained, blockchains may breach applicable rules and regulations, and smart contracts may contain bugs. To elegantly address a series of issues arising from the immutability of the blockchain, we propose a redactable blockchain that utilizes trapdoor-hash to repair harmful data in the blockchain, allowing for efficient transaction editing, deletion, and smart contract repair without destroying the blockchain structure or security features. Additionally, our solution provides fine-grained control over modification authority, allowing the user to change the transaction he initiated and the regulators to delete the erroneous transaction. Finally, we integrate and test the suggested technique with one of the most influential enterprise-grade permissioned blockchain platforms, Hyperledger Fabric. The results demonstrate that our technique has a low overhead when compared to an immutable blockchain. As compared to other existing redactable blockchains, our technique provides greater permission control flexibility without sacrificing efficiency or performance.

An efficient multi-data owner cooperative resource sharing scheme against key regeneration in edge computing

In cloud storage, attribute-based keyword searchable encryption realizes multi-user fine-grained authorization access control and outsourcing data sharing. However, (1) resisting key regeneration is an urgent problem to be solved in practical applications of attribute-based keyword search; (2) the general attribute-based keyword search scheme ignores the application scenario where multiple data owners jointly manage files. In addition, the interaction of data between intelligent terminals and cloud servers has become more frequent, leading to issues such as central load, transmission efficiency, and data privacy protection in cloud computing. To address the above issues, we propose an attribute-based searchable encryption scheme with the multi-data owner based on edge computing. In this scheme, the Pedersen (k,n) secret sharing protocol and the Reciprocal protocol are used to realize the common management and sharing of data by the multi-data owner to enhance data security. Secondly, the attribute set and identity information of each user are embedded into the private key to prevent key regeneration and resist user collusion attacks. Thirdly, edge computing is introduced, and part of the storage and decryption work is outsourced to edge nodes to reduce the computing load of users and data transmission bandwidth. Security proof and performance analysis show that the proposed scheme has strong security and practicability in multi-user sharing scenarios such as Online Education with multi-data owners.

Performance analysis of parallel programming models for C++

As multicore processors become more common in today’s computing systems and parallel programming models are enriched, programmers must consider how to choose the appropriate and parallel programming model when writing parallel code. The purpose of this paper is to compare and analyze the performance gap between different C++ parallel programming models, such as C++ standard library threads, OpenMP and Pthreads, in terms of matrix operations. The experiments use different libraries to implement matrix multiplication separately and then analyze their performance. The experimental data show that the data size has a significant impact on the performance of the different models. For very small matrices of size magnitude less than or close to the number of threads, the performance of parallel implementations is much lower than that of serial implementations. For small matrices with magnitudes larger than the number of threads, the C++ standard library threads outperforms Pthreads and OpenMP due to its lightweight thread performance on relatively small matrices. pthreads shows the best performance on very large matrices due to its fine-grained control over thread management, communication, and synchronization operations. openMP’s is not as stable as the other two libraries, especially for smaller matrices. This paper provides a comparative analysis that can help programmers choose the most appropriate library for their specific computational needs.

A License Management and Fine-Grained Verifiable Data Access Control System for Online Catering

A License Management and Fine-Grained Verifiable Data Access Control System for Online Catering

MNET++: Music-Driven Pluralistic Dancing Toward Multiple Dance Genre Synthesis.

Numerous task-specific variants of autoregressive networks have been developed for dance generation. Nonetheless, a severe limitation remains in that all existing algorithms can return repeated patterns for a given initial pose, which may be inferior. We examine and analyze several key challenges of previous works, and propose variations in both model architecture (namely MNET++) and training methods to address these. In particular, we devise the beat synchronizer and dance synthesizer. First, generated dance should be locally and globally consistent with given music beats, circumvent repetitive patterns, and look realistic. To achieve this, the beat synchronizer implicitly catches the rhythm enabling it to stay in sync with the music as it dances. Then, the dance synthesizer infers the dance motions in a seamless patch-by-patch manner conditioned by music. Second, to generate diverse dance lines, adversarial learning is performed by leveraging the transformer architecture. Furthermore, MNET++ learns a dance genre-aware latent representation that is scalable for multiple domains to provide fine-grained user control according to the dance genre. Compared with the state-of-the-art methods, our method synthesizes plausible and diverse outputs according to multiple dance genres as well as generates remarkable dance sequences qualitatively and quantitatively.

Rewarded Meta-Pruning: Meta Learning with Rewards for Channel Pruning

Convolutional neural networks (CNNs) have gained recognition for their remarkable performance across various tasks. However, the sheer number of parameters and the computational demands pose challenges, particularly on edge devices with limited processing power. In response to these challenges, this paper presents a novel approach aimed at enhancing the efficiency of deep learning models. Our method introduces the concept of accuracy and efficiency coefficients, offering a fine-grained control mechanism to balance the trade-off between network accuracy and computational efficiency. At our core is the Rewarded Meta-Pruning algorithm, guiding neural network training to generate pruned model weight configurations. The selection of this pruned model is based on approximations of the final model’s parameters, and it is precisely controlled through a reward function. This reward function empowers us to tailor the optimization process, leading to more effective fine-tuning and improved model performance. Extensive experiments and evaluations underscore the superiority of our proposed method when compared to state-of-the-art techniques. We conducted rigorous pruning experiments on well-established architectures such as ResNet-50, MobileNetV1, and MobileNetV2. The results not only validate the efficacy of our approach but also highlight its potential to significantly advance the field of model compression and deployment on resource-constrained edge devices.

Attribute and User Trust Score-Based Zero Trust Access Control Model in IoV

The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, thereby endangering vehicle users’ privacy and personal safety. An Attribute and User Trust Score-based Zero Trust Access Control Model (AU-ZTAC) is proposed, combining the zero-trust and attribute-based access control models to meet network protection requirements while achieving fine-grained dynamic access control and incorporating trust evaluation in the access control process to better reflect users’ intent. Experimental results demonstrate the effectiveness and feasibility of trust assessment through the proposed model. A comparison with the classical schemes illustrates that AU-ZTAC allows for more flexible and fine-grained access control in complex access control environments while improving IoV security.

Smart Public Transportation Sensing: Enhancing Perception and Data Management for Efficient and Safety Operations.

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data.

Novel lightweight and fine-grained fast access control using RNS properties in fog computing

Novel lightweight and fine-grained fast access control using RNS properties in fog computing

SensiX++: Bringing MLOps and Multi-tenant Model Serving to Sensory Edge Devices

We present SensiX++, a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e.g., a camera, a microphone, or IoT sensors. SensiX++ operates on two fundamental principles: highly modular componentisation to externalise data operations with clear abstractions and document-centric manifestation for system-wide orchestration. First, a data coordinator manages the lifecycle of sensors and serves models with correct data through automated transformations. Next, a resource-aware model server executes multiple models in isolation through model abstraction, pipeline automation, and feature sharing. An adaptive scheduler then orchestrates the best-effort executions of multiple models across heterogeneous accelerators, balancing latency and throughput. Finally, microservices with REST APIs serve synthesised model predictions, system statistics, and continuous deployment. Collectively, these components enable SensiX++ to serve multiple models efficiently with fine-grained control on edge devices while minimising data operation redundancy, managing data and device heterogeneity, and reducing resource contention. We benchmark SensiX++ with 10 different vision and acoustics models across various multi-tenant configurations on different edge accelerators (Jetson AGX and Coral TPU) designed for sensory devices. We report on the overall throughput and quantified benefits of various automation components of SensiX++ and demonstrate its efficacy in significantly reducing operational complexity and lowering the effort to deploy, upgrade, reconfigure, and serve embedded models on edge devices.

Capability and Blockchain-Based Fine-Grained and Flexible Access Control Model

Currently, integrating blockchain into access control systems has become a new research trend. However, existing access control systems still have problems such as easy confusion of capability authorisation subjects, inflexible capability granting and revocation, insufficient granularity of capability, insecure capability transfer and lack of capability verification. To solve these problems, this work proposes Capability- & Blockchainbased Fine-grained and Flexible Access Control (CB2FAC), defines new capability authorisation rules, and designs an authorisation tree with capability revocation list to meet the needs of flexibility and timeliness of capability revocation. Finally, simulation experiments show that CB2FAC is flexible and secure in granting, revoking, granularity control and verification of capability, and significantly outperforms the existing schemes in terms of performance.

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.

The wave equation as a differentiable-programmable computer for the design of acoustic metamaterials

Designing new metamaterials enables fine-grain control of wave dynamics. Applications of these materials range from wave steering devices for seismic wave manipulation, to “super-focusing” devices for high-resolution ultrasound imaging and surgery. Control of systems governed by partial differential equations is an inherently hard problem. Specifically, control of wave dynamics is a challenging problem, because of the additional physical constraints and intrinsic properties of wave phenomena such as attenuation, reflection, and scattering. In this work, we propose a novel method for transient control of wave equations with Model Predictive Control. The proposed model incorporates the essential physical properties observed in the original dynamics directly into its lower dimensional latent space. We show that our model is capable of using its latent dynamics to predict scalar integral quantities over a variable length timespan. Moreover, it is fully interpretable by the corresponding physical quantities, which allows it to guarantee solution properties. We demonstrate this method by solving important cases of wave equation, which have not been solved before, such as control of transient waves. We make our code publicly available.

Unmodificated stepless regulation of CRISPR/Cas12a multi-performance.

As CRISPR technology is promoted to more fine-divided molecular biology applications, its inherent performance finds it increasingly difficult to cope with diverse needs in these different fields, and how to more accurately control the performance has become a key issue to develop CRISPR technology to a new stage. Herein, we propose a CRISPR/Cas12a regulation strategy based on the powerful programmability of nucleic acid nanotechnology. Unlike previous difficult and rigid regulation of core components Cas nuclease and crRNA, only a simple switch of different external RNA accessories is required to change the reaction kinetics or thermodynamics, thereby finely and almost steplessly regulating multi-performance of CRISPR/Cas12a including activity, speed, specificity, compatibility, programmability and sensitivity. In particular, the significantly improved specificity is expected to mark advance the accuracy of molecular detection and the safety of gene editing. In addition, this strategy was applied to regulate the delayed activation of Cas12a, overcoming the compatibility problem of the one-pot assay without any physical separation or external stimulation, and demonstrating great potential for fine-grained control of CRISPR. This simple but powerful CRISPR regulation strategy without any component modification has pioneering flexibility and versatility, and will unlock the potential for deeper applications of CRISPR technology in many finely divided fields.

Proof of retrievability with flexible designated verification for cloud storage

With numerous applications in cloud storage, Proof of Retrievability (PoR) is an efficient solution for verification and retrieval of big data on a remote server. Existing PoR schemes can support two mutually exclusive verification modes, namely either private verifiability or public verifiability, depending on whether the outsourced data is verifiable by the data owner (user) only or by everyone. In order to allow the user to flexibly designate the capability of verification, we propose a novel framework for PoR schemes that enables fine-grained control of remote data verification for cloud storage. Our idea is enabling the user to dynamically release some tokens (verification keys) to one or more third parties (designated verifiers), such that only the user and the designated verifiers can verify and retrieve the outsourced data from the server. We formalize this notion as PoR with Flexible Designated Verification (PoR-FDV), and define an extended adversarial model for PoR-FDV based on Juel et al.'s PoR model. Under the new framework, we propose a generic, simple and elegant construction of PoR-FDV from public verifiable PoR and identity-based KEM/DEM, which then gives birth to a concrete and efficient PoR-FDV scheme. Compared to existing PoR schemes, PoR-FDV provides higher flexibility and scalability, as well as many useful features.