Reviewerly/exHarmony huggingface dataset:
Ensemble Learning for Predicting Task Connectivity Over Time in Cloud Data Centers

The rapid growth and interdependencies of cloud-hosted services and applications have made it imperative to optimize data center resource management while maintaining low operating costs. Inefficient resource use, rising energy consumption, and operating costs impact cloud provider's ability to provide high-quality services on an elastic basis. However, changing the selection and decision-making processes based on how the tasks are set up can improve scheduling and resource management in cloud data centers. In this paper, we develop a multivariate time series prediction model for task connectivity based on windowing characterization and ensemble learning methods. The high cardinality features are handled using a counter encoder, and the task trace data is transformed using a sliding window, from which features are extracted and used in conjunction with the task profile data to train and tune the candidate estimators. The best model outcomes are then used to construct an ensembled estimator. As part of the evaluation, a baseline comparison is performed in order to determine how well ensemble learning predicts task connectivity over time. The model outcomes are assisted using standard classification metrics such as accuracy, precision, and recall, including the F1 score, Kappa, and Matthews correlation coefficient. The results show that the proposed model outperformed the traditional models in most performance metrics, indicating the successful implementation of an ensemble learning approach for task connectivity predictions in large-scale cloud data centers.
Robust Federated Learning against Backdoor Attackers

Federated learning is a privacy-preserving alter-native for distributed learning with no involvement of data transfer. As the server does not have any control on clients' actions, some adversaries may participate in learning to introduce corruption into the underlying model. Backdoor attacker is one such adversary who injects a trigger pattern into the data to manipulate the model outcomes on a specific sub-task. This work aims to identify backdoor attackers and to mitigate their effects by isolating their weight updates. Leveraging the correlation between clients' gradients, we propose two graph theoretic algorithms to separate out attackers from the benign clients. Under a classification task, the experimental results show that our algorithms are effective and robust to the attackers who add backdoor trigger patterns at different location in targeted images. The results also evident that our algorithms are superior than existing methods especially when numbers of attackers are more than the normal clients.
Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks

The growing number of devices using the wireless spectrum makes it important to find ways to minimize interference and optimize the use of the spectrum. Deep learning models, such as convolutional neural networks (CNNs), have been widely utilized to identify, classify, or mitigate interference due to their ability to learn from the data directly. However, there have been limited research on the complexity of such deep learning models. The major focus of deep learning-based wireless classification literature has been on improving classification accuracy, often at the expense of model complexity. This may not be practical for many wireless devices, such as, internet of things (IoT) devices, which usually have very limited computational resources and cannot handle very complex models. Thus, it becomes important to account for model complexity when designing deep learning based models for interference classification. To address this, we conduct an analysis of CNN based wireless classification that explores the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty: namely, interference classification, heterogeneous transmitter classification, and homogeneous transmitter classification. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model, providing important insights into the use of CNNs in computationally constrained applications.
Enabling Security Research Through Efficient Partial Deployment Topology Configuration and Validation

How to measure security value in partial deployments has long been a consideration for the Internet research community. Without clear security outcomes, adoption of security mechanisms may take years before users begin to see any benefit. This lack of clarity can serve to further delay adoption as incentives to implement are often outweighed by additional costs or complexity. While prior efforts have looked at theoretical approaches to estimate this critical mass of partial deployment within a topology, no effort has been able to effectively simulate and measure such an outcome. In this work, we provide an early effort to demonstrate how topology simulation can be used to effectively deploy and measure partial deployments of RPKI utilizing the SEED Internet Emulator. Our efforts show that this approach can be used to simulate large networks and provide an effective means to measure partial deployment value of security protocol deployments. Further, we demonstrate that adoption rates of greater than fifty percent begin to show exponential return on security outcomes for both adopters and non-adopters alike.
A Fully Distributed Training for Class Incremental Learning in Multihead Networks

Due to good elastic scalability, multi-head network is favored in incremental learning (IL). During IL process, the model size of multi-head network continually grows with the increasing number of branches, which makes it difficult to store and train within a single node. To this end, within model parallelism framework, a distributed training architecture together with its pre-requisite is proposed. Based on the assumption that the pre-requisite is satisfied, a distributed training algorithm is proposed. In addition, to avoid the dilemma that prevalent cross-entropy (CE) loss function does not fit distributed setting, a fully distributed cross-entropy (D-CE) loss function is proposed, which avoids information exchange among nodes. Corresponding training based on D-CE is proposed (D-CE-Train). This method avoids model size expansion problem in centralized training. It employs distributed implementation to speed up training, and reduces the interaction between multiple nodes that may significantly slow down the training. A series of experiments verify the effectiveness of the proposed method.
Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning

Federated learning (FL) has been proposed as a promising distributed learning paradigm to realize edge artificial intelligence (AI) without revealing the raw data.Nevertheless, it would incur inevitable costs in terms of training latency and energy consumption, due to periodical communication between user equipments (UEs) and the geographically remote central parameter server.Thus motivated, we study the joint edge aggregation and association problem to minimize the total cost, where the model aggregation over multiple cells just happens at the network edge.After proving its hardness with complex coupled variables, we transform it into a set function optimization problem and prove the objective function is neither submodular nor supermodular, which further complicates the problem.To tackle this difficulty, we first split it into multiple edge association subproblems, where the optimal solution to the computation resource allocation can be efficiently obtained in the closed form.We then construct a substitute function with the supermodularity and provable upper bound.On this basis, we reformulate an equivalent set function minimization problem under a matroid base constraint.We then propose an approximation algorithm to the original problem based on the two-stage search strategy with theoretical performance guarantee.Both extensive simulations and field experiments are conducted to validate the effectiveness of our proposed solution.
Asynchronous Federated Unlearning

Thanks to regulatory policies such as the General Data Protection Regulation (GDPR), it is essential to provide users with the right to erasure regarding their own private data, even if such data has been used to train a neural network model. Such a machine unlearning problem becomes even more challenging in the context of federated learning, where clients collaborate to train a global model with their private data. When a client requests its data to be erased, its effects have already gradually permeated through a large number of clients, as the server aggregates client updates over multiple communication rounds. All of these affected clients need to participate in the retraining process, leading to prohibitive retraining costs with respect to the wall-clock training time.In this paper, we present the design and implementation of Knot, a new clustered aggregation mechanism custom-tailored to asynchronous federated learning. The design of Knot is based upon our intuition that, with asynchronous federated learning, clients can be divided into clusters, and aggregation can be performed within each cluster only so that retraining due to data erasure can be limited to within each cluster as well. To optimize client-cluster assignment, we formulated a lexicographical minimization problem that could be transformed into a linear programming problem and solved efficiently. Over a variety of datasets and tasks, we have shown clear evidence that Knot outperformed the state-of-the-art federated unlearning mechanisms by up to 85% in the context of asynchronous federated learning.
Pragmatic Communication: Bridging Neural Networks for Distributed Agents

In this paper, an intelligence-to-intelligence communication design with a language generation scheme is studied. The concepts and features of pragmatics and pragmatic communication are first discussed and defined from a linguistic point of view: intelligence-to-intelligence communication in a certain environment, using task performance as the evaluation criterion, with the inputs of the goal and the construction of the environment, and the output of task completion. Then, we propose the “glue neural layer” (GNL) design to bridge two intelligence to form a deeper neural network for effective and efficient communication training. Based on the design of GNL, we shed light on the thoughts about the relationship between the structure of languages and neural networks. Furthermore, a neuromorphic framework of pragmatic communication is proposed to find a base for further discussion. Experiments show that GNL design can dramatically change performance. Finally, the advantage of pragmatic and several open research problems are discussed.
Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency

Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private raw data. Prior works on the convergence analysis of FL have focused on mini-batch size and aggregation frequency separately. However, increasing the batch size and the number of local updates can differently affect model performance and system overhead. This paper proposes a novel model in quantifying the interplay of FL mini-batch size and aggregation frequency to navigate the unique trade-offs among convergence, completion time, and resource cost. We obtain a new convergence bound for synchronous FL with respect to these decision variables under heterogeneous training datasets at different devices. Based on this bound, we derive closed-form solutions for co-optimized mini-batch size and aggregation frequency, uniformly among devices. We then design an efficient exact algorithm to optimize heterogeneous mini-batch configurations, further improving the model accuracy. An adaptive control algorithm is also proposed to dynamically adjust the batch sizes and the number of local updates per round. Extensive experiments demonstrate the superiority of our offline optimized solutions and online adaptive algorithm.
NFChain: A Practical Fingerprinting Scheme for NFC Tag Authentication

NFC tag authentication is highly demanded to avoid tag abuse.Recent fingerprinting methods employ the physicallayer signal, which embeds the tag hardware imperfections for authentication.However, existing NFC fingerprinting methods suffer from either low scalability for a large number of tags or incompatibility with NFC protocols, impeding the practical application of NFC authentication systems.To fill this gap, we propose NFChain, a new NFC fingerprinting scheme that excavates the tag hardware uniqueness from the protocol-agnostic tag response signal.Specifically, we harness an agile and compatible frequency band of NFC to extract the tag fingerprint from a chain of tag responses over multiple frequencies, which significantly improves fingerprint scalability.However, extracting the desired fingerprint encounters two practical challenges: (1) fingerprint inconsistency under different NFC reader and tag configurations and (2) fingerprint variations across multiple measurements of the same tag due to the signal noise in generic readers.To tackle these challenges, we first design an effective nulling method to eliminate the effect of device configurations.Second, we employ contrastive learning to reduce fingerprint variations for accurate authentication.Extensive experiments show we can achieve as low as 3.7% FRR and 4.1% FAR for over 600 tags.
ASTrack: Automatic Detection and Removal of Web Tracking Code with Minimal Functionality Loss

Recent advances in web technologies make it more difficult than ever to detect and block web tracking systems. In this work, we propose ASTrack, a novel approach to web tracking detection and removal. ASTrack uses an abstraction of the code structure based on Abstract Syntax Trees to selectively identify web tracking functionality shared across multiple web services. This new methodology allows us to: (i) effectively detect web tracking code even when using evasion techniques (e.g., obfuscation, minification, or webpackaging); and (ii) safely remove those portions of code related to tracking purposes without affecting the legitimate functionality of the website. Our evaluation with the top 10k most popular Internet domains shows that ASTrack can detect web tracking with high precision (98%), while discovering about 50k tracking code pieces and more than 3,400 new tracking URLs not previously recognized by most popular privacy-preserving tools (e.g., uBlock Origin). Moreover, ASTrack achieved a 36% reduction in functionality loss in comparison with the filter lists, one of the safest options available. Using a novel methodology that combines computer vision and manual inspection, we estimate that full functionality is preserved in more than 97% of the websites.
Communication Efficient Secret Sharing with Dynamic Communication-Computation Conversion

Secret Sharing (SS) is widely adopted in secure Multi-Party Computation (MPC) with its simplicity and computational efficiency. However, SS-based MPC protocol introduces significant communication overhead due to interactive operations on secret sharings over the network. For instance, training a neural network model with SS-based MPC may incur tens of thousands of communication rounds among parties, making it extremely hard for real-world deployment.To reduce the communication overhead of SS, prior works statically convert interactive operations to equivalent non-interactive operations with extra computation cost. However, we show that such static conversion misses chances for optimization, and further present SOLAR, an SS-based MPC framework that aims to reduce the communication overhead through dynamic communication-computation conversion. At its heart, SOLAR converts interactive operations that involve communication among parties to equivalent non-interactive operations within each party with extra computations and introduces a speculative strategy to perform opportunistic conversion when CPU is idle for network transmission. We have implemented and evaluated SOLAR on several popular MPC applications, and achieved 1.6-8.1 times speedup in multi-thread setting compared to the basic SS and 1.2-8.6 times speedup over static conversion.
Demo: Missed An Exit? Confusion Zone Detection

Drivers may get confused for any number of reasons. For example, due to confusion, they miss their intended exits in some regions. In such cases, confused drivers should loop back around. However, some drivers exhibit risky driving (e.g., back up on the highway or perform abrupt lane changes). Inferring such zones where most drivers get confused could be helpful. Drivers can use such prior knowledge and plan their exits or turns accordingly. In this paper, we focus on this use case. Connected vehicles share their location data with the remote server, and the remote server examines data to identify turn loops in real time. The region becomes a confusion zone when a turn loop is executed by several vehicles. We demonstrate the feasibility of confusion zone detection through a simulation-based demonstration.
Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression

Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. However, the existing works fail to address all these practical concerns in FL: limited communication resources, dynamic network conditions and heterogeneous client properties, which slow down the convergence of FL. To tackle the above challenges, we propose a heterogeneity-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Specifically, the parameter server (PS) selects a representative client subset considering statistical heterogeneity and sends the global model to them. After local training, these selected clients upload compressed model updates matching their capabilities to the PS for aggregation, which significantly alleviates the communication load and mitigates the straggler effect. We theoretically analyze the impact of both client selection and gradient compression on convergence performance. Guided by the derived convergence rate, we develop an iteration-based algorithm to jointly optimize client selection and compression ratio decision using submodular maximization and linear programming. Extensive experiments on both real-world prototypes and simulations show that FedCG can provide up to 5.3× speedup compared to other methods.
A Noise-Robust Radio Frequency Fingerprint Identification Scheme for Internet of Things Devices

Radio frequency fingerprint (RFF) identification is a potentially effective technique to address the authentication security of Internet of Things (IoT) devices. Since the complex working environment and limited resources of IoT devices, noise is non-negligible in RFF identification of IoT devices. It is a challenge to suppress the noise without damaging the RFF information. In this paper, we propose a robust RFF identification scheme, which consists of a frequency point selection (FPS) based denoising algorithm, and a convolutional neural network (CNN) classifier. The FPS algorithm performs denoising by filtering out all the frequency components that are independent of the RFF. The CNN is designed with a dynamically decreasing learning rate to accelerate learning and obtain optimal identification performance. Experiments were conducted with 54 ZigBee devices to evaluate the performance of the proposed scheme under three different RFF identification scenarios. The results show that the FPS algorithm brings the highest accuracy improvement of about 25 % when the training signal-to-noise ratio (SNR) is hybrid and the test SNR is 0 dB.
StreamSwitch: Fulfilling Latency Service-Layer Agreement for Stateful Streaming

Distributed stream systems provide low latency by processing data as it arrives. However, existing systems do not provide latency guarantee, a critical requirement of real-time analytics, especially for stateful operators under burst and skewed workload. We present StreamSwitch, a control plane for stream systems to bound operator latency while optimizing resource usage. Based on a novel stream switch abstraction that unifies dynamic scaling and load balancing into a holistic control framework, our design incorporates reactive and predictive metrics to deduce the healthiness of executors and prescribes practically optimal scaling and load balancing decisions in time. We implement a prototype of StreamSwitch and integrate it with Apache Flink and Samza. Experimental evaluations on real-world applications and benchmarks show that StreamSwitch provides cost-effective solutions for bounding latency and outperforms the state-of-the-art alternative solutions.
Fast Generation-Based Gradient Leakage Attacks against Highly Compressed Gradients

Federated learning (FL) is a distributed machine learning technology that preserves data privacy. However, it has been shown to be vulnerable to gradient leakage attacks (GLA), which can reconstruct private training data from public gradients with an overwhelming probability. Nevertheless, these attacks either require modification of the FL model (analytics-based) or take a long time to converge (optimization-based) and fail in dealing with highly compressed gradients in practical FL systems. In this paper, we pioneer a generation-based GLA method called FGLA that can reconstruct batches of user data, forgoing the optimization process. Specifically, we design a feature separation technique that extracts the feature of each data in a batch and then generates user data directly. Extensive experiments on multiple image datasets demonstrate that FGLA can reconstruct user images in milliseconds with a batch size of 256 from highly compressed gradients (0.8% compression ratio or higher), thus substantially outperforming state-of-the-art methods.
PENGEMBANGAN E-KOMIK SEBAGAI MEDIA PEMBELAJARAN KEAMANAN JARINGAN MATERI KRIPTOGRAFI

Technology advances do not always have a positive effect on interest reading and learning student. From the facts that are often encountered, indicates that there are many students who do not like learning network security caused by several factors. Problems in this study is how the qualifications and the response of students to education media e-comics in cryptographic material. This study aimed to get a description of the qualifications e-comics and get response from the students to e-comics in the learning network security cryptographic material. This research method use a method of development research ADDIE (Analysis, Design, Development, Implementation, Evaluation). Instrument data sources using a questionnaire. validation of this research involve materials validator and media validator. The subjects were 25 students of Information Technology Education 2013 Unesa. The results showed appraisement of qualication from the validator is 84.5%. Thus e-Comics media can be said to be Very Well worth as a education learning of students.