Reviewerly/exHarmony huggingface dataset:
Joint heterogeneity-aware personalized federated search for energy efficient battery-powered edge computing

The limited battery capacity of edge devices has a significant impact on the deployment of Federated Learning (FL). As a result, maintaining computation sustainability is a critical issue for edge FL. Furthermore, the heterogeneities of deployed edge devices reduce FL efficiency and effectiveness, making FL computation sustainability more challenging to maintain. To address these issues raised by heterogeneities, we perform a joint heterogeneity-aware personalized federated search for energy-efficient edge computing. To achieve energy-efficient on-device inference and training, a one-training process is adopted to search for personalized partial network structures on each device. We begin by tailoring the network scale on each node based on the efficiency of model inference, which also serves as the search space for optimization. This strategy can mitigate the straggler problem and improve the energy efficiency of FL by guiding the efficient FL training process in each round. To further optimize the energy consumption of edge devices, we design a lightweight search controller during the search process. This controller meets the low energy consumption requirements of the edge devices and reduces their energy consumption during the search process. Finally, we introduce an adaptive search strategy to guarantee personalized training convergence on each device. By reducing the energy consumption of each training round and ensuring the training convergence of personalized models, we can significantly improve the energy efficiency of FL on battery-powered edge devices. Our framework can obtain competitive accuracy with state-of-the-art methods while improving inference efficiency by up to 1.43× and training energy efficiency by up to 2.63×.
Automatic information extraction in the AI chip domain using gated interactive attention and probability matrix encoding method

Artificial intelligence (AI) that utilizes neural networks (NNs) has a broad range of applications. However, NNs necessitate significant amounts of computation and data storage, which imposes considerable hardware demands and drives the need for AI chip research. To address this need, AI chip researchers must delve into extensive literature for entity information on hardware issues, architectures, optimizations, performances, and applications. Nevertheless, the influx of relevant papers has far surpassed their ability to read and absorb all the information. In this article, automatic information extraction in the AI chip domain using entity recognition techniques is conducted to alleviate the burden of paper reading for AI chip researchers. Our approach involves creating a manually annotated dataset called the ACER dataset to support automatic information extraction from literature in the field of AI chips. To address the challenge of recognizing entities with complex structures and lacking explicit features, the GIA-PME model is proposed, which utilizes a gated interaction attention mechanism and probability-matrix encoding. Our proposed approach enhances entity cognition by utilizing the keyword sequence of each entity type as prior knowledge. It also incorporates a dedicated embedding to learn the eigenvectors of entity structures. Finally, it combines the prior knowledge with eigenvectors using a designed gated interactive attention mechanism to assist with recognition. In addition, the proposed probability matrix encoding is used to detect nested entities to avoid information loss. Experimental results show that our GIA-PME model achieves the best performance compared to existing models, thus improving the strict/relaxed F1-score to 70.9/81.7 (+5.3 ∼ 9.1) on the ACER dataset.
Gradient distribution-aware INT8 training for neural networks

Recently, low bit-width quantization (e.g., INT8) has been commonly used in deep neural network inference acceleration, but fewer researchers have focused on low-precision training quantization techniques. Considering that the backward propagation in deep neural network training is more computationally intensive and has a heavier energy overhead than the inference process, the quantization of backward propagation is of great interest for the training of very large-scale neural networks as well as for low-power devices with online training requirements. However, the shape specificity and continuous variability of the gradient distribution make gradient quantization difficult, and many studies propose various complex quantization methods for the gradient to reduce training accuracy loss. In this paper, we propose two innovative techniques mainly for INT8 quantization training, including the Data-aware Dynamic Segmentation Quantization scheme to quantize various special gradient distributions and the Update Direction Periodic Search strategy to achieve lower quantization errors. Then, we build a distribution-aware INT8 quantization training framework based on these two methods and conduct experiments on various models and tasks. Experimental results show that our proposed INT8 quantization training method achieves a negligible loss in final training accuracy compared to the full-precision floating-point counterpart on different models, including ResNet, MobileNetV2, VGG, AlexNet, and LSTM. By replacing floating-point computing with 8-bit integer computing for network training, this INT8 quantization training framework provides the possibility of deploying online training directly on low-power devices in the future.
Evolving stochastic configure network: A more compact model with interpretability

Stochastic Configure Networks (SCNs) are an incremental variant of randomly weighted neural networks of which one of the key highlights lies in the constraints while adding hidden layer nodes. Recent studies show two weaknesses of SCNs. One is the redundancy of added hidden layer nodes while the other is the lack of interpretability to the designed constraints. Motivated by overcoming these weaknesses, this paper proposes a new model named Evolving SCN to increase the interpretability of the design for constraints from the viewpoint of evolution by a sampling mechanism and to promote the model compactness by optimizing random weights within the space of constraint parameters. Surprisingly, although an evolving method is used in our model, the effectiveness and efficiency are significantly improved with respect to running time and prediction accuracy in comparison with the existing versions of SCNs. This work makes a first attempt to enhance the interpretability of incrementally adding nodes and simultaneously reduce the redundancy of hidden nodes in SCNs, which brings some new insights to understand the model compactness from the view of Occam's razor and further to the nature of incremental learning.
Do all roads lead to Rome? Studying distance measures in the context of machine learning

Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an overview of the use of these measures in the most common machine learning problems, pointing out those aspects to consider to choose the most appropriate measure for a particular task. For this purpose, the most recent works addressing the subject were reviewed and seven of the most commonly used measures were analyzed, investigating in detail their main properties and applications. Different experiments were carried out to study their relationships and compare their performance. The degradation of the results in the presence of noise was also considered, as well as the execution time required by each measure.
Semantic-agnostic progressive subtractive network for image manipulation detection and localization

In this paper, we propose a new detection and localization framework capable of detecting suspicious forgeries using the Semantic-Agnostic Progressive Subtractive Network (SAPS-Net). Our approach is based on the key observation that fluctuations in image content severely interfere with the capture of general manipulations by existing convolutional architecture. Distinct from the aggregation attention employed by traditional methods, we design the Semantic-Agnostic Manipulation Attention (SAMA) based on subtractive operation for mitigating the effect of rich image semantics on manipulation extraction. Initially, the Multi-Scale feature Iterative Fusion Block (MSIFB) and Multi-Kernel feature Fusion Residual Block (MKFRB) are designed to iteratively crawl potential semantic associations of different hierarchical feature mappings. Then, we further devise the subtractive operation to effectively remove the semantic associations as distractors and promote the network to adaptively learn general forgery. Notably, these semantic associations based on image content may be fundamentally different from the manipulation traces that alter the internal patterns of images. By progressively utilizing SAMAs, the network remains robust to image content manipulation with rich semantics. Extensive experiments on six challenge datasets show that our approach has more than 3.03% pixel-level AUC gains and 3.70% image-level AUC gains in cross-dataset scenarios compared to state-of-the-art methods, especially on the IMD20 dataset (pixel-level AUC: 0.859) and Wild dataset (pixel-level AUC: 0.821) with realistic scenarios.
Ensemble strategy learning for imperfect information games

Algorithms with several paradigms (such as rule-based methods, game theory and reinforcement learning) have achieved great success in solving imperfect information games (IIGs). However, agents based on a single paradigm tend to be brittle in certain aspects due to the paradigm’s weaknesses. In this paper, we first present three base-solvers with diversified paradigms for IIGs, and then combine them to design three ensemble-solvers (including an attention ensemble-solver, a gradient ensemble-solver and an evolution ensemble-solver) to learn ensemble strategies given base-solvers’ strengths. We evaluate our methods on Leduc poker with nonstationary opponents and limited games. The results show that our ensemble strategy learning method can effectively integrate the advantages of various advanced individual algorithms and significantly outperform them.
A kernel-enriched order-dependent nonparametric spatio-temporal process

Spatio-temporal processes are necessary modeling tools for various environmental, biological, and geographical problems. The underlying model is commonly considered to be parametric and to be a Gaussian process. Additionally, the covariance function is expected to be stationary and separable. This structure need not always be realistic. Moreover, attempts have been made to construct nonparametric processes of neither stationary nor separable covariance functions. Nevertheless, as we elucidate, some desirable and necessary spatio-temporal properties are not guaranteed by the existing approaches, thus, calling for further innovative ideas. In this article, using kernel convolution of order-based dependent Dirichlet process, we construct a novel spatio-temporal model. We show that this satisfies desirable properties and includes the stationary, separable, parametric processes as special cases. Our resultant posterior distribution is variable dimensional, which we attack using Transdimensional Transformation based Markov Chain Monte Carlo, which can update all the variables and change dimensions using deterministic transformations of a random variable drawn from some arbitrary density defined on relevant support. We demonstrate our model’s performance on simulated and real data sets. In all situations, the findings are highly encouraging.
A Solid Electrolyte ZnO Thin Film Transistor for classification of spoken digits using Reservoir Computing

The duration and interval of the input signal in two terminal memristor based reservoir systems is constrained by the decay time of a conventional memristor. Here, we demonstrate that the third terminal of a Solid Electrolyte <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ZnO}/\text{Ta}_{2}\mathrm{O}_{5}$</tex> Thin-film Transistor (SE-FET) can be used to control the decay time by using a variable read voltage, without any additional circuit elements. Using this approach, we have benchmarked the performance of our SE-FET based RC system for a task of recognition of spoken digits with a high accuracy of 99.4%.
Neural Network Quantization is All You Need for Energy Efficient ISP

It is common to devise and develop a Hand Crafted Feature for the modules constituting the CIS ISP Chain. This not only requires specialized domain knowledge, but also has a limitation in the degree of image quality improvement, and there have been many attempts to introduce deep learning to overcome this. However, it is very challenging to circuit the deep learning model directly on the CMOS sensor. Therefore, this study improves energy efficiency through Ultra Low Bit Quantization for demosaicing functions existing in the ISP chain, and proposes an ultra-high-speed/ultra-light model that enables Integer Only Inference. The proposed methodology can be utilized as an element technology that can be applied to deep learning models for other functions constituting ISP.
Reliability Prediction for Automotive 5nm and 7nm Technology node by using Machine Learning based Solution

Automotive semiconductor products demand high reliability requirements. Thus, only electrical test after fab-out may not be sufficient for a reliability management. This paper proposes a machine learning (ML) based solution to predict reliability in real time during the middle of wafer processing. The solution includes fab-data augmentation (FDA) to estimate missing values by using partially available measurement data. This is followed by building a ML model to identify and rank the critical process steps that impact reliability. ML model is also used to predict reliability of wafers to screen low reliability wafers out in advance during the fab process by real time monitoring.
Human, What Must I Tell You?

Artificial intelligence (AI)-assisted code generation is everywhere these days. Undoubtedly, AI will help near-future developers substantially by providing code suggestions and automation. In this application, explainability will be a key quality attribute. But what needs to be explained to whom? And how to deliver the explanations nonintrusively?
Don’t Lie to Me: Avoiding Malicious Explanations With <i>STEALTH</i>

STEALTH is a method for using some artificial intelligence-generated models without suffering from malicious attacks or associated unfairness issues. STEALTH asks so few queries (one per data cluster) that malicious algorithms cannot detect its operation or know when to lie.
A side-channel attack on a masked and shuffled software implementation of Saber

Abstract In this paper, we show that a software implementation of IND-CCA-secure Saber key encapsulation mechanism protected by first-order masking and shuffling can be broken by deep learning-based power analysis. Using an ensemble of deep neural networks trained at the profiling stage, we can recover the session key and the secret key from $$257 \times N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>257</mml:mn> <mml:mo>×</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> and $$24 \times 257 \times N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>24</mml:mn> <mml:mo>×</mml:mo> <mml:mn>257</mml:mn> <mml:mo>×</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> traces, respectively, where N is the number of repetitions of the same measurement. The value of N depends on the implementation of the algorithm, the type of device under attack, environmental factors, acquisition noise, etc.; in our experiments $$N = 10$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>10</mml:mn> </mml:mrow> </mml:math> is sufficient for a successful attack. The neural networks are trained on a combination of 80% of traces from the profiling device with a known shuffling order and 20% of traces from the device under attack captured for all-0 and all-1 messages. “Spicing” the training set with traces from the device under attack helps us minimize the negative effect of inter-device variability.
Robust Recommendation with Adversarial Gaussian Data Augmentation

Recommender system holds the promise of accurately understanding and estimating the user preferences. However, due to the extremely sparse user-item interactions, the learned recommender models can be less robust and sensitive to the highly dynamic user preferences and easily changed recommendation environments. To alleviate this problem, in this paper, we propose a simple yet effective robust recommender framework by generating additional samples from the Gaussian distributions. In specific, we design two types of data augmentation strategies. For the first one, we directly produce the data based on the original samples, where we simulate the generation process in the latent space. For the second one, we firstly change the original samples towards the direction of maximizing the loss function, and then produce the data based on the altered samples to make more effective explorations. Based on both of the above strategies, we leverage adversarial training to optimize the recommender model with the generated data which can achieve the largest losses. In addition, we theoretically analyze our framework, and find that the above two data augmentation strategies equal to impose a gradient based regularization on the original recommender models. We conduct extensive experiments based on six real-world datasets to demonstrate the effectiveness of our framework.
Secure and resilient remote access is what really matters

Forget the ‘edge vs cloud’ debate. These technologies each have their place, but they are both vulnerable to certain kinds of threats. And it is these risks that should be front of mind – especially for organisations adapting to new kinds of working.
An Autonomous Resource Management Model towards Cloud Morphing

This paper proposes an autonomous resource management model for future edge clouds with abundant and diverse computing resources. The model utilizes a pseudo cost function to allocate resources based on load, and a convex cost function to enable load balancing and idle-resource pooling. Stakeholders can manipulate the cost function or resource weights for micro-jobs to control resource utilization. We explore various cost tuning methods and present their performance through simulations.
ESCEPE: Early-Exit Network Section-Wise Model Compression Using Self-distillation and Weight Clustering

Deploying deep learning models on resource-constrained (edge) devices is challenging due to their high computational demands and large model sizes. Early-exit neural networks are one of the approaches to make deep learning models more efficient for resource-constrained devices by reducing computational cost and latency. However, even with early-exit neural networks, the model size may remain a problem when deploying them on edge devices. To address this problem, we propose a section-wise model compression technique for compressing an early-exit neural network with intermediate classifiers. Our approach divides the model into a few sections and uses different compression settings in the weight clustering-based compression for each section to prevent accuracy loss in the intermediate sections. We demonstrate that knowledge distillation can be used in the retraining phase to transfer knowledge from uncompressed to compressed sections and to accelerate the recovery of performance reduction after the weight clustering stages. The performance evaluation of our proposed method on CIFAR10 and CIFAR100 datasets using ResNet and WideResNet architectures demonstrates that the proposed technique can compress an early-exit neural network with a high compression ratio with minimal impact on the accuracy of intermediate classifiers. The proposed method achieves compression ratios of more than 36 and 22 times for ResNet18 with three shallow classifiers on CIFAR10 and CIFAR100, respectively, with an ensemble accuracy loss of less than 1%. By eliminating shallow classifiers from the early-exit model, the static model can achieve compression ratios of up to 64 and 52 times for ResNet18 and WideResNet50, respectively, on the CIFAR10 dataset with an accuracy loss of less than 2.5%.
VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild

Deep neural network models for IMU sensor-based human activity recognition (HAR) that are trained from controlled, well-curated datasets suffer from poor generalizability in practical deployments. However, data collected from naturalistic settings often contains significant label noise. In this work, we examine two in-the-wild HAR datasets and DivideMix, a state-of-the-art learning with noise labels (LNL) method to understand the extent and impacts of noisy labels in training data. Our empirical analysis reveals that the substantial domain gaps among diverse subjects cause LNL methods to violate a key underlying assumption, namely, neural networks tend to fit simpler (and thus clean) data in early training epochs. Motivated by the insights, we design VALERIAN, an invariant feature learning method for in-the-wild wearable sensor-based HAR. By training a multi-task model with separate task-specific layers for each subject, VALERIAN allows noisy labels to be dealt with individually while benefiting from shared feature representation across subjects. We evaluated VALERIAN on four datasets, two collected in a controlled environment and two in the wild. Experimental results show that VALERIAN significantly outperforms baseline approaches. VALERIAN can correct 75% – 93% of label errors in the source domains. When only 10-second clean labeled data per class is available from a new target subject, even with 40% label noise in training data, it achieves test accuracy.
Optimizing Neural Network Efficiency with Hybrid Magnitude-Based and Node Pruning for Energy-efficient Computing in IoT

The Deep Neural Networks (DNN) are computationally intensive in terms of processing, energy and memory which becomes a bottleneck to run these models on edge devices. This research study provides a technique for pruning the neural networks to enhance the performance of deep learning models in IoT devices. The proposed method combines magnitude-based pruning, which merges insignificant weights based on their magnitude, with node pruning, which eliminates insignificant nodes based on their contribution to the network. The hybrid pruning technique is designed to be energy-efficient, reducing the computational overhead of deep learning models while maintaining their accuracy. The experimental results demonstrate that the proposed method can achieve significant reductions in model size and energy consumption with minimal loss in accuracy. The technique has the potential to enable the deployment of deep learning models on resource constrained IoT devices.
Poster Abstract: Learning-based Sensor Scheduling for Event Classification on Embedded Edge Devices

Incremental learning on embedded edge devices is feasible nowadays due to the increasing computational power of these devices and the reduction techniques applied to simplify the model. However, edge devices still require significant time to update the learning model and such time is hard to be obtained due to other tasks, such as sensor data pulling, data preprocessing, and classification. In order to secure the time for incremental learning and to reduce energy consumption, we need to schedule sensing activities without missing any events in the environment. In this paper, we propose a reinforcement learning-based sensor scheduler that dynamically determines the sensing interval for each classification moment by learning the patterns of event classes. The initial results are promising compared to the existing scheduling approach.