OpenRaiser/Intern-Atlas huggingface dataset:
Applying Evolutionary Algorithms Successfully: A Guide Gained from Real-world Applications
Venue: arXiv cs.NE 2021
Year: 2021

Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization. This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated.
Mitigating Power Attacks through Fine-Grained Instruction Reordering
Venue: arXiv cs.CR 2021
Year: 2021

Side-channel attacks are a security exploit that take advantage of information leakage. They use measurement and analysis of physical parameters to reverse engineer and extract secrets from a system. Power analysis attacks in particular, collect a set of power traces from a computing device and use statistical techniques to correlate this information with the attacked application data and source code. Counter measures like just-in-time compilation, random code injection and instruction descheduling obfuscate the execution of instructions to reduce the security risk. Unfortunately, due to the randomness and excess instructions executed by these solutions, they introduce large overheads in performance, power and area. In this work we propose a scheduling algorithm that dynamically reorders instructions in an out-of-order processor to provide obfuscated execution and mitigate power analysis attacks with little-to-no effect on the performance, power or area of the processor. We exploit the time between operand availability of critical instructions (slack) to create high-performance random schedules without requiring additional instructions or static prescheduling. Further, we perform an extended security analysis using different attacks. We highlight the dangers of using incorrect adversarial assumptions, which can often lead to a false sense of security. In that regard, our advanced security metric demonstrates improvements of 34$\times$, while our basic security evaluation shows results up to 261$\times$. Moreover, our system achieves performance within 96% on average, of the baseline unprotected processor.
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0
Venue: arXiv cs.CL 2021
Year: 2021

Deep learning techniques have been shown to be efficient in various tasks, especially in the development of speech recognition systems, that is, systems that aim to transcribe an audio sentence in a sequence of written words. Despite the progress in the area, speech recognition can still be considered difficult, especially for languages lacking available data, such as Brazilian Portuguese (BP). In this sense, this work presents the development of an public Automatic Speech Recognition (ASR) system using only open available audio data, from the fine-tuning of the Wav2vec 2.0 XLSR-53 model pre-trained in many languages, over BP data. The final model presents an average word error rate of 12.4% over 7 different datasets (10.5% when applying a language model). According to our knowledge, the obtained error is the lowest among open end-to-end (E2E) ASR models for BP.
HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis
Venue: arXiv cs.AI 2021
Year: 2021

Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge. However, many of the existing drive-by monitoring approaches are based on supervised learning models that require labeled data from every bridge of interest, which is expensive and time-consuming, if not impossible, to obtain. To this end, we introduce a new framework that transfers the model learned from one bridge to diagnose damage in another bridge without any labels from the target bridge. Our framework trains a hierarchical neural network model in an adversarial way to extract task-shared and task-specific features that are informative to multiple diagnostic tasks and invariant across multiple bridges. We evaluate our framework on experimental data collected from 2 bridges and 3 vehicles. We achieve accuracies of 95% for damage detection, 93% for localization, and up to 72% for quantification, which are ~2 times improvements from baseline methods.
Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images
Venue: arXiv cs.LG 2021
Year: 2021

We analyze a dataset of retinal images using linear probes: linear regression models trained on some "target" task, using embeddings from a deep convolutional (CNN) model trained on some "source" task as input. We use this method across all possible pairings of 93 tasks in the UK Biobank dataset of retinal images, leading to ~164k different models. We analyze the performance of these linear probes by source and target task and by layer depth. We observe that representations from the middle layers of the network are more generalizable. We find that some target tasks are easily predicted irrespective of the source task, and that some other target tasks are more accurately predicted from correlated source tasks than from embeddings trained on the same task.
Error Floor Analysis of LDPC Column Layered Decoders
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we analyze the error floor of column layered decoders, also known as shuffled decoders, for low-density parity-check (LDPC) codes under saturating sum-product algorithm (SPA). To estimate the error floor, we evaluate the failure rate of different trapping sets (TSs) that contribute to the frame error rate in the error floor region. For each such TS, we model the dynamics of SPA in the vicinity of the TS by a linear state-space model that incorporates the information of the layered message-passing schedule. Consequently, the model parameters and the failure rate of the TS change as a result of the change in the order by which the messages of different layers are updated. This, in turn, causes the error floor of the code to change as a function of scheduling. Based on the proposed analysis, we then devise an efficient search algorithm to find a schedule that minimizes the error floor. Simulation results are presented to verify the accuracy of the proposed error floor estimation technique.
Efficient Inverse Design of 2D Elastic Metamaterial Systems Using Invertible Neural Networks
Venue: arXiv cs.CE 2021
Year: 2021

Locally resonant elastic metamaterials (LREM) can be designed, by optimizing the geometry of the constituent self-repeating unit cells, to potentially damp out vibration in selected frequency ranges, thus yielding desired bandgaps. However, it remains challenging to quickly arrive at unit cell designs that satisfy any requested bandgap specifications within a given global frequency range. This paper develops a computationally efficient framework for (fast) inverse design of LREM, by integrating a new type of machine learning models called invertible neural networks or INN. An INN can be trained to predict the bandgap bounds as a function of the unit cell design, and interestingly at the same time it learns to predict the unit cell design given a bandgap, when executed in reverse. In our case the unit cells are represented in terms of the width's of the outer matrix and middle soft filler layer of the unit cell. Training data on the frequency response of the unit cell is provided by Bloch dispersion analyses. The trained INN is used to instantaneously retrieve feasible (or near feasible) inverse designs given a specified bandgap constraint, which is then used to initialize a forward constrained optimization (based on sequential quadratic programming) to find the bandgap satisfying unit cell with minimum mass. Case studies show favorable performance of this approach, in terms of the bandgap characteristics and minimized mass, when compared to the median scenario over ten randomly initialized optimizations for the same specified bandgaps. Further analysis using FEA verify the bandgap performance of a finite structure comprised of $8\times 8$ arrangement of the unit cells obtained with INN-accelerated inverse design.
SODA: A Semantics-Aware Optimization Framework for Data-Intensive Applications Using Hybrid Program Analysis
Venue: arXiv cs.DC 2021
Year: 2021

In the era of data explosion, a growing number of data-intensive computing frameworks, such as Apache Hadoop and Spark, have been proposed to handle the massive volume of unstructured data in parallel. Since programming models provided by these frameworks allow users to specify complex and diversified user-defined functions (UDFs) with predefined operations, the grand challenge of tuning up entire system performance arises if programmers do not fully understand the semantics of code, data, and runtime systems. In this paper, we design a holistic semantics-aware optimization for data-intensive applications using hybrid program analysis} (SODA) to assist programmers to tune performance issues. SODA is a two-phase framework: the offline phase is a static analysis that analyzes code and performance profiling data from the online phase of prior executions to generate a parameterized and instrumented application; the online phase is a dynamic analysis that keeps track of the application's execution and collects runtime information of data and system. Extensive experimental results on four real-world Spark applications show that SODA can gain up to 60%, 10%, 8%, faster than its original implementation, with the three proposed optimization strategies, i.e., cache management, operation reordering, and element pruning, respectively.
Two Headed Dragons: Multimodal Fusion and Cross Modal Transactions
Venue: arXiv cs.CV 2021
Year: 2021

As the field of remote sensing is evolving, we witness the accumulation of information from several modalities, such as multispectral (MS), hyperspectral (HSI), LiDAR etc. Each of these modalities possess its own distinct characteristics and when combined synergistically, perform very well in the recognition and classification tasks. However, fusing multiple modalities in remote sensing is cumbersome due to highly disparate domains. Furthermore, the existing methods do not facilitate cross-modal interactions. To this end, we propose a novel transformer based fusion method for HSI and LiDAR modalities. The model is composed of stacked auto encoders that harness the cross key-value pairs for HSI and LiDAR, thus establishing a communication between the two modalities, while simultaneously using the CNNs to extract the spectral and spatial information from HSI and LiDAR. We test our model on Houston (Data Fusion Contest - 2013) and MUUFL Gulfport datasets and achieve competitive results.
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
Venue: arXiv cs.NI 2021
Year: 2021

The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizing communication time. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially by considering either the communication rounds or per-round latency, while the total communication time is determined by both metrics. To close this gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the communication time through cross-disciplinary effort involving both learning theory for convergence analysis and communication theory for per-round latency analysis. Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.
A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
Venue: arXiv cs.LG 2021
Year: 2021

This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features {\sl regardless} of the model used. This limit, namely the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given.
LAConv: Local Adaptive Convolution for Image Fusion
Venue: arXiv cs.CV 2021
Year: 2021

The convolution operation is a powerful tool for feature extraction and plays a prominent role in the field of computer vision. However, when targeting the pixel-wise tasks like image fusion, it would not fully perceive the particularity of each pixel in the image if the uniform convolution kernel is used on different patches. In this paper, we propose a local adaptive convolution (LAConv), which is dynamically adjusted to different spatial locations. LAConv enables the network to pay attention to every specific local area in the learning process. Besides, the dynamic bias (DYB) is introduced to provide more possibilities for the depiction of features and make the network more flexible. We further design a residual structure network equipped with the proposed LAConv and DYB modules, and apply it to two image fusion tasks. Experiments for pansharpening and hyperspectral image super-resolution (HISR) demonstrate the superiority of our method over other state-of-the-art methods. It is worth mentioning that LAConv can also be competent for other super-resolution tasks with less computation effort.
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
Venue: arXiv cs.CV 2021
Year: 2021

Defects are unavoidable in casting production owing to the complexity of the casting process. While conventional human-visual inspection of casting products is slow and unproductive in mass productions, an automatic and reliable defect detection not just enhances the quality control process but positively improves productivity. However, casting defect detection is a challenging task due to diversity and variation in defects' appearance. Convolutional neural networks (CNNs) have been widely applied in both image classification and defect detection tasks. Howbeit, CNNs with frequentist inference require a massive amount of data to train on and still fall short in reporting beneficial estimates of their predictive uncertainty. Accordingly, leveraging the transfer learning paradigm, we first apply four powerful CNN-based models (VGG16, ResNet50, DenseNet121, and InceptionResNetV2) on a small dataset to extract meaningful features. Extracted features are then processed by various machine learning algorithms to perform the classification task. Simulation results demonstrate that linear support vector machine (SVM) and multi-layer perceptron (MLP) show the finest performance in defect detection of casting images. Secondly, to achieve a reliable classification and to measure epistemic uncertainty, we employ an uncertainty quantification (UQ) technique (ensemble of MLP models) using features extracted from four pre-trained CNNs. UQ confusion matrix and uncertainty accuracy metric are also utilized to evaluate the predictive uncertainty estimates. Comprehensive comparisons reveal that UQ method based on VGG16 outperforms others to fetch uncertainty. We believe an uncertainty-aware automatic defect detection solution will reinforce casting productions quality assurance.
Graph Convolutional Network with Generalized Factorized Bilinear Aggregation
Venue: arXiv cs.LG 2021
Year: 2021

Although Graph Convolutional Networks (GCNs) have demonstrated their power in various applications, the graph convolutional layers, as the most important component of GCN, are still using linear transformations and a simple pooling step. In this paper, we propose a novel generalization of Factorized Bilinear (FB) layer to model the feature interactions in GCNs. FB performs two matrix-vector multiplications, that is, the weight matrix is multiplied with the outer product of the vector of hidden features from both sides. However, the FB layer suffers from the quadratic number of coefficients, overfitting and the spurious correlations due to correlations between channels of hidden representations that violate the i.i.d. assumption. Thus, we propose a compact FB layer by defining a family of summarizing operators applied over the quadratic term. We analyze proposed pooling operators and motivate their use. Our experimental results on multiple datasets demonstrate that the GFB-GCN is competitive with other methods for text classification.
A Survey of Wearable Devices Pairing Based on Biometric Signals
Venue: arXiv cs.CR 2021
Year: 2021

With the growth of wearable devices, which are usually constrained in computational power and user interface, this pairing has to be autonomous. Considering devices that do not have prior information about each other, a secure communication should be established by generating a shared secret key derived from a common context between the devices. Context-based pairing solutions increase the usability of wearable device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing onboard sensors (with the same sensing modalities) to capture a common physical context (e.g., body motion, gait, heartbeat, respiration, and EMG signal). A wide range of approaches has been proposed to address autonomous pairing in wearable devices. This paper surveys context-based pairing in wearable devices by focusing on the signals and sensors exploited. We review the steps needed for generating a common key and provide a survey of existing techniques utilized in each step.
Revisiting Analog Over-the-Air Machine Learning: The Blessing and Curse of Interference
Venue: arXiv cs.IT 2021
Year: 2021

We study a distributed machine learning problem carried out by an edge server and multiple agents in a wireless network. The objective is to minimize a global function that is a sum of the agents' local loss functions. And the optimization is conducted by analog over-the-air model training. Specifically, each agent modulates its local gradient onto a set of waveforms and transmits to the edge server simultaneously. From the received analog signal the edge server extracts a noisy aggregated gradient which is distorted by the channel fading and interference, and uses it to update the global model and feedbacks to all the agents for another round of local computing. Since the electromagnetic interference generally exhibits a heavy-tailed intrinsic, we use the $α$-stable distribution to model its statistic. In consequence, the global gradient has an infinite variance that hinders the use of conventional techniques for convergence analysis that rely on second-order moments' existence. To circumvent this challenge, we take a new route to establish the analysis of convergence rate, as well as generalization error, of the algorithm. Our analyses reveal a two-sided effect of the interference on the overall training procedure. On the negative side, heavy tail noise slows down the convergence rate of the model training: the heavier the tail in the distribution of interference, the slower the algorithm converges. On the positive side, heavy tail noise has the potential to increase the generalization power of the trained model: the heavier the tail, the better the model generalizes. This perhaps counterintuitive conclusion implies that the prevailing thinking on interference -- that it is only detrimental to the edge learning system -- is outdated and we shall seek new techniques that exploit, rather than simply mitigate, the interference for better machine learning in wireless networks.
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we focus on the challenging multicategory instance segmentation problem in remote sensing images (RSIs), which aims at predicting the categories of all instances and localizing them with pixel-level masks. Although many landmark frameworks have demonstrated promising performance in instance segmentation, the complexity in the background and scale variability instances still remain challenging for instance segmentation of RSIs. To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB). The SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map and reduce the background noise's interference. To handle the under-segmentation of geospatial instances with large varying scales, we design the SCMB that extends the original single mask branch to trident mask branches and introduces complementary mask supervision at different scales to sufficiently leverage the multi-scale information. We conduct comprehensive experiments to evaluate the effectiveness of our proposed method on the iSAID dataset and the NWPU Instance Segmentation dataset and achieve promising performance.
Binary sequences with low correlation via cyclotomic function fields
Venue: arXiv cs.IT 2021
Year: 2021

Due to wide applications of binary sequences with low correlation to communications, various constructions of such sequences have been proposed in literature. However, most of the known constructions via finite fields make use of the multiplicative cyclic group of $\F_{2^n}$. It is often overlooked in this community that all $2^n+1$ rational places (including "place at infinity") of the rational function field over $\F_{2^n}$ form a cyclic structure under an automorphism of order $2^n+1$. In this paper, we make use of this cyclic structure to provide an explicit construction of families of binary sequences of length $2^n+1$ via the finite field $\F_{2^n}$. Each family of sequences has size $2^n-1$ and its correlation is upper bounded by $\lfloor 2^{(n+2)/2}\rfloor$. Our sequences can be constructed explicitly and have competitive parameters. In particular, compared with the Gold sequences of length $2^n-1$ for even $n$, we have larger length and smaller correlation although the family size of our sequences is slightly smaller.