OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Text Normalization
Venue: arXiv cs.CL 2022
Year: 2022

Text-based adversarial attacks are becoming more commonplace and accessible to general internet users. As these attacks proliferate, the need to address the gap in model robustness becomes imminent. While retraining on adversarial data may increase performance, there remains an additional class of character-level attacks on which these models falter. Additionally, the process to retrain a model is time and resource intensive, creating a need for a lightweight, reusable defense. In this work, we propose the Adversarial Text Normalizer, a novel method that restores baseline performance on attacked content with low computational overhead. We evaluate the efficacy of the normalizer on two problem areas prone to adversarial attacks, i.e. Hate Speech and Natural Language Inference. We find that text normalization provides a task-agnostic defense against character-level attacks that can be implemented supplementary to adversarial retraining solutions, which are more suited for semantic alterations.
A Flash(bot) in the Pan: Measuring Maximal Extractable Value in Private Pools
Venue: arXiv cs.CR 2022
Year: 2022

The rise of Ethereum has lead to a flourishing decentralized marketplace that has, unfortunately, fallen victim to frontrunning and Maximal Extractable Value (MEV) activities, where savvy participants game transaction orderings within a block for profit. One popular solution to address such behavior is Flashbots, a private pool with infrastructure and design goals aimed at eliminating the negative externalities associated with MEV. While Flashbots has established laudable goals to address MEV behavior, no evidence has been provided to show that these goals are achieved in practice. In this paper, we measure the popularity of Flashbots and evaluate if it is meeting its chartered goals. We find that (1) Flashbots miners account for over 99.9% of the hashing power in the Ethereum network, (2) powerful miners are making more than $2\times$ what they were making prior to using Flashbots, while non-miners' slice of the pie has shrunk commensurately, (3) mining is just as centralized as it was prior to Flashbots with more than 90% of Flashbots blocks coming from just two miners, and (4) while more than 80% of MEV extraction in Ethereum is happening through Flashbots, 13.2% is coming from other private pools.
Analyzing Folktales of Different Regions Using Topic Modeling and Clustering
Venue: arXiv cs.CL 2022
Year: 2022

This paper employs two major natural language processing techniques, topic modeling and clustering, to find patterns in folktales and reveal cultural relationships between regions. In particular, we used Latent Dirichlet Allocation and BERTopic to extract the recurring elements as well as K-means clustering to group folktales. Our paper tries to answer the question what are the similarities and differences between folktales, and what do they say about culture. Here we show that the common trends between folktales are family, food, traditional gender roles, mythological figures, and animals. Also, folktales topics differ based on geographical location with folktales found in different regions having different animals and environment. We were not surprised to find that religious figures and animals are some of the common topics in all cultures. However, we were surprised that European and Asian folktales were often paired together. Our results demonstrate the prevalence of certain elements in cultures across the world. We anticipate our work to be a resource to future research of folktales and an example of using natural language processing to analyze documents in specific domains. Furthermore, since we only analyzed the documents based on their topics, more work could be done in analyzing the structure, sentiment, and the characters of these folktales.
Enhancement of Healthcare Data Transmission using the Levenberg-Marquardt Algorithm
Venue: arXiv cs.LG 2022
Year: 2022

In the healthcare system, patients are required to use wearable devices for the remote data collection and real-time monitoring of health data and the status of health conditions. This adoption of wearables results in a significant increase in the volume of data that is collected and transmitted. As the devices are run by small battery power, they can be quickly diminished due to the high processing requirements of the device for data collection and transmission. Given the importance attached to medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission will improve the device battery life via using inference algorithm. There is an issue of improving transmission metrics with accuracy and efficiency, which trade-off each other such as increasing accuracy reduces the efficiency. This paper demonstrates that machine learning can be used to analyze complex health data metrics such as the accuracy and efficiency of data transmission to overcome the trade-off problem using the Levenberg-Marquardt algorithm to enhance both metrics by taking fewer samples to transmit whilst maintaining the accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The result shows that the LMA has best performed with an efficiency of 3.33 times for reduced sample data size and accuracy of 79.17%, which has the similar accuracies in 7 different sampling cases adopted for testing but demonstrates improved efficiency. These proposed methods significantly improved both metrics using machine learning without sacrificing a metric over the other compared to the existing methods with high efficiency.
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
Venue: arXiv cs.LG 2022
Year: 2022

The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multi-class classification has not been solved: how to improve the classification accuracy of multi-class classification problems when only imprecise observations are available. Hence, in this paper, we propose a novel framework to address a new realistic problem called multi-class classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. Firstly, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. Experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.
Language Identification for Austronesian Languages
Venue: arXiv cs.CL 2022
Year: 2022

This paper provides language identification models for low- and under-resourced languages in the Pacific region with a focus on previously unavailable Austronesian languages. Accurate language identification is an important part of developing language resources. The approach taken in this paper combines 29 Austronesian languages with 171 non-Austronesian languages to create an evaluation set drawn from eight data sources. After evaluating six approaches to language identification, we find that a classifier based on skip-gram embeddings reaches a significantly higher performance than alternate methods. We then systematically increase the number of non-Austronesian languages in the model up to a total of 800 languages to evaluate whether an increased language inventory leads to less precise predictions for the Austronesian languages of interest. This evaluation finds that there is only a minimal impact on accuracy caused by increasing the inventory of non-Austronesian languages. Further experiments adapt these language identification models for code-switching detection, achieving high accuracy across all 29 languages.
Learning to generate imaginary tasks for improving generalization in meta-learning
Venue: arXiv cs.LG 2022
Year: 2022

The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of the assumption in applications with either insufficient tasks or a very narrow meta-training task distribution leads to memorization or learner overfitting. Recent solutions have pursued augmentation of meta-training tasks, while it is still an open question to generate both correct and sufficiently imaginary tasks. In this paper, we seek an approach that up-samples meta-training tasks from the task representation via a task up-sampling network. Besides, the resulting approach named Adversarial Task Up-sampling (ATU) suffices to generate tasks that can maximally contribute to the latest meta-learner by maximizing an adversarial loss. On few-shot sine regression and image classification datasets, we empirically validate the marked improvement of ATU over state-of-the-art task augmentation strategies in the meta-testing performance and also the quality of up-sampled tasks.
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning alleviates the privacy risk in distributed learning by transmitting only the local model updates to the central server. However, it faces challenges including statistical heterogeneity of clients' datasets and resource constraints of client devices, which severely impact the training performance and user experience. Prior works have tackled these challenges by combining personalization with model compression schemes including quantization and pruning. However, the pruning is data-dependent and thus must be done on the client side which requires considerable computation cost. Moreover, the pruning normally trains a binary supermask $\in \{0, 1\}$ which significantly limits the model capacity yet with no computation benefit. Consequently, the training requires high computation cost and a long time to converge while the model performance does not pay off. In this work, we propose HideNseek which employs one-shot data-agnostic pruning at initialization to get a subnetwork based on weights' synaptic saliency. Each client then optimizes a sign supermask $\in \{-1, +1\}$ multiplied by the unpruned weights to allow faster convergence with the same compression rates as state-of-the-art. Empirical results from three datasets demonstrate that compared to state-of-the-art, HideNseek improves inferences accuracies by up to 40.6\% while reducing the communication cost and training time by up to 39.7\% and 46.8\% respectively.
Open ERP System Data For Occupational Fraud Detection
Venue: arXiv cs.AI 2022
Year: 2022

Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems to track vast amounts of information regarding their business process, researchers have in the past shown interest in automatically detecting fraud through ERP system data. Current research in this area, however, is hindered by the fact that ERP system data is not publicly available for the development and comparison of fraud detection methods. We therefore endeavour to generate public ERP system data that includes both normal business operation and fraud. We propose a strategy for generating ERP system data through a serious game, model a variety of fraud scenarios in cooperation with auditing experts, and generate data from a simulated make-to-stock production company with multiple research participants. We aggregate the generated data into ready to used datasets for fraud detection in ERP systems, and supply both the raw and aggregated data to the general public to allow for open development and comparison of fraud detection approaches on ERP system data.
Individually Fair Learning with One-Sided Feedback
Venue: arXiv cs.LG 2022
Year: 2022

We consider an online learning problem with one-sided feedback, in which the learner is able to observe the true label only for positively predicted instances. On each round, $k$ instances arrive and receive classification outcomes according to a randomized policy deployed by the learner, whose goal is to maximize accuracy while deploying individually fair policies. We first extend the framework of Bechavod et al. (2020), which relies on the existence of a human fairness auditor for detecting fairness violations, to instead incorporate feedback from dynamically-selected panels of multiple, possibly inconsistent, auditors. We then construct an efficient reduction from our problem of online learning with one-sided feedback and a panel reporting fairness violations to the contextual combinatorial semi-bandit problem (Cesa-Bianchi & Lugosi, 2009, György et al., 2007). Finally, we show how to leverage the guarantees of two algorithms in the contextual combinatorial semi-bandit setting: Exp2 (Bubeck et al., 2012) and the oracle-efficient Context-Semi-Bandit-FTPL (Syrgkanis et al., 2016), to provide multi-criteria no regret guarantees simultaneously for accuracy and fairness. Our results eliminate two potential sources of bias from prior work: the "hidden outcomes" that are not available to an algorithm operating in the full information setting, and human biases that might be present in any single human auditor, but can be mitigated by selecting a well chosen panel.
cycle text2face: cycle text-to-face gan via transformers
Venue: arXiv cs.CV 2022
Year: 2022

Text-to-face is a subset of text-to-image that require more complex architecture due to their more detailed production. In this paper, we present an encoder-decoder model called Cycle Text2Face. Cycle Text2Face is a new initiative in the encoder part, it uses a sentence transformer and GAN to generate the image described by the text. The Cycle is completed by reproducing the text of the face in the decoder part of the model. Evaluating the model using the CelebA dataset, leads to better results than previous GAN-based models. In measuring the quality of the generate face, in addition to satisfying the human audience, we obtain an FID score of 3.458. This model, with high-speed processing, provides quality face images in the short time.
An FPGA-based Solution for Convolution Operation Acceleration
Venue: arXiv cs.AR 2022
Year: 2022

Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerate the convolution operation - a complex and expensive computing step that appears in many Convolutional Neural Network models. We target the design to the standard convolution operation, intending to launch the product as an edge-AI solution. The project's purpose is to produce an FPGA IP core that can process a convolutional layer at a time. System developers can deploy the IP core with various FPGA families by using Verilog HDL as the primary design language for the architecture. The experimental results show that our single computing core synthesized on a simple edge computing FPGA board can offer 0.224 GOPS. When the board is fully utilized, 4.48 GOPS can be achieved.
Joint radar and communications with multicarrier chirp-based waveform
Venue: arXiv cs.IT 2022
Year: 2022

We consider a multicarrier chirp-based waveform for joint radar and communication (JRC) systems and derive its time discrete periodic ambiguity function (AF). An advantage of the waveform is that it includes a set of waveform parameters (e.g., chirp rate) which together with the transmit sequence, can be selected to flexibly shape the AF to be thumbtack-like, or to be ridge-like, either along the delay axis or the Doppler axis. These shapes are applicable for different use cases, e.g., target detection or timeand frequency synchronization. The results show that better signal detection performance than OFDM and DFT-s-OFDM can be achieved on channels with large Doppler frequency. Furthermore, it is shown how transmit sequences can be selected in order to achieve 0 dB peak-to-average-power-ratio (PAPR) of the waveform.
Functional Code Building Genetic Programming
Venue: arXiv cs.AI 2022
Year: 2022

General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally. Code Building Genetic Programming (CBGP) is a recently introduced GP method for general program synthesis that leverages reflection and first class specifications to support the evolution of programs that may use arbitrary data types, polymorphism, and functions drawn from existing codebases. However, neither a formal description nor a thorough benchmarking of CBGP have yet been reported. In this work, we formalize the method of CBGP using algorithms from type theory. Specially, we show that a functional programming language and a Hindley-Milner type system can be used to evolve type-safe programs using the process abstractly described in the original CBGP paper. Furthermore, we perform a comprehensive analysis of the search performance of this functional variant of CBGP compared to other contemporary GP program synthesis methods.
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding
Venue: arXiv cs.RO 2022
Year: 2022

Semantic 3D scene understanding is a problem of critical importance in robotics. While significant advances have been made in simultaneous localization and mapping algorithms, robots are still far from having the common sense knowledge about household objects and their locations of an average human. We introduce a novel method for leveraging common sense embedded within large language models for labelling rooms given the objects contained within. This algorithm has the added benefits of (i) requiring no task-specific pre-training (operating entirely in the zero-shot regime) and (ii) generalizing to arbitrary room and object labels, including previously-unseen ones -- both of which are highly desirable traits in robotic scene understanding algorithms. The proposed algorithm operates on 3D scene graphs produced by modern spatial perception systems, and we hope it will pave the way to more generalizable and scalable high-level 3D scene understanding for robotics.
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
Venue: arXiv cs.CR 2022
Year: 2022

We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP-based ML implementations are so loose that they do not offer the ex ante privacy guarantees of DP. Instead, what they deliver is basically noise addition similar to the traditional (and often criticized) statistical disclosure control approach. Due to the lack of formal privacy guarantees, the actual level of privacy offered must be experimentally assessed ex post, which is done very seldom. In this respect, we present empirical results showing that standard anti-overfitting techniques in ML can achieve a better utility/privacy/efficiency trade-off than DP.
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition
Venue: arXiv cs.LG 2022
Year: 2022

We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., after any convolutional layer or block, to create intermediate connections between individual streams responsible for processing each modality. Additionally, our method benefits from two properties. First, it can share information uni-directionally (from one modality to the other) or bi-directionally. Second, it can be integrated into multiple stages at the same time to further allow network gradients to be exchanged in several touch-points. We perform extensive experiments on three public multimodal wearable datasets, WESAD, SWELL-KW, and CASE, and demonstrate that our method can effectively regulate and share information between different modalities to learn better representations. Our experiments further demonstrate that once integrated into simple CNN-based multimodal solutions (2, 3, or 4 modalities), our method can result in superior or competitive performance to state-of-the-art and outperform a variety of baseline uni-modal and classical multimodal methods.
A Resilient Distributed Boosting Algorithm
Venue: arXiv cs.LG 2022
Year: 2022

Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize. We present a distributed boosting algorithm which is resilient to a limited amount of noise. Our algorithm is similar to classical boosting algorithms, although it is equipped with a new component, inspired by Impagliazzo's hard-core lemma [Impagliazzo95], adding a robustness quality to the algorithm. We also complement this result by showing that resilience to any asymptotically larger noise is not achievable by a communication-efficient algorithm.
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data
Venue: arXiv cs.LG 2022
Year: 2022

Existing theory predicts that data heterogeneity will degrade the performance of the Federated Averaging (FedAvg) algorithm in federated learning. However, in practice, the simple FedAvg algorithm converges very well. This paper explains the seemingly unreasonable effectiveness of FedAvg that contradicts the previous theoretical predictions. We find that the key assumption of bounded gradient dissimilarity in previous theoretical analyses is too pessimistic to characterize data heterogeneity in practical applications. For a simple quadratic problem, we demonstrate there exist regimes where large gradient dissimilarity does not have any negative impact on the convergence of FedAvg. Motivated by this observation, we propose a new quantity, average drift at optimum, to measure the effects of data heterogeneity, and explicitly use it to present a new theoretical analysis of FedAvg. We show that the average drift at optimum is nearly zero across many real-world federated training tasks, whereas the gradient dissimilarity can be large. And our new analysis suggests FedAvg can have identical convergence rates in homogeneous and heterogeneous data settings, and hence, leads to better understanding of its empirical success.
HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration
Venue: arXiv cs.LG 2022
Year: 2022

HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other Machine Learning (ML) approaches. Frameworks enabling fast design space exploration to find practical algorithms are necessary to make HD computing competitive with other ML techniques. To this end, we introduce HDTorch, an open-source, PyTorch-based HDC library with CUDA extensions for hypervector operations. We demonstrate HDTorch's utility by analyzing four HDC benchmark datasets in terms of accuracy, runtime, and memory consumption, utilizing both classical and online HD training methodologies. We demonstrate average (training)/inference speedups of (111x/68x)/87x for classical/online HD, respectively. Moreover, we analyze the effects of varying hyperparameters on runtime and accuracy. Finally, we demonstrate how HDTorch enables exploration of HDC strategies applied to large, real-world datasets. We perform the first-ever HD training and inference analysis of the entirety of the CHB-MIT EEG epilepsy database. Results show that the typical approach of training on a subset of the data does not necessarily generalize to the entire dataset, an important factor when developing future HD models for medical wearable devices.