Reviewerly/exHarmony huggingface dataset:
Federated Learning Framework for IID and Non-IID datasets of Medical Images

Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fields such as healthcare, a primary reason being the issue of privacy. Federated Learning (FL) is a technique that can be used to overcome the limitation of availability of data at a central location and allows for training machine learning models on private data or data that cannot be directly accessed. It allows the use of data to be decoupled from the governance (or control) over data. In this paper, we present an easy-to-use framework that provides a complete pipeline to let researchers and end users train any model on image data from various sources in a federated manner. We also show a comparison in results between models trained in a federated fashion and models trained in a centralized fashion for Independent and Identically Distributed (IID) and non IID datasets. The Intracranial Brain Hemorrhage dataset and the Pneumonia Detection dataset provided by the Radiological Society of North America (RSNA) are used for validating the FL framework and comparative analysis.
Ethereum Blockchain Smart Contract Vulnerability Detection Using Deep Learning

Blockchain technology, employing advanced cryptography, stands as an optimal means to establish trust among unfamiliar online counterparts. It facilitates secure transactions and consensus among participants. Ethereum, a prominent blockchain network, extends this utility by introducing smart contracts. These are predefined programs containing data and methods for execution. Once deployed, these contracts remain unalterable due to blockchain’s immutable nature. However, unlike conventional software that can be readily patched, they may harbor vulnerabilities. Smart contracts operate with the Ethereum cryptocurrency Ether, rendering fixes intricate and economically impactful. Static analyzers exist to spot vulnerabilities in smart contacts during development, but they are time-intensive. We propose a machine learning-based approach for detecting reentrancy vulnerabilities in smart contracts. Our system comprises three components: data preparation, Op2Vec, and an LSTM model. We collected 30,000 smart contracts, dividing them into two sets of 15,000 each for Op2Vec generation and LSTM training, respectively. We mapped opcode keywords to vector representations using a Skip-Gram algorithm, resulting in a 100-dimensional dictionary with 72 unique opcodes. Labeling was done using the Slither static analyzer, with 116 contracts identified as vulnerable and an additional 132 clean contracts for dataset balance. A Bidirectional LSTM (Bi-LSTM) model was devised by employing assembly data to detect flaws. The developed Bi-LSTM model demonstrated promise in reentrancy vulnerability detection, achieving a 96% accuracy rate in testing and reducing the analysis time to less than a fifth of that required by static analyzers. The codes and data are shared on GitHub as an open-source software package in a way that benefits everyone interested: https://github.com/miralabai/blockchain-vulnerability-detection.
RobustHealthFL: Robust Strategy Against Malicious Clients in Non-iid Healthcare Federated Learning*

Due to the sensitive and confidential nature of healthcare data, it cannot be freely transmitted, resulting in the challenge of data silos. While federated learning offers a solution to this data island dilemma, it also faces potential threats from malicious client attacks. The system becomes more vulnerable when dealing with non-independent and identically distributed (non-IID) data. Hence, a robust federated learning framework is indispensable to defend against malicious attacks. In this study, we employ a dynamic feature extractor based on the sparse representation, and the parameters of each iteration construct the feature extractor for the subsequent parameters. Then, we use optimized k-means clustering to get benign clients and aggregate them. The experimental results show that the system robustness is lower for non-IID datasets compared to IID healthcare datasets. And our RobustHealthFL can significantly enhance the robustness of the system when facing non-IID data. Our code is available at https://github.com/xipengp/RobustHealthFL.
Self-distillation Augmented Masked Autoencoders for Histopathological Image Understanding

Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years. Compared to contrastive learning which is troubled with the false negative problem, i.e., semantically similar images are selected as negative samples, masked autoencoders (MAE) build SSL from a generative paradigm which is probably a more appropriate pretraining. In this paper, we introduce MAE to histopathological image understanding, and moreover, verify the effect of visible patches in this task. Specifically, a novel SD-MAE model is proposed to enable a self-distillation augmented MAE. Besides the reconstruction loss on masked image patches, SD-MAE further imposes the self-distillation loss on visible patches to enhance the representational capacity of encoder located in the shallow layers. It generates a more effective feature pre-training and benefits downstream applications. We apply SD-MAE to histopathological image classification, cell segmentation and cell detection. Experiments demonstrate that SD-MAE shows highly competitive performance compared with other SSL methods in these tasks. Code is available at https://github.com/irsLu/SD-MAE/
Federated Learning Applications for Breast Cancer

Breast cancer stands as the leading cause of mortality among women worldwide, encompassing all types of cancer. It can affect women of all age groups post-puberty in any country, with its incidence generally rising as women age. Early detection of breast cancer, prior to its spreading, enables more effective treatment and markedly improves survival rates. Employing techniques like mammography, ultrasound, and magnetic resonance imaging (MRI) for breast cancer screening can greatly improve a patient’s prognosis and overall outlook.In the realm of medical imaging, there is a growing interest in the utilization of Federated Learning (FL). This approach entails training deep learning models using expansive datasets dispersed across various data centers. Crucially, it upholds privacy by eliminating the requirement to transmit sensitive patient data. The paper aims to analyze current solutions in the state-of-the-art, highlighting workflows and key solutions, taking into consideration the datasets used and architectural choices. Finally, common problems encountered in the works and those typical of the medical domain are analyzed, focusing on possible future solutions to solve them.
Vision Transformers(ViT) Pretraining on 3D ABUS Image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.
Computing the optimal BWT of very large string collections

It is known that the exact form of the Burrows-Wheeler Transform (BWT) of a string collection depends, in most implementations, on the input order of the strings in the collection. Reordering strings of an input collection affects the number of equal-letter runs r, arguably the most important parameter of BWT-based data structures, such as the FM-index or the r-index. Bentley, Gibney, and Thankachan [ESA 2020] introduced a linear-time algorithm for computing the permutation of the input collection which yields the minimum number of runs of the resulting BWT. In this paper, we present the first tool that guarantees a Burrows-Wheeler Transform with minimum number of runs (optBWT), by combining i) an algorithm that builds the BWT from a string collection (either SAIS-based [Boucher et al., SPIRE 2021] or BCR [Bauer et al., CPM 2011]); ii) the SAP array data structure introduced in [Cox et al., Bioinformatics, 2012]; and iii) the algorithm by Bentley et al. We present results both on real-life and simulated data, showing that the improvement achieved in terms of r with respect to the input order is significant and the overhead created by the computation of the optimal BWT negligible, making our tool competitive with other tools for BWT-computation in terms of running time and space usage. In particular, on real data the optBWT obtains up to 31 times fewer runs with only a 1.39$\times$ slowdown. Source code is available at https://github.com/davidecenzato/optimalBWT.git.
A domain adaptive pre-training language model for sentence classification of Chinese electronic medical record

Accurately extracting and classifying Chinese electronic medical record (EMR), which contain huge amounts of valuable medical information, have promising practical application and medical value in the health care of China. While the pivotal issue has gathered escalating attention, the bulk of current research is directed towards operations conducted at the document or entity level within medical records. Only a restricted body of work addresses these concerns at the sentence level, a critical aspect for downstream tasks like medical information retrieval, diagnosis normalization, and question answering. In this paper, we present a domain adaptive pre-training language model named CEMR-LM for sentence classification of Chinese EMRs. CEMR-LM acquires Chinese medical domain knowledge through the utilization of copious unlabeled clinical corpus for pre-training the language model. This is fortified by combining fine-tuning strategy and a dual-channel mechanism, which collectively contribute to the model’s heightened performance. Experiments on the benchmark dataset and real world hospital dataset both demonstrate that CEMR-LM is superior to the state-of-the-art methods. Furthermore, CEMR-LM possesses the capability to elucidate indicative elements within medical records by visualizing of the attention weights embedded within the model. The implemented code and experimental datasets are available online at https://github.com/BioMedBigDataCenter/CEMR-LM.
Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation

The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high annotation cost, the medical image segmentation (MIS) community has been encouraged to investigate SAM’s zero-shot capabilities to facilitate automatic annotation. Inspired by the extraordinary accomplishments of the interactive medical image segmentation (IMIS) paradigm, this paper focuses on assessing the potential of SAM’s zero-shot capabilities within the IMIS paradigm to amplify its benefits in the MIS domain. Regrettably, we observe that SAM’s vulnerability to prompt forms (e.g., points, bounding boxes) becomes notably pronounced in IMIS. This leads us to develop a mechanism that adaptively offers suitable prompt forms for human experts. We refer to the mechanism above as temporally-extended prompts optimization (TEPO) and model it as a Markov decision process, solvable through reinforcement learning. Numerical experiments on the standardized benchmark Brats2020 demonstrate that the learned TEPO agent can further enhance SAM’s zero-shot capability in the MIS context.
Emergent Coordination Through Game-Induced Nonlinear Opinion Dynamics

We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided. Dynamic non-cooperative games have been used to encode multi-agent interaction, but ambiguity arising from factors such as goal preference or the presence of multiple equilibria may lead to coordination issues, ranging from the “freezing robot” problem to unsafe behavior in safety-critical events. The recently developed nonlinear opinion dynamics (NOD) [1] provide guarantees for breaking deadlocks. However, choosing the appropriate model parameters automatically in general multi-agent settings remains a challenge. In this paper, we first propose a novel and principled procedure for synthesizing NOD based on the value functions of dynamic games conditioned on agents' intents. In particular, we provide for the two-player two-option case precise stability conditions for equilibria of the game-induced NOD based on the mismatch between agents' opinions and their game values. We then propose an optimization-based trajectory optimization algorithm that computes agents' policies guided by the evolution of opinions. The efficacy of our method is illustrated with a simulated toll station coordination example.
Differentially Private Stochastic Convex Optimization for Network Routing Applications

Network routing problems are common across many engineering applications. Computing optimal routing policies requires knowledge about network demand, i.e., the origin and destination (OD) of all requests in the network. However, privacy considerations make it challenging to share individual OD data that would be required for computing optimal policies. Privacy can be particularly challenging in standard network routing problems because sources and sinks can be easily identified from flow conservation constraints, making feasibility and privacy mutually exclusive. In this paper, we present a differentially private algorithm for network routing problems. The main ingredient is a reformulation of network routing which moves all user data-dependent parameters out of the constraint set and into the objective function. We then present an algorithm for solving this formulation based on a differentially private variant of stochastic gradient descent. In this algorithm, differential privacy is achieved by injecting noise, and one may wonder if this noise injection compromises solution quality. We prove that our algorithm is both differentially private and asymptotically optimal as the size of the training set goes to infinity. We corroborate the theoretical results with numerical experiments on a road traffic network which show that our algorithm provides differentially private and near-optimal solutions in practice.
A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis

Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. The conventional way to deal with a multitask problem is to establish a scalar objective function based on a linear combination of different objectives. However, for the case where we have conflicting objectives with different scales, this method needs a trial-and-error approach to properly find proper weights for the combination. As such, in most cases, this approach cannot guarantee an optimal Pareto solution. In this paper, we develop a single-agent scale-independent multi-objective reinforcement learning on the basis of the Advantage Actor-Critic (A2C) algorithm. A convergence analysis is then done for the devised multi-objective algorithm providing a convergence-in-mean guarantee. We then perform some experiments over a multitask problem to evaluate the performance of the proposed algorithm. Simulation results show the superiority of developed multi-objective A2C approach against the single-objective algorithm.
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning (Co-MARL), a team of agents must jointly optimize the team's longterm rewards to learn a designated task. Optimizing rewards as a team often requires inter-agent communication and data sharing, leading to potential privacy implications. We assume privacy considerations prohibit the agents from sharing their environment interaction data. Accordingly, we propose Privacy-Engineered Value Decomposition Networks (PE-VDN), a Co-MARL algorithm that models multi-agent coordination while provably safeguarding the confidentiality of the agents' environment interaction data. We integrate three privacy-engineering techniques to redesign the data flows of the VDN algorithm-an existing Co-MARL algorithm that consolidates the agents' environment interaction data to train a central controller that models multi-agent coordination-and develop PE-VDN. In the first technique, we design a distributed computation scheme that eliminates Vanilla VDN's dependency on sharing environment interaction data. Then, we utilize a privacy-preserving multi-party computation protocol to guar-antee that the data flows of the distributed computation scheme do not pose new privacy risks. Finally, we enforce differential privacy to preempt inference threats against the agents' training data-past environment interactions-when they take actions based on their neural network predictions. We implement PE-VDN in StarCraft Multi-Agent Competition (SMAC) and show that it achieves 80% of Vanilla VDN's win rate while maintaining differential privacy levels that provide meaningful privacy guarantees. The results demonstrate that PE-VDN can safeguard the confidentiality of agents' environment interaction data without sacrificing multi-agent coordination.
Convex Q Learning in a Stochastic Environment

The paper introduces the first formulation of convex Q-learning for Markov decision processes with function approximation. The algorithms and theory rest on a relaxation of a dual of Manne's celebrated linear programming characterization of optimal control. The main contributions firstly concern properties of the relaxation, described as a deterministic convex program: we identify conditions for a bounded solution, and a significant relationship between the solution to the new convex program, and the solution to standard Q-learning. The second set of contributions concern algorithm design and analysis: (i) A direct model-free method for approximating the convex program for Q-learning shares properties with its ideal. In particular, a bounded solution is ensured subject to a simple property of the basis functions; (ii) The proposed algorithms are convergent and new techniques are introduced to obtain the rate of convergence in a mean-square sense; (iii) The approach can be generalized to a range of performance criteria, and it is found that variance can be reduced by considering “relative” dynamic programming equations; (iv) The theory is illustrated with an application to a classical inventory control problem.
Managing E-Reviews: A Performance Enhancement Technique Using Deep Learning

E-business has converted the entire world into a global market where client feedback plays a substantial role in improving the business's productivity as it helps the companies to meet the public demands. Reviews on the products' services helps in knowing the public requirements. Consumer reviews help the business owner to know the success of the product, consequently, the growth of the business as well as the future of the business. Various techniques had emerged in order to gather consumer responses. Sentiment analysis targets to garner the responses and categorize those reviews into some fixed patterns, for example- positive, negative or neutral. This methodology helps to study a large amount of data effectively, efficiently and quickly. This study aims to propose a system that accurately integrates text-based feedback into the thumbs up, thumbs down or non-partisan set. The proffered approach uses deep learning for sentimental analysis to figure out the text and convert it into valuable feedback. The proposed study incorporates a machine learning procedure called the TextEmbedNet Model, which incorporates word embedding, global average pooling, and Dense layers. The performance of the proposed work is measured using F1 Score, Precision, Recall and Specificity constraints. In order to produce the results dataset from Amazon has been taken into consideration.
Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition

The integration of Language Models (LMs) has proven to be an effective way to address domain shifts in speech recognition. However, these approaches usually require a significant amount of target domain text data for the training of LMs. Different from these methods, in this work, with only a domain-specific text prompt, we propose two zero-shot ASR domain adaptation methods using LLaMA, a 7-billionparameter large language model (LLM). LLM is used in two ways: 1) second-pass rescoring: reranking N-best hypotheses of a given ASR system with LLaMA; 2) deep LLM-fusion: incorporating LLM into the decoder of an encoder-decoder based ASR system. Experiments show that, with only one domain prompt, both methods can effectively reduce word error rates (WER) on out-of-domain TedLium-2 and SPGISpeech datasets. Especially, the deep LLM-fusion has the advantage of better recall of entity and out-of-vocabulary words.
The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR

With the success of the first Multi-channel Multi-party Meeting Transcription challenge (M2MeT), the second M2MeT challenge (M2MeT 2.0) held in ASRU2023 particularly aims to tackle the complex task of speaker-attributed ASR (SAASR), which directly addresses the practical and challenging problem of “who spoke what at when” at typical meeting scenario. We particularly established two sub-tracks. The fixed training condition sub-track, where the training data is constrained to predetermined datasets, but participants can use any open-source pre-trained model. The open training condition sub-track, which allows for the use of all available data and models without limitation. In addition, we release a new 10-hour test set for challenge ranking. This paper provides an overview of the dataset, track settings, results, and analysis of submitted systems, as a benchmark to show the current state of speaker-attributed ASR.