Reviewerly/exHarmony huggingface dataset:
Relative Entropy Regularization for Robust Submodular Multi-Task Subset Selection

We propose a novel formulation for multi-task subset selection with the aim of finding a solution that is locally distributionally robust in the "neighborhood" of a reference distribution, assigning an importance score to each task in a multi-task objective, using relative entropy as a regularizer. Using duality, we show that this novel formulation is equivalent to a normalized, monotone nondecreasing submodular function, which can be optimized very efficiently with standard greedy-based methods. This approach bridges the existing gap in optimization of performance-robustness tradeoff in multi-task subset selection. We then experimentally corroborate our theoretical results, comparing it with two other algorithms focused on optimizing the performance of the worst-case task, and on directly optimizing the performance on the reference distribution itself. We conclude that solving our novel formulation produces a solution that performs well on the reference distribution, is locally distributionally robust, and is quick in terms of computation time.
Uncertainty Estimation in Deep Bayesian Survival Models

Bayesian methods can express uncertainty about their predictions, but have seen little adaptation in survival analysis using neural networks. Proper uncertainty estimation is important in high-risk domains, such as the healthcare or medical field, if machine learning methods are to be adopted for decision-making purposes, however, uncertainty estimation is a known shortcoming of neural networks. In this paper, we introduce the use of Bayesian inference techniques for survival analysis in neural networks that rely on the Cox proportional hazard assumption, for which we discuss a new flexible and effective architecture. We implement three architectures: a fully-deterministic neural network that acts as a baseline, a Bayesian model using variational inference, and one using Monte-Carlo Dropout. Our comprehensive experiments show that on the WHAS500 dataset, Bayesian techniques improve predictive performance over the state-of-the-art neural networks and on the larger SEER and SUPPORT datasets provide comparable performance. In all experiments, training with Monte Carlo Dropout is significantly faster than training with variational inference. Our Bayesian models additionally provide quantification of both aleatoric and epistemic uncertainty, which we exhibit by plotting 95% confidence intervals around the survival function and showing a probability density function of the survival time. Our work motivates further work in leveraging uncertainty for survival analysis using neural networks.
Straggler Exploitation in Distributed Computing Systems with Task Grouping

We consider the problem of stragglers in distributed computing systems. Stragglers are compute nodes that unpredictably become slow. This often increases the completion times of tasks. One popular method for mitigating stragglers is to replicate work. Out of all replications of a task, only the first to complete is accepted. The rest are discarded. Discarding work completed by some of the workers leads to wastage of resources. In this paper, we propose a method for exploiting the work completed by stragglers rather than discarding it. We increase the granularity of the assigned work and the frequency of worker updates. We demonstrate that the proposed method reduces the completion time of tasks via experiments performed on a simulated cluster.
Exploration of System-on-Chip Secure-Boot Vulnerability to Fault-Injection by Side-Channel Analysis

Fault-Injection might be a useful tool to bypass security features that may obstruct the work of forensic experts. For instance, injecting a fault could modify the target control-flow and compromise its security. When the attacker knowledge about the target software implementation and hardware architecture is limited, discovering a Fault-Injection vulnerability becomes a serious challenge. Another issue is identifying when the targeted vulnerability is executed. To the best of our knowledge, this paper proposes a new methodology to solve these problems for the first time on System-on-Chip (SoC). The first step is to improve the knowledge of security feature implementations. Then deviations in the control-flow induced by forged inputs can be combined with Side-Channel observations to identify vulnerabilities. The next step is to define a trigger as close as possible in time and prior to these vulnerabilities. At this stage, Electromagnetic Fault-Injection (EMFI) can be put in practice to bypass the targeted security feature. As a proof of concept, we bypassed the Secure-Boot of a smartphone grade SoC. Three theoretical vulnerabilities in the Secure-Boot architecture of our target are identified using this new methodology and successfully exploited by EMFI.
Image-based Live Tracking and Registration for AR-Guided Liver Surgery Using Hololens2: A Phantom Study

We investigate AR-based tracking and registration of the liver surface for potential surgical applications. Our approach consists of streaming RGBD data from a Hololens2 device, RGBD segmentation using a deep learning model and registering the acquired partial liver surface point cloud with the corresponding virtual liver model. We aim to derive basic requirements for AR-guided liver surgery, thus consider several test cases of partially occluded liver as it would appear in surgical scenarios. To evaluate our approach, we use a 3D-printed phantom with basic texture and rigid structure. Our results show that the visible liver section has a substantial impact of feature extraction and matching, thus the registration process. Test cases, where specific anatomical features are visible, e.g. the right liver lobe, yielded superior outcomes compared to other cases, e.g. only the left liver lobe visible. Moreover, our results showed that large scale Hololens movements during the tracking process affected the registration performance. Our implementation achieved 2-3 frames per second for tracking and registration. We discuss the potential and limitations of utilizing Hololens2 for real-time tracking and registration of the liver surface. To our knowledge this is the first experimental approach for real-time markerless tracking and registration for AR-guided surgery guidance using the Hololens2 sensors only.
Empirical Study on Secure Software Development

The integration of security into the software development process, through the Security Software Development Life Cycle (SSDLC), involves continuous efforts from individuals and practices that ensure application confidentiality, integrity, and availability. For critical applications or those handling sensitive information, security measures must be thoughtfully planned and meticulously managed at every stage of the software development lifecycle in order to maximize their effectiveness. The purpose of the paper is to study the implementation of this approach and how effective it is by examining students' projects and identifying the types of threats and vulnerabilities which they have discovered and fixed in their software course project systems. We have validated this approach by analyzing the results that students who are studying to become software engineers obtained after its application while designing their software projects.
Transforming Adolescent Healthcare in Rwanda: Sustainable and Scalable Features in Digital Health

CyberRwanda is a digital health intervention designed to increase knowledge of family planning and reproductive health (FP/RH) and access to youth-friendly services in Rwanda. Previous studies showed high acceptability and feasibility through user surveys, success in combating social stigma, and an effective educational impact on adolescents. In order to further enhance user experience and overcome resource limitations, the authors designed and developed new features, including an integrated dashboard and chatbot, leveraging data engineering and natural language processing. The experiments showed that new features substantially reduce the time required to access information for user groups, potentially leading to an improved user experience.
A New Double-flow Network via Decision Tree for Human Action Recognition

Traditional convolutional neural networks tend to lose time flow information when extracting video action features. The feature extraction ability of the novel model is reduced. To solve the above problems, first of all, RGB images in each video and their corresponding vertical dense optical flow sequence map are extracted to obtain the spatial and temporal information of the actions in the video. They input the spatial domain network and the temporal domain network respectively for pre-training, and then perform feature extraction after pre-training. Then, the parallel fusion strategy is implemented for the feature vectors with the same dimension. This process can enhance the representation ability of the feature vectors. Finally, the fused feature vectors are input into the decision tree for training and classification. Experiments on public datasets show that the new proposed method has lower error rate and high recognition rate. Human behavior analysis technology is of great significance to the current theoretical research and development. And how to make the algorithm applicable to different scenes, more accurate detection and improve the fall detection effect in complex environment has become a problem to be solved.
On Subsampled Quantile Randomized Kaczmarz

When solving noisy linear systems Ax = b + c, the theoretical and empirical performance of stochastic iterative methods, such as the Randomized Kaczmarz algorithm, depends on the noise level. However, if there are a small number of highly corrupt measurements, one can instead use quantile-based methods to guarantee convergence to the solution x of the system, despite the presence of noise. Such methods require the computation of the entire residual vector, which may not be desirable or even feasible in some cases. In this work, we analyze the sub-sampled quantile Randomized Kaczmarz (sQRK) algorithm for solving large-scale linear systems which utilize a sub-sampled residual to approximate the quantile threshold. We prove that this method converges to the unique solution to the linear system and provide numerical experiments that support our theoretical findings. We additionally remark on the extremely small sample size case and demonstrate the importance of interplay between the choice of quantile and subset size.
Exploring Gradient Oscillation in Deep Neural Network Training

Understanding optimization in deep learning is a fundamental problem, and recent findings have challenged the previously held belief that gradient descent stably trains deep networks. In this study, we delve deeper into the instability of gradient descent during the training of deep networks. By employing gradient descent to train various modern deep networks, we provide empirical evidence demonstrating that a significant portion of the optimization progress occurs through the utilization of oscillating gradients. These gradients exhibit a high negative correlation between adjacent iterations. Furthermore, we make the following noteworthy observations about these gradient oscillations (GO): (i) GO manifests in different training stages for networks with diverse architectures; (ii) when using a large learning rate, GO consistently emerges across all layers of the networks; and (iii) when employing a small learning rate, GO is more prominent in the input layers compared to the output layers. These discoveries indicate that GO is an inherent characteristic of training different types of neural networks and may serve as a source of inspiration for the development of novel optimizer designs.
Overall Structure Modeling and Modal Analysis of Liquid Rocket Engine Based on ABAQUS Secondary Development Platform

With the development of computer technology, dynamic simulation technology becomes more and more important in the engine design and development cycle. The development and use of relevant special programs greatly shorten the engine development cycle. In this paper, the components and connection modes of liquid rocket engine are simplified reasonably and efficiently, and a high-precision equivalent model with the maximum deviation of 9.754% and the minimum deviation of 0.423% of the natural frequency obtained by modal analysis is established; The ABAQUS secondary development technology is used to build a modal analysis platform, so as to quickly and accurately realize modal calculation, which is of guiding significance to the designers of liquid rocket engines.
Software Countermeasures against DVFS fault Attack for AES

Most modern processors utilize Dynamic Voltage and Frequency Scaling (DVFS) technology to dynamically adjust voltage and frequency according to the workloads of processors to reduce power consumption. However, DVFS can be exploited by attackers to introduce transient hardware faults. Combined with Differential Fault Analysis (DFA), the secure execution environment of processors is threatened. In order to minimize the threat of DVFS fault attack, we analyze its principle and focus on countermeasures for AES applications. We provide a detailed analysis of three software countermeasures, namely redundancy-based methods, parity code, and infective computation. Among them, the temporal redundant method with random latency between executions defends against high-order attacks with 34.18% timing overhead, while the infective computation-based method achieves the best security with nearly 124.35% timing overhead.
Visual Feedback Using VR Patient Avatar in Care and Nursing Training Environments

Care and Nursing Training (CNT) refers to developing the ability to respond effectively to patient needs by examining patients’ needs and improving trainees’ nursing skills in a nursing environment. Existing methods have been performed based on videos and books, but the best way is to experiment with actual humans. However, patients must be continuously recruited for training, and inexperienced trainees may make mistakes when practicing at medical institutions or public health centers for CNT training. Thus, in this paper, we propose a VR-based virtual patient avatar to perform a CNT program and to implement an effective feedback system; visual feedback is provided to trainees using VR to express the patient’s pain state in real-time. This study is anticipated to provide a new pathway for developing advanced care training methods by being the most effective method for caregivers who must check whether the patient feels pain periodically.
DVTest: Deep Neural Network Visualization Testing Framework

Deep neural networks have shown their powerful potential in various fields, especially vision classification problems, even though it still has black-box property. Visualization methods for deep neural networks, as a common technique to improve the interpretability of neural networks, can help researchers better understand the various properties of neural networks. However, current neural network visualization methods provide a limited explanation of the network’s testing process and simultaneously rely on the diversity of test data sets.This paper proposes DVTest, a Deep Neural Network Visualization Testing Framework to evaluate and improve the testing efficiency, interpretability, and reliability of deep neural networks. By (1) constructing various test samples and (2) utilizing the mutation testing method in the visualization process, our framework can (3) locate the problematic neurons and improve the precision of the model. (4) Rich experiments demonstrate that our method can not only visualize the prediction defects of neural networks but also increase the prediction accuracy of models by neuron freezing method and retraining technique. We also build a local website usage platform based on DVTest, and the source code can be downloaded from https://github.com/tianfy1258/aim and https://github.com/tianfy1258/aim_front, representing back end and front end of the system, respectively.
Assessing ChatGPT’s Comprehension of Perturbed Text through Text Linguistic Features

This paper presents an evaluation method for assessing ChatGPT’s ability to understand perturbed text, offering a novel approach to evaluating the robustness of large language models from a text understanding perspective. The proposed method analyzes ChatGPT’s comprehension of perturbed text across three dimensions: static sensitivity, dynamic sensitivity, and comprehensive ability. This is achieved through the utilization of traditional metrics and a difference norm based on textual linguistic features. The integration of linguistic features into the assessment of text perturbation degree provides a fast, efficient, and accurate evaluation method that can be easily applied to other large language models. Analysing of results identifies perturbations with high static and dynamic sensitivity for ChatGPT, and assesses its comprehensive comprehension both qualitatively and quantitatively.
Distributed Storage Over a Public Channel: Trade-off between Privacy and Shared Key Lengths

Consider a user who wants to store a file across multiple servers such that at least t servers are needed to reconstruct the file and any z colluding servers cannot learn more than a fraction α of the file. Unlike traditional secret-sharing models that assume the availability of secure channels at no cost, we assume that the user can only transmit data to the servers through a public channel and that the user and each server share an individual secret key of length n bits. For a fixed key length n, we characterize the fundamental trade-off between the privacy leakage parameter α and the file length that the user can store in the servers. Furthermore, for the optimal trade-off between α and the file length, we determine (i) the minimum amount of local randomness needed by the user, (ii) the minimum amount of public communication from the user to the servers, and (iii) the minimum storage requirement at the servers.
Privacy-Preservation for Distributed Quantized k-means Clustering

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node’s state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph.
Point-process-based Representation Learning for Electronic Health Records

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions of clinicians and the state of the patient. Point process is a mathematical framework for analyzing event sequence data that is consistent with irregular sampling patterns. To tackle the challenges posed by EHRs, we propose a transformer event encoder with point process loss that encodes the pattern of laboratory tests in EHRs. We conduct experiments on two real-world EHR databases to evaluate our proposed approach. Firstly, we learn the transformer event encoder jointly with an existing state encoder in a self-supervised learning approach which gives superior performance in negative log-likelihood and future event prediction. Additionally, we propose an algorithm for aggregating attention weights that can reveal the interaction between the events. Secondly, we transfer and freeze the learned transformer event encoder to the downstream task for the outcome prediction (mortality and sepsis shock), where it outperforms state-of-the-art models for handling irregularly-sample time series. Our results demonstrate that our approach can improve representation learning in EHRs and can be useful for clinical prediction tasks.
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems

Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protecting privacy by avoiding data exchange between clients and servers, it has been shown that the server can infer user ratings based on updated non-zero gradients obtained from two consecutive rounds of user-uploaded gradients. Moreover, federated recommendation systems (FRS) face the challenge of heterogeneity, leading to decreased recommendation performance. In this paper, we propose FedRec+, an ensemble framework for FRS that enhances privacy while addressing the heterogeneity challenge. FedRec+ employs optimal subset selection based on feature similarity to generate near-optimal virtual ratings for pseudo items, utilizing only the user’s local information. This approach reduces noise without incurring additional communication costs. Furthermore, we utilize the Wasserstein distance to estimate the heterogeneity and contribution of each client, and derive optimal aggregation weights by solving a defined optimization problem. Experimental results demonstrate the state-of-the-art performance of FedRec+ across various reference datasets.
Data-Leashing: Towards a Characterization of The Problem and Its Solution

Once sensitive digital content is shared or leaked, it is not feasible for the content owner to control it. This has led to issues whereby the privacy of leaked information is compromised. There is a need for a solution to allow digital content creators to maintain control of their digital assets even after they have been shared, downloaded, and saved by the receiver. In this paper, we propose a novel approach called Data-Leashing - inspired by the common practice of putting a leash on a pet. The proposed approach solves the post-sharing control of data problem by encapsulating digital assets (such as personal pictures) in a special container program that protects the digital assets. The specialized container program allows only authorized users to access protected digital assets. Should the receiver of the digital asset decide to (re)share the protected digital assets with unauthorized receivers, the container program notifies the asset’s owner and gives them the option to either grant or deny access to the previously unauthorized subjects. In this paper, we outline the key features and properties of the proposed Data-Leashing approach.