Reviewerly/exHarmony huggingface dataset:
Along Similar Lines: Local Obstacle Avoidance for Long-Term Autonomous Path Following

Visual Teach and Repeat 3 (VT&R3), a generalization of stereo VT&R, achieves long-term autonomous path-following using topometric mapping and localization from a single rich sensor stream. In this paper, we improve the capabilities of a LiDAR implementation of VT &R3 to reliably detect and avoid obstacles in changing environments. Our architecture simplifies the obstacle-perception problem to that of place-dependent change detection. We then extend the behaviour of generic sample-based motion planners to better suit the teach-and-repeat problem structure by introducing a new edge-cost metric paired with a curvilinear planning space. The resulting planner generates naturally smooth paths that avoid local obstacles while minimizing lateral path deviation to best exploit prior terrain knowledge. While we use the method with VT &R, it can be generalized to suit arbitrary path-following applications. Experimental results from online run-time analysis, unit testing, and qualitative experiments on a differential drive robot show the promise of the technique for reliable long-term autonomous operation in complex un-structured environments.
Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets

Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications. Due to a lack of verification methods for high-dimensional DNNs, a trade-off is needed between accepted performance and handling of out-of-distribution (OOD) samples.This work evaluates rejecting outputs from semantic segmentation DNNs by applying a Mahalanobis distance (MD) based on the most probable class-conditional Gaussian distribution for the predicted class as an OOD score. The evaluation follows three DNNs trained on the Cityscapes dataset and tested on four automotive datasets and finds that classification risk can drastically be reduced at the cost of pixel coverage, even when applied on unseen datasets. The applicability of our findings will support legitimizing safety measures and motivate their usage when arguing for safe usage of DNNs in automotive perception.
Measurement and Identification of Informative Reviews for Automated Summarization

This research investigates the impact of data quality to the quality of text summarization using the software review summarization as a case study. It answers three research questions: 1. What is the most important quality dimension for measuring the quality of software reviews for fitting the review summarization purpose? Our answer is the informativeness of reviews. We propose a metric to measure informativeness and use it to identify highly informative reviews for training review summarization models. 2. How does the review quality affect the quality of review summarization? We conducted the review summarization experiments with a group of datasets that have different quality settings to answer the question. Based on the experiment results, we propose a sampling method for identifying high quality reviews, and the experiment results indicate that the method can significantly improve quality of review summarization on two large review datasets. Furthermore, the results show that the models trained on the selected dataset maintain a balance of bias and variance. 3. Do all text summarization models perform equally well on the datasets? We conduct a comparative study of review summarization on two state-of-the-art deep learning models BART and T5 to answer the question. The research results showing that identifying highly informative reviews is a new direction for improving quality of review summarization.
Time Series Anomaly Detection using Convolutional Neural Networks in the Manufacturing Process of RAN

The traditional approach of categorizing test results as “Pass” or “Fail” based on fixed thresholds can be labor-intensive and lead to dropping test data. This paper presents a framework to enhance the semi-automated software testing process by detecting deviations in executed data and alerting when anomalous inputs fall outside data-driven thresholds. In detail, the proposed solution utilizes classification with convolutional neural networks and prediction modeling using linear regression, Ridge regression, Lasso regression, and XGBoost. The study also explores transfer learning in a highly correlated use case. Empirical evaluation at a leading Telecom company validates the effectiveness of the approach, showcasing its potential to improve testing efficiency and accuracy. Despite its significance, limitations include the need for further research in different domains and industries to generalize the findings, as well as the potential biases introduced by the selected machine learning models. Overall, this study contributes to the field of semi-automated software testing and highlights the benefits of leveraging data-driven thresholds and machine learning techniques for enhanced software quality assurance processes.
The Rise of Super Apps: Insights and Opportunities from a UX Perspective

The term Super App has become commonplace in Asia and has been reshaping digital ecosystems globally. This study reviews the unique features of super apps and addresses the reasons for their success. Using WeChat as an example, the study adopts Rayport and Jaworski’s 7Cs user interface framework (context, content, community, customization, communication, connection, and commerce) to examine WeChat’s interface. The research first reviews the 7Cs framework to situate it in the context of Web 2.0 and then uses it to explore WeChat’s interface. The study finds that WeChat is a rich ecosystem that allows users to perform daily tasks within a single app, and elements within the framework have evolved beyond the framework initially discussed. Additionally, the study finds that WeChat tailors its interface to meet local user needs and embeds cultural elements into its design.
Posthumanism and Technical Communication: Using an Assemblage Assignment in an Interdisciplinary Seminar

As technical communicators contend with increasingly complex techno-social systems of activity, a posthumanist approach to shared agency across humans and nonhuman components becomes more vital to the field’s development. This paper suggests that the theoretic concept of “assemblage” productively supports such an approach. The authors trace an assemblage analysis assignment as a key process and deliverable in an interdisciplinary seminar to demonstrate its scope and benefit. A key takeaway is that the complexity and indeterminate parameters of the assemblage concept itself demands engagement with a posthumanist lens of technical communication.
Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection

We propose a method that augments a simulated dataset using diffusion models to improve the performance of pedestrian detection in real-world data. The high cost of collecting and annotating data in the real-world has motivated the use of simulation platforms to create training datasets. While simulated data is inexpensive to collect and annotate, it unfortunately does not always closely match the distribution of real-world data, which is known as the sim2real gap. In this paper we propose a novel method of synthetic data creation meant to close the sim2real gap for the challenging pedestrian detection task. Our method uses a diffusion-based architecture to learn a real-world distribution which, once trained, is used to generate datasets. We mix this generated data with simulated data as a form of augmentation and show that training on a combination of generated and simulated data increases average precision by as much as 27.3% for pedestrian detection models in real-world data, compared against training on purely simulated data.
CANDID: Correspondence AligNment for Deep-burst Image Denoising

With the advent of mobile phone photography and point-and-shoot cameras, deep-burst imaging is widely used for a number of photographic effects such as depth of field, super-resolution, motion deblurring, and image denoising. In this work, we propose to solve the problem of deep-burst image denoising by including an optical flow-based correspondence estimation module which aligns all the input burst images with respect to a reference frame. In order to deal with varying noise levels the individual burst images are pre-filtered with different settings. Exploiting the established correspondences one network block predicts a pixel-wise spatially-varying filter kernel to smooth each image in the original and prefiltered bursts before fusing all images to generate the final denoised output. The resulting pipeline achieves state-of-the-art results by combining all available information provided by the burst.
Naive Scene Graphs: How Visual is Modern Visual Relationship Detection?

Modern approaches to scene graph generation still struggle with their performance, with even state of the art approaches hovering under a 15% mean recall on certain evaluation modes. This poor performance is partially a result of networks heavily relying and fixating on non-visual data, such as class statistics, instead of the pixel-level signals present in the images. We demonstrate this by examining the 'visual-ness' of visual relationship detection approaches. We first describe and implement a new Naive Bayes-based statistical baseline for scene graph generation. Most notably, this basic classifier does not utilize the image pixels, but relies on the properties of the bounding boxes (class labels, topological configuration, … etc.) to predict the relationship labels. We demonstrate that our classical machine learning approach, one as simple as a categorical Naive Bayes classifier, can perform relationship detection in a manner that achieves relatively competitive performance to that of modern scene graph generators. This is an alarming finding regarding scene graph generation that implies that visual data in images may not be utilized in modern visual relationship detection past the point of object detection. We finally discuss how more visual modern approaches to scene graph generation appear to remedy some of these shortcomings.
Gradient-Based Maximally Interfered Retrieval for Domain Incremental 3D Object Detection

Accurate 3D object detection in all weather conditions remains a key challenge to enable the widespread deployment of autonomous vehicles, as most work to date has been performed on clear weather data. In order to generalize to adverse weather conditions, supervised methods perform best if trained from scratch on all weather data instead of finetuning a model pretrained on clear weather data. Training from scratch on all data will eventually become computationally infeasible and expensive as datasets continue to grow and encompass the full extent of possible weather conditions. On the other hand, naive finetuning on data from a different weather domain can result in catastrophic forgetting of the previously learned domain. Inspired by the success of replay-based continual learning methods, we propose Gradient-based Maximally Interfered Retrieval (GMIR), a gradient based sampling strategy for replay. During finetuning, GMIR periodically retrieves samples from the previous domain dataset whose gradient vectors show maximal interference with the gradient vector of the current update. Our 3D object detection experiments on the SeeingThroughFog (STF) dataset [1] show that GMIR not only overcomes forgetting but also offers competitive performance compared to scratch training on all data with a 46.25% reduction in total training time.
Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach.
Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness

Deep learning models can perform well when evaluated on images from the same distribution as the training set. However, applying small perturbations in the forms of noise, artifacts, occlusions, blurring, etc. to a model's input image and feeding the model with out-of-distribution (OOD) data can significantly drop the model's accuracy, making it not applicable to real-world scenarios. Data augmentation is one of the well-practiced methods to improve model robustness against OOD data; however, examining which augmentation type to choose and how it affects the OOD robustness remains understudied. There is a growing belief that augmenting datasets using data augmentations that improve a model's bias to shape-based features rather than texture-based features results in increased OOD robustness for Convolutional Neural Networks trained on the ImageNet-1K dataset. This is usually stated as “an increase in the model's shape bias results in an increase in its OOD robustness”. Based on this hypothesis, some works in the literature aim to find augmentations with higher effects on model shape bias and use those for data augmentation. By evaluating 39 types of data augmentations on a widely used OOD dataset, we demonstrate the impact of each data augmentation on the model's robustness to OOD data and further show that the mentioned hypothesis is not true; an increase in shape bias does not necessarily result in higher OOD robustness. By analyzing the results, we also find some biases in the ImageNet-1K dataset that can easily be reduced using proper data augmentation. Our evaluation results further show that there is not necessarily a trade-off between in-domain accuracy and OOD robustness, and choosing the proper augmentations can help increase both in-domain accuracy and OOD robustness simultaneously.
A QoE-Fair Synchronized Transmission Scheme for Edge-Assisted Interactive Virtual Reality

In multi-user interactive virtual reality scenarios, synchronization among users is crucial for interactions smoothness. In this paper, we focus on the comprehensive synchronization requirements, not only the immersive timing experience, but also the QoE fairness among multiple sessions. A QoE-fair optimization problem is formulated, aiming at maximizing QoE fairness while providing immersive experience with high frame transmission rate, low end-to-end delay, and synchronization among users. The proposed approach requires edge-cloud collaboration, consisting of a compression control algorithm in edge servers and a synchronization decision algorithm in cloud servers. The former algorithm restricts the range of compression ratio according to the network condition and considers cumulative delay to satisfy the low end-to-end delay requirement, maximizing the QoE in each session. The latter algorithm selects the optimal aggregation decision to maximize QoE fairness among synchronized users. Simulation results demonstrate that the proposed scheme outperforms other solutions and variants in terms of QoE performance as well as QoE fairness.
The Emergence of Cryptography in Blockchain Technology

This chapter explores the evolution of cryptography in blockchain technology, studying its significance, challenges, and implications. It analyzes the various cryptographic algorithms employed in blockchain systems, emphasizing their significance in ensuring transaction security and anonymity. The chapter looks at the challenges of using cryptography in blockchain, such as scalability, key management, and the arrival of quantum computing. It also looks at how cryptography has influenced the emergence of cryptocurrencies and smart contracts. It also examines current cryptographic trends and their possible impact on blockchain security, highlighting the importance of using best practices while implementing cryptography in blockchain systems. Overall, this chapter presents a detailed review of the critical role that cryptography plays in maintaining blockchain technology's integrity, privacy, and trustworthiness.
Enabling CBRS Experimentation through an OpenSAS and SDR-based CBSD

The increased demand for spectrum has motivated the Federal Communications Commission (FCC) to open band 48, also known as the Citizen Broadband Radio Service (CBRS) band, spanning 3.55 to 3.7 GHz, for shared wireless broadband use. Access and operations in the CBRS band is managed by a dynamic Spectrum Access System (SAS) that enables seamless spectrum sharing between incumbent and secondary users. In this paper, we demonstrate an enhanced version of an open source SAS [1], OpenSAS, with added functionality to showcase interaction between General Authorized Access (GAA) and Priority Access License (PAL) user tiers. We further showcase the use of the Google SAS Test Environment [2] with OpenSAS and a Software-defined Radio (SDR)-based CBRS Base Station Device (CBSD) developed by our team. Spectrum sharing is achieved by using a client on the CBSD that is in continuous communication with the SAS to conform to its spectrum usage and transmission power rules at a given location. Our demo steps through the entire SAS-CBSD cycle which includes registration, spectrum inquiry, grant request, heartbeat request, grant relinquishment, and de-registration.
Avoid attacks: A Federated Data Sanitization Defense in IoMT Systems

Malicious falsification of medical data destroys the training process of the medical-aided diagnosis models and causes serious damage to Healthcare IoMT Systems. To solve this unsupervised problem, this paper finds a robust data filtering method for various data poisoning attacks. First, we adapt the federated learning framework to project all of the clients' data features into the public subspace domain, allowing unified feature mapping to be established while their data remains stored locally. Then we adopt the federated clustering to re-group their features to clarify the poisoned data. The federated clustering is based on the consistent association of data and its semantics. Finally, we do the data sanitization with a simple yet efficient strategy. Extensive experiments are conducted to evaluate the accuracy and efficacy of the proposed defense method against data poisoning attacks.
RansomCoin: A New Dataset for Analysing Cryptocurrency Transactions - Addressing a Gap in the Literature

This paper presents a ransomware payment transactions repository, RansomCoin, and showcases the pattern analysis to understand the behaviour of ransomware attackers' money laundering tactics. The surge in ransomware attacks globally is an exciting research challenge and needs a sustainable solution. In this work, we created a bitcoin transaction dataset related to ransomware. In particular, we have focused on Qlocker, Medusa-Locker, BitPaymer, DarkSide, and Netwalker ransomware attacks to create an automation process that extracts data associated with these attacks from the blockchain. The automation details are publicly available via GitHub. The RansomCoin dataset will help law enforcement agencies trace the transaction and analyse the bitcoin movements in the blockchain. The dataset contains suspicious/normal flags, which can help focus on the wallet address flagged as suspicious. The k-NN algorithm from the family of anomaly detection techniques performs better in identifying suspicious transactions.
AR-Span: A Multi-Screen Adaptive Displaying System Using Smart Handheld Devices Based on Mobile Networks

In this paper, we design and implement a multi-screen adaptive displaying system, called AR-Span, which can enable the assembling of heterogeneous mobile devices into one multi-screen displaying equipment. The AR-Span system consists of multiple mobile devices with various screen sizes and a dedicated server communicating and coordinating among these mobile devices. AR-Span can cooperatively display a complete image on multiple mobile devices. In addition, AR-Span can precisely partition a video and synchronically play the video on multiple mobile devices. Furthermore, AR-Span can arbitrarily customize an electronic marquee on multiple mobile devices. In particular, AR-Span can immediately adjust and control the displayed content in an augmented reality based manipulation manner. Experimental results show that AR-Span manipulates the desired content much faster than existing methods/systems and can significantly reduce the total operating time. Moreover, AR-Span can improve the displaying time inconsistency and unsynchronized content gap on heterogeneous mobile devices.
An Extraction-based Approach for Vietnamese Legal Text Summarization

The development of extractive text summarization by the support of deep learning makes a great chance for more and more methods proposed. However, with legal text, this seems to be a great challenge. Apart from the quite large number of researches on general text summarization, there are still few on the legal text summarization. The main problem may due to the complicated structures with long length, specialized vocabulary of each sentences in a legal document. To be specific, unlike general text, legal text requires a document format containing redundant formal sentences, while the main idea is just in a few sentences but widely distributed, not just in a single or few sentences. Moreover, it is also usually structured as an imperative clause, not just a normal statement. Especially with Vietnamese language, this topic seems to be entirely new with the researchers. In this paper, we will use a framework using a pretrained model and a multi-layer classification approach with different ranking methods. We will also compare different pre-trained model versions on the Vietnamese legal text dataset in order to find the best way for the summarizing task.
A Deep Learning-Based Strategy for Vietnamese Incorrect Pronunciation Detection

Despite the growing interest in learning Vietnamese, pronunciation remains a significant challenge for many language learners. This study explores the use of deep learning techniques to automatically detect incorrect pronunciation in Vietnamese. Our approach utilizes a multi-task setup that incorporates an Audio Encoder and a Phoneme Recognizer, enabling the model to learn the alignment between phonemes and acoustic features. This alignment information is then employed by the Incorrect Pronunciation Detector to identify words with incorrect pronunciation. Notably, we propose a novel strategy for generating pronunciation features, which involves “manually” grouping phonemes of the same word, thereby facilitating the model’s learning process. To evaluate the effectiveness of the proposed method, we build a small non-native (L2) Vietnamese speech dataset for training and testing. Compared to the baseline model, our final result improves the accuracy by 5.2% and $F_{1}$ score by 21.14%.