Reviewerly/exHarmony huggingface dataset:
QTC4SO: Automatic Question Title Completion for Stack Overflow

Question posts with low-quality titles often discourage potential answerers in Stack Overflow. In previous studies, researchers mainly focused on directly generating question titles by analyzing the contents of the posts. However, the quality of the generated titles is still limited by the information available in the post contents. A more effective way is to provide accurate completion suggestions when developers compose titles. Inspired by this idea, we are the first to study the problem of automatic question title completion for Stack Overflow and then propose a novel approach QTC4SO. Specifically, we first preprocess the gathered post titles to form incomplete titles (i.e., tip information provided by developers) for simulating the scene of this task. Then we construct the multi-modal input by concatenating the incomplete title with the post’s contents (i.e., the problem description and the code snippet). Later, we adopt multi-task learning to the question title completion task for multiple programming languages. Finally, we adopt a pre-trained model T5 to learn the title completion patterns automatically. To evaluate the effectiveness of QTC4SO, we gathered 164,748 high-quality posts from Stack Overflow by covering eight popular programming languages. Our empirical results show that compared with the approaches of directly generating question titles, our proposed approach QTC4SO is more practical in automatic and human evaluation. Therefore, our study provides a new direction for automatic question title generation and we hope more researchers can pay attention to this problem in the future.
Generating near-ideal nonces for cryptographic processes using Quantum Cryptography

Quantum cryptography and quantum computing explore the physics and principles of randomness achieved through quantum mechanics. The aim of quantum cryptography is to transfer data from the sender to the receiver without any information loss, error, or violation of the needed security aspects. Ideally, the data should be transferred with basic standards like Confidentiality, Integrity, and Availability (CIA). Also, other services like authentication, authorization, access control, digital signatures, etc. need to be implemented depending on the requirement of the application. For all these services, random numbers are required to be used as nonces, session keys, identifiers, initialization vectors, seed values, etc. This work primarily focuses on generating random numbers using the Quantum Key Distribution (QKD) as a parallel benefit. BB84 and Six State Protocol (SSP) are the protocols used in Quantum Key Distribution (QKD). In this work, these protocols are simulated. The security levels of these protocols are analyzed based on the parameters like randomization and probability. The protocols are implemented in Qiskit software. Filter arrangement plays an important role in Quantum Cryptography. This offers an additional axis compared to the BB84 protocol. The approach thereby improves the randomization in SSP protocol. The comparison of both protocols shows that the SSP protocol gives better randomization when compared to the BB84 protocol.
FedDK: Improving Cyclic Knowledge Distillation for Personalized Healthcare Federated Learning

For most healthcare organizations, a significant challenge today is predicting diseases with incomplete data information, often resulting in isolation.Federated learning (FL) solves the issue of data silos by enabling remote local machines to train a globally optimal model collaboratively without the need for sharing data.In this research, we present FedDK, a serverless framework designed to obtain personalized models for each federation through data from local federations using convolutional neural networks and training through FL.Our approach involves using convolutional neural networks (CNNs) to accumulate common knowledge and transfer it using knowledge distillation, which helps prevent common knowledge forgetting.Additionally, the missing common knowledge is filled circularly between each federation, culminating in a personalized model for each group.This novel design leverages federated, deep, and integrated learning methods to produce more accurate machine-learning models.Our federated model exhibits superior performance to local and baseline FL methods, achieving significant advantages.
Pedestrian Tracking Based on Receptive Field Improvement: A One-Shot Multiobject Tracking Approach Based on Vision Sensors

Multiobject tracking (MOT) in video sequences has gradually become one of the most essential fields in computer vision tasks. As the use of two separate models for feature extraction in two-shot methods precludes them from achieving real-time inference, one-shot methods have been developed to accelerate the running speed by integrating the position estimation of interested targets and extraction of the corresponding appearance embedding features. However, the one-shot MOT accuracy is not as good as that of the two-shot methods. In our study, we design a reinforced one-shot MOT system to further promote the MOT capability. First of all, for the problem of target scale transformation, a receptive field module (RFM) that applies dilated convolutions to acquire diverse receptive fields is presented. Then, we design an attention mechanism network to extract channel and positional information. Finally, we combine the circle loss with Euclidean distance optimization and cross-entropy loss to enhance the learning of discriminative embeddings. Three public pedestrian tracking datasets are applied to verify the effectiveness and superiority of our presented algorithm. In particular, we achieve 79.0 MOT accuracy (MOTA) and 78.4 identification F-score (IDF1) on the train set of MOT16 with 24.1 frames/s inference speed on an NVIDIA GeForce RTX 3090 GPU.
Non-destructive estimation of three-dimensional inelastic strain via nonlinear inverse analysis using displacement

The service life of mechanical structures is influenced by the inelastic strain generated during manufacturing and operation. Therefore, non-destructive estimation of three-dimensional inelastic strain is essential to ensure structure safety even during operation. Accordingly, an inverse problem method was proposed to estimate the inelastic strain over the entire component based on the eigenstrain theory using non-destructively measured surface displacement. However, this method assumes micro-deformation, in which the causal inelastic strain and resulting displacement are linearly related. In practice, the value of inelastic strain is large, which results in large deformation and a nonlinear effect on displacement determined by the magnitude of the inelastic strain. This study considers the inverse problem of estimating the creep strain in a turbine blade for power generation from the displacement caused by the creep strain and proposes a method to derive the exact solution even for large deformation problems. Numerical simulations using a simplified turbine model show that the estimates converge to the correct solution value with relatively high accuracy in the absence of measurement errors. Moreover, the result of the iterative calculations used in the proposed method converge around the correct value, even in the presence of measurement errors in the displacement by the laser displacement meter.
Using chatbots for English language learning in higher education

Despite the advancement of Chatbot technology, little is known about its affordances for language learning, especially in the context of Malaysian higher education institutions. Adapting the Push-Pull Mooring-Habit (PPMH) theoretical framework, this mixed-methods study examined Malaysian university students' experience in using Chatbots for English language learning, focusing on four main aspects; Performance Expectancy, Effort Expectancy, Social Isolation and Covid-19 Fear. A total of 360 undergraduate students from three public universities in Malaysia completed the questionnaire after being engaged with chatbots for three months. Twenty of them were also interviewed for an in-depth understanding of the phenomenon being investigated. The analysis reveals that Performance and Effort Expectancies contribute to the positive experience of using Chatbots for language learning hence serving as the Pull factors. Whereas Social Isolation contributed by the sense of robotic interaction, emotionlessness, and lack of flow in conversation serves as the Push Factor in using Chatbots for language learning. Despite the students' disagreements with the social influence of Chatbots on their behavioural intentions, Chatbots are still considered beneficial enough to serve as an interlocutor for English language learning.
Supervised Copy Mechanism for Grammatical Error Correction

AI has introduced a new reform direction for traditional education, such as automating Grammatical Error Correction (GEC) to reduce teachers' workload and improve efficiency.However, current GEC models still have flaws because human language is very variable, and the available labeled datasets are often too small to learn everything automatically.One of the key principles of GEC is to preserve correct parts of the input text while correcting grammatical errors.However, previous sequence-to-sequence (Seq2Seq) models may be prone to over-correction as they generate corrections from scratch.Over-correction is a phenomenon where a grammatically correct sentence is incorrectly flagged as containing errors that require correction, leading to incorrect corrections that can change the meaning or structure of the original sentence.This can significantly reduce the accuracy and usefulness of GEC systems, highlighting the need for improved approaches that can reduce over-correction and ensure more accurate and natural corrections.Recently, sequence tagging-based models have been used to mitigate this issue by only predicting edit operations that convert the source sentence to a corrected one.Despite their good performance on datasets with minimal edits, they struggle to restore texts with drastic changes.This issue artificially restricts the type of changes that can be made to a sentence and does not reflect those required for native speakers to find sentences fluent or natural sounding.Moreover, sequence tagging-based models are usually conditioned on human-designed language-specific tagging labels, hindering generalization and the real error distribution generated by diverse learners from different nationalities.In this work, we introduce a novel Seq2Seq-based approach that can handle a wide variety of grammatical errors on a low-fluency dataset.Our approach enhances the Seq2Seq architecture with a novel copy mechanism based on supervised attention.Instead of merely predicting the next token in context, the model predicts additional correctness-related information for each token.This auxiliary objective propagates into the weights of the model during training without requiring extra labels at testing time.Experimental results on benchmark datasets show that our model achieves competitive performance compared to state-of-the-art(SOTA) models.
BlockMeter: An Application Agnostic Performance Measurement Framework for Private Blockchain Platforms

Blockchain Technology is an emerging technology with the potential to disrupt a number of application domains. Though blockchain platforms like Bitcoin and Ethereum have seen immense success and acceptability, their nature of being public and anonymous makes them unsuitable for many enterprise-level use-cases. To address this issue, Linux Foundation has started an open-source umbrella initiative, known as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hyperledger Platforms</i> . Under this initiative, a number of private blockchain platforms have been developed. However, the scalability and performance of these private blockchains must be examined to understand their suitability for different use cases. Recent researches and projects on performance benchmarking for private blockchain systems are specific to use cases and generally tied to a blockchain platform. In this article, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMeter</i> , an application-agnostic performance benchmarking framework for private blockchain platforms. BlockMeter can be utilised to measure the key performance metrics of any application deployed on top of an external private blockchain application in real-time. In this article, we present the architecture of the framework and discuss its different implementation aspects. Then, to showcase the applicability of the framework, we use BlockMeter to evaluate the two most widely used Hyperledger platforms, Hyperledger Fabric and Hyperledger Sawtooth, against a number of use-cases.
A Privacy-Preserving Ride-Matching Scheme Without a Trusted Third-Party Server

In the past few years, online taxi-hailing services have gained acceptance as a convenient way to travel and are becoming increasing popular. However, privacy concerns come into being along with the online taxi-hailing service. Most of the existing privacy-preserving online taxi-hailing systems rely on a central third party to manage the whole system. This construction has three drawbacks. First, the online taxi-hailing platforms and the drivers can view the passenger's entire ride trajectory freely. Second, the platforms tend to prioritize matching orders with longer distances. Third, the platforms are vulnerable to a single point of failure attack. This article proposes a privacy-preserving ride-matching scheme without a trusted third-party server. In this article, we construct an efficient driver-passenger matching algorithm by using the public key encryption with equality test and blockchain with smart contracts, which efficiently match the ride request and ride offer in the encrypted form. Theoretical analysis shows that our scheme is feasible and practical. Experimental analysis provides reliable support for theoretical analysis.
Reducing dehumanisation through virtual reality: prospects and pitfalls

This paper presents evidence that Virtual reality (VR) has the potential to reduce dehumanisation via a range of different routes, notably the experience of being present in a situation with outgroup members, experiencing virtual contact and interaction with the outgroup and taking on the perspective or even body of an outgroup member. In addition, it hightlights key questions that require future research, including the strength of empirical evidence that VR can indeed reduce dehumanisation, the mechanisms by which such a process occurs and the ethical issues in treating VR as an 'ultimate empathy machine'.
Breast ultrasound image classification with hard sample generation and semi-supervised learning

Ultrasound is a fast and non-invasive imaging technique widely used in the early-stage diagnosis of breast cancer. Ultrasound images are also commonly used in deep learning, but traditional computer algorithms and even ultrasound diagnosticians often struggle to accurately distinguish between benign and malignant cases, as some ultrasound images exhibit mixed characteristics of both. To address the above problems, we defined hard sample mining rules firstly. To address the above problems, we defined rules for hard sample mining and utilized shape descriptors (Concavity, Growth Direction, Compactness, Circle Variance, and Elliptic Variance) to describe the samples. For those samples with overlapped features of both categories, i.e., benign and malignant, we identify them as hard samples. In order to make the network pay more attention to hard samples, we propose a Phased GAN (PGAN) that can generate hard samples to increase the number of such samples in the dataset. However, since the generated hard samples lack labels, they cannot be used in supervised training. To overcome this limitation, we propose a new semi-supervised network (SSN) that trains them with labeled samples. We also add a hard sample loss function to the SSN to regularize the features of the hard samples, which helps the network learn these features better. In summary, we propose a breast ultrasound image classification using PGAN and SSN, which achieves a classification accuracy of 97.7% on a public breast dataset (445 cases of benign and 210 cases of malignant).
Online learning using deep random vector functional link network

Deep neural networks have shown their promise in recent years with their state-of-the-art results. Yet, backpropagation-based methods may suffer from time-consuming training process and catastrophic forgetting when performing online learning. In this work we attempt to curtail them by employing the ensemble deep Random Vector Functional Link (edRVFL). As opposed to backpropagation-based neural networks that adjust weights iteratively, RVFL uses a closed-form solution method without iterative parameter learning. In addition, our approach allows the model to grow incrementally as new data is made available so that it can more resemble real-life learning scenarios. Our proposed online learning models were able to perform better on 72% of the datasets in the classification scenario and 80% of the datasets in the regression scenario, when compared to other available randomization-based online learning models in the literature. This is further supported by statistical comparisons which also show the stability of our network.
Position‐aware spatio‐temporal graph convolutional networks for skeleton‐based action recognition

Abstract Graph Convolutional Networks (GCNs) have been widely used in skeleton‐based action recognition. Though significant performance has been achieved, it is still challenging to effectively model the complex dynamics of skeleton sequences. A novel position‐aware spatio‐temporal GCN for skeleton‐based action recognition is proposed, where the positional encoding is investigated to enhance the capacity of typical baselines for comprehending the dynamic characteristics of action sequence. Specifically, the authors’ method systematically investigates the temporal position encoding and spatial position embedding, in favour of explicitly capturing the sequence ordering information and the identity information of nodes that are used in graphs. Additionally, to alleviate the redundancy and over‐smoothing problems of typical GCNs, the authors’ method further investigates a subgraph mask, which gears to mine the prominent subgraph patterns over the underlying graph, letting the model be robust against the impaction of some irrelevant joints. Extensive experiments on three large‐scale datasets demonstrate that our model can achieve competitive results comparing to the previous state‐of‐art methods.
MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing

In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems. MESSES helps transform tabular data from multiple sources into a Metabolomics Workbench specific deposition format. The package provides three commands, extract, validate, and convert, that implement a natural data transformation workflow. Moreover, MESSES facilitates richer metadata capture than is typically attempted by manual efforts. The source code and extensive documentation is hosted on GitHub and is also available on the Python Package Index for easy installation.
LightSeg: Local Spatial Perception Convolution for Real-Time Semantic Segmentation

Semantic segmentation is increasingly being applied on mobile devices due to advancements in mobile chipsets, particularly in low-power consumption scenarios. However, the lightweight design of mobile devices poses limitations on the receptive field, which is crucial for dense prediction problems. Existing approaches have attempted to balance lightweight designs and high accuracy by downsampling features in the backbone. However, this downsampling may result in the loss of local details at each network stage. To address this challenge, this paper presents a novel solution in the form of a compact and efficient convolutional neural network (CNN) for real-time applications: our proposed model, local spatial perception convolution (LSPConv). Furthermore, the effectiveness of our architecture is demonstrated on the Cityscapes dataset. The results show that our model achieves an impressive balance between accuracy and inference speed. Specifically, our LightSeg, which does not rely on ImageNet pretraining, achieves an mIoU of 76.1 at a speed of 61 FPS on the Cityscapes validation set, utilizing an RTX 2080Ti GPU with mixed precision. Additionally, it achieves a speed of 115.7 FPS on the Jetson NX with int8 precision.
Biased Random-Key Genetic Algorithm with Local Search Applied to the Maximum Diversity Problem

The maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computational time. In this study, we propose a novel approach that combines the biased random-key genetic algorithm (BRKGA) with local search to tackle the MDP. Our computational study utilizes a comprehensive set of MDPLib instances, and demonstrates the superior average performance of our proposed algorithm compared to existing literature results. The MDP has a wide range of practical applications, including biology, ecology, and management. We provide future research directions for improving the algorithm’s performance and exploring its applicability in real-world scenarios.
Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition

Activity recognition using data collected with smart devices such as mobile and wearable sensors has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of smart devices in the internet-of-things era has limited the adoption of activity recognition models for use across different devices. This lack of cross-domain adaptation is particularly notable across sensors of different modalities where the mapping of the sensor data in the traditional feature level is highly challenging. To address this challenge, we propose ActiLabel, a combinatorial framework that learns structural similarities among the events that occur in a target domain and those of a source domain and identifies an optimal mapping between the two domains at their structural level. The structural similarities are captured through a graph model, referred to as the dependency graph, which abstracts details of activity patterns in low-level signal and feature space. The activity labels are then autonomously learned in the target domain by finding an optimal tiered mapping between the dependency graphs. We carry out an extensive set of experiments on three large datasets collected with wearable sensors involving human subjects. The results demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods. In particular, ActiLabel outperforms such algorithms by average F1-scores of 36.3%, 32.7%, and 9.1% for cross-modality, cross-location, and cross-subject activity recognition, respectively.