Reviewerly/exHarmony huggingface dataset:
Eliciting Environmental Opposites for Requirements-Based Testing

Many software failures originate in the environment. To make the environment assumptions explicit in the requirements-based testing process, we propose to construct opposites where an implemented software feature can be observed and evaluated in different yet related contexts. In this paper, we share our manual analysis of such environmental opposites. Specifically, we identified 127 opposite pairs of 32 Webex’s continuously released features over three months. Our results not only demonstrate the feasibility by covering all the 32 features, but also suggest that semantic roles, such as "Temporal", "Stakeholder", and "Instrument", emerge as the dominant categories conducive to eliciting environmental opposites.
Secure Revocation Features in eKYC - Privacy Protection in Central Bank Digital Currency

Central bank digital currencies require the implementation of eKYC to verify whether a trading customer is eligible online. When an organization issues an ID proof of a customer for eKYC, that proof is usually achieved in practice by a hierarchy of issuers. However, the customer wants to disclose only part of the issuer's chain and documents to the trading partner due to privacy concerns. In this research, delegatable anonymous credential (DAC) and zero-knowledge range proof (ZKRP) allow customers to arbitrarily change parts of the delegation chain and message body to range proofs expressed in inequalities. That way, customers can protect the privacy they need with their own control. Zero-knowledge proof is applied to prove the inequality between two time stamps by the time stamp server (signature presentation, public key revocation, or non-revocation) without disclosing the signature content and stamped time. It makes it possible to prove that the registration information of the national ID card is valid or invalid while keeping the user's personal information anonymous. This research aims to contribute to the realization of a sustainable financial system based on self-sovereign identity management with privacy-enhanced PKI.
Continual Variational Autoencoder Learning via Online Cooperative Memorization

Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks. However, their ability to generate images with specifications corresponding to the classes and databases learned during Continual Learning (CL) is not well understood and catastrophic forgetting remains a significant challenge. In this paper, we firstly analyze the forgetting behaviour of VAEs by developing a new theoretical framework that formulates CL as a dynamic optimal transport problem. This framework proves approximate bounds to the data likelihood without requiring the task information and explains how the prior knowledge is lost during the training process. We then propose a novel memory buffering approach, namely the Online Cooperative Memorization (OCM) framework, which consists of a Short-Term Memory (STM) that continually stores recent samples to provide future information for the model, and a Long-Term Memory (LTM) aiming to preserve a wide diversity of samples. The proposed OCM transfers certain samples from STM to LTM according to the information diversity selection criterion without requiring any supervised signals. The OCM framework is then combined with a dynamic VAE expansion mixture network for further enhancing its performance.
A Large-Scale Multiple-objective Method for Black-box Attack Against Object Detection

Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model information. Most existing attack methods aim to minimize the true positive rate, which often shows poor attack performance, as another sub-optimal bounding box may be detected around the attacked bounding box to be the new true positive one. To settle this challenge, we propose to minimize the true positive rate and maximize the false positive rate, which can encourage more false positive objects to block the generation of new true positive bounding boxes. It is modeled as a multi-objective optimization (MOP) problem, of which the generic algorithm can search the Pareto-optimal. However, our task has more than two million decision variables, leading to low searching efficiency. Thus, we extend the standard Genetic Algorithm with Random Subset selection and Divide-and-Conquer, called GARSDC, which significantly improves the efficiency. Moreover, to alleviate the sensitivity to population quality in generic algorithms, we generate a gradient-prior initial population, utilizing the transferability between different detectors with similar backbones. Compared with the state-of-art attack methods, GARSDC decreases by an average 12.0 in the mAP and queries by about 1000 times in extensive experiments. Our codes can be found at https://github.com/LiangSiyuan21/GARSDC .
Revisiting -Reciprocal Distance Re-Ranking for Skeleton-Based Person Re-Identification

Person re-identification (re-ID) as a retrieval task often utilizes a re-ranking model to improve performance. Existing re-ranking methods are typically designed for conventional person re-ID with RGB images, while skeleton representation re-ranking for skeleton-based person re-ID still remains to be explored. To fill this gap, we revisit the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -reciprocal distance re-ranking model in this letter, and propose a generic re-ranking method that exploits the salient skeleton features to perform <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -reciprocal distance encoding for skeleton-based person re-ID re-ranking. In particular, we devise the skeleton sequence pooling to aggregate the most salient features of skeletons within a sequence, and combine both original Euclidean distance and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -reciprocal distance to re-rank the skeleton sequence representations for person re-ID. Furthermore, we propose the context-based Rank-1 voting that jointly exploits the initial ranking list and re-ranking list to vote for the top candidate to enhance the Rank-1 matching. Extensive experiments on three public benchmarks demonstrate that our approach can effectively re-rank different state-of-the-art skeleton representations and significantly improve their person re-ID performance.
INTERCORRELATION ANALYSIS OF VOCATIONAL EDUCATION TEACHERS’ COMPETENCY DEFICITS

The article states the need to ensure a high level of human resources for the vocational education system and it requires constant monitoring of the difficulties (deficits) that teachers face in their professional and pedagogical activities. The article presents the results of a study of professional and pedagogical deficits of vocational education teachers. The article provides the analysis and interpretation of the relationships (correlations) between the identified deficits and factors influencing both their formation (professional experience) and replenishment (advanced training programs based on individual trajectories).
UCTNet: Uncertainty-Aware Cross-Modal Transformer Network for Indoor RGB-D Semantic Segmentation

In this paper, we tackle the problem of RGB-D Semantic Segmentation. The key challenges in solving this problem lie in 1) how to extract features from depth sensor data and 2) how to effectively fuse the features extracted from the two modalities. For the first challenge, we found that the depth information obtained from the sensor is not always reliable (e.g. objects with reflective or dark surfaces typically have inaccurate or void sensor readings), and existing methods that extract depth features using ConvNets did not explicitly consider the reliability of depth value at different pixel locations. To tackle this challenge, we propose a novel mechanism, namely Uncertainty-Aware Self-Attention that explicitly controls the information flow from unreliable depth pixels to confident depth pixels during feature extraction. For the second challenge, we propose an effective and scalable fusion module based on Cross-Attention that can perform adaptive and asymmetric information exchange between the RGB and depth encoder. Our proposed framework, namely UCTNet, is an encoder-decoder network that naturally incorporates these two key designs for robust and accurate RGB-D Segmentation. Experimental results show that UCTNet outperforms existing works and achieves state-of-the-art performances on two RGB-D Semantic Segmentation benchmarks.
AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction

Recent work achieved impressive progress towards joint reconstruction of hands and manipulated objects from monocular color images. Existing methods focus on two alternative representations in terms of either parametric meshes or signed distance fields (SDFs). On one side, parametric models can benefit from prior knowledge at the cost of limited shape deformations and mesh resolutions. Mesh models, hence, may fail to precisely reconstruct details such as contact surfaces of hands and objects. SDF-based methods, on the other side, can represent arbitrary details but are lacking explicit priors. In this work we aim to improve SDF models using priors provided by parametric representations. In particular, we propose a joint learning framework that disentangles the pose and the shape. We obtain hand and object poses from parametric models and use them to align SDFs in 3D space. We show that such aligned SDFs better focus on reconstructing shape details and improve reconstruction accuracy both for hands and objects. We evaluate our method and demonstrate significant improvements over the state of the art on the challenging ObMan and DexYCB benchmarks.
EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching

Recent advanced studies have spent considerable human efforts on optimizing network architectures for stereo matching but hardly achieved both high accuracy and fast inference speed. To ease the workload in network design, neural architecture search (NAS) has been applied with great success to various sparse prediction tasks, such as image classification and object detection. However, existing NAS studies on the dense prediction task, especially stereo matching, still cannot be efficiently and effectively deployed on devices of different computing capability. To this end, we propose to train an elastic and accurate network for stereo matching (EASNet) that supports various 3D architectural settings on devices with different compute capability. Given the deployment latency constraint on the target device, we can quickly extract a sub-network from the full EASNet without additional training while the accuracy of the sub-network can still be maintained. Extensive experiments show that our EASNet outperforms both state-of-the-art human-designed and NAS-based architectures on Scene Flow and MPI Sintel datasets in terms of model accuracy and inference speed. Particularly, deployed on an inference GPU, EASNet achieves a new SOTA 0.73 EPE on the Scene Flow dataset with 100 ms, which is 4.5 $$\times $$ faster than LEAStereo with a better quality model. The codes of EASNet are available at: https://github.com/HKBU-HPML/EASNet.git .
Blind Modulation Recognition of UWA Signals with Semi-Supervised Learning

Blind modulation recognition is one of the most important component of the intelligent communication. Recent years, supervised learning is proved to be an effective way to solve this problem. But in time space varying underwater acoustic (UWA) channels, high quality labeled data is very difficult to obtain. To solve this problem, we put forward a semi-supervised learning-based blind modulation recognition scheme SSLUWA for UWA signals. This scheme linearly interpolates the unlabeled signal as fake label, and trains it by interpolation consistency. It can test the knowledge learned from the labeled signals by the accuracy of training on unlabeled signals, to improve the recognition accuracy when the labeled signals are insufficient. In order to verify the performance of SSLUMA, extensive pool and simulation experiments have been conducted. The results show that, compared with 100% label samples, the recognition accuracy of the SSLUWA decreases slightly at low SNR when the label samples are only 10%.
ANALYSIS OF CAPSULE NETWORKS FOR IMAGE CLASSIFICATION

Recently, the interest in convolutional neural networks have grown exponentially for image classification.Their success is based on the ability to learn hierarchical and meaningful image representation that results in a feature extraction technique which is general, flexible and can encode complex patterns.However, these networks have some drawbacks.For example, they need a large number of labeled data, lose valuable information (about the internal properties, such as shape, location, pose and orientation in an image and their relationships) in the pooling and are not able to encode deformation information.Therefore, capsule based networks have been introduced as an alternative to convolutional neural networks.Capsules are a group of neurons (logistic units) representing the presence of an entity and the vector indicating the relationship between features by encoding instantiation parameters.Unlike convolutional neural networks, maximum pooling layers are not employed in a capsule network, but a dynamic routing mechanism is applied iteratively in order to decide the coupling of capsules between successive layers.In other words, training between capsule layers is provided with a routing-by-agreement method.However, capsule networks and their properties to provide high accuracy for image classification have not been sufficiently investigated.Therefore, this paper aims (i) to point out drawbacks of convolutional networks, (ii) to examine capsule networks, (iii) to present advantages, weaknesses and strengths of capsule networks proposed for image classification.
AN IMPROVEMENT OF TRUSTED SAFE SEMI-SUPERVISED FUZZY CLUSTERING METHOD WITH MULTIPLE FUZZIFIERS

Data clustering are applied in various fields such as document classification, dental X-ray image segmentation, medical image segmentation, etc. Especially, clustering algorithms are used in satellite image processing in many important application areas, including classification of vehicles participating in traffic, logistics, classification of satellite images to forecast droughts, floods, forest fire, etc. In the process of collecting satellite image data, there are a number of factors such as clouds, weather, ... that can affect to image quality. Images with low quality will make the performance of clustering algorithms decrease. Apart from that, the parameter of fuzzification in clustering algorithms also affects to clustering results. In the past, clustering methods often used the same fuzzification parameter, m = 2. But in practice, each element should have its own parameter m. Therefore, determining the parameters m is necessary to increase fuzzy clustering performance. In this research, an improvement algorithm for the data partition with confidence problem and multi fuzzifier named as TS3MFCM is introduced. The proposed method consists of three steps namely as “FCM for labeled data”, “Data transformation”, and “Semi-supervised fuzzy clustering with multiple point fuzzifiers”. The proposed TS3MFCM method is implemented and experimentally compared against with the Confidence-weighted Safe Semi-Supervised Clustering (CS3FCM). The performance of proposed method is better than selected methods in both computational time and clustering accuracy on the same datasets
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning

Semi-supervised learning (SSL) leverages both labeled and unlabeled data to train machine learning (ML) models. State-of-the-art SSL methods can achieve comparable performance to supervised learning by leveraging much fewer labeled data. However, most existing works focus on improving the performance of SSL. In this work, we take a different angle by studying the training data privacy of SSL. Specifically, we propose the first data augmentation-based membership inference attacks against ML models trained by SSL. Given a data sample and the black-box access to a model, the goal of membership inference attack is to determine whether the data sample belongs to the training dataset of the model. Our evaluation shows that the proposed attack can consistently outperform existing membership inference attacks and achieves the best performance against the model trained by SSL. Moreover, we uncover that the reason for membership leakage in SSL is different from the commonly believed one in supervised learning, i.e., overfitting (the gap between training and testing accuracy). We observe that the SSL model is well generalized to the testing data (with almost 0 overfitting) but “memorizes” the training data by giving a more confident prediction regardless of its correctness. We also explore early stopping as a countermeasure to prevent membership inference attacks against SSL. The results show that early stopping can mitigate the membership inference attack, but with the cost of model’s utility degradation. (Our code is available at https://github.com/xinleihe/Semi-Leak .)
Partial Attention Modeling for Sentiment Analysis of Big Data

Sentiment analysis has grown in importance as a component of NLP. Sentiment analysis in service assessment can help leaders modify programs correctly and in a timely manner to reflect users’ true feelings and sentiment about the service. This will raise the standard of teaching and learning. To address the inefficiencies and heavy workload of assessment methods in all areas, a Partial Attention (PA) model that integrates global and local attention through gated unit control is proposed. The model yields reasonable contextual representations and achieves improved classification results. According to the experimental results, the PA model outperforms current methods for applications in education and other fields.
Artificial Intelligence based Visual Aid with Live Tracking of Visually Impaired People

Blind people cannot do things like navigate on their own, recognize objects, or avoid obstacles because they cannot see where they are going or what is around them. In the future, there will be an article about a new visual aid for people who are completely deaf or blind. As a result, the Raspberry Pi 3 Model B+ was chosen because it is cheap, small, and easy to work with other systems. This model was made to show how the prototype would work and how it would look. The system's obstacle avoidance configuration includes a camera and other sensors and object detection and tracking algorithms to help it avoid things. People can use ultrasonic technology and a camera to figure out how far away they are from a specific object or person, so they can figure out how far away they are. As an extra layer of protection, the GPS system tracks people's movements who cannot see. The products will be lightweight and easy to move, making them easy to move. Regular eyeglasses can be used as a base for these glasses so that you do not have to spend extra money or make things more complicated. Using this idea to make eyeglasses would be easy, and it would not make them more expensive or more complicated. The price or complexity of eyeglasses would not change if we made one of these products, so it would be easy to put one in.
User Feature Similarity Supported Collaborative Filtering for Page Recommendation Using Hybrid Shuffled Frog Leaping Algorithm

The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to.To overcome the product overload of online shoppers, a variety of recommendation methods have been developed.Collaborative Filtering (CF) is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations.This work proposes a recommendation methodology based on Web Usage Mining (WUM), and machine learning methods to enhance the recommendation quality and the system performance of current CF-based recommender systems.WUM populates the rating database by tracking learners' behaviors on the Web, thereby leading to better quality recommendations.The data is collected from user profile and their preferences and also the weblink and usage.Based on the CF and WUM data, the recommendations are provided.The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers is used to improve the performance of searching for nearest neighbors through Shuffled Frog Leaping Algorithm (SFLA).Experimental results show that the proposed model is effective and can enhance the performance of recommendation.Results show that the proposed SFLA-XGBoost has higher average sensitivity: rating <=15 for CF by 11.49% for RF, by 7.52% for XGBoost and by 4.58% for proposed SFLA-RF respectively.
Docker Container Performance Comparison on Windows and Linux Operating Systems

Docker virtualization technology is now widely used not just in Linux, but also in Windows. When working with containers, it's critical to understand how Docker's performance on Windows compares to Docker's performance on Linux. Is it reasonable to execute a Linux program in a Docker container on Windows? A test tool was created to evaluate Docker's performance on Linux and Windows. This software determines the speed at which simple arithmetic operations are performed, the speed at which RAM is used, and the speed at which read/write operations on a disk drive are performed. This software has been tested on both Linux and Windows operating systems using a Docker container. Docker's arithmetic and memory performance on Linux is marginally better than on Windows, according to testing. Significant performance reduction is found only when communicating with an NVMe SSD under Windows. Thus, in the majority of scenarios, running Docker on Windows makes sense and does not result in substantial performance reduction.
Perceptions and conceptions of learning in smart healthcare technology contexts: a draw-a-picture analysis of the differences between nurses and nurse preceptors

In recent years, smart healthcare technologies used to facilitate precision medicine in clinical practice have increasingly become a topic of discussion. Nurses and nurse preceptors are critical professionals in hospitals, and are also the frontline staff using smart healthcare. The purpose of this study was to investigate nurses’ and nurse preceptors’ perceptions of smart healthcare (including learning motivation, learning attitudes, and personal innovativeness) and the differences in their conceptions of learning in smart healthcare technology contexts. The research findings showed that nurse preceptors had higher mean scores than nurses for learning motivation, learning attitudes, and personal innovativeness in smart healthcare, especially for learning attitudes and personal innovativeness. The nurse preceptors’ conceptions of learning in smart healthcare technology contexts were also more diverse than those of nurses. The main differences are that nurses’ drawings showed a healthcare setting where learners (both patients and nurses) use computers to connect to cloud services for online visits, and where patients can engage in online health education learning activities via computers or mobile devices. The nurse preceptors’ drawings focused on the delivery of nursing knowledge and skills to learners via VR technologies, helping diagnose patients, analyzing medical data, and predicting healthcare through smart healthcare technologies.