Reviewerly/exHarmony huggingface dataset:
A Multi-Phase Camera-LiDAR Fusion Network for 3D Semantic Segmentation With Weak Supervision

Camera and LiDAR are indispensable perception units in autonomous driving, providing complementary environmental information for 3D semantic segmentation. It is the key point that fuses the information of two modalities to accurate and robust semantic segmentation. However, three major factors will restrict the performance of fusion-based methods, i.e., the reliability of image features, the contribution of different image features, and the trade-off between results of image and point cloud. This paper proposes a novel multi-phase fusion network for 3D semantic segmentation. For the first factor, this paper takes the lead in regarding the problem that image features may be wrong due to the lack of dense annotations in the common datasets as a weak supervision problem and introduces the weakly supervised loss. Second, the proposed attention based feature fusion module can filter and reweight the image features effectively. Third, the results of the two modalities are further fused by self-confidence based late fusion module at pixel-level to complement their advantages. The proposed scheme has been evaluated on nuScenes and SemanticKITTI benchmarks, and the results show the competitiveness with state-of-the-art methods. The ablation studies demonstrate the superiority of the method in sparse classes segmentation. In addition, the robustness is also evaluated, and the results of the proposed method can keep relatively accurate even when faults in one of the sensors.
A novel dataset for encrypted virtual private network traffic analysis

Encryption of network traffic should guarantee anonymity and prevent potential interception of information. Encrypted virtual private networks (VPNs) are designed to create special data tunnels that allow reliable transmission between networks and/or end users. However, as has been shown in a number of scientific papers, encryption alone may not be sufficient to secure data transmissions in the sense that certain information may be exposed. Our team has constructed a large dataset that contains generated encrypted network traffic data. This dataset contains a general network traffic model consisting of different types of network traffic such as web, emailing, video conferencing, video streaming, and terminal services. For the same network traffic model, data are measured for different scenarios, i.e., for data traffic through different types of VPNs and without VPNs. Additionally, the dataset contains the initial handshake of the VPN connections. The dataset can be used by various data scientists dealing with the classification of encrypted network traffic and encrypted VPNs.
SECS: An effective CNN joint construction strategy for breast cancer histopathological image classification

Breast cancer is one of the most prevalent cancers in women. Reliable pathology identification can help histopathologists make accurate diagnosis of breast cancer but require specialized histopathological knowledge and a significant amount of manpower and medical resources. In this study, we fuse the coordinated attention mechanism to enhance the image texture analysis capability of the DenseNet, and build the CA-BreastNet model to classify microscopic histopathological images of specific types of breast cancers in the BreakHis dataset. More crucially, convolutional decision trees based on the specialized enhanced classifying strategy(SECS) are built to increase the overall accuracy of the network by reducing the model's accuracy restriction imposed by dataset structures. The related experimental results show that our network has strong performance and the SECS offers researchers reliable and effective performance enhancement guidelines. The accuracy of the convolutional decision trees reaches 99.75% for binary classification and 95.69% for eight-class classification, which means our model and strategy will be useful in the field of automatic diagnosis of breast cancer.
Skeleton‐aware implicit function for single‐view human reconstruction

Abstract The aim is to reconstruct a complete and detailed clothed human from a single‐view input. Implicit function is suitable for this task because it represents fine shape details and varied topology. Current methods, however, often suffer from artefacts such as broken or disembodied body parts, missing details, or depth ambiguity due to the ambiguity and complexity of human articulation. The main issue observed by the authors is structure‐agnostic. To address these problems, the authors fully utilise the skinned multi‐person linear (SMPL) model and propose a method using the Skeleton‐aware Implicit Function (SIF). To alleviate the broken or disembodied body parts, the proposed skeleton‐aware structure prior makes the skeleton awareness into an implicit function, which consists of a bone‐guided sampling strategy and a skeleton‐relative encoding strategy. To deal with the missing details and depth ambiguity problems, the authors’ body‐guided pixel‐aligned feature exploits the SMPL to enhance 2D normal and depth semantic features, and the proposed feature aggregation uses the extra geometry‐aware prior to enabling a more plausible merging with less noisy geometry. Additionally, SIF is also adapted to the RGB‐D input, and experimental results show that SIF outperforms the state‐of‐the‐arts methods on challenging datasets from Twindom and Thuman3.0.
Client-Specific Upgrade Compatibility Checking via Knowledge-Guided Discovery

Modern software systems are complex, and they heavily rely on external libraries developed by different teams and organizations. Such systems suffer from higher instability due to incompatibility issues caused by library upgrades. In this article, we address the problem by investigating the impact of a library upgrade on the behaviors of its clients. We developed CompCheck , an automated upgrade compatibility checking framework that generates incompatibility-revealing tests based on previous examples. CompCheck first establishes an offline knowledge base of incompatibility issues by mining from open source projects and their upgrades. It then discovers incompatibilities for a specific client project, by searching for similar library usages in the knowledge base and generating tests to reveal the problems. We evaluated CompCheck on 202 call sites of 37 open source projects and the results show that CompCheck successfully revealed incompatibility issues on 76 call sites, 72.7% and 94.9% more than two existing techniques, confirming CompCheck ’s applicability and effectiveness.
Control of Open Mobile Robotic Platform Using Deep Reinforcement Learning

Advanced control for mobile robotic platforms allows efficient real-time navigation in structured and unstructured environments in various industry applications. Deep reinforcement learning is an emerging control strategy where an agent is trained iteratively according to an optimisation objective by using reward and penalty actions. The agent generates the neural network weights used for computing the robot command towards the reference set point. We present an application for an open hardware mobile robotic platform navigation that integrates the sensing, communication, computing and control functions into a single system for navigation in unstructured environments. Implementation is performed through a dedicated software and communication layer that integrates the hardware platform with the MATLAB environment using standardized Robot Operating System (ROS) libraries. Quantitative testing results are presented, in order to prove the viability of the solution, by defining both simulation and laboratory setting scenarios.
An Open-Set Modulation Recognition Scheme With Deep Representation Learning

This letter proposes a deep representation learning based automatic modulation recognition (AMR) algorithm in the open-set recognition (OSR) regime. The challenging recognition risk of unknown modulation classes is first analyzed for most state-of-the-art approaches, and interesting insights into this problem is then provided. Based on this, an open-set AMR scheme is proposed with a combination of feature representation and classification, where a triplet loss function from metric learning is employed for the representor to form distinct clusters for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> known modulation classes. Then, the degree of membership is calculated via extreme value theory (EVT) by modeling the distance between known training data to its corresponding clustering center, followed by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> binary classifiers. Comprehensive experiments on public dataset confirm that the proposed scheme outperforms the other state-of-the-arts in terms of both balanced accuracy and openness.
Weaknesses of Popular and Recent Covert Channel Detection Methods and a Remedy

Network covert channels are applied for the secret exfiltration of confidential data, the stealthy operation of malware, and legitimate purposes, such as censorship circumvention. In recent decades, some major detection methods for network covert channels have been developed. In this paper, we investigate two highly cited detection methods for covert timing channels, namely <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -similarity and compressibility score from Cabuk et al. (jointly cited by 930 papers and applied by thousands of researchers). We additionally analyze two recent ML-based detection methods: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAS</i> (2022) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SnapCatch</i> (2021). While all these detection methods must be considered valuable for the analysis of typical covert timing channels, we show that these methods are not reliable when a covert channel's behavior is slightly modified. In particular, we demonstrate that when confronted with a simple covert channel that we call <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> libur, all detection methods can be circumvented or their performance can be significantly reduced although the covert channel still provides a high bitrate. In comparison to previous timing channels that circumvent these methods, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> libur is much simpler and eliminates the need of altering previously recorded traffic. Moreover, we propose an enhanced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -similarity that can detect the classical covert timing channel as well as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> libur.
A Generalized Explanation Framework for Visualization of Deep Learning Model Predictions

Attribution-based explanations are popular in computer vision but of limited use for fine-grained classification problems typical of expert domains, where classes differ by subtle details. In these domains, users also seek understanding of "why" a class was chosen and "why not" an alternative class. A new GenerAlized expLanatiOn fRamEwork (GALORE) is proposed to satisfy all these requirements, by unifying attributive explanations with explanations of two other types. The first is a new class of explanations, denoted deliberative, proposed to address the "why" question, by exposing the network insecurities about a prediction. The second is the class of counterfactual explanations, which have been shown to address the "why not" question but are now more efficiently computed. GALORE unifies these explanations by defining them as combinations of attribution maps with respect to various classifier predictions and a confidence score. An evaluation protocol that leverages object recognition (CUB200) and scene classification (ADE20 K) datasets combining part and attribute annotations is also proposed. Experiments show that confidence scores can improve explanation accuracy, deliberative explanations provide insight into the network deliberation process, the latter correlates with that performed by humans, and counterfactual explanations enhance the performance of human students in machine teaching experiments.
Evaluation of modern educational software and basic digital competences among teachers in Italy

The aim of this paper is to evaluate the usefulness of educational software among teachers in Italy (N = 559). The evaluation of usefulness was conducted on the latest and most popular web-based software used by European teachers, namely: Quizizz, Mentimeter, Wakelet, Padlet, Canva, Emaze, Answergarden, Jamboard, Coggle, Createley, Wordwall, LearningApps, Prezi, Kahoot, Plickers, Trimino, Dobble, Genial.ly, ClassDojo, Explain Everything, KhanAcademy, and Easelly. Based on data collected in the first half of 2022 using an online survey, it was noted that: 1) The majority of teachers are not able to evaluate modern software because they do not use such teaching resources as those listed; 2) About 9.3% of teachers rate the listed software highly; 3) Most commonly teachers use and also rate highly: Canva, Wordwall, Padlet, LearningApps, Kahoot, Coggle, Jamboard, and Prezi; 4) Seniority and workplace does not always differentiate the evaluation of educational software.
Digital intervention in inclusive education: is it necessary?

Dear Editor, In a recently published correspondence in the Journal of Public Health, ‘Wanted: quality telehealth for all’ by Ibita1, this topic is very interesting if it is discussed in terms of education that should be able to adapt to this, especially if it is related to the situation that is happening in our country. In Indonesia, the implementation of the education system continues to transform depending on the era. Every individual has an equal right to be literate regardless of their social, economic and physical status. This is in line with Law Number 20 of 2003 concerning the National Education System, article 5 paragraph 1, which concludes that every citizen has the same right to obtain a quality education. Based on this, inclusive education is present in the implementation of the school system in Indonesia. Inclusive education is a system that provides equal opportunities for people to be literate and is in the form of formal learning. The implementation is carried out by teachers who collaborate in helping all students learn how to grow into tolerant characters2. However, inclusive education for children with special needs is only perceived as an ordinary school and yet to be understood as an effort to improve the education quality3.
MIRN: A multi-interest retrieval network with sequence-to-interest EM routing

Vector-based retrieval have been widely adopted to process online users' diverse interests for recommendations. However, most of them utilize a single vector to represent user multiple interests (UMI), inevitably impairing the accuracy and diversity of item retrieval. In addition, existing work often does not take into account the scale and speed of the model, and high-dimensional user representation vectors need high computation cost, leading to inefficient item retrieval. In this paper, we propose a novel lightweight multi-interest retrieval network (MIRN) by incorporating sequence-to-interest Expectation Maximization (EM) routing to deal with users' multiple interests. By leveraging representation ability of the Capsule network, we design a multi-interest representation learning module that clusters multiple Capsule vectors from the user's behavior sequence to represent each of their interests respectively. In addition, we introduce a composite capsule clustering strategy for the Capsule network framework to reduce the scale of the network model. Furthermore, a Capsule-aware module incorporating an attention mechanism has been developed to guide model training by adaptively learning multiple Capsule vectors of user representations. The experimental results demonstrate MIRN outperforms the state-of-the-art approaches for item retrieval and gains significant improvements in terms of metric evaluations.
Spatial–temporal transformer for end-to-end sign language recognition

Abstract Continuous sign language recognition (CSLR) is an essential task for communication between hearing-impaired and people without limitations, which aims at aligning low-density video sequences with high-density text sequences. The current methods for CSLR were mainly based on convolutional neural networks. However, these methods perform poorly in balancing spatial and temporal features during visual feature extraction, making them difficult to improve the accuracy of recognition. To address this issue, we designed an end-to-end CSLR network: Spatial–Temporal Transformer Network (STTN). The model encodes and decodes the sign language video as a predicted sequence that is aligned with a given text sequence. First, since the image sequences are too long for the model to handle directly, we chunk the sign language video frames, i.e., ”image to patch”, which reduces the computational complexity. Second, global features of the sign language video are modeled at the beginning of the model, and the spatial action features of the current video frame and the semantic features of consecutive frames in the temporal dimension are extracted separately, giving rise to fully extracting visual features. Finally, the model uses a simple cross-entropy loss to align video and text. We extensively evaluated the proposed network on two publicly available datasets, CSL and RWTH-PHOENIX-Weather multi-signer 2014 (PHOENIX-2014), which demonstrated the superior performance of our work in CSLR task compared to the state-of-the-art methods.
Use of Social Media by Academic Libraries in Australia: Review and a Case Study

ABSTRACTABSTRACTThis study examines social media use by academic libraries and consists of two parts. The first part surveys websites, and from the survey provides a picture of the current state of social media usage among university libraries in Australia. It also identifies Twitter, Facebook, Instagram, and YouTube as the most widely used social media platforms, and the University of Melbourne Library (UML) as the library which has attracted the most social media followers. In the second part of the paper, a content analysis is carried out on the UML's social media accounts. Findings demonstrate that the UML library uses Facebook more for sharing information and knowledge and showcasing facilities, places, and services; while the main purposes of Twitter are mainly for communicating and interacting, making news and announcements, and promoting library collections. 'Communication and interaction' is shown as the category of information most often posted on Twitter, the UML's main social media platform. It is also the one that attracts the highest level of engagement.KEYWORDS: Social mediaacademic librariesAustralia Disclosure StatementNo potential conflict of interest was reported by the author.Additional informationNotes on contributorsMinh NguyenMinh Nguyen is a Reference Librarian (After Hours) based in the Library Services team at City East and City West campuses, University of South Australia. He supports the teaching, learning, and research needs of academic staff, researchers, and students in various areas of the University.
Assembly training system on HoloLens using embedded algorithm

In this article, we demonstrate an implementation on Microsoft HoloLens, deep learning supported in the context of object detection. The main aim of the training system is to create the more accurate object detection model for augmented reality using deep learning models for image recognition directly on the HoloLens 2. In terms of the object detection approach, a deep learning model called YOLOv5 has been used for the implementation of this system. This article uses the Windows ML API to implement machine learning in augmented reality applications. A simple and easy method of drawing lines between specified 2D coordinates on a canvas is proposed. The module division and development steps of the development of augmented reality training system are given. Our system provides the annotation of augmented object detected and its bounding box via HoloLens. It allows to detect the new object in a few milliseconds. Preliminary results show a great rate of object detection and reasonable detection time.
A Novel Traffic Classifier With Attention Mechanism for Industrial Internet of Things

With the development of the Industrial Internet of Things (IIoT), the complex traffic generated by large-scale IIoT devices presents challenges for traffic analysis. Most of existing deep learning-based traffic analysis methods use a single flow for classification, resulting in being misled by the irrelevant flow. Thus, it is necessary to use flow sequences for traffic analysis. However, existing models fail to effectively distinguish unimportant flows in flow sequence, which affects the classification performance. To address the aforementioned challenges, we propose a novel traffic classifier called flow transformer to perform traffic analysis with flow sequences, which leverages multihead attention mechanism to strengthen the information interaction between related flows. Besides, the RF-based feature selection method is designed to select the optimal feature combination, avoiding insignificant features from reducing the performance of the classifier. Experimental results on three real-world traffic datasets demonstrate that our method outperforms state-of-the-art methods with a large margin.