Reviewerly/exHarmony huggingface dataset:
A Transformer Based Approach for Abuse Detection in Code Mixed Indic Languages.

The advancement in the number of online social media platforms has entailed active participation from the web users globally. This has also lead to subsequent increase in the cyberbullying cases online. Such incidents diminish an individual’s reputation or defame a community, also posing a threat to the privacy of users in cyberspace. Traditionally, manual checks and handling mechanisms have been used to deal with such textual content. However, an automatic computer-based approach would provide far better solutions to this problem. Existing approaches to automate this task majorly involves classical machine learning models which tend to perform poorly on low resource languages. Owing to the varied background and language of web users, the cyberspace witnesses the presence of multilingual text. An integrated approach to accommodate multilingual text could be the appropriate solution. This paper explores various methods to detect abusive content in 13 Indic code-mixed languages. Firstly, baseline classical machine learning models are compared with Transformer based architecture. Secondly, the paper presents the experimental analysis of four state-of-the-art transformer-based models vis à vis XLM-RoBERTa, indic-BERT, MurilBert and mBERT, out of which XLM Roberta with BiGRU outperforms. Thirdly, the experimental setup of the best performing model XLM-RoBERTa is fed with emoji embeddings that leads to further enhancement of overall performance of the employed model. Finally, the model is trained with the combined dataset of 13 Indic languages, to compare its performance with those of individual language models. The performance of combined model surpassed those of the individual models in terms of F1 score and accuracy, supporting the fact that combined model fits the data better possibly due to its code-mixed nature. This model reports a F1 score of 0.88 on test data while rendering a training loss of 0.28, validation loss of 0.31 and an AUC score of 0.94 for both training and validation.
Robust mixture regression modeling based on the normal mean-variance mixture distributions

Mixture regression models (MRMs) are widely used to capture the heterogeneity of relationships between the response variable and one or more predictors coming from several non-homogeneous groups. Since the conventional MRMs are quite sensitive to departures from normality caused by extra skewness and possible heavy tails, various extensions built on more flexible distributions have been put forward in the last decade. The class of normal mean-variance mixture (NMVM) distributions that arise from scaling both the mean and variance of a normal random variable with a common mixing distribution encompasses many prominent (symmetric or asymmetrical) distributions as special cases. A unified approach to robustifying MRMs is proposed by considering the class of NMVM distributions for component errors. An expectation conditional maximization either (ECME) algorithm, which incorporates membership indicators and the latent scaling variables as the missing data, is developed for carrying out maximum likelihood (ML) estimation of model parameters. Four simulation studies are conducted to examine the finite-sample property of ML estimators and the robustness of the proposed model against outliers for contaminated and noisy data. The usefulness and superiority of our methodology are demonstrated through applications to two real datasets.
Keystroke and swipe biometrics fusion to enhance smartphones authentication

Several authentication techniques are required to preserve smartphone users' privacy. Part of these authentication mechanisms are based on Keystroke Dynamics (KD) or Swipe Dynamics (SD); however, these mechanisms have performance challenges due to the following; limited capability in handling user behavioral variations, inefficient feature extraction, and alternate usages that are not restricted to a specific method typing or swiping. This work presents an improved smartphone continuous authentication model by integrating free text-based KD and SD. The proposed model adopts feature-level fusion by concatenating free-text KD and SD features. This Feature level fusion was evaluated based on a comprehensive and benchmark dataset and Random Forest (RF) classifier. Results have confirmed the proposed model's performance, in which accuracy was 99.98%, with the lowest Equal Error Rate (EER) rate of 0.02% based on multi-class classification.
Single shot multibox detector for honeybee detection

In recent years, deep learning has made great strides in automatic feature extraction through learning. Our target is to operate computers to analyze honeybee behavior and facilitate computational behavior in biology through object detection and reveal a wealth of previously unknown information. Although the object detection task has been well developed, there is no relevant bee dataset. Therefore, in this study, we first created a honeybee dataset using manual annotation and applied data augmentation to it. Furthermore, this study trains a single-shot multiple-box detector (SSD) for object detection to detect honeybee regions using a manually created dataset. We check the accuracy of the proposed method using the mean percentage of fit (mAP) commonly used in SSD. The experimental results show that our method works well.
Enhanced mechanisms of pooling and channel attention for deep learning feature maps

The pooling function is vital for deep neural networks (DNNs). The operation is to generalize the representation of feature maps and progressively cut down the spatial size of feature maps to optimize the computing consumption of the network. Furthermore, the function is also the basis for the computer vision attention mechanism. However, as a matter of fact, pooling is a down-sampling operation, which makes the feature-map representation approximately to small translations with the summary statistic of adjacent pixels. As a result, the function inevitably leads to information loss more or less. In this article, we propose a fused max-average pooling (FMAPooling) operation as well as an improved channel attention mechanism (FMAttn) by utilizing the two pooling functions to enhance the feature representation for DNNs. Basically, the methods are to enhance multiple-level features extracted by max pooling and average pooling respectively. The effectiveness of the proposals is verified with VGG, ResNet, and MobileNetV2 architectures on CIFAR10/100 and ImageNet100. According to the experimental results, the FMAPooling brings up to 1.63% accuracy improvement compared with the baseline model; the FMAttn achieves up to 2.21% accuracy improvement compared with the previous channel attention mechanism. Furthermore, the proposals are extensible and could be embedded into various DNN models easily, or take the place of certain structures of DNNs. The computation burden introduced by the proposals is negligible.
The Key Assistant of Smart City

One of the key internal processes for smart cities is the data transfer process. That is, proper data management can help you track the beginning, present, and end of any event. Security is important for any field. In this regard, various measures should be taken to ensure security. In the simplest example, the loss of personal information of an enterprise by its customers (in other words, theft by hackers) is one of the primary risks that will bring the end of this enterprise. The same is true for smart cities. In addition, the security of a (smart) city should not be limited to information alone because the protection of public order is more important than the protection of their information.
Cyber risk management for autonomous passenger ships using threat-informed defense-in-depth

Abstract Recent innovations in the smart city domain have led to the proposition of a new mode of transportation utilizing Autonomous Passenger Ships (APS) or ferries in inland waterways. The novelty of the APS concept influenced the cyber risk paradigm and led to different considerations regarding attack objectives, techniques as well as risk management approaches. The main factor that has led to this is the autoremote operational mode, which refers to autonomous operations and remote supervision and control in case of emergency. The autoremote operational mode influences the risk of cyber attacks due to the increased connectivity and reliance on technology for automating navigational functions. On the other hand, the presence of passengers without crew members imposes a safety risk factor in cyber attacks. In this paper, we propose a new cyber risk management approach for managing the cyber risks against cyber physical systems in general and Autonomous Passenger Ships in particular. Our proposed approach aims to improve the Defense-in-Depth risk management strategy with additional components from the Threat-Informed Defense strategy allowing for more evolved cyber risk management capabilities. Moreover, we have utilized the proposed cyber risk management approach for the proposition of a cybersecurity architecture for managing the cyber risks against an APS use case named milliAmpere2. Additionally, we present our results after conducting a Systematic Literature Review (SLR) in cybersecurity evaluation in the maritime domain. Then, the findings of the SLR were utilized for a suitable evaluation of the proposed risk management approach. Our findings suggest that our proposed risk management approach named Threat-Informed Defense-in-Depth is capable of enriching several risk management activities across different stages in the system development life cycle. Additionally, a comprehensive evaluation of the cybersecurity posture of milliAmpere2 has been conducted using several approaches including risk evaluation, simulation, checklist, and adversary emulation. Our evaluation has uncovered several limitations in the current cybersecurity posture and proposed actions for improvement.
The ASEAN blockchain roadmap

Although blockchain technology is disruptive, revolutionary, and foundational, its application across different countries with varying levels of socio/economic development is uneven. Accordingly, this paper examines blockchain technology in ASEAN (Association of Southeast Asian Nations). With its sizeable populations and economic diversity, ASEAN provides a suitable staging ground for examining the factors that facilitate or impede blockchain technology. This paper discusses the preconditions for blockchain adoption, enabling factors relating to regulatory policies, and illustrative cases depicting new blockchain solutions or improvements over current practices. Based on these narratives, a six-step roadmap delineates the need for regulatory clarity, the balance between public versus private policies, and pathways for securing competitive strategies and organizational advantages.
MIGT: Multi-modal image inpainting guided with text

In this work, we propose MIGT, a novel framework for multi-modal image inpainting that introduces textual description as guidance. We divide MIGT into three components: Coarse-to-Fine Image Inpainting Module (CFIM), Visual-Textual modalities Fusion Module (VTFM), and Multi-modal Semantic Alignment Module (MSAM). CFIM is a Unet-based inpainting model in a coarse-to-fine manner. The coarse inpainting stage produces images of rough shape and color according to the source corrupted images and the corresponding textual descriptions. The fine inpainting stage generates the final high-quality images with fine-grained textures. VTFM aims to reasonably fuse visual-textual modalities. First, we feed visual and textual features into the proposed visual-aware textual filtering mechanism to adaptively focus on desired words related to the missing areas. Then the filtered visual-aware textual features pass through an Attentional Generative Network (AGN) to obtain fusion features that are fed into the middle layers of the coarse stage for subsequent image inpainting. MSAM takes the generated image as input, reconstructing a textual description that semantically aligns the given one to guarantee the semantic consistency between the generated image and the input textual description. Extension experiments conducted on Oxford-102 flower and CUB-200–2011 bird datasets demonstrate the effectiveness of our proposed method.
LSH-DETR: object detection algorithm for marine benthic organisms based on improved DETR

A recent area of study focus is object detecting technologies for marine benthic organisms. A marine benthic object detection algorithm, LSH-DETR, based on DETR, is proposed to address the detection issue in the case of dense distribution of marine benthic small objects. The locality-sensitive hashing attention is used instead of self attention in the transformer to enhance the learning ability of context information. The multilevel feature pyramid networks fusion is introduced to improve the detection performance of small targets. At the same time, repulsion loss is used to optimize the prediction loss of the bounding box to improve the missing detection phenomenon caused by the target stack. According to the findings, LSH-DETR enhanced the mAP of marine benthos to 62.8% and obtained a performance of 13.8% MR when compared to the original DETR. This improvement demonstrates that LSH-DETR is more effective at locating small, dense marine benthic organisms.
Using a virtual reality mobile application for interpreting learning: listening to the students’ voice

ABSTRACTABSTRACTThis research aims to examine students' perceptions of the benefits and constraints of using Virtual Interpreting Practice (VIP), a mobile-based virtual reality application for bi-directional English/Chinese consecutive and sight interpreting learning that was developed within the project. Thirty-one Chinese university students participated in an in-depth interview. Based on a thematic analysis, six main types of benefit were identified: (1) interpreting and language proficiency, (2) authentic and immersive interpreting practice, (3) learning flexibility and effectiveness, (4) learning experience, (5) cognitive development and (6) affective development. While some constraints relating to functionality and design were identified, the students generally perceived VIP as a useful tool for improving both interpreting and language competencies, facilitating situated and simulated learning, increasing learning opportunities and efficacy, creating a joyful and ubiquitous learning experience, enhancing independent learning and critical thinking as well as boosting their motivation and confidence.KEYWORDS: Interpretingmobile-assisted language learningstudent perceptionsthematic analysisvirtual reality Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Hong Kong Metropolitan University: [Grant Number No. 2019/1.2].Notes on contributorsVenus ChanVenus Chan is an assistant professor at the Department of Humanities, Language and Translation of Hong Kong Metropolitan University, Hong Kong. She is the secretary of the Association of Translation Technology (ATT). Her main research interests are education technologies, applied linguistics and interpreter and translator training. She is the Principal Investigator of some internal and external funded research projects, e.g. Faculty Development Grant (University Grants Committee). In addition, she has served as a journal reviewer, academic advisor and external examiner.
A plague on both your houses: The debate about how to deal with ‘inconclusive’ conclusions when calculating error rates

Journal Article A plague on both your houses: The debate about how to deal with 'inconclusive' conclusions when calculating error rates Get access Geoffrey Stewart Morrison Geoffrey Stewart Morrison Forensic Data Science Laboratory, Aston University, Birmingham, UK; Forensic Evaluation Ltd, Birmingham, UK E-mail: geoff-morrison@forensic-evaluation.net https://orcid.org/0000-0001-8608-8207 Search for other works by this author on: Oxford Academic Google Scholar Law, Probability and Risk, Volume 21, Issue 2, June 2022, Pages 127–129, https://doi.org/10.1093/lpr/mgac015 Published: 23 November 2022 Article history Accepted: 16 November 2022 Published: 23 November 2022
Kernelized transformed subspace clustering with geometric weights for non-linear manifolds

The naive assumption of subspace clustering is that the data should be separable into separate subspaces. Another consideration of the conventional subspace clustering methods is the linear manifolds. What if, the data doesn’t hold this assumption? We propose a novel subspace clustering framework that works even if the raw data is not separable into separate subspaces. It also generalizes it for non-linear manifolds. To achieve the intended goal, we embed subspace clustering techniques into kernelized transform learning with weighting matrix regularization which accounts for nonlinearity. For the weighting matrix, we use similarity between tangent spaces on data manifolds for local structure and euclidean distances for capturing global geometric structure. The complete optimization problem is solved using alternate minimization. The weighted norm regularization is solved by designing a fixed-point continuation algorithm to obtain an approximate closed solution. To test the performance of the proposed framework, we provide the experimental results on handwritten digits clustering, face image clustering, and motion segmentation. The superiority of the results proves the effectiveness of the weighting matrix in kernelized transformed subspace clustering formulation.
Intelligent characterisation of space objects with hyperspectral imaging

This paper presents some initial results on the use of hyperspectral imaging technology and machine learning to characterise the surface composition of space objects and reconstruct their attitude motion. The paper provides a preliminary demonstration that hyperspectral and multispectral analysis of the light absorbed, emitted and reflected by space objects can be used to identify, with some degree of accuracy, the materials composing their surface. The paper introduces a high-fidelity simulation model, developed to test this concept, and a validation of the model against experimental tests in a laboratory environment. The paper shows how to unmix the spectra to provide an estimation of the materials composing the surface facing the sensor. A machine learning approach is then proposed to reconstruct the attitude motion from the time series of spectra.
TENet: Accurate light-field salient object detection with a transformer embedding network

Current light-field salient object detection methods have difficulty in accurately distinguishing objects from complex backgrounds. In this paper, we believe that this problem can be mitigated by optimizing feature fusion and enlarging receptive field, and thus propose a novel transformer embedding network named TENet. The main idea of the network is to (1) selectively aggregate multi-features for fuller feature fusion; (2) integrate the Transformer for larger receptive field, so as to accurately identify salient objects. For the former, firstly, a multi-modal feature fusion module (MMFF) is designed to mine the different contributions of multi-modal features (i.e., all-in-focus image features and focal stack features). Then, a multi-level feature fusion module (MLFF) is developed to iteratively select and fuse complementary cues from multi-level features. For the latter, we integrate the Transformer for the first time and propose a transformer-based feature enhancement module (TFE), to provide a wider receptive field for each pixel of high-level features. To validate our idea, we comprehensively evaluate the performance of our TENet on three challenging datasets. Experimental results show that our method outperforms the state-of-the-art method, e.g., the detection accuracy is improved by 28.1%, 20.3%, and 14.9% in MAE metric, respectively.
Virtual Reality in Vocational Training: A Study Demonstrating the Potential of a VR-based Vehicle Painting Simulator for Skills Acquisition in Apprenticeship Training

Abstract Previous studies on Virtual Reality (VR)-enriched learning pointed out the advantages of immersive learning for the development of competencies. In the context of vocational education in vehicle painting, training opportunities are severely limited for many reasons. VR can be utilized to develop a comprehensive learning environment with authentic training tasks. Besides the need to train psychomotor skills, vehicle painting procedures are complex tasks requiring incremental training to develop knowledge, skills, and attitudes. This study aims to evaluate a VR training application for vehicle painting, focusing on the development of professional competencies regarding skills, knowledge, and attitudes. 47 apprentices participated in the evaluation study. A VR-simulated painting booth was developed based on the 4 C/ID model by van Merriënboer, where they dealt with typical painting jobs on 3D workpieces (e.g., car wings, engine hood). Within the descriptive-inferential study, no significant differences between the types of competencies were revealed. The training application supports the acquisition of skills, knowledge, and attitudes equally. Further results regarding usability, cognitive load, etc., are promising. The essential finding of this study is that the VR training application is generally suitable for supporting craftsmanship within the field of vehicle painting. Since training opportunities for apprentices in this context are often rare, VR offers a unique solution especially for skills training if it follows a proven instructional model for the development of competencies.
Artificial Intelligence in Technology-Enhanced Assessment: A Survey of Machine Learning

Intelligent assessment, the core of any AI-based educational technology, is defined as embedded, stealth and ubiquitous assessment which uses intelligent techniques to diagnose the current cognitive level, monitor dynamic progress, predict success and update students’ profiling continuously. It also uses various technologies, such as learning analytics, educational data mining, intelligent sensors, wearables and machine learning. This can be the key to Precision Education (PE): adaptive, tailored, individualized instruction and learning. This paper explores (a) the applications of Machine Learning (ML) in intelligent assessment, and (b) the use of deep learning models in ‘ knowledge tracing and student modeling’. The paper concludes by discussing barriers involved in using state-of-the-art ML methods and some suggestions to unleash the power of data and ML to improve educational decision-making.
A Ship Detection Model Based on YOLOX with Lightweight Adaptive Channel Feature Fusion and Sparse Data Augmentation

Accurate and real-time detection of ships has become an essential part in maritime video surveillance and plays a vital role in national territorial water security. However, the existing ship detection models have poor recognition performance for some types of ships, such as small ships and ultra-long ships. Meanwhile, light and weather factors also affect the accuracy of the ship detection model, which blurs the boundaries between ships and the background In this paper, a new ship detection model ShipYOLOX based on YOLOX is designed to solve those problems in existing models. Specifically, the modelrs ability to discriminate ships with complex contours is enhanced by adding a feature fusion module called Lightweight Adaptive Channel Feature Fusion (LACFF). Additionally, a new data augmentation algorithm Sparse Target Mosaic is designed to replace the original Mosaic. The new model ShipYOLOX has been validated in experiments with results showing that it can improve the accuracy of ship detection, which achieves an excellent performance of 87.4% AP75 on Seaships.