Reviewerly/exHarmony huggingface dataset:
Learning spatial variance-key surrounding-aware tracking via multi-expert deep feature fusion

The continuous advancement of visual object tracking (VOT) algorithms has made significant contributions to computer vision and video processing. Correlation filter (CF)-based trackers have achieved promising tracking performance in VOT, but still face some problems under scale changes, occlusion, and boundary effects. To deal with these constraints, the learning spatial variance-key surrounding-aware correlation filter (LSVKSCF) via multi-expert deep feature fusion is proposed in this work. Initially, we extract the surrounding samples from the input based on the target size and shape, which integrates the context information and maintains the integrity of the target. Then, we introduce selective spatial information to the filter for penalizing the background and preserving the valid information of the target. Additionally, the spatial variance information is utilized to find the second-order difference of the filter which avoids over-fitting problems. Furthermore, we introduce the keyframes by periodically selecting the surrounding patches which handle the background distractions and improve the tracking speed. Finally, a multi-expert strategy is used in the filter learning stage which shows the reliability of the proposed method. Extensive experiments on OTB2013, OTB2015, TempleColor128, UAV123, UAVDT and DTB70 datasets prove that our method performs well against several existing trackers.
An Innovative approach towards image encryption by using novel PRNs and S-boxes Modeling techniques

Efficient multiple pseudo-random number sequences (PRNS) and substitution boxes (S-boxes) are one of the most significant building blocks, which are jointly adopted normally for secure data encryption. Multiple aspects pave the way to handle large-scale multimedia data. However, the computational efforts on multiple constructions may certainly lead to limits the required ciphering through-put. Therefore, reducing the computational cost of multiple patterns such as PRNS and S-boxes is the core requirement for an efficient cryptosystem. For this achievement, we exploited the indexing technique over elliptic curves with small prime fields to introduce a computationally efficient mechanism for both multiple PRNS and multiple S-boxes. In the newly constructed PRNS and S-boxes, we used collectively EC group law and simple algebraic operations to get the security strength as well as low computational cost respectively. Based on statistical results, the proposed S-box mechanism is the most effective method that generates efficient multiple S-boxes on minimum prime fields. Likewise, the PRNS's simulation results shows that the proposed mechanism is the highly effective model for generating multiple verified pseudo random patterns on small prime fields in a single round. These assessments indicates that the proposed mechanism offers desired key-space, better statistical features of encrypted data and less computational cost.
Cross on Cross Attention: Deep Fusion Transformer for Image Captioning

Numerous studies have shown that in-depth mining of correlations between multi-modal features can help improve the accuracy of cross-modal data analysis tasks. However, the current image description methods based on the encoder-decoder framework only carry out the interaction and fusion of multi-modal features in the encoding stage or the decoding stage, which cannot effectively alleviate the semantic gap. In this paper, we propose a Deep Fusion Transformer (DFT) for image captioning to provide a deep multi-feature and multi-modal information fusion strategy throughout the encoding to decoding process. We propose a novel global cross encoder to align different types of visual features, which can effectively compensate for the differences between features and incorporate each other’s strengths. In the decoder, a novel cross on cross attention is proposed to realize hierarchical cross-modal data analysis, extending complex cross-modal reasoning capabilities through the multi-level interaction of visual and semantic features. Extensive experiments conducted on the MSCOCO dataset prove that our proposed DFT can achieve excellent performance and outperform state-of-the-art methods. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/weimingboya/DFT</uri> .
Discriminative and Robust Attribute Alignment for Zero-Shot Learning

Zero-shot learning (ZSL) aims to learn models that can recognize images of semantically related unseen categories, through transferring attribute-based knowledge learned from training data of seen classes to unseen testing data. As visual attributes play a vital role in ZSL, recent embedding-based methods usually focus on learning a compatibility function between the visual representation and the class semantic attributes. While in this work, in addition to simply learning the region embedding of different semantic attributes to maintain the generalization capability of the learned model, we further consider to improve the discrimination power of the learned visual features themselves by contrastive embedding. It exploits both the class-wise and instance-wise supervision for GZSL, under the attribute guided weakly supervised representation learning framework. To further improve the robustness of the ZSL model, we also propose to train the model under the consistency regularization constraint, through taking full advantages of self-supervised signals of the image under various perturbed augmentation situations, which could make the model robust to some occluded or un-related attribute regions. Extensive experimental results demonstrate the effectiveness of the proposed ZSL method, achieving superior performances to state-of-the-art methods on three widely-used benchmark datasets, namely CUB, SUN, and AWA2. Our source code is released at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/KORIYN/CC-ZSL</uri></monospace> .
Reskilling Workforce for the Artificial Intelligence Age: Challenges and the Way Forward

Introduction: Artificial intelligence (AI) has progressed significantly over the past few years, evolving into a collection of innovative tools that provide a competitive advantage to businesses. The acceptance and investment in AI are skyrocketing over the globe. The entry of AI in the workplace automates tasks and impacts making a timely decisions. At the same time, the workforce is not ready to welcome the new technology due to the skill gap. The organisation has to face many challenges in reskilling and convincing the workforce to incorporate AI in their work.Purpose: With this study, the authors aim to analyse and highlight the introduction of AI in the organisation and the call for the reskilling of the workforce. To figure out what skills are most important for employees to learn to advance their careers.Methodology: Given the deductive nature of the study, the researchers used secondary data collected and compiled from research papers, publications, websites, HR blogs, survey reports, etc. Research papers from reputed journals, reports of consultancies and agencies have been considered to synthesise the information and present it in a systematic manner and to derive the conclusion.Findings: The findings indicate AI's capabilities and applications have grown considerably, which shows the importance of AI in a growing number of fields, yet several hurdles need to be overcome, the most prominent one being the issues concerning upskilling the workforce for the future of AI. This study reveals the change in the perceived importance of the skills in the present and future times. Reskilling and upskilling the workforce and creating new talent to meet the changing employment demands is becoming increasingly important.
Is adopting artificial intelligence in libraries urgency or a buzzword? A systematic literature review

This study aims to investigate the implementation of artificial intelligence (AI) in libraries from 2011 to 2020. This study uses PRISMA guidelines to perform a systematic literature review (SLR). The articles were obtained mainly from the SCOPUS database, with Google Scholar as the supporting database. AI can easily be adopted in libraries, especially for technical services such as classification and cataloguing, library management such as staffing and decision-making, library services such as referencing and information service, and for information literacy. Successful AI adoption is, however, still debatable, because there are many requirements that need to be met, so that it can be inclusively adopted in libraries. There is a lack of research on the application of AI in libraries, especially in the context of its actual implementation. The results of this study offer insights on the implementation of AI in library support services.
Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

Abstract The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A‐Share listed firms. Based on a sample of 33,544 Chinese firm‐year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out‐of‐sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision‐Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A‐listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)‐based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML‐based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.
Promoting international high‐school students' <scp>C</scp>hinese language learning achievements and perceptions: A mind mapping‐based spherical video‐based virtual reality learning system in <scp>C</scp>hinese language courses

Abstract Background As adequate support, virtual reality (VR) has been increasingly introduced into the classroom to help students learn a new language. However, empirical studies exploring the educational potential of using 360° spherical video‐based virtual reality (SVVR) in high‐school Chinese language classrooms are still lacking. Objectives A mind mapping‐based SVVR learning system is proposed for international high‐school students' Chinese courses and examines its effects on improving Chinese learning achievements and perceptions among Grade 10 students who learn Chinese as a foreign language. Methods This study adopted a quasi‐experiment design. Participants ( N = 66) were assigned to one of three conditions: mind mapping‐based SVVR (MMBSVVR), conventional SVVR (CSVVR), or traditional lecturing (TL). Our mixed‐method approach employed pre‐and post‐tests to measure Chinese learning achievements, questionnaires to measure Chinese learning motivation, problem‐solving ability, and self‐efficacy, and semi‐structured interviews to further explore students' feelings and attitudes about Chinese courses in corresponding learning environments. Results and Conclusions The results showed that the MMBSVVR classroom significantly improved high students' Chinese learning achievements in Chinese vocabulary and making sentence, problem‐solving ability, and self‐efficacy. Moreover, SVVR stimulates students' learning interest and class participation, and mind mapping provides students with a clear guide to arrange the knowledge. These findings help interpret the relationships between MMBSVVR and learning achievements, Chinese learning motivation, problem‐solving ability, and self‐efficacy in an international high school Chinese courses, thus providing insight on integrating MMBSVVR into existing classrooms.
NAM+: Towards Scalable End-to-End Contextual Biasing for Adaptive ASR

Attention-based biasing techniques for end-to-end ASR systems are able to achieve large accuracy gains without requiring the inference algorithm adjustments and parameter tuning common to fusion approaches. However, it is challenging to simultaneously scale up attention-based biasing to realistic numbers of biased phrases; maintain in-domain WER gains, while minimizing out-of-domain losses; and run in real time. We present NAM+, an attention-based biasing approach which achieves a 16X inference speedup per acoustic frame over prior work when run with 3,000 biasing entities, as measured on a typical mobile CPU. NAM+ achieves these run-time gains through a combination of Two-Pass Hierarchical Attention and Dilated Context Update. Compared to the adapted baseline, NAM+ further decreases the in-domain WER by up to 12.6% relative, while incurring an out-of-domain WER regression of 20% relative. Compared to the non-adapted baseline, the out-of-domain WER regression is 7.1 % relative.
Explaining Technology We Do Not Understand

Since 2016 a significant program of work has been initiated by the U.S. Defense Advanced Research Projects Agency (DARPA) under the title of explainable artificial intelligence (XAI). This program is seen as important for AI adoption, in this case to include the needs of warfighters to effectively collaborate with AI “partners.” Technology adoption is often promoted based on beliefs, which bears little relationship to the benefit a technology will provide. These beliefs include “progress,” technology superiority, and technology as cornucopia. The XAI program has widely promoted a new belief: that AI is in general explainable. As AI systems often have concealed or black box characteristics, the problem of explainability is significant. This paper argues that due to their complexity, AI systems should be approached in a way similar to the way the scientific method is used to approach natural phenomena. One approach encouraged by DARPA, model induction, is based on post-hoc reasoning. Such inductive reasoning is consistent with the scientific method. However, that method has a history of controls that are applied to create confidence in an uncertain, inductive, outcome. The paper proposes some controls consistent with a philosophical examination of black boxes. As AI systems are being used to determine who should have access to scarce resources and who should be punished and in what way, the claim that AI can be explained is important. Widespread recent experimentation with ChatGPT has also highlighted the challenges and expectations of AI systems.
A blockchain non-fungible token-enabled ‘passport’ for construction waste material cross-jurisdictional trading

The waste material passport (WMP) has been proposed for construction waste material (CWM) cross-border trading to reduce information asymmetry between parties. However, a major concern is that such WMPs are not issued by authorities that can guarantee authentication. This research aims to develop a blockchain non-fungible token (NFT)-enabled WMP. By using design science research, we develop a framework to (1) digitize CWM into NFT-enabled passports via tokenization; (2) prevent duplicated issuance of passports with unique NFT identifiers; (3) enhance information transparency via blockchain distributed ledgers; (4) improve trading efficiency via its decentralized consensus; and (5) secure transaction records via its cryptographic algorithms. The framework is prototyped and simulated in a case study. The prototype is found feasible with satisfactory performance. It can serve as a reference for future endeavors in harnessing the power of blockchain NFTs, particularly for mobilizing blockchain NFT-enabled passports for various value-added applications in the circular economy.
A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique

This study aims to develop a better Financial Statement Fraud (FSF) detection model by utilizing data from publicly available financial statements of firms in the MENA region. We develop an FSF model using a powerful ensemble technique, the XGBoost (eXtreme Gradient Boosting) algorithm, that helps to identify fraud in a set of sample companies drawn from the Middle East and North Africa (MENA) region. The issue of class imbalance in the dataset is addressed by applying the Synthetic Minority Oversampling Technique (SMOTE) algorithm. We use different Machine Learning techniques in Python to predict FSF, and our empirical findings show that the XGBoost algorithm outperformed the other algorithms in this study, namely, Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), AdaBoost, and Random Forest (RF). We then optimize the XGBoost algorithm to obtain the best result, with a final accuracy of 96.05% in the detection of FSF.
Comparative assessment of future solar power potential based on CMIP5 and CMIP6 multi-model ensembles

While the popularity and feasibility of solar power have increased toward achieving a low-carbon and climate-resilient society, it is uncertain how changes in climate attributes will affect the future potential of solar power output. This study presents a comparative assessment of future changes in solar power in terms of the technology for harnessing energy from insolation (PVP vs CSP), climate projections (CMIP5 vs CMIP6), and emission scenarios. Both CMIP5 and CMIP6 multi-model projections capture the major characteristics of the global distribution seen in PVP and CSP calculated using the reanalysis data during the historical period. However, despite the general similarity to CMIP5-based results, CMIP6 models slightly outperform their CMIP5 counterparts regarding quantitative metrics and enhance the robustness of the future change signal estimated by the statistical significance and inter-model consistency. The future changes in PVP and CSP patterns are sensitive to the emission scenarios that can control the degree of warming. Under the fossil-fueled development scenarios, the greater increase in temperatures may lead to a high vulnerability of the solar power supply by reducing the output of both PVP and CSP. This study is timely and relevant to emphasizing the benefits of climate change mitigation, which can support the sustainable development of solar energy.
An experimental study measuring the generalization of fine‐tuned language representation models across commonsense reasoning benchmarks

Abstract In the last 5 years, language representation models, such as BERT and GPT‐3, based on transformer neural networks, have led to enormous progress in natural language processing (NLP). One such NLP task is commonsense reasoning, where performance is usually evaluated through multiple‐choice question answering benchmarks. Till date, many such benchmarks have been proposed, and ‘leaderboards’ tracking state‐of‐the‐art performance on those benchmarks suggest that transformer‐based models are approaching human‐like performance. Because these are commonsense benchmarks, however, such a model should be expected to generalize, that is, at least in aggregate, should not exhibit excessive performance loss across independent commonsense benchmarks regardless of the specific benchmark on (the training set of) which it has been fine‐tuned. In this article, we evaluate this expectation by proposing a methodology and experimental study to measure the generalization ability of language representation models using a rigorous and intuitive metric. Using five established commonsense reasoning benchmarks, our experimental study shows that the models do not generalize well, and may be (potentially) susceptible to issues such as dataset bias. The results therefore suggest that current performance on benchmarks may be an over‐estimate, especially if we want to use such models on novel commonsense problems for which a ‘training’ dataset may not be available, for the language representation model, to fine‐tune on.
3D Statistical Head Modeling for Face/head-Related Product Design: A State-of-the-Art Review

In the field of face/head-related product design, 3D statistical models (3DSMs) of human heads and faces are widely used to model geometric variations, generate representative models and reconstruct head geometries of a target population. They therefore have many applications to face/head-related products, including ergonomic product design, sizing system establishment, fit assessment, individual product customization, and virtual try-on. Numerous studies have created 3DSMs for various populations and further applied them in the design of various products. This paper conducts a state-of-the-art review of 3DSMs of human heads and faces for facewear/headwear design. It focuses on different essential processes in the modeling pipeline, including 3D head data capture techniques and instruments, 3D head scan datasets, statistical modeling pipelines (particularly facial mesh alignment, facial landmark detection and facial shape correspondence), and product application scenarios. Finally, the paper explicitly highlights unsolved challenges and proposes the potential directions for future research. The paper provides an overview of 3D statistical head modeling and its applications, and will hopefully help inspire researchers to solve the existing challenges and promote ergonomic design development.
Varied Image Data Augmentation Methods for Building Ensemble

Convolutional Neural Networks (CNNs) are used in many domains but the requirement of large datasets for robust training sessions and no overfitting makes them hard to apply in medical fields and similar fields. However, when large quantities of samples cannot be easily collected, various methods can still be applied to stem the problem depending on the sample type. Data augmentation, rather than other methods, has recently been under the spotlight mostly because of the simplicity and effectiveness of some of the more adopted methods. The research question addressed in this work is whether data augmentation techniques can help in developing robust and efficient machine learning systems to be used in different domains for classification purposes. To do that, we introduce new image augmentation techniques that make use of different methods like Fourier Transform (FT), Discrete Cosine Transform (DCT), Radon Transform (RT), Hilbert Transform (HT), Singular Value Decomposition (SVD), Local Laplacian Filters (LLF) and Hampel filter (HF). We define different ensemble methods by combining various classical data augmentation methods with the newer ones presented here. We performed an extensive empirical evaluation on 15 different datasets to validate our proposal. The obtained results show that the newly proposed data augmentation methods can be very effective even when used alone. The ensembles trained with different augmentations methods can outperform some of the best approaches reported in the literature as well as compete with state-of-the-art custom methods. All resources are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LorisNanni</uri> .