Reviewerly/exHarmony huggingface dataset:
ViTALnet: Anomaly on Industrial Textured Surfaces With Hybrid Transformer

The coexistence of subtle and long-range anomalies in real-world industrial applications brings significant challenges for anomaly localization. Existing methods typically train deep models by utilizing the multilevel patches or layers-fusion approaches for learning the global-local distribution; however, these methods do not consider learning local and global features simultaneously, which suffer from inaccurate localization results. To this end, a hybrid transformer model, ViTALnet, is proposed here, which is established based on fine-grained feature reconstruction. Our ViTALnet first adopts the vision transformer (ViT) to extract local discriminatory features as feature representation, which leverages the global semantic capturing capability. Then, an anomaly estimation module is proposed by integrating global attention and a pyramidal architecture to enhance contextual information for fine-grain anomaly localization. The experiments were extensively conducted on industrial anomaly localization datasets MVTec Anomaly Detection (MVTec AD)-Texture, NanoTWICE, and general textured datasets KolektorSDD2, MT Defect, and Dot-patterned Fabric, where our proposed ViTALnet outperformed major state-of-the-art (SOTA) methods.
LFRNet: Localizing, Focus, and Refinement Network for Salient Object Detection of Surface Defects

Salient object detection of surface defects is one of the surface defect detection tasks, which aims at highlighting the defect regions from the surface of strip steel, magnetic tale, road, and so on. However, the performance of existing methods degrades dramatically when dealing with complex scenarios such as low-contrast of defect regions and various defect shapes. Therefore, in this paper, we propose a novel saliency model, namely localizing, focus, and refinement network (LFRNet), which consists of the semantic-guided localizing module, the context-driven focus module, and the edge-aware refinement module. Firstly, the semantic-guided localizing module deploys the graph reasoning (GR) unit and global attention (GA) unit to localize the potential defect regions from a global view. Secondly, the context-driven focus module employs the split context (SC) unit and the mutual attention (MA) unit to perform the identification process via the introduction of spatial detail features. Lastly, to further improve the accuracy of the detection results, we deploy the edge-aware refinement (ER) module, which introduces the boundary cues via the edge generation (EG) unit and aggregates the localizing result, the focus results, and the edge information into the high-quality detection map. Extensive experiments on four public defect datasets clearly show the effectiveness and superiority of the proposed LFRNet, where the LFRNet obtains an improvement of 4.1%, 5.7%, 1.0%, and 0.8% on FM, WF, EM, and SM respectively when compared with the top-level method AEP.
A Scanner Darkly: Copyright Infringement in Artificial Intelligence Inputs and Outputs

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Multimodal Sentiment Analysis Representations Learning via Contrastive Learning with Condense Attention Fusion

Multimodal sentiment analysis has gained popularity as a research field for its ability to predict users' emotional tendencies more comprehensively. The data fusion module is a critical component of multimodal sentiment analysis, as it allows for integrating information from multiple modalities. However, it is challenging to combine modalities and remove redundant information effectively. In our research, we address these challenges by proposing a multimodal sentiment analysis model based on supervised contrastive learning, which leads to more effective data representation and richer multimodal features. Specifically, we introduce the MLFC module, which utilizes a convolutional neural network (CNN) and Transformer to solve the redundancy problem of each modal feature and reduce irrelevant information. Moreover, our model employs supervised contrastive learning to enhance its ability to learn standard sentiment features from data. We evaluate our model on three widely-used datasets, namely MVSA-single, MVSA-multiple, and HFM, demonstrating that our model outperforms the state-of-the-art model. Finally, we conduct ablation experiments to validate the efficacy of our proposed method.
On the importance of interpretable machine learning predictions to inform clinical decision making in oncology

Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient's future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.
Syntactic complexity of interpreted, L2 and L1 speech: A constrained language perspective

This study investigates the differences in syntactic complexity among three language varieties: interpreted speech, nonnative English (L2) speech, and native English (L1) speech. The examination was conducted through the evaluation of 14 measures across five subconstructs: the length of the production unit, the amount of subordination, the amount of coordination, phrasal sophistication, and overall sentence complexity. We used a self-compiled comparable corpus of these three language varieties and tested the simplification hypothesis under the framework of constrained language. Our results showed that the two spoken constrained varieties, interpreted speech and L2 speech, had significantly lower scores on most of the syntactic complexity measures compared to non-constrained L1 speech. However, there was no consistent pattern between the two constrained varieties. Specifically, interpreted speech had longer language units and more coordination than L2 speech, which contained more subordination. Overall, this study provides new insights for simplification research by examining syntactic complexity measures from a constrained language perspective.
B-SMART: A reference architecture for artificially intelligent autonomic smart buildings

The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building.
Extended reality for biomedicine

Extended reality (XR) refers to an umbrella of methods that allows users to be immersed in a 3D or a 4D (spatial + temporal) virtual environment to various extents, including virtual reality (VR), augmented reality (AR) and mixed reality (MR). Whereas VR allows a user to be fully immersed in a virtual environment, AR and MR overlay virtual objects onto the real physical world. The immersion and interaction of XR provide unparalleled opportunities to extend our world beyond conventional lifestyles. Although XR has extensive applications in fields such as entertainment and education, its numerous applications in biomedicine create transformative opportunities in both fundamental research and health care. This Primer outlines XR technology from instrumentation to software computation methods, delineating the biomedical applications that have been advanced by state-of-the-art techniques. We further describe the technical advances overcoming current limitations in XR and its applications, providing an entry point for professionals and trainees to thrive in this emerging field. Extended reality allows users to be immersed in a virtual environment, with implications for biomedical research and applications. Yuan, Hassan et al. discuss the adoption of extended reality in biomedical research, with a focus on technical advances that overcome current limitations.
A systematic review of Mobile-Assisted Vocabulary Learning research

Abstract With rapid development of mobile technology, the number of researches on its use in language education process has increased. This growing body of interest has led to a need for review studies that are expected to explain the literature and the trend in the field succinctly. Based on this ground, this study aimed to review the studies conducted on mobile-assisted vocabulary learning in two dimensions. Firstly, it tried to reveal a general map of the domain through bibliometric analysis. With this aim, Web of Science database was scanned and 687 publications were included in the analysis. Then, among these publications 19 articles were selected to identify the technological and pedagogical focus of research papers to determine the trend. The results of bibliometric analysis explained the most frequent keywords, the most productive countries, and the most influential authors and articles. Furthermore, it was discovered that achievement and perceptions were the most common topics in terms of language learning focus and it was gamification in the dimension of technological focus in empirical studies. Despite its preliminary character, the findings have provided insights for future studies.
Improving Fraud Detection in An Imbalanced Class Distribution Using Different Oversampling Techniques

Credit card fraud detection is essential for financial institutions to avoid charging customers for items they did not purchase. Fraud detection can be implemented through ML by building a model trained on a dataset containing transactions with fraud and non-fraud classes. The dataset available for this task is usually highly imbalanced. Therefore, the goal of this paper is to conduct a comprehensive comparison between five oversampling techniques. The oversampling techniques are the Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), borderline1 SMOTE, borderline2 SMOTE, and Support Vector Machine SMOTE (SVM SMOTE) to generate an enhanced model which can solve the imbalanced problem. The comparison is conducted by computing the geometric mean, recall, precision, and F1-score of six machine learning models with and without applying oversampling. The ML models experimented with are logistic regression, random forest, K-nearest neighbor, naive Bayes, support vector machine, and decision tree. Experimental results show that the oversampling techniques have improved the models' performance.
Discriminative Frequency Information Learning for End-to-End Speech Anti-Spoofing

End-to-end technology is an active research topic in speech anti-spoofing. Although end-to-end methods have achieved remarkable success in the speech anti-spoofing, channel effects brought by telephony transmission and certain challenging forms of spoofing attacks still plague them. We observe that differences in the high-frequency components between bonafide and spoofed speech help detect some most troublesome attack forms and the differences also remain after the signals are affected by transmission and codecs. Based on this observation, we aim to utilize the high-frequency information of speech signals to develop better generalization ability to unknown attacks and stronger robustness against transmission and codecs. We propose a raw waveform processing module based on sinc convolution and multiple pre-emphasis to obtain discriminative shallow feature representations. Additionally, we propose an improved backbone to learn discriminative feature embeddings, and a feature classification loss to optimize intra-class and inter-class distances simultaneously. The above modules constitute the proposed Discriminative Frequency-information SincNet, namely DFSincNet. Our proposed algorithm demonstrates competitive performance on both ASVspoof 2019 and 2021 logical access (LA) scenarios.
Two Conjectures on the Largest Minimum Distances of Binary Self-Orthogonal Codes With Dimension 5

The purpose of this paper is to solve the two conjectures on the largest minimum distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{so}(n,5)$ </tex-math></inline-formula> of a binary self-orthogonal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n, 5]$ </tex-math></inline-formula> code proposed by Kim and Choi (2022). The determination of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{so}(n,k)$ </tex-math></inline-formula> has been a fundamental and difficult problem in coding theory because there are too many binary self-orthogonal codes as the dimension <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> increases. Recently, Kim et al. (2021) considered the shortest self-orthogonal embedding of a binary linear code, and many binary optimal self-orthogonal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> codes were constructed for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=4,5$ </tex-math></inline-formula> . Kim and Choi (2022) improved some results of Kim et al. (2021) and made two conjectures on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{so}(n,5)$ </tex-math></inline-formula> . In this paper, we develop a general method to determine the exact value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{so}(n,k)$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=5,6$ </tex-math></inline-formula> and show that the two conjectures made by Kim and Choi (2022) are true.
Augmented Reality With Authentic GeometryGo App to Help Geometry Learning and Assessments

We developed an Authentic GeometryGo (AGG) app to help students application of geometry in authentic contexts through authentic measurements and peer assessments. The study included an experimental group (EG) using an AGG app and a control group (CG) using paper-based methods. The results showed that the EG outperformed the CG in terms of estimation. Measuring authentic geometry shapes using the app could foster students’ estimation. Furthermore, meaningful peer assessments with visual representations in authentic contexts can facilitate the evaluation of peers’ work and improve students’ geometry understanding. Therefore, we encourage educators to use augmented reality with appropriate learning activities to sharpen students’ measurement quality and visual representations by measuring real geometry shapes and applying mathematics knowledge in authentic contexts.
Cryptocurrency Acceptance Model to Analyze Consumers’ Usage Intention: Evidence From Pakistan

Blockchain and cryptocurrencies are transformative fintech breakthroughs that infiltrate the financial sector; however, they have many drawbacks and limits. Consumers have shown an insufficient level of acceptance of these innovations. This paper elucidates the main reasons for the effective growth of a cryptocurrency from user behavior. The objective is to determine what factors influence consumers’ intentions to engage in blockchain-based cryptocurrency dealings. Considering the complexity of emerging technologies, this paper applied an integration model which assumes various external elements such as financial literacy, performance expectancy, facilitating conditions, effort expectancy, awareness, trust, design, and social influence. Smart PLS3 has been used. The most critical determinant of a cryptocurrency’s growth is “design.” On the other hand, “design” affects effort expectancy positively, and social influence affects trust. Awareness, performance expectancy, financial literacy, and effort expectancy significantly affect intentions to use cryptocurrency. It’s worth mentioning that it is the first research to examine Pakistani customers’ perceptions of Bitcoin and their ability to partake. As a result, it is intended to serve as a base for potential research in this area.
Biomedical Image Encryption with a Novel Memristive Chua Oscillator Embedded in a Microcontroller

In this paper, a chaos-based encryption/decryption scheme using a novel memristive Chua oscillator to protect medical images is presented. The novel Chua oscillator is constructed by using the active voltage memristor in the nonlinear branch of the Chua oscillator. The chaotic dynamics behaviors are investigated using 1-D, 2-D bifurcation diagrams, time traces, basin of attractions, and largest Lyapunov exponent plot. The study reveals that the novel memristive Chua oscillator exhibits versatile transitions to chaos with interesting dynamics like multistability, spiking, and bursting oscillations just to name a few. These remarkable features are experimentally confirmed by a laboratory microcontroller-based setup. Thereafter, a chaos-based cryptography algorithm designed for biomedical images is built using pseudorandom number generated from the oscillator. The robustness and security tests undergone by the algorithm yielded high sensitivity on the encryption keys and resisted noise contamination as well as data loss. These results are encouraging and prove that the chaos-based cryptosystem built with the memristive Chua circuit–generated pseudorandom number is suitable for securing images in a healthcare system.
Interval Type-2 Fuzzy-Logic-Based Constant Switching Frequency Control of a Sliding-Mode-Controlled DC–DC Boost Converter

The inherent unlimited high switching frequency of the sliding mode controller (SMC) is limited by practical constraints of the hysteresis modulation (HM) technique. The inductor current and output voltage of a converter can be regulated using a combination of HM-SMC. However, HM-SMC results in a variable switching frequency operation, which is not preferred due to Electromagnetic Interference (EMI) issues. In this paper, an interval fuzzy controller is designed and developed as a solution to enable HM-SMC. In addition, a robust sliding surface is proposed, which provides an improved dynamic response. The two proposed controllers’ compatibility with one another has been tested via experiments such as a step change in input voltage, load resistance variation, and finally, a step change in output voltage reference value. The test results validate that while the interval type-2 fuzzy maintains a constant switching frequency with acceptable dynamic responses, it successfully regulates the state variables of the system. A comparison of the performance of the proposed control method with existing techniques in the literature is presented.
Blockchain for social good and stakeholder engagement: Evidence from a case study

Abstract Blockchain is a promising and emerging technology. Despite the number of studies on the subject, several studies require further exploration of the relationship between blockchain and social innovation. Moreover, there is an increasing interest in social entrepreneurship and in how technical solutions may address social or environmental issues. Hence, this work aims at understanding how a venture can apply blockchain technology for social good. The study adopts a qualitative approach based on a case study and builds on stakeholder theory as a theoretical background. The case study under review is a social venture working on Sustainable Development Goal (SDG) number 10. Our findings present four peculiarities of blockchain for social good: (i) reliability, (ii) transparency, (iii) decentralization, and (iv) accessibility. Moreover, the present study develops a framework on blockchain for social good based on the possible stakeholders' involvement. Finally, four challenges related to blockchain for social good are presented and discussed.
Metaheuristics for optimizing weights in neural networks

The multilayer perceptron (MLP) neural network is the most popular feedforward neural network widely used to tackle different classification and prediction problems. The successful behavior of MLP depends on the proper configurations of its input parameters (i.e., weights and biases), which are adjusted in the learning process using a gradient-based mechanism. Two chronic problems of gradient-based mechanisms can occur: slow convergence and local optima trap. To avoid these problems, the process of the gradient-based mechanism is replaced by a recent metaheuristic swarm-based method called horse herd optimization algorithm (HOA). In this chapter, HOA serves as a training algorithm in MLP to find optimal configurations of its input parameters, thus improving its classification performance. The proposed HOA-MLP is evaluated using 15 popular classification datasets with 2–10 label classes. The proposed HOA-MLP is compared against five other comparative methods, which are bat algorithm, harmony search, flower pollination algorithm, sine cosine algorithm, and JAYA algorithm. Interestingly, the HOA-MLP is able to outperform others in 5 out of 15 datasets. Furthermore, the HOA-MLP can achieve high-quality results when compared with the comparative methods.