Successive data injection in conditional quantum GAN applied to time series anomaly detection

Abstract: Abstract Classical GAN architectures have shown interesting results for solving anomaly detection problems in general and for time series anomalies in particular, such as those arising in communication networks. In recent years, several quantum GAN (QGAN) architectures have been proposed in the literature. When detecting anomalies in time series using QGANs, huge challenges arise due to the limited number of qubits compared to the size of the data. To address these challenges, a new high‐dimensional encoding approach, named Successive Data Injection (SuDaI) is proposed. In this approach, SuDaI explores a larger portion of the quantum state, compared to the conventional angle encoding method used predominantly in the literature. This is achieved through repeated data injections into the quantum state. SuDaI encoding allows the authors to adapt the QGAN for anomaly detection with network data of a much higher dimensionality than with the existing known QGANs implementations. In addition, SuDaI encoding applies to other types of high‐dimensional time series and can be used in contexts beyond anomaly detection and QGANs, opening up therefore multiple fields of application.


Physics-Guided Data Augmentation Combined with Unsupervised Learning Improves Stability and Accuracy of Bit Wear Deep Learning Model

Abstract: Abstract Data is one of the most important limiting factors of deep machine learning (ML) model in drilling applications. Though a big size of historical data can be available, high-quality cleaned and labeled data is usually limited. In this case study, we show that with limited labeled data, physics-based data augmentation combined with unsupervised learning significantly improves both stability and accuracy in bit wear ML model. It provides a pathway to overcome labeled data shortage and field data quality limitations. Labeled bit wear data is usually limited because only the final bit dull state can be labeled using dull photos for the entire drilling distance. To overcome this difficulty, an encoder-decoder unsupervised ML framework based on Bi-directional LSTM architecture is first applied to the data of entire drilling distance to extract and maximize data learnings. Then, a physics-based wear estimator is implemented in the learned latent space to augment labeled wear dataset, guided by true wear labels and offset-well data. Weighting factor in loss function is applied to optimize augmented and true datasets in final supervised learning step. The proposed real-time bit wear model is built on a combination of physics-based data augmentation and unsupervised data learning method. The model is applied on multiple field bit runs. Results show that by implementing the unsupervised data learnings only, the prediction accuracy is improved by 30% compared to the baseline ML model. By combining physics-based augmentation, the accuracy is further improved by 10%. More importantly, adding physics-based data augmentation significantly reduces prediction variance and unphysical wear outputs, therefore improving prediction stability by more than 30%. It should be noted that the prediction stability of AI/ML model is crucial in real-time application and decision making. The results show physics-based data augmentation not only increases the size of label dataset and prevents model overfitting, but also applies physics-based guidance to the ML model effectively. It is learned weighting factor plays a crucial role in balancing loss contributions from true wear labels and physics-based labels. While low weighting factor of physics-based labels diminishes the augmented data, high weighting factor disrespects the true wear labels leading to a high prediction bias. Overall, the bit wear model study shows physic-based data augmentation combined with unsupervised data learning can effectively improve model accuracy, stability, and overcome labeled data shortage difficulty. The proposed paper shows a case study for the bit wear ML model using a combination of physics-based data augmentation and unsupervised data learning. While labeled data is one of the major challenges of ML models in many drilling applications, this study provides a pathway to improve both accuracy and stability of deep ML models with limited labeled data.


Drill Bit Forensics Using Videos Captured on Mobile Phones

Abstract: Abstract Still images captured using mobile phone cameras have been shown to be very useful for bit forensics purposes. However, since still images can only capture views at certain viewing angles, they often provide insufficient information on each cutter's diamond table and substrate. Videos solve the problem of lack of view angles. This paper demonstrates how a video captured on a phone can provide additional bit information benefiting forensics, and provides recommendations for capturing videos to maximize information content. Bit forensics as well as the future IADC dull grading system require meticulous data collection for various drill bit regions, including critical information such as blade numbers and cutter locations. To automate this process, this paper introduces a multi-stage approach using bit videos. Initially, a detection model is employed to accurately locate the drill bit within the video. Next, computer vision algorithms are utilized to segment the different blades. Spatial geometry algorithms are then applied to reconstruct the camera trajectory, which aids in estimating the blade numbers. Finally, each cutter within the segmented blades is further segmented into different regions. This study explores the utilization of videos for the automated location segmentation of drill bits, a crucial aspect of the revised IADC dull grading system currently being proposed. The videos capture the entire drill bit from multiple angles, encompassing top-down views and a full 360-degree rotation. The IADC dull grading system necessitates the precise recording of position information including blade numbers, pocket numbers, and bit zones. By employing videos instead of still images, this study demonstrates that spatial geometric information of the drill bit can be obtained more completely and efficiently. Given a video that conforms to established shooting standards, the proposed automatic position calculation algorithm efficiently completes the segmentation of different parts of the drill bit and labels them in accordance with the relevant standards. Notably, video capturing offers several advantages over still photography; it obviates the need for complex training for operators, only necessitating adherence to basic camera trajectory guidelines, and substantially reduces the time needed to collect such data. Importantly, the location segmentation algorithm employed in this study is capable of running in real-time, thereby streamlining and accelerating the IADC dull grading and bit forensics processes. This paper introduces a novel video-based algorithm for drill bit segmentation. The algorithm automatically segments and labels various components of the drill bit as per established criteria, generating comprehensive data vital for damage analysis. By employing this algorithm, videos of PDC drill bits can be processed with remarkable speed and accuracy. This represents a substantial advancement in data collection methods, with implications for improving the quality of bit damage assessment.


The Implementation of Safety-II: Learning from Normal Work in Practice

Abstract: Abstract The oil and gas industry has for decades learned about safety from the analysis of failure, adverse events, incidents, and accidents. This approach is called "safety-I". Safety-I has helped the industry reach a plateau of general high levels safety, measured as the absence of incidents, compared to other industries, but there is still room for further and continuous improvements. Researchers in human factors and safety have suggested that safety critical industries, in the future, should not only learn from failure, but also from the normal everyday successful work. This approach is called "safety-II" (Hollnagel et al. 2015), and, in a report from The International Association of Oil & Gas Producers (2022), "learning from normal work". The paper describes the practical implementation and roll-out of safety-II and learning from normal work on board operating drilling rigs. The learning from normal work project covers 3 phases: Phase 1: The onboard studies of positive practice, i.e., ethnographic observations of normal daily work and conversations with crew members, e.g., on the drill floor.Phase 2: The development and prototype testing of learning material based on the onboard observations and training of onboard facilitators in the learning team format.Phase 3: The final validation of the learning from normal work concept in a ramp-up case study on a rig starting a new drilling campaign. More than 160 examples of positive practice from 5 different rigs (2 drillships, 2 jackups and 1 semisubmersibles) were collected in the first phase of the learning from normal work project. The examples illustrate how crews add safety to the process by, e.g., the way they communicate and interact, the way they use technology or the way they plan and discuss their work. The positive examples were filtered and used to generate learning material in the form of learning cards for interactive discussions of safety in learning teams and positive examples to be used in safety meetings and presentations for inspiration as an alternative to failure and incident cases. One important conclusion from the learning from normal work project is that the crews contribute to the safety of the process through their behavior and small actions in the daily routine work. Thereby, they put many small "safety nets" in place in the system making it resilient and strong. Looking at positive practice and positive events is new when discussing safety. It changes the narrative and the focus of the discussion from "what to avoid" to "what to do more" and "what to develop and support". This philosophy is based on positive reinforcement as a motivating and driving factor.


Human-Centered Interaction in Virtual Worlds: A New Era of Generative Artificial Intelligence and Metaverse

Abstract: The metaverse has emerged as an exciting new paradigm for human-computer interaction (HCI) and virtual collaboration. This paper presents a comprehensive review of the metaverse to address the gap in the existing literature where there is a lack of a survey that reviews the nature of the metaverse and its building blocks from a human-centric perspective. We first synthesize a definition of the metaverse from existing literature and delineate key affordances. We then introduce a detailed framework encompassing the metaverse’s nature, infrastructure technologies, and input/output technologies that facilitate multi-sensory HCI, alongside applications across diverse domains. The components within this framework are explained in depth, offering insights into the metaverse’s nature and the readiness level of current technologies. Based on this comprehensive analysis, we outline major open challenges and propose promising directions demanding further exploration and investigation. By clarifying the vision for the metaverse and characterizing the building blocks required to realize it, this review provides essential insights and serves as an invaluable resource for metaverse developers and researchers working to advance this transformative new medium.


Bringing digital competence to the disciplines

Abstract: How can upper secondary teachers accommodate the increasing demand for integrating digital competence in their disciplines? Research on digital competence typically operates independently from specific disciplines and may be difficult to translate into teaching practices. Based on a three-year action research project, the paper presents the results of a study of teacher’s understanding of digital technologies in connection to their disciplines. The study involves two rounds of interviews with 40 teachers. From the interviews, different perspectives emerge with 1) technology-driven, 2) pedagogy-driven, and 3) discipline-specific use of digital technologies. By further exploring discipline-specific use of digital technologies, digital competence is seen as expanding the disciplines where technology directly impacts and transforms subject areas of disciplines. Further, drawing from teacher experiences from completed courses, the paper develops a framework for accommodating the interplay between digital competence and the disciplines. The framework presents boundary concepts that are meant to bridge the gap between teaching practices within the disciplines and contributions from research. The framework is intended for teachers in developing their disciplines from digital competence. Finally, the paper discusses how expanding the disciplines through the lens of digital competence may call for moving across and possibly beyond the disciplines.


Brief study on the emergence and preliminary development of China’s marine higher education in the early and mid-20th century

Abstract: Abstract China’s marine higher education originated in the early twentieth century and was led and promoted by a group of educators and marine scientists in the Republic of China. There are three sources of marine higher education, namely, National Shandong University, Xiamen University, and Fudan University, which, over time, have formed three unique paths of marine higher education in China. With the establishment of the People’s Republic of China, from 1949 to 1952, there was a reform in marine higher education, in which the teachers and students of the Oceanography Group of Fudan University and the Department of Oceanography of Xiamen University were merged into Shandong University, and the three sources were then merged into one. Among them, the students of the Oceanography Group of Fudan University were adjusted to Shandong University by policy, which opened the prelude to China’s ‘colleges and departments adjustment’. After the ‘colleges and departments adjustment’, the characteristics of marine discipline at Shandong University became increasingly prominent, which drove the development of China’s marine higher education from less to more vigorous. As a whole, it has formed the unique characteristics and patterns of the emergence and the initial development of marine higher education in China.


Real‐time route planning for low observable <scp>unmanned combat aerial vehicle</scp>

Abstract: Abstract The next generation of low observable (LO) unmanned combat aerial vehicle (UCAV) with highly autonomy to implement a penetration mission requires advanced methods for flyable and safe route planning (i.e., respecting physical capability of vehicle and threat coverage by hostile air defense radars) at a real‐time manner. Currently, the main challenge of real‐time route planning for LO UCAV is to achieve computationally efficiency under dynamic (pop‐up/moving) threats by air defense radars. In this paper, a real‐time planning paradigm in compliance with complex penetration requirements is proposed, and a complete modeling of route planning for LO UCAV's penetration as an optimal control problem is designed. The paper at first devises a direct method to transform the optimal control problem into a nonlinear programming (NLP) problem and then solves the formulated NLP problem under a moving planning horizon. The proposed method can give computationally efficient route planning results for LO UCAV's penetration under multiple kinds of radar threats. Numerical test results based on F‐16 uninhabited platform demonstrate the effectiveness of the proposed method.


Multi‐exposure embeddings for graph learning: Towards high dynamic range image saliency prediction

Abstract: Abstract Identifying saliency in high dynamic range (HDR) images is a fundamentally important issue in HDR imaging, and plays critical roles towards comprehensive scene understanding. Most of existing studies leverage hand‐crafted features for HDR image saliency prediction, lacking the capabilities of fully exploiting the characteristics of HDR image (i.e. wider luminance range and richer colour gamut). Here, systematical studies are carried out on HDR image saliency prediction by proposing a new framework to single out the contributions from multi‐exposure images. Specifically, inspired by the mechanism of HDR imaging, the method first utilizes graph neural networks to model the relations among multi‐exposure images and the tone‐mapped image obtained from an HDR image, enabling more discriminative saliency‐related feature representations. Subsequently, the saliency features driven by global semantic knowledge are aggregated from the tone‐mapped image through enhancing global context‐aware semantic information. Finally, a fusion module is designed to integrate saliency‐oriented feature representations originated from multi‐exposure images and the tone‐mapped image, producing the saliency maps of HDR images. Moreover, a new challenging HDR eye fixation database (HDR‐EYEFix) is created, expecting to further contribute the research on HDR image saliency prediction. Experiment results show that the method obtains superior performance compared to the state‐of‐the‐art methods.


Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services

Abstract: Abstract Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry.


Exploring English Vocabulary Learning of Vietnamese Secondary School Students with VoiceGPT Assistance

Abstract: With the advent of AI chatbots, many teachers’ teaching practices of English as a foreign language have undergone many changes. Many of them have become accustomed to employing ChatGPT to assist their work, bringing many benefits and potential challenges that, to date, have yet to be fully tested in any aspect. Particularly, two notable research gaps involve how Vietnamese secondary school students use VoiceGPT, the Vietnamese version of ChatGPT, to assist them in learning new English words and how they perceive this support. The current case study aimed to address these gaps by employing a quasi-experimental design at Lam Son Secondary School in Ho Chi Minh City with the participation of ten sixth-grade students in two English-intensive classes. In this investigation, the teacher used the Presentation-Practice-Production teaching method to teach vocabulary to her students, who were randomly assigned into two groups with the same number of members in each group, and the data for analysis was collected from their writing samples and semi-structured interviews. The findings indicate that sixth-grade students had different ways of using VoiceGPT to help them learn English words. The participants with VoiceGPT assistance outperformed those without this A.I. support in terms of lexical performance in the writing productions on five topics surveyed. In addition, they expressed favorable attitudes toward VoiceGPT’s benefits, but some concerns were raised about login difficulties, vocabulary range, and long response time.


Blockchain for finance: A survey

Abstract: Abstract As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point‐to‐point transactions, and decentralized autonomous organizations. Here, the authors focus on blockchain‐based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third‐party verification by using consensus‐based verification. The 12 most popular blockchain platforms are investigated and 6 platforms that are related to finance are elaborated on, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain‐based securities trading applications. Numerous practical applications of blockchain‐based securities trading are gathered and they are categorized into four distinct categories. For each category, a typical example is introduced and how blockchain contributes to solving the key problems faced by FinTech companies and researchers explained. Finally, interesting observations are provided ranging from mainstream blockchain‐based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance.


Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction

Abstract: Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity concepts. To address this, our paper proposes a two-stage mention-level framework based on an enhanced heterogeneous graph attention network for document-level relation extraction. Our framework employs two different strategies to model intra-sentential and inter-sentential relations between fine-grained entity mentions, yielding local mention representations for intra-sentential relation prediction and global mention representations for inter-sentential relation prediction. For inter-sentential relation prediction and inference, we propose an enhanced heterogeneous graph attention network to better model the long-distance semantic relationships and design an entity-coreference path-based inference strategy to conduct relation inference. Moreover, we introduce a novel cross-entropy-based multilabel focal loss function to address the class imbalance problem and multilabel prediction simultaneously. Comprehensive experiments have been conducted to verify the effectiveness of our framework. Experimental results show that our approach significantly outperforms the existing methods.


DeforestVis: Behaviour Analysis of Machine Learning Models with Surrogate Decision Stumps

Abstract: Abstract As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model‐agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier‐to‐explain. Yet, rule sets can become very lengthy, with many if–else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models by providing surrogate decision stumps (one‐level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity versus fidelity trade‐off by incrementally generating more stumps, creating attribute‐based explanations with weighted stumps to justify decision making, and analysing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case‐by‐case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.


High capacity reversible data hiding in radiographic images with optimal bit allocation

Abstract: Abstract This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method.


Modeling digital skills beyond the IT workforce: construct definition, measurement and impact on digitalization value

Abstract: Purpose Information technology (IT) personnels’ technical, business and behavioral skills are critical enablers for generating IT value. In an increasingly digitalized working environment where non-IT employees participate in digital innovations, a focus on IT personnels’ skills only doesn’t meet researchers’ need for a framework to study digital skills and managers’ need to address digital skills challenges across an enterprise’s workforce. Nevertheless, the digital skills topic is complicated by conceptual ambiguity and a lack of theoretically derived and empirically validated model. The purpose of this study is to address this problem. Design/methodology/approach Theoretically, this study draws on human capital (HC) and resource-based view (RBV) theories. Empirically, it follows mixed method combining interviews and a survey. Findings The digital skills construct is a multidimensional second order reflective construct. While its development is influenced by an organization’s commitment and exposure to digitalization, it influences the value organizations obtain from digitalization. Research limitations/implications This study conceptualizes the digital skills construct, identifying technology agnostic subdimensions that are meaningful beyond a particular digital domain [information and communication technology (ICT), information, Internet, Inter of Things (IoT)] and establishing a valid measure. Other researchers can improve both the indicators of the existing four conceptually distinct and managerially recognizable workplace digital skills dimensions as well as testing new ones. Practical implications Managers can use the instrument to assess the extent to which their non-IT workforces are equipped with digital skills and get strategic insights for specific interventions such as upskilling or buying in skills. Originality/value The main theoretical contribution of the paper is the conceptualization and validation of the digital skills construct for the non-IT workforce. Furthermore, we provide a theoretical framework to explain the factors that could influence the development of digital skills and demonstrate the impact that digital skills have on selected digitalization value indicators. This contribution provides the foundation for investigating the drivers, outcomes and the relationship of digital skills to other constructs such as digital transformation, innovation and firm performance.


A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

Abstract: A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.


Evaluating end‐users' digital competencies and ethical perceptions of <scp>AI</scp> systems in the context of sustainable digital banking

Abstract: Abstract In the pursuit of fostering sustainable digital banking (SDB), this study investigates the nexus between end‐users' digital competencies, perceptions of artificially intelligent systems, and sustainable practices. With a focus on addressing inclusivity barriers and promoting ethical considerations, expert interviews were employed to provide nuanced insights. The study's foundation rests on the significance of sustainable development goals, digital competencies, and artificial intelligence (AI) ethics in shaping the future of digital banking. An interview schedule was developed and distributed online to IT experts and researchers in the field of AI. The instrument examined the major challenges faced by customers in accessing digital banking, the expected timeline for the complete digitalization of the South African banking sector, customers' anticipated competency, and comfort levels with a 100% digital banking interface, and their perspectives on the overall impact of such a transition on humanity. The interview gathered participants' views on AI‐based banking systems that determine credit scores and creditworthiness. The study also included questions to obtain expert opinions on customers' digital capabilities, access to resources, and the impact of AI trust issues on financial service customers. The findings of this study provided valuable insights for stakeholders, including governments, NGOs, and financial institutions. Implications derived from expert perspectives illuminate strategies for enhancing digital competencies, addressing inclusivity hurdles, and ensuring ethical AI integration. This study contributes to the sustainable development discourse by offering actionable recommendations for stakeholders in the pursuit of a more inclusive, digitally competent, and ethically responsible digital banking landscape.


Navigating the technical analysis in stock markets: Insights from bibliometric and topic modeling approaches

Abstract: In stock markets, technical analysis plays a vital role by offering valuable insights into price trends, patterns, and anticipated market movements, aiding investors in making well-informed decisions. This study employs bibliometric and topic modelling approaches on 589 English-language journal articles indexed in Scopus in the last two decades (from 2003 to 2023), exclusively focusing on technical analysis in stock markets. The keyword co-occurrence analysis identifies five topic clusters. The application of structural topic modelling also unravels five prominent thematic clusters, namely pattern-based forecasting, rule-based trading, algorithmic trading, techno-fundamental trading, and machine learning &amp;amp; sentiment analysis. The topic of pattern-based forecasting involves researching the application of various patterns or models to predict stock prices. Rule-based trading concentrates on utilizing technical analysis tools to generate buy and sell signals, aiming for profitability. The algorithmic trading cluster explores the use of algorithms to systematically execute buy and sell actions, especially in high-frequency trading scenarios. Techno-fundamental trading investigates the integration of both fundamental and technical analysis in trading and investment decisions. Lastly, machine learning &amp;amp; sentiment analysis focus on applying advanced machine learning techniques and sentiment analysis for predicting stock prices, highlighting the use of sophisticated methods in this domain. The three predominant topics in the dataset are &quot;rule-based trading,&quot; &quot;machine learning &amp;amp; sentiment analysis,&quot; and &quot;algorithmic trading&quot; constituting 26.79%, 23.52%, and 21.11% of the dataset, respectively. These findings underscore the prominence and significance of these themes within the context of the research domain.


Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning

Abstract: Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data’s integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, F-measure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction.


Like a human: The social facilitation/inhibition effect in presence of a virtual observer depends on arousal

Abstract: Abstract Better (social facilitation) or worse (social inhibition) performance in a task while being observed has repeatedly been demonstrated with real human observers, yet it has not been fully tested with virtual observers. We tested this effect in a virtual environment using a visual search task to verify if the mere presence of a virtual observer would boost participants’ performance in the easy variant of the task and hinder it in the difficult one. We used electrodermal (EDA) activity measurement to test whether physiological arousal would moderate this effect. The presence of a virtual character affected search performance, which was moderated by arousal, with lower arousal leading to better performance. These results confirm that the mere presence of virtual characters can evoke a social facilitation/inhibition effect similar to that of human beings; this could confirm Zajonc’s theory and serve as encouragement for further development of VR-based training & educational tools with virtual agents.


Enhancing Deep Learning and Computer Image Analysis in Petrography through Artificial Self-Awareness Mechanisms

Abstract: In this paper, we discuss the implementation of artificial self-awareness mechanisms and self-reflection abilities in deep neural networks. While the current limitations of research prevent achieving cognitive capabilities on par with natural biological entities, the incorporation of basic self-awareness and self-reflection mechanisms in deep learning architectures offers substantial advantages in tackling specific problems across various scientific fields, including geosciences. In the first section, we outline the foundational architecture of our deep learning approach termed Self-Aware Learning (SAL). The subsequent part of the paper highlights the practical benefits of this machine learning methodology through synthetic tests and applications addressed to automatic classification and image analysis of real petrological data sets. We show how Self-Aware Learning allows enhanced accuracy, reduced overfitting problems, and improved performances compared to other existing methods.


A Systematic Evaluation of Recurrent Neural Network Models for Edge Intelligence and Human Activity Recognition Applications

Abstract: The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local or cloud-based GPU machines are used to train them. However, inference is now shifting to miniature, mobile, IoT devices and even micro-controllers. Due to their colossal memory and computing requirements, mapping RNNs directly onto resource-constrained platforms is arcane and challenging. The efficacy of edge-intelligent RNNs (EI-RNNs) must satisfy both performance and memory-fitting requirements at the same time without compromising one for the other. This study’s aim was to provide an empirical evaluation and optimization of historic as well as recent RNN architectures for high-performance and low-memory footprint goals. We focused on Human Activity Recognition (HAR) tasks based on wearable sensor data for embedded healthcare applications. We evaluated and optimized six different recurrent units, namely Vanilla RNNs, Long Short-Term Memory (LSTM) units, Gated Recurrent Units (GRUs), Fast Gated Recurrent Neural Networks (FGRNNs), Fast Recurrent Neural Networks (FRNNs), and Unitary Gated Recurrent Neural Networks (UGRNNs) on eight publicly available time-series HAR datasets. We used the hold-out and cross-validation protocols for training the RNNs. We used low-rank parameterization, iterative hard thresholding, and spare retraining compression for RNNs. We found that efficient training (i.e., dataset handling and preprocessing procedures, hyperparameter tuning, and so on, and suitable compression methods (like low-rank parameterization and iterative pruning) are critical in optimizing RNNs for performance and memory efficiency. We implemented the inference of the optimized models on Raspberry Pi.


Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

Abstract: Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system’s performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study’s findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.


A Multi-Task Network Based on Dual-Neck Structure for Autonomous Driving Perception

Abstract: A vision-based autonomous driving perception system necessitates the accomplishment of a suite of tasks, including vehicle detection, drivable area segmentation, and lane line segmentation. In light of the limited computational resources available, multi-task learning has emerged as the preeminent methodology for crafting such systems. In this article, we introduce a highly efficient end-to-end multi-task learning model that showcases promising performance on all fronts. Our approach entails the development of a reliable feature extraction network by introducing a feature extraction module called C2SPD. Moreover, to account for the disparities among various tasks, we propose a dual-neck architecture. Finally, we present an optimized design for the decoders of each task. Our model evinces strong performance on the demanding BDD100K dataset, attaining remarkable accuracy (Acc) in vehicle detection and superior precision in drivable area segmentation (mIoU). In addition, this is the first work that can process these three visual perception tasks simultaneously in real time on an embedded device Atlas 200I A2 and maintain excellent accuracy.


DeepMCGCN: Multi-channel Deep Graph Neural Networks

Abstract: Abstract Graph neural networks (GNNs) have shown powerful capabilities in modeling and representing graph structural data across various graph learning tasks as an emerging deep learning approach. However, most existing GNNs focus on single-relational graphs and fail to fully utilize the rich and diverse relational information present in real-world graph data. In addition, deeper GNNs tend to suffer from overfitting and oversmoothing issues, leading to degraded model performance. To deeply excavate the multi-relational features in graph data and strengthen the modeling and representation abilities of GNNs, this paper proposes a multi-channel deep graph convolutional neural network method called DeepMCGCN. It constructs multiple relational subgraphs and adopts multiple GCN channels to learn the characteristics of different relational subgraphs separately. Cross-channel connections are utilized to obtain interactions between different relational subgraphs, which can learn node embeddings richer and more discriminative than single-channel GNNs. Meanwhile, it alleviates overfitting issues of deep models by optimizing convolution functions and adding residual connections between and within channels. The DeepMCGCN method is evaluated on three real-world datasets, and the experimental results show that its node classification performance outperforms that of single-channel GCN and other benchmark models, which improves the modeling and representation capabilities of the model.


Convenient Site Selection of a Floating PV Power Plant in Türkiye by using GIS-Fuzzy Analytical Hierarchy Process

Abstract: Floating photovoltaics (FPVs) are appearing as a promising and an alternative renewable energy opinion in which PV panels are mounted on floating platforms in order to produce electricity from renewable energy on water such as seas, dams, rivers, oceans, canals, fish farms, and reservoirs. So far, such studies related to the body knowledge on financial, technical, and environmental aspects of installation of FPV have not been performed in Turkey while expanding steadily in other countries. In this study, suitable site selection for installation of FPV power plants on three lakes in Turkey was studied by performing geographic information system (GIS) and the fuzzy analytic hierarchy process (FAHP) as multi-criteria decision-making (MCDM) method. This detailed study revealed that the criterion of global horizontal irradiance (GHI) was determined as the most crucial criterion for the installation of FPV on Beysehir Lake, Lake of Tuz, and Van Lake. Additionally, it was clearly seen that the Beysehir Lake had the highest value approximately 52% among other lakes for installation, that is why Beysehir Lake is selected as the best option for installation of an FPV system with this multi-criteria approach.


Using of the Weibull distribution in developing global solar radiation forecasting models

Abstract: Abstract In this study, a solar radiation prediction model was developed using the Weibull distribution function (WDF). The main purpose of this study is to develop a new model for solar radiation (SR) estimation using the WDF and to bring this model to the literature. Although the WDF is widely used in wind energy forecasting due to its compatibility with wind speed data, it has not been used before in solar radiation forecasting model development. In this study, it is aimed to make SR estimation with the WDF for the first time. SR data for all regions where this new model was developed and its performance was examined were obtained from the General Directorate of Meteorology. In order to decide the success of these developed models, five different statistical metrics (RPE, MPE, MAPE, SSRE, and t ‐stat) were discussed in the study. When the results are evaluated in general, it is seen that the new developed model has an acceptable performance. According to all test results in the region where the model was developed, it is seen that the new developed model showed the best performance with 0.0353, 0.4068, 7.1851, 0.1144, and 0.0162 values, respectively. The performance of this new developed model was observed in three different regions. When the statistical test results in these regions were examined, it was seen that the new model developed had a similar performance to the popular solar forecasting models widely used in the literature.


A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Abstract: Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.


Regional Geochemical Characteristics of Lithium in the Mufushan Area, South China

Abstract: With the explosive growth in demand for lithium (Li) resources, the Mufushan area has been a hotspot for Li deposit exploration in China in recent years. Geochemical maps and geochemical anomaly maps are basic maps in the geochemical exploration of mineral resources. A fixed-value method to contour a Li geochemical map is presented here, in which Li concentrations are divided into 19 levels on 18 fixed values, ranging from 5 μg/g (corresponding to the detection limit) to 1858 μg/g (corresponding to the cut-off grade of Li deposit in hard-rock type) and illustrated in six color tones corresponding to Li areas of low background, high background, low anomaly, high anomaly, mineralization in clay-type, and mineralization in hard-rock type. The geochemical map of Li in the Mufushan area using the new fixed-value method indicates that the study area belongs to the high background area, and the known Li deposits are located in the high anomaly areas. In addition, the geochemical anomaly map of the Mufushan area is drawn using the method of seven levels of classification, and indicates that the known Li deposits are all in the anomaly areas, with anomaly levels not lower than the second level. Furthermore, four other areas are recognized for Li resource potential based on the geochemical map and geochemical anomaly map in the Mufushan area.


Toxic Element Contamination Sources in the Surface and Groundwater of the Elbrus Region: Geochemistry and Health Risks

Abstract: The region surrounding the Elbrus volcano in the Great Caucasus is a popular tourist destination, home to native people and an area of the richest ore mineral resources. Based on the chemical composition of surface water, mineral, and groundwater in the Elbrus area, as well as statistical and thermodynamic calculations, waters with high concentrations of Mn, Cu, Zn, Mo, Ba, W, Pb, and As were identified, possible mineral phases that may lead to these metal concentrations in water were estimated, and risks to human health were assessed. Two main sources of surface water pollution have been identified: (1) water from tailings that are enriched due to oxidation of sulfide minerals is considered a source of As (181 µg/L) and Mo (2070 µg/L) pollution in the area of the Tyrnyauz W-Mo deposit; and (2) CO2-rich waters of deep horizons discharge on the earth surface with high concentrations of As (828 µg/L) and W (504 µg/L). It has been shown that compared to all toxic elements studied, only Mo and As have adverse non-carcinogenic effects on adult health. The estimated carcinogenic risk of As in the mine area was 1.7 × 10−3 and exceeded the generally accepted range of 10−4 to 10−6. In order to minimize the health risks, regular monitoring of water quality is required when mining activities on the Tyrnyauz W-Mo deposit are renewed.


Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Abstract: The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures.


The modal behaviour of a violin corpus

Abstract: Violins incorporate numerous interfaces and anisotropic behaviour due to natural materials. Hence, the modelling of these instruments is extremely complex. The present work investigates the structural dynamics of a specific violin corpus (violin resonant body) with the aim of using numerical modelling to accurately understand the modal behaviour of the violin using a novel stepwise approach. In the first step, an inverse material parameter identification via experimental and numerical modal analysis of the raw woods is performed to obtain the material parameters of the individual woods. In the second step, an experimental modal analysis of the violin substructures is performed using 2D scanning laser Doppler vibrometry and building finite element models based on accurate 3D scans of the parts manufactured by a luthier. The experimentally obtained modal parameters of the individual parts are compared with the numerical results. In the third step, the whole violin corpus, i.e. the resonant body of the instrument, is assessed. The good agreement between the numerical and the experimental results demonstrates that the application of standard techniques of modal analysis on a very complex mechanical system, the violin body, is possible. Our methodology offers a novel approach to analysing individual components of bowed string instruments, facilitating a comprehensive vibrational analysis with implications for broader musical research, including luthier optimisation tools, digital reconstruction of historical violins, and advancements in instrument design for improved playability and tonal quality.


Causal Conditionals, Tendency Causal Claims and Statistical Relevance

Abstract: Abstract Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ in their predictive power for both causal and conditional claims.


Invariant Feature Learning Based on Causal Inference from Heterogeneous Environments

Abstract: Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimization, which typically fails to converge to the optimal solution. Therefore, obtaining the variables that cause the target outcome through a causal inference method is a more direct and effective method. This paper presents a new approach for invariant feature learning based on causal inference (IFCI). IFCI detects causal variables unaffected by the environment through the causal inference method. IFCI focuses on partial causal relationships to work efficiently even in the face of high-dimensional data. Our proposed causal inference method can accurately infer causal effects even when the treatment variable has more complex values. Our method can be viewed as a pretreatment of data to filter out variables whose distributions change between different environments, and it can then be combined with any learning method for classification and regression. The result of empirical studies shows that IFCI can detect and filter out environmental variables affected by the environment. After filtering out environmental variables, even a model with a simple structure and common loss function can have strong OOD generalization capability. Furthermore, we provide evidence to show that classifiers utilizing IFCI achieve higher accuracy in classification compared to existing OOD generalization algorithms.


Joint Distortion Restoration and Quality Feature Learning for No-Reference Image Quality Assessment

Abstract: No-reference image quality assessment (NR-IQA) methods inspired by the free energy principle, improve the accuracy of image quality prediction by simulating the human brain’s repair process for distorted images. However, existing methods use separate optimisation schemes for distortion restoration and quality prediction, which undermines the accurate mapping of feature representations to quality scores. To address this issue, we propose a joint restoration and quality feature learning NR-IQA (RQFL-IQA) method to jointly tackle distortion image restoration and quality prediction within a unified framework. To accurately establish the quality reconstruction relationship between distorted and restored images, a hybrid loss function based on pixel-wise and structure-wise representations is used to improve the restoration capability of the image restoration network. The proposed RQFL-IQA exploits rich labels, including restored images and quality scores, to enable the model to learn more discriminative features and establish a more accurate mapping from feature representation to quality scores. In addition, to avoid the impact of poor restoration on quality prediction, we propose a module with a cleaning function to reweight the fusion of restored and primitive features to achieve more perceptual consistency in feature fusion. Experimental results on public IQA datasets show that the proposed RQFL-IQA is superior over existing methods.


The Complexity of LTL Rational Synthesis

Abstract: In rational synthesis , we automatically construct a reactive system that satisfies its specification in all rational environments, namely environments that have objectives and act to fulfill them. We complete the study of the complexity of LTL rational synthesis, when the objectives are given by formulas in Linear Temporal Logic. Our contribution is threefold. First, we tighten the known upper bounds for settings that were left open in earlier work. Second, our complexity analysis is parametric, and we describe tight upper and lower bounds in each of the problem parameters: the game graph, the objectives of the system components, and the objectives of the environment components. Third, we generalize the definition of rational synthesis by adding hostile players to the setting and by combining the cooperative and non-cooperative approaches studied in earlier work.


Emission Reduction Decisions in Blockchain-Enabled Low-Carbon Supply Chains under Different Power Structures

Abstract: With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers’ environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers’ profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises’ decision-making supported by blockchain.


Real-Time Multi-Robot Mission Planning in Cluttered Environment

Abstract: Addressing a collision-aware multi-robot mission planning problem, which involves task allocation and path-finding, poses a significant difficulty due to the necessity for real-time computational efficiency, scalability, and the ability to manage both static and dynamic obstacles and tasks within a complex environment. This paper introduces a parallel real-time algorithm aimed at overcoming these challenges. The proposed algorithm employs an approximation-based partitioning mechanism to partition the entire unassigned task set into several subsets. This approach decomposes the original problem into a series of single-robot mission planning problems. To validate the effectiveness of the proposed method, both numerical and hardware experiments are conducted, involving dynamic obstacles and tasks. Additionally, comparisons in terms of optimality and scalability against an existing method are provided, showcasing its superior performance across both metrics. Furthermore, a computational burden analysis is conducted to demonstrate the consistency of our method with the observations derived from these comparisons. Finally, the optimality gap between the proposed method and the global optima in small-size problems is demonstrated.


Attribute Graph Embedding Based on Multi-Order Adjacency Views and Attention Mechanisms

Abstract: Graph embedding plays an important role in the analysis and study of typical non-Euclidean data, such as graphs. Graph embedding aims to transform complex graph structures into vector representations for further machine learning or data mining tasks. It helps capture relationships and similarities between nodes, providing better representations for various tasks on graphs. Different orders of neighbors have different impacts on the generation of node embedding vectors. Therefore, this paper proposes a multi-order adjacency view encoder to fuse the feature information of neighbors at different orders. We generate different node views for different orders of neighbor information, consider different orders of neighbor information through different views, and then use attention mechanisms to integrate node embeddings from different views. Finally, we evaluate the effectiveness of our model through downstream tasks on the graph. Experimental results demonstrate that our model achieves improvements in attributed graph clustering and link prediction tasks compared to existing methods, indicating that the generated embedding representations have higher expressiveness.


Differential Fault and Algebraic Equation Combined Analysis on PICO

Abstract: In modern information technology, research on block cipher security is imperative. Concerning the ultra lightweight block cipher PICO, there has been only one study focused on recovering its complete master key, with a large search space of 264, and no fault analysis yet. This paper proposes a new fault analysis approach, combining differential fault and algebraic equation techniques. It achieved the recovery of PICO’s entire master key with 40 faults in an average time of 0.57 h. S-box decomposition was utilized to optimize our approach, reducing the time by a remarkable 75.83% under the identical 40-fault condition. Furthermore, PICO’s complete master key could be recovered with 28 faults in an average time of 0.78 h, indicating a significant 237 reduction in its search space compared to the previous study. This marks the first fault analysis on PICO. Compared to conventional fault analysis methods DFA (differential fault analysis) and AFA (algebraic fault analysis), our approach outperforms in recovering PICO’s entire master key, highlighting the cruciality of key expansion complexity in block cipher security. Therefore, our approach could serve to recover master keys of block ciphers with comparably complicated key expansions, and production of more secure block ciphers could result.


DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network

Abstract: Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not node’s local topology structure. However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology structures in a payment network may reveal sellers’ business roles ( e.g., supplier or retailer). To model both connectivity and local topology structure for better node classification performance, we present DP-GCN, a dual-path graph convolution network. DP-GCN consists of three main modules: (i) a C-GCN module to capture the connectivity relationships between nodes, (ii) a T-GCN module to capture the topology structure similarity among nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines to demonstrate its effectiveness. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, a leading payment service provider, for risky seller detection. Experimental results show DP-GCN’s effectiveness and practicability in large-scale settings. PayPal’s internal testing also show DP-GCN’s effectiveness in defending real risks from transaction networks.


State-Difference Feedback Control for Discrete-Time Takagi–Sugeno Fuzzy Descriptor Systems with Parameter Uncertainties and External Noises

Abstract: This research focuses on the development of state-difference feedback controllers for discrete-time (DT) nonlinear descriptor systems. Discrete-time nonlinear DA systems consist of difference and algebraic equations and play a crucial role in describing dynamic behavior and capturing the constraints or relationships within the system. However, analytical stability may pose additional challenges due to the unique characteristics of the system. Utilizing fuzzy model-based techniques, the DT nonlinear DA system discussed in this study can be effectively represented using the Takagi–Sugeno (T-S) fuzzy model. After linearizing the nonlinear system through the T-S fuzzy model, traditional linear control techniques become applicable. These techniques are then applied to T-S fuzzy systems to establish stability criteria. This article chooses the Lyapunov function as the method used to analyze system stability. Additionally, we use a free-weighting matrix to introduce additional degrees of freedom. In summary, this paper presents simulation results and discussions to verify the effectiveness of the proposed design approach.


Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians

Abstract: Importance sampling techniques significantly reduce variance in physically-based rendering. In this paper we propose a novel online framework to learn the spatial-varying distribution of the full product of the rendering equation, with a single small neural network using stochastic ray samples. The learned distributions can be used to efficiently sample the full product of incident light. To accomplish this, we introduce a novel closed-form density model, called the Normalized Anisotropic Spherical Gaussian mixture, that can model a complex light field with a small number of parameters and that can be directly sampled. Our framework progressively renders and learns the distribution, without requiring any warm-up phases. With the compact and expressive representation of our density model, our framework can be implemented entirely on the GPU, allowing it to produce high-quality images with limited computational resources. The results show that our framework outperforms existing neural path guiding approaches and achieves comparable or even better performance than state-of-the-art online statistical path guiding techniques.


Tensor-Based Sparse Representation for Hyperspectral Image Reconstruction Using RGB Inputs

Abstract: Hyperspectral image (HSI) reconstruction from RGB input has drawn much attention recently and plays a crucial role in further vision tasks. However, current sparse coding algorithms often take each single pixel as the basic processing unit during the reconstruction process, which ignores the strong similarity and relation between adjacent pixels within an image or scene, leading to an inadequate learning of spectral and spatial features in the target hyperspectral domain. In this paper, a novel tensor-based sparse coding method is proposed to integrate both spectral and spatial information represented in tensor forms, which is capable of taking all the neighboring pixels into account during the spectral super-resolution (SSR) process without breaking the semantic structures, thus improving the accuracy of the final results. Specifically, the proposed method recovers the unknown HSI signals using sparse coding on the learned dictionary pairs. Firstly, the spatial information of pixels is used to constrain the sparse reconstruction process, which effectively improves the spectral reconstruction accuracy of pixels. In addition, the traditional two-dimensional dictionary learning is further extended to the tensor domain, by which the structure of inputs can be processed in a more flexible way, thus enhancing the spatial contextual relations. To this end, a rudimentary HSI estimation acquired in the sparse reconstruction stage is further enhanced by introducing the regression method, aiming to eliminate the spectral distortion to some extent. Abundant experiments are conducted on two public datasets, indicating the considerable availability of the proposed framework.


Parametric RSigELU: a new trainable activation function for deep learning

Abstract: Abstract Activation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. Thus, the development of activation functions which affect deep learning models’ performances is of great interest to researchers. In the literature, mostly, nonlinear activation functions are preferred since linear activation functions limit the learning performances of the deep learning models. Non-linear activation functions can be classified as fixed-parameter and trainable activation functions based on whether the activation function parameter is fixed (i.e., user-given) or modified during the training process of deep learning models. The parameters of the fixed-parameter activation functions should be specified before the deep learning model training process. However, it takes too much time to determine appropriate function parameter values and can cause the slow convergence of the deep learning model. In contrast, trainable activation functions whose parameters are updated in each iteration of deep learning models training process achieve faster and better convergence by obtaining the most suitable parameter values for the datasets and deep learning architectures. This study proposes parametric RSigELU (P+RSigELU) trainable activation functions, such as P+RSigELU Single (P+RSigELUS) and P+RSigELU Double (P+RSigELUD), to improve the performance of fixed-parameter activation function of RSigELU. The performances of the proposed trainable activation functions were evaluated on the benchmark datasets of MNIST, CIFAR-10, and CIFAR-100 datasets. Results show that the proposed activation functions outperforms PReLU, PELU, ALISA, P+FELU, PSigmoid, and GELU activation functions found in the literature. To access the codes of the activation function; https://github.com/serhatklc/P-RsigELU-Activation-Function .


Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer

Abstract: Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object detection. Despite the above advantages, Transformer-based architectures tend to incur significant computational costs, limiting the model’s real-time application potential. To address this problem, we propose a U-shaped network with Transformer as the decoder and CNN as the encoder to segment remote sensing urban scene images. For efficient segmentation, we design a window-based, multi-head, focused linear self-attention (WMFSA) mechanism and further propose the global–local information modeling module (GLIM), which can capture both global and local contexts through a dual-branch structure. Experimenting on four challenging datasets, we demonstrate that our model not only achieves a higher segmentation accuracy compared with other methods but also can obtain competitive speeds to enhance the model’s real-time application potential. Specifically, the mIoU of our method is 68.2% and 52.8% on the UAVid and LoveDA datasets, respectively, while the speed is 114 FPS, with a 1024 × 1024 input on a single 3090 GPU.


A unifying perspective on non-stationary kernels for deeper Gaussian processes

Abstract: The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of machine learning (ML) in the last two decades because of their superior prediction abilities, especially in data-sparse scenarios, and their inherent ability to provide robust uncertainty estimates. Even so, their performance highly depends on intricate customizations of the core methodology, which often leads to dissatisfaction among practitioners when standard setups and off-the-shelf software tools are being deployed. Arguably, the most important building block of a GP is the kernel function, which assumes the role of a covariance operator. Stationary kernels of the Matérn class are used in the vast majority of applied studies; poor prediction performance and unrealistic uncertainty quantification are often the consequences. Non-stationary kernels show improved performance but are rarely used due to their more complicated functional form and the associated effort and expertise needed to define and tune them optimally. In this perspective, we want to help ML practitioners make sense of some of the most common forms of non-stationarity for Gaussian processes. We show a variety of kernels in action using representative datasets, carefully study their properties, and compare their performances. Based on our findings, we propose a new kernel that combines some of the identified advantages of existing kernels.


Quartz: A Template for Quantitative Corpus Data Visualization Tools

Abstract: Quantitative data visualization is an important element of corpus linguistics, and visualization tools are commonly available in corpus management systems (CMSs) or developed for custom tasks. Nonetheless, the implementation and advancement of visualization methods can be a challenge, both for individual projects and collectively. This article introduces a web application template that integrates with the Sketch Engine application programming interface (API) to encourage the development of new visualization tools that can provide advanced visualization features while directly communicating with the CMS. The application template, written in Python and called Quartz, is described and implemented for the Humanitarian Encyclopedia as part of ongoing efforts to conduct analyses of humanitarian concepts. The application’s features, purpose, and limitations are described, and an example case study utilizing the software is offered, focusing on usage patterns of the concept localization in the humanitarian domain. Some of the challenges of improving visualization techniques for corpus-based analysis are discussed, including data interoperability and the role of visualization in data exploration.


Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities

Abstract: In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy.


Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues

Abstract: The purpose of semi-supervised video object segmentation (VOS) is to predict and generate object masks in subsequent video frames after being provided with the initial frame’s object mask. Currently, mainstream methods leverage historical frame information for enhancing the network’s performance. However, this approach faces the following issues: (1) They often overlook important shape information, leading to decreased accuracy in segmenting object-edge areas. (2) They often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggle against occlusion or fast/blurry motion. For the first problem, this paper introduces an object shape extraction module that exploits both the high-level and low-level features to obtain object shape information, by which the shape information can be used to further refine the predicted masks. For the second problem, this paper introduces a novel object-level motion prediction module, in which it stores the representative motion features during the training stage, and predicts the object motion by retrieving them during the inference stage. We evaluate our method on benchmark datasets compared with recent state-of-the-art methods, and the results demonstrate the effectiveness of the proposed method.


Teacher Agency in the Pedagogical Uses of ICT: A Holistic Perspective Emanating from Reflexive Practice

Abstract: This article discusses a case study on teacher agency about the pedagogical uses of information and communication technologies (ICTs). The investigation explored a school ecosystem that has developed a model of reflexive practice aimed at promoting teacher agency from a holistic and relational perspective. The primary case comprised six sub-cases using ICTs with students and deliberated on these uses by applying the reflexive model promoted within the school boundaries. Data were analyzed thematically. Observations of teaching practices with ICTs, reflexive practice sessions on ICT uses, and interviews with the heads of departments of the observed teachers yielded the relevance of collaborative agency in the context of the digital age as it brings together policy, theory, and practice. In line with the claims of relevant literature, the study shows that an articulated dialogue between these dimensions is relevant for using technologies in education according to the specificities of teachers’ institutional ecosystem.


AI in education risk assessment mechanism analysis

Abstract: Artificial intelligence (AI) has had a significant impact on several sectors and fields. Failure Modes and Effects Analysis (FMEA) is a powerful risk management and prevention tool that can help companies identify and address any weaknesses in their practices. The Risk Priority Number (RPN) approach has been criticized for its flaws since it calculates the product of severity, occurrence, and detection ratings of threats. By considering a variety of criteria and aspects, and by thoughtfully and carefully combining expert perspectives, the proposed FMEA model may more accurately evaluate the risks associated with AI in Education. Picture Fuzzy Sets (PFSs) and Grey Relational Analysis-TOPSIS (GRA-TOPSIS) are combined to achieve this. The goal of this research is to use an updated FMEA model to assess and rank the risk hierarchy of the seven identified hazards. According to the data, algorithmic risk is the most significant issue that requires urgent attention. Relevant constraints and suggestions are also provided to encourage research on the dangers of AI in Education. Suggestions include creating a national or international regulatory authority to control the use of artificial intelligence in education, promoting the use of the FMEA technique as a common framework for assessing the risks connected to AI in Education.


Integrating QuickBooks® in financial accounting classrooms: evidence from the UAE

Abstract: The surge in technology adoption has transformed students into ‘digital natives’ who require innovative teaching methods to enhance their engagement and learning. This paper investigates the impact of integrating QuickBooks®, an accounting mobile app, on students’ learning, engagement, and interest in the course. It utilizes a survey questionnaire and focus group design to collect data from a sample of 160 undergraduate financial accounting students at a leading public university in the UAE. The findings reveal that students positively viewed the use of QuickBooks® on their learning experience. Students reported enhanced class engagement and interest in the course, which was otherwise viewed as generally dull. Using QuickBooks® in solving assessments was perceived more effective relative to pen-and-paper assessments and during uncertain times. This paper contributes to the literature on the impact of mobile technology in accounting education and suggests innovative tools to enhance the graduate attributes and employability for educators’ consideration.


Functional quantum abstract detecting systems

Abstract: Abstract Quantum abstract detecting systems (QADS) provide a common framework to address detection problems in quantum computers. A particular QADS family, that of combinatorial QADS, has been proved to be useful for decision problems on eigenvalues or phase estimation methods. In this paper, we consider functional QADS, which not only have interesting theoretical properties (intrinsic detection ability, relation to the QFT), but also yield improved decision and phase estimation methods, as compared to combinatorial QADS. A first insight into the comparison with other phase estimation methods also shows promising results.


A multimodal deep learning architecture for smoking detection with a small data approach

Abstract: Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Our model can achieve 74% accuracy for images and 98% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data.


The Potential of Diffusion-Based Near-Infrared Image Colorization

Abstract: Camera traps, an invaluable tool for biodiversity monitoring, capture wildlife activities day and night. In low-light conditions, near-infrared (NIR) imaging is commonly employed to capture images without disturbing animals. However, the reflection properties of NIR light differ from those of visible light in terms of chrominance and luminance, creating a notable gap in human perception. Thus, the objective is to enrich near-infrared images with colors, thereby bridging this domain gap. Conventional colorization techniques are ineffective due to the difference between NIR and visible light. Moreover, regular supervised learning methods cannot be applied because paired training data are rare. Solutions to such unpaired image-to-image translation problems currently commonly involve generative adversarial networks (GANs), but recently, diffusion models gained attention for their superior performance in various tasks. In response to this, we present a novel framework utilizing diffusion models for the colorization of NIR images. This framework allows efficient implementation of various methods for colorizing NIR images. We show NIR colorization is primarily controlled by the translation of the near-infrared intensities to those of visible light. The experimental evaluation of three implementations with increasing complexity shows that even a simple implementation inspired by visible-near-infrared (VIS-NIR) fusion rivals GANs. Moreover, we show that the third implementation is capable of outperforming GANs. With our study, we introduce an intersection field joining the research areas of diffusion models, NIR colorization, and VIS-NIR fusion.


Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys

Abstract: Abstract Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.


Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

Abstract: This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.


Passive dynamical decoupling of trapped-ion qubits and qudits

Abstract: We propose a method to dynamically decouple every magnetically sensitive hyperfine sublevel of a trapped ion from magnetic field noise, simultaneously using integrated circuits to adiabatically rotate its local quantization field. These integrated circuits allow passive adjustment of the effective polarization of any external (control or noise) field. By rotating the ion's quantization direction relative to this field's polarization, we can perform ``passive'' dynamical decoupling (PDD), inverting the linear Zeeman sensitivity of every hyperfine sublevel. This dynamically decouples the entire ion, rather than just a qubit subspace. Fundamentally, PDD drives the transition ${m}_{F}\ensuremath{\rightarrow}\ensuremath{-}{m}_{F}$ for every magnetic quantum number ${m}_{F}$ in the system---with only one operation---indicating it applies to qudits with constant overhead in the dimensionality of the qudit. We show how to perform pulsed and continuous PDD, weighing each technique's insensitivity to external magnetic fields versus their sensitivity to diabaticity and control errors. Finally, we show that we can tune the sinusoidal oscillation of the quantization axis to a motional mode of the crystal in order to perform a laser-free two-qubit gate that is insensitive to magnetic field noise.


Robust gates with spin-locked superconducting qubits

Abstract: Dynamical decoupling is effective in reducing gate errors in most quantum computation platforms and is therefore projected to play an essential role in future fault-tolerant constructions. In superconducting circuits, however, it has proven difficult to utilize the benefits of dynamical decoupling. In this work, we present a theoretical proposal that incorporates a continuous version of dynamical decoupling, namely, spin locking, with a coupler-based CZ gate for transmons and provide analytical and numerical results that demonstrate its effectiveness.


Abstract: research-article Open Access Share on What Does 'Failure' Mean in Civic Tech? We Need Continued Conversations About Discontinuation Authors: Andrea Hamm Weizenbaum Institute for the Networked Society Weizenbaum Institute for the Networked SocietySearch about this author , Yuya Shibuya University of Tokyo University of TokyoSearch about this author , Teresa Cerratto Pargman Stockholm University Stockholm UniversitySearch about this author , Roy Bendor Delft University of Technology Delft University of TechnologySearch about this author , Christoph Raetzsch Aarhus University Aarhus UniversitySearch about this author , Mennatullah Hendawy Ain Shams University Ain Shams UniversitySearch about this author , Rainer Rehak Weizenbaum Institute for the Networked Society Weizenbaum Institute for the Networked SocietySearch about this author , Gwen Klerks Eindhoven University of Technology Eindhoven University of TechnologySearch about this author , Ben Schouten Eindhoven University of Technology Eindhoven University of TechnologySearch about this author , Nicolai Brodersen Hansen Aalborg University Aalborg UniversitySearch about this author Authors Info & Claims InteractionsVolume 31Issue 2March - April 2024pp 34–38https://doi.org/10.1145/3641815Published:28 February 2024Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF


Nonlinear dimensionality reduction with q-Gaussian distribution

Abstract: Abstract In recent years, the dimensionality reduction has become more important as the number of dimensions of data used in various tasks such as regression and classification has increased. As popular nonlinear dimensionality reduction methods, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) have been proposed. However, the former outputs only one low-dimensional space determined by the t-distribution and the latter is difficult to control the distribution of distance between each pair of samples in low-dimensional space. To tackle these issues, we propose novel t-SNE and UMAP extended by q-Gaussian distribution, called q-Gaussian-distributed stochastic neighbor embedding (q-SNE) and q-Gaussian-distributed uniform manifold approximation and projection (q-UMAP). The q-Gaussian distribution is a probability distribution derived by maximizing the tsallis entropy by escort distribution with mean and variance, and a generalized version of Gaussian distribution with a hyperparameter q. Since the shape of the q-Gaussian distribution can be tuned smoothly by the hyperparameter q, q-SNE and q-UMAP can in- tuitively derive different embedding spaces. To show the quality of the proposed method, we compared the visualization of the low-dimensional embedding space and the classification accuracy by k-NN in the low-dimensional space. Empirical results on MNIST, COIL-20, OliverttiFaces and FashionMNIST demonstrate that the q-SNE and q-UMAP can derive better embedding spaces than t-SNE and UMAP.


Photonic Which-Path Entangler Based on Longitudinal Cavity-Qubit Coupling

Abstract: We show that a modulated longitudinal cavity-qubit coupling can be used to control the path taken by a multiphoton coherent-state wave packet conditioned on the state of a qubit, resulting in a qubit--which-path (QWP) entangled state. QWP states can generate long-range multipartite entanglement using strategies for interfacing discrete- and continuous-variable degrees of freedom. Using the approach presented here, entanglement can be distributed in a quantum network without the need for single-photon sources or detectors.