FFT-Based Simultaneous Calculations of Very Long Signal Multi-Resolution Spectra for Ultra-Wideband Digital Radio Frequency Receiver and Other Digital Sensor Applications

Abstract: The discrete Fourier transform (DFT) is the most commonly used signal processing method in modern digital sensor design for signal study and analysis. It is often implemented in hardware, such as a field programmable gate array (FPGA), using the fast Fourier transform (FFT) algorithm. The frequency resolution (i.e., frequency bin size) is determined by the number of time samples used in the DFT, when the digital sensor's bandwidth is fixed. One can vary the sensitivity of a radio frequency receiver by changing the number of time samples used in the DFT. As the number of samples increases, the frequency bin width decreases, and the digital receiver sensitivity increases. In some applications, it is useful to compute an ensemble of FFT lengths; e.g., 2P-j for j=0, 1, 2, …, J, where j is defined as the spectrum level with frequency resolution 2j·Δf. Here Δf is the frequency resolution at j=0. However, calculating all of these spectra one by one using the conventional FFT method would be prohibitively time-consuming, even on a modern FPGA. This is especially true for large values of P; e.g., P≥20. The goal of this communication is to introduce a new method that can produce multi-resolution spectrum lines corresponding to sample lengths 2P-j for all J+1 levels, concurrently, while one long 2P-length FFT is being calculated. That is, the lower resolution spectra are generated naturally as by-products during the computation of the 2P-length FFT, so there is no need to perform additional calculations in order to obtain them.


Precise path planning and trajectory tracking based on improved A-star algorithm

Abstract: Path planning and trajectory tracking are very meaningful for the field of autonomous driving, but currently path planning still has problems such as non-optimal paths and insufficiently accurate paths. This paper addresses the issue of path planning by proposing a improved A-star algorithm and locally zooming on the map technique to achieve precise path planning. Compared with the conventional method, this method reduces the time by 23% and the path length by 21% in the scenarios shown in the paper, respectively, and provides a reference for related research. Moreover, trajectory tracking was achieved using the improved LQR control. Compared with the conventional method, the improved LQR control algorithm reduces the average error by 80% in the scenario shown in the paper. Firstly, the A-star algorithm is enhanced by incorporating an unknown path cost estimation function, thereby improving the effect of its path planning in complex environments. Additionally, the method of locally zooming on the map is incorporated, effectively enhancing the accuracy and safety of path planning. Building upon the path planning, further improvements are made to the LQR control algorithm, enabling autonomous deceleration in complex sections, which facilitates better trajectory tracking and enhances the motion control performance of the robot during practical operations.


Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Abstract: Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.


Physics-Constrained Hardware-Efficient Ansatz on Quantum Computers That Is Universal, Systematically Improvable, and Size-Consistent

Abstract: Variational wave function ansätze are at the heart of solving quantum many-body problems in physics and chemistry. Previous designs of hardware-efficient ansatz (HEA) on quantum computers are largely based on heuristics and lack rigorous theoretical foundations. In this work, we introduce a physics-constrained approach for designing HEA with rigorous theoretical guarantees by imposing a few fundamental constraints. Specifically, we require that the target HEA to be universal, systematically improvable, and size-consistent, which is an important concept in quantum many-body theories for scalability but has been overlooked in previous designs of HEA. We extend the notion of size-consistency to HEA and present a concrete realization of HEA that satisfies all these fundamental constraints while only requiring linear qubit connectivity. The developed physics-constrained HEA is superior to other heuristically designed HEA in terms of both accuracy and scalability, as demonstrated numerically for the Heisenberg model and some typical molecules. In particular, we find that restoring size-consistency can significantly reduce the number of layers needed to reach a certain accuracy. In contrast, the failure of other HEA to satisfy these constraints severely limits their scalability to larger systems with more than 10 qubits. Our work highlights the importance of incorporating physical constraints into the design of HEA for efficiently solving many-body problems on quantum computers.


Avoiding embarrassment online: Response to and inferences about chatbots when purchases activate self‐presentation concerns

Abstract: Abstract We explore how self‐presentation concerns and the desire to avoid embarrassment impact two distinct types of interactions consumers have with chatbots: interactions when a chatbot's identity is (1) not disclosed and therefore ambiguous or (2) disclosed. We propose that consumers feel less embarrassed with a chatbot than a human service agent in purchase contexts where self‐presentation concerns are active because consumers ascribe less mind to chatbots. Therefore, when a chat agent's identity is ambiguous, consumers with greater self‐presentation concerns are more likely to infer that an agent is human because this judgment allows consumers to proactively protect themselves from potential embarrassment in the event they are interacting with a human. We further show that when agent identity is clearly disclosed, consumers respond more positively to chatbots than human agents. However, this effect is contingent on the extent to which the chatbot is imbued with human characteristics: Anthropomorphizing chatbots leads consumers with higher self‐presentation concerns to ascribe more mind to even clearly identified chatbots, resulting in a more negative consumer response.


Adoption of distributed ledger technology for construction projects: a study of the challenges in a developing country

Abstract: Purpose The construction industry in Nigeria, like many others worldwide, grapples with increasing demands for enhanced efficiency, transparency and security. While distributed ledger technology (DLT) offers substantial potential in tackling these issues, its implementation faces numerous obstacles. This study aims to investigate the challenges hindering the adoption of DLT within the Nigerian construction sector, capitalizing on a recognized research gap in this field. Design/methodology/approach A comprehensive literature review identified 20 challenges to DLT adoption. A structured questionnaire was developed and distributed to 98 construction professionals selected from a pool of 5,330 members using purposive and snowball sampling. Background information was assessed using frequency and percentage, data normality with the Shapiro–Wilk test and nonparametric data compared using the Kruskal–Wallis H -test. Factor analysis was then used to reduce the challenges into a more coherent set. Findings Findings revealed the top five challenges: power supply issues, data privacy and compliance, an uncertain legal framework, insufficient training and education and a lack of skilled workforce. Further analysis revealed four clusters of challenges, namely, technical and infrastructure barriers, organizational and cultural challenges, financial and economic challenges and regulatory and legal challenges. Practical implications Through the evaluation of challenges and potential strategies to overcome them, this study aims to lay a foundation that assists construction entities in effectively harnessing the benefits of DLT while navigating the obstacles inherent in its adoption. Originality/value This study contributes to the existing body of knowledge by offering insights into the challenges surrounding the adoption of DLT, specifically within the Nigerian construction sector. By juxtaposing the findings from this study with existing research conducted in other countries such as India, South Africa and the USA, a more comprehensive understanding of the challenges unique to the Nigerian context can be gained.


Is deep learning a useful tool for the pure mathematician?

Abstract: A personal and informal account of what a pure mathematician might expect when using tools from deep learning in their research.


Investigation on storage level data integrity strategies in cloud computing: classification, security obstructions, challenges and vulnerability

Abstract: Abstract Cloud computing provides outsourcing of computing services at a lower cost, making it a popular choice for many businesses. In recent years, cloud data storage has gained significant success, thanks to its advantages in maintenance, performance, support, cost, and reliability compared to traditional storage methods. However, despite the benefits of disaster recovery, scalability, and resource backup, some organizations still prefer traditional data storage over cloud storage due to concerns about data correctness and security. Data integrity is a critical issue in cloud computing, as data owners need to rely on third-party cloud storage providers to handle their data. To address this, researchers have been developing new algorithms for data integrity strategies in cloud storage to enhance security and ensure the accuracy of outsourced data. This article aims to highlight the security issues and possible attacks on cloud storage, as well as discussing the phases, characteristics, and classification of data integrity strategies. A comparative analysis of these strategies in the context of cloud storage is also presented. Furthermore, the overhead parameters of auditing system models in cloud computing are examined, considering the desired design goals. By understanding and addressing these factors, organizations can make informed decisions about their cloud storage solutions, taking into account both security and performance considerations.


Joint streaming model for backchannel prediction and automatic speech recognition

Abstract: Abstract In human conversations, listeners often utilize brief backchannels such as “uh‐huh” or “yeah.” Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human–machine conversation systems, users can engage in natural conversations when a conversational agent generates backchannels like a human listener. We propose a method that simultaneously predicts backchannels and recognizes speech in real time. We use a streaming transformer and adopt multitask learning for concurrent backchannel prediction and speech recognition. The experimental results demonstrate the superior performance of our method compared with previous works while maintaining a similar single‐task speech recognition performance. Owing to the extremely imbalanced training data distribution, the single‐task backchannel prediction model fails to predict any of the backchannel categories, and the proposed multitask approach substantially enhances the backchannel prediction performance. Notably, in the streaming prediction scenario, the performance of backchannel prediction improves by up to 18.7% compared with existing methods.


Framework for evaluating code generation ability of large language models

Abstract: Abstract Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, , which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the metric.


Named entity recognition using transfer learning and small human‐ and meta‐pseudo‐labeled datasets

Abstract: Abstract We introduce a high‐performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta‐pseudo‐label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human‐ and pseudo‐labeled data. Second, the influence of noisy pseudo‐labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human‐labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.


Real-time sign language recognition based on YOLO algorithm

Abstract: Abstract This study focuses on real-time hand gesture recognition in the Turkish sign language detection system. YOLOv4-CSP based on convolutional neural network (CNN), a state-of-the-art object detection algorithm, is used to provide real-time and high-performance detection. The YOLOv4-CSP algorithm is created by adding CSPNet to the neck of the original YOLOv4 to improve network performance. A new object detection model has been proposed by optimizing the YOLOv4-CSP algorithm in order to provide more efficient detection in Turkish sign language. The model uses CSPNet throughout the network to increase the learning ability of the network. However, Proposed YOLOv4-CSP has a learning model with Mish activation function, complete intersection of union (CIoU) loss function and transformer block added. The Proposed YOLOv4-CSP algorithm has faster learning with transfer learning than previous versions. This allows the proposed YOLOv4-CSP algorithm to perform a faster restriction and recognition of static hand signals simultaneously. To evaluate the speed and detection performance of the proposed YOLOv4-CSP model, it is compared with previous YOLO series, which offers real-time detection, as well. YOLOv3, YOLOv3-SPP, YOLOv4-CSP and proposed YOLOv4-CSP models are trained with a labeled dataset consisting of numbers in Turkish Sign language, and their performances on the hand signals recognitions are compared. With the proposed method, 98.95% precision, 98.15% recall, 98.55 F1 score and 99.49% mAP results are obtained in 9.8 ms. The proposed method for detecting numbers in Turkish sign language outperforms other algorithms with both real-time performance and accurate hand sign prediction, regardless of background.


Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study

Abstract: This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors.The dataset includes 1,009 cases and 1,009 controls, with comprehensive data on lifestyle, health-behavior, reproductive and sociodemographic factors. Different machine learning models, namely Random Forest (RF), Neural Networks (NN), Bootstrap Aggregating Classification and Regression Trees (Bagged CART), and Extreme Gradient Boosting Tree (XGBoost), were employed to analyze the data.The findings highlight the significance of a chest X-ray history, deliberate weight loss, abortion history, and post-menopausal status as predictors. Factors such as second-hand smoking, lower education, menarche age (>14), occupation (employed), first delivery age (18-23), and breastfeeding duration (>42 months) were also identified as important predictors in multiple models. The RF model exhibited the highest Area Under the Curve (AUC) value of 0.9, as indicated by the Receiver Operating Characteristic (ROC) curve. Following closely was the Bagged CART model with an AUC of 0.89, while the XGBoost model achieved a slightly lower AUC of 0.78. In contrast, the NN model demonstrated the lowest AUC of 0.74. On the other hand, the RF model achieved an accuracy of 83.9% and a Kappa coefficient of 67.8% and the XGBoost, achieved a lower accuracy of 82.5% and a lower Kappa coefficient of 0.6.This study could be beneficial for targeted preventive measures according to the main risk factors for BC among high-risk women.


Study protocol for factors influencing the adoption of ChatGPT technology by startups: Perceptions and attitudes of entrepreneurs

Abstract: Generative Artificial Intelligence (AI) technology, for instance Chat Generative Pre-trained Transformer (ChatGPT), is continuously evolving, and its userbase is growing. These technologies are now being experimented by the businesses to leverage their potential and minimise their risks in business operations. The continuous adoption of the emerging Generative AI technologies will help startups gain more and more experience with adoptions, helping them to leverage continuously evolving technological innovation landscape. However, there is a dearth of prior research on ChatGPT adoption in the startup context, especially from Entrepreneur perspective, highlights the urgent need for a thorough investigation to identify the variables influencing this technological adoption. The primary objective of this study is to ascertain the factors that impact the uptake of ChatGPT technology by startups, anticipate their influence on the triumph of companies, and offer pragmatic suggestions for various stakeholders, including entrepreneurs, and policymakers.This study attempts to explore the variables impacting startups' adoption of ChatGPT technology, with an emphasis on comprehending entrepreneurs' attitudes and perspectives. To identify and then empirically validate the Generative AI technology adoption framework, the study uses a two-stage methodology that includes experience-based research, and survey research. The research method design is descriptive and Correlational design. Stage one of the research study is descriptive and involves adding practical insights, and real-world context to the model by drawing from the professional consulting experiences of the researchers with the SMEs. The outcome of this stage is the adoption model (also called as research framework), building Upon Technology Adoption Model (TAM), that highlight the technology adoption factors (also called as latent variables) connected with subset of each other and finally to the technology adoption factor (or otherwise). Further, the latent variables and their relationships with other latent variables as graphically highlighted by the adoption model will be translated into the structured questionnaire. Stage two involves survey based research. In this stage, structured questionnaire is tested with small group of entrepreneurs (who has provided informed consent) and finally to be distributed among startup founders to further validate the relationships between these factors and the level of influence individual factors have on overall technology adoption. Partial Least Squares Structural Equation Modeling (PLS-SEM) will be used to analyze the gathered data. This multifaceted approach allows for a comprehensive analysis of the adoption process, with an emphasis on understanding, describing, and correlating the key elements at play.This is the first study to investigate the factors that impact the adoption of Generative AI, for instance ChatGPT technology by startups from the Entrepreneurs perspectives. The study's findings will give Entrepreneurs, Policymakers, technology providers, researchers, and Institutions offering support for entrepreneurs like Academia, Incubators and Accelerators, University libraries, public libraries, chambers of commerce, and foreign embassies important new information that will help them better understand the factors that encourage and hinder ChatGPT adoption. This will allow them to make well-informed strategic decisions about how to apply and use this technology in startup settings thereby improving their services for businesses.


Blockchain and public service delivery: a lifetime cross-referenced model for e-government

Abstract: The article presents the results of field studies, analysing the perspectives of blockchain developers on decentralised service delivery and elaborating on unique algorithms for lifetime ledgers to reliably and safely record e-government transactions in an intrinsically cross-referenced manner. New interesting technological niches of service delivery and emerging models of related data management in the industry were proposed and further elaborated such as the generation of unique lifetime personal data profiles, blockchain-driven cross-referencing of e-government metadata, parallel maintenance of serviceable ledgers for data identifiers and phenomena of blockchain 'black holes' to ensure reliable protection of important public, corporate and civic information.


A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack

Abstract: Abstract In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack instances (MKP), presenting a novel deep reinforcement learning (DRL) framework. In this DRL framework, we train different agents compatible with a discrete action space for sequential decision-making while still satisfying any resource constraint of the MKP. This novel framework incorporates the decision variable values in the 2D DRL where the agent is responsible for assigning a value of 1 or 0 to each of the variables. To the best of our knowledge, this is the first DRL model of its kind in which a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our framework is configured to solve MKP instances of different dimensions and distributions. We propose a K-means approach to obtain an initial feasible solution that is used to train the DRL agent. We train four different agents in our framework and present the results comparing each of them with the CPLEX commercial solver. The results show that our agents can learn and generalize over instances with different sizes and distributions. Our DRL framework shows that it can solve medium-sized instances at least 45 times faster in CPU solution time and at least 10 times faster for large instances, with a maximum solution gap of 0.28% compared to the performance of CPLEX. Furthermore, at least 95% of the items are predicted in line with the CPLEX solution. Computations with DRL also provide a better optimality gap with respect to state-of-the-art approaches.


LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction

Abstract: Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.


Integrating foreground-background feature distillation and contrastive feature learning for ultra-fine-grained visual classification

Abstract: In pattern recognition, ultra-fine-grained visual classification (ultra-FGVC) has emerged as a paramount challenge, focusing on sub-category distinction within fine-grained objects. The near-indistinguishable similarities among such objects, combined with the dearth of sample data, intensify this challenge. In response, our FDCL-DA method is introduced, which integrates Foreground-background feature Distillation (FD) and Contrastive feature Learning (CL) with Dual Augmentation (DA). This method uses two different data augmentation techniques, standard and auxiliary augmentation, to enhance model performance and generalization ability. The FD module reduces superfluous features and augments the contrast between the principal entity and its backdrop, while the CL focuses on creating unique data imprints by reducing intra-class resemblances and enhancing inter-class disparities. Integrating this method with different architectures, such as ResNet-50, Vision Transformer, and Swin-Transformer (Swin-T), significantly improves these backbone networks, especially when used with Swin-T, leading to promising results on eight popular datasets for ultra-FGVC tasks.1


When I use a word . . . Medical blue plaques in London

Abstract: In 1863 the MP William Ewart suggested that “it might be practicable... to have inscribed on those houses in London which have been inhabited by celebrated persons, the names of such persons.” Accordingly, in 1867 the first such inscriptions, which came to be known as blue plaques, were put up by the Society of Arts, commemorating Lord Byron and Napoleon III at places in London where they had lived. The society put up 35 such plaques over the next 35 years when the scheme was taken over by the London County Council, which gave way to the Greater London Council in 1965 and finally English Heritage, in 1986. Among London’s 1000 or so blue plaques several medical men and women, doctors, nurses, and other healthcare workers, are commemorated. They include Cecil Belfield-Clarke, Hannah Billig, Richard Bright, Edith Louisa Cavell, Henry Hallett Dale, Charles Darwin, Henry Havelock Ellis, Elizabeth Garrett Anderson, Thomas Hodgkin, William Hunter and his brother John, Joseph Lister, James Mackenzie, Rachel McMillan and her sister Margaret, William Marsden, Florence Nightingale, Ronald Ross, Mary Seacole, Hans Sloane, and George Frederick Still.


Dynamic S-Box Construction Using Mordell Elliptic Curves over Galois Field and Its Applications in Image Encryption

Abstract: Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.


Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer

Abstract: Abstract In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification.


Link prediction using low-dimensional node embeddings: The measurement problem

Abstract: Graph representation learning is a fundamental technique for machine learning (ML) on complex networks. Given an input network, these methods represent the vertices by low-dimensional real-valued vectors. These vectors can be used for a multitude of downstream ML tasks. We study one of the most important such task, link prediction. Much of the recent literature on graph representation learning has shown remarkable success in link prediction. On closer investigation, we observe that the performance is measured by the AUC (area under the curve), which suffers biases. Since the ground truth in link prediction is sparse, we design a vertex-centric measure of performance, called the VCMPR@k plots. Under this measure, we show that link predictors using graph representations show poor scores. Despite having extremely high AUC scores, the predictors miss much of the ground truth. We identify a mathematical connection between this performance, the sparsity of the ground truth, and the low-dimensional geometry of the node embeddings. Under a formal theoretical framework, we prove that low-dimensional vectors cannot capture sparse ground truth using dot product similarities (the standard practice in the literature). Our results call into question existing results on link prediction and pose a significant scientific challenge for graph representation learning. The VCMPR plots identify specific scientific challenges for link prediction using low-dimensional node embeddings.


Multiple objectives dynamic VM placement for application service availability in cloud networks

Abstract: Abstract Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement problem in order to optimize application availability in cloud computing paradigm. The problem is modeled as an integer nonlinear programming (INLP) optimization with multiple objectives and constraints. The framework comprises three major modules that use optimization methods and algorithms to determine the most effective VM placement strategy in cases of application deployment, failure, and scaling. Our primary goals are to minimize power consumption, resource waste, and server failures while also ensuring that application availability requirements are met. We compare our proposed heuristic VM placement solution with three related algorithms from the literature and find that it outperforms them in several key areas. Our solution is able to admit more applications, reduce power consumption, and increase CPU and RAM utilization of the servers. Moreover, we use a deep learning method that has high accuracy and low error loss to predict application task failures, allowing for proactive protection actions to reduce service outage. Overall, our framework provides a comprehensive solution by optimizing dynamic VM placement. Therefore, the framework can improve the quality of cloud computing services and enhance the experience for users.


A Noval Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Abstract: This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.


Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment

Abstract: Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.


Report for ISSMGE TC309/TC304/TC222 fourth ML dialogue on “Machine learning supremacy projects”

Abstract: The ISSMGE TC309/TC304/TC222 and ASCE Geo-Institute Risk Assessment Management Committee Fourth Machine Learning (ML) in Geotechnics Dialogue was held at the Okayama Convention Center on 5 December 2023. The dialogue focused on “machine learning supremacy projects”, which involve the use of novel ML techniques to analyse emerging data, potentially bringing disruptive value to transform research and practice. The discussions centred around four real-world projects covering tunnelling, pile foundations, and geophysical investigation, to showcase the latest developments and implementations of ML, exchange views on the associated technical and non-technical challenges, and explore further developments to transform practice on a broader scale. These projects highlight the need for efficient ML techniques to analyse large volumes of real-time data for 3D heterogeneous soil/rock domains, and the benefits of standardising data formats among practitioners and researchers. Meanwhile, as data become valuable assets, there is a need to establish guidelines or protocols to protect data privacy and ensure data trustworthiness while creating incentives for owners to share data. Several recommendations include (1) creating benchmark examples using real data and Class A prediction (i.e. prediction made before the event) exercises from real projects to accelerate research in ML methods and (2) involving regulators in future dialogue and exploring the incorporation of data-driven approaches in design codes to complement traditional methods.


Which pedagogical approaches are more effective in mobile learning? A meta‐analysis and research synthesis

Abstract: Abstract Background Study Several meta‐analysis studies have investigated the effects of mobile learning on learning performance. However, limited attention has been paid to pedagogy in mobile learning, making quantitative evidence of the effects of pedagogical approaches on learning performance in mobile learning scarce. Filling this gap can therefore help stakeholders understand which mobile pedagogical approaches might work or not under which learning conditions, hence achieving better learning experience and performance. Objectives To address this gap, this study conducted a meta‐analysis and research synthesis of the effects of integrated pedagogical approaches on students' learning performance in mobile learning. Additionally, this study analysed the field of education, level of education, learning setting, sample size, and mobile device as moderating variables of the effect of pedagogical approaches. Methods The software Comprehensive Meta‐Analysis V.3 was used for this meta‐analysis, where Hedges' g was calculated for the effect sizes. Specifically, 70 quantitative studies ( N = 5575 participants) were coded and analysed. Results The results indicate that pedagogical approaches in mobile learning have a large effect on students' learning performance ( g = 0.93, p < 0.001). The most effective pedagogical approach was project‐based learning (huge effect), while collaborative learning, situated learning and game‐based learning had large effects. Finally, cognitive theory of multimedia learning and inquiry‐based learning had medium effects. The results also indicate that the effect is moderated by the field of education, the level of education, the learning setting, the sample size, and the mobile device. Finally, it is found that study quality might influence the overall effect size. Conclusions The findings of this study can be beneficial to both researchers and practitioners as they highlight and discuss which pedagogical approaches could be more effective in mobile learning under specific conditions. Further research, on the other hand, should focus on covering additional moderators of learning performance like mobile device type and screen size which is one of the limitations of this study.


Blockchain Empowered Solar Energy Trading: A Decentralized Grid Integration Revolution

Abstract: The growing prominence of solar energy in decentralized renewable energy landscapes underscores the need for efficient solar energy trading mechanisms and seamless grid integration. This decentralized renewable energy sources, especially solar energy, present a significant prospect for the production of sustainable energy. This investigation explores the use of blockchain technology as a basic framework to tackle the problems associated with grid integration and solar energy trading in a decentralized setting. It uses the built-in advantages of blockchain technology, such as trust, transparency, and smart contract automation, to address the design and execution of a platform tailored for solar energy. As a result, the system efficiency increased to 93% and the transaction confirmation time was cut from 4.5 to 2.5 s with the lowest possible transaction cost. Furthermore, this study intends to offer insightful information about the technological viability, security, and scalability of blockchain solutions in the field of solar energy, while highlighting the significance of indicators of performance for an in-depth evaluation. This novel strategy is a crucial development for the field of green energy as it promises to revolutionize the way solar energy is traded, used, and integrated into modern grids.


Semantic and Geometric-Aware Day-to-Night Image Translation Network

Abstract: Autonomous driving systems heavily depend on perception tasks for optimal performance. However, the prevailing datasets are primarily focused on scenarios with clear visibility (i.e., sunny and daytime). This concentration poses challenges in training deep-learning-based perception models for environments with adverse conditions (e.g., rainy and nighttime). In this paper, we propose an unsupervised network designed for the translation of images from day-to-night to solve the ill-posed problem of learning the mapping between domains with unpaired data. The proposed method involves extracting both semantic and geometric information from input images in the form of attention maps. We assume that the multi-task network can extract semantic and geometric information during the estimation of semantic segmentation and depth maps, respectively. The image-to-image translation network integrates the two distinct types of extracted information, employing them as spatial attention maps. We compare our method with related works both qualitatively and quantitatively. The proposed method shows both qualitative and qualitative improvements in visual presentation over related work.


The efficacy of augmented reality exposure therapy in the treatment of spider phobia—a randomized controlled trial

Abstract: The evidence for the use of Augmented Reality (AR) in treating specific phobias has been growing. However, issues of accessibility persist, especially in developing countries. The current study examined a novel, but relatively simple therapist guided smartphone-based AR Exposure Treatment (ARET) of spider phobia. Participants who reported symptoms of Arachnophobia were randomized into one of three comparison groups: ARET (n = 20), traditional in vivo exposure therapy (IVET; n = 18) and a waitlist control group (n = 17). Behavioral approach, subjective symptom measures, and galvanic skin response were assessed pre- and post-treatment. The study was concluded with a one-month follow up assessment. Results indicated that both treatment groups showed statistically significant and clinically meaningful improvements in behavioral approach at post-test that were maintained at 1 month follow- up, compared to the wait-listed group. Moreover, the treatment groups demonstrated significant improvements in subjective symptom report at 1-month follow up. Given its utility and potential accessibility, our findings suggest that future AR evaluation research could be conducted in therapy settings with minimal resources.


A Path Planning System for Orchard Mower Based on Improved A* Algorithm

Abstract: The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower’s traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments.


Investigating translanguaging strategies and online self-presentation through internet slang on Douyin (Chinese TikTok)

Abstract: Abstract This study explores the translanguaging strategies used in internet slang on Douyin, a popular social media platform in China. By analyzing 331 examples (encompassing 313 translanguaging strategies), we investigate how individuals utilize various translanguaging resources, including multimodal, multilingual, multisemiotic, and multisensory resources, to manage their online self-presentation. Our findings suggest that multilingual resources, such as new Chinglish, Pinyin initialism, and hybrid words (combining Chinese, English, and Arabic numerals), were the most frequently used strategies. Additionally, we found that multisensory resources, including homophonic puns and modal particles, were also commonly utilized. In contrast, multimodal resources, such as emojis, and multisemiotic resources, such as keyboard-generated emoticon, hashtag, and punctuation mark, were less frequently employed. These findings reveal that Chinese internet users display a high degree of creativity and adaptability in their online communication, drawing on a wide range of linguistic and semiotic resources to express their identities and project their desired image. Overall, this study highlights the importance of translanguaging in online communication and its role in shaping self-presentation in the digital age.


Ensuring Consistency between Noise and Detection in Hierarchical Bayesian Inference

Abstract: Abstract Hierarchical Bayesian inference can simultaneously account for both measurement uncertainty and selection effects within astronomical catalogs. In particular, the imposed hierarchy encodes beliefs about the interdependence of the physical processes that generate the observed data. We show that several proposed approximations within the literature actually correspond to inferences that are incompatible with any physical detection process. This generically leads to biases and is associated with the assumption that detectability is independent of the observed data given the true source parameters. We show several examples of how this error can affect astrophysical inferences based on catalogs of coalescing binaries observed through gravitational waves, including misestimating the redshift evolution of the merger rate as well as incorrectly inferring that general relativity is the correct theory of gravity when it is not. In general, one cannot directly fit for the “detected distribution” and “divide out” the selection effects in post-processing. Similarly, when comparing theoretical predictions to observations, it is better to simulate detected data (including both measurement noise and selection effects) rather than comparing estimates of the detected distributions of event parameters (which include only selection effects). While the biases introduced by model misspecification from incorrect assumptions may be smaller than statistical uncertainty for moderate catalog sizes ( O (100) events), they will nevertheless pose a significant barrier to precision measurements of astrophysical populations.


Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

Abstract: Abstract This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.


A Comprehensive Examination of Mobile Augmented Reality in Tourism (MART) Adoption

Abstract: This research investigates the adoption of mobile augmented reality in tourism (MART) utilizing the unified theory of acceptance and use of technology two (UTAUT2) framework. Centered on Pondicherry, India, the research specifically investigates the integration of AI-powered services through the MART platform in the tourism sector. Analyzing data from 137 participants who engaged with MART for service bookings, the study employs descriptive and inferential statistics. It scrutinizes the influence of perceived effectiveness, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit on users' behavioral intention. The findings unveil significant positive impacts, underscoring the pivotal role these factors play in shaping users' intentions to embrace MART. This research contributes valuable insights to the comprehension of technology adoption within the tourism domain, offering practical implications for designers and policymakers aiming to enhance user experiences and foster widespread MART adoption.


Learning likelihood ratios with neural network classifiers

Abstract: A bstract The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of data- or simulation-driven models for which computing the likelihood ratio can be very difficult or even impossible. By applying the so-called “likelihood ratio trick,” approximations of the likelihood ratio may be computed using clever parametrizations of neural network-based classifiers. A number of different neural network setups can be defined to satisfy this procedure, each with varying performance in approximating the likelihood ratio when using finite training data. We present a series of empirical studies detailing the performance of several common loss functionals and parametrizations of the classifier output in approximating the likelihood ratio of two univariate and multivariate Gaussian distributions as well as simulated high-energy particle physics datasets.


A hybrid quantum algorithm to detect conical intersections

Abstract: Conical intersections are topologically protected crossings between the potential energy surfaces of a molecular Hamiltonian, known to play an important role in chemical processes such as photoisomerization and non-radiative relaxation. They are characterized by a non-zero Berry phase, which is a topological invariant defined on a closed path in atomic coordinate space, taking the value <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&#x03C0;</mml:mi></mml:math> when the path encircles the intersection manifold. In this work, we show that for real molecular Hamiltonians, the Berry phase can be obtained by tracing a local optimum of a variational ansatz along the chosen path and estimating the overlap between the initial and final state with a control-free Hadamard test. Moreover, by discretizing the path into <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> points, we can use <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> single Newton-Raphson steps to update our state non-variationally. Finally, since the Berry phase can only take two discrete values (0 or <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&#x03C0;</mml:mi></mml:math>), our procedure succeeds even for a cumulative error bounded by a constant; this allows us to bound the total sampling cost and to readily verify the success of the procedure. We demonstrate numerically the application of our algorithm on small toy models of the formaldimine molecule (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:msub><mml:mi>H</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mi>N</mml:mi><mml:mi>H</mml:mi></mml:mrow></mml:math>).


Self-Supervised High Dynamic Range Imaging: What Can Be Learned from a Single 8-bit Video?

Abstract: Recently, Deep Learning-based methods for inverse tone mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. To be effective, deep learning-based methods need to learn from large datasets and transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR 8-bit video? With the presented self-supervised approach, we show that, in many cases, a single SDR video is sufficient to generate an HDR video of the same quality or better than other state-of-the-art methods.


Chasing spammers: Using the Internet protocol address for detection

Abstract: Abstract The proliferation of reviews evaluating different services on social networks and online platforms and their importance in consumer decision‐making has led some unscrupulous individuals to take advantage of the anonymity offered by the Internet to manipulate these reviews and influence customers' decisions. The main objectives of this study are: (1) to test whether spammers usually perform their misdemeanors from the same IP address; (2) to explore whether there are differences between stated sexes in this regard; (3) to detect the main motivations for posting fraudulent reviews; and (4) to determine the motivations for doing so from the same IP address. These objectives were achieved by means of a quasi‐experiment with a sample of 7,192,487 users, and a qualitative investigation in which 37 users who had falsified information were interviewed. The results show that spammers who tend to fake their identity do so from the same IP address and that they tend to be male. Four types of motivation are presented: revenge, entertainment, opportunity for profit, and self‐esteem; as well as a further three to explain the use of the same IP: convenience, limited resources, and complacency.


The mediating role of employee performance in the relationship between human resource management (HRM) practices and police service effectiveness

Abstract: Purpose The efficiency of each of an organization’s individual workers determines its effectiveness. The study aims to explore the relationship between human resource management (HRM) practices and organizational effectiveness with employee performance as a mediating variable. Design/methodology/approach Data were collected from 800 police officers in the Greater Accra and Tema regions. The data were supported by the hypothesized relationship. Construct reliability and validity was established through confirmatory factor analysis. The proposed model and hypotheses were evaluated using structural equation modeling. Findings The results show that career planning and employee performance were significantly related. Self-managed teams and employee performance were shown to be nonsignificantly related. Similarly, performance management and employee performance were shown to be nonsignificantly related. Employee performance significantly influenced organizational effectiveness. The results further indicate that employee performance mediates the relationship between HRM practices and organizational effectiveness. Research limitations/implications The generalizability of the findings will be constrained due to the research’s police service focus and cross-sectional data. Practical implications The study’s findings will serve as valuable pointers for the police administration in the adoption, design and implementation of well-articulated and proactive HRM practices to improve the abilities, skills, knowledge and motivation of officer’s to inordinately enhance the effectiveness of the service. Originality/value By evidencing empirically that employee performance mediates the relationship between HRM practice and organizational effectiveness, the study extends the literature.


"How fancy you are to make us use your fancy tool": Coordinating Individuals' Tool Preference over Group Boundaries

Abstract: When a group makes a decision, it necessitates the understanding and amalgamation of information from different group members. This process becomes particularly intricate in cross-boundary teams, which consist of individuals from diverse organizational backgrounds, each bringing in unique informational tools and representation modalities. People share information generated from their personal tools, and the variance in representation of such information makes it challenging to form cohesive group decisions. We conducted workshop studies with 11 knowledge workers to understand current practices of tool adaptation and negotiation in such teams. The results indicate a reluctance to adopt new tools due to perceived violations of social acceptance, often leading to negative judgments of those suggesting new tools. Consequently, participants in cross-boundary teams gravitated towards their preferred tools, complicating the aggregation of inputs and impeding cohesive decision-making. To address these challenges, we developed a platform facilitating sensemaking and decision-making without necessitating compromises on tool preferences. In our mixed-method within-subject experiments, this approach enabled faster, more informed decision-making with reduced mental load and increased engagement through enhanced social interaction and acknowledgment of diverse contributions.


Large language models for science and medicine

Abstract: Abstract Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task‐specific LLMs have demonstrated potential across diverse applications. Science and medicine have many data types that are highly suitable for LLMs, such as scientific texts (publications, patents and textbooks), electronic medical records, large databases of DNA and protein sequences and chemical compounds. Carefully validated systems that can understand and reason across all these modalities may maximize benefits. Despite the inevitable limitations and caveats of any new technology and some uncertainties specific to LLMs, LLMs have the potential to be transformative in science and medicine.


Short-Term Photovoltaic Power Prediction Based on Extreme Learning Machine with Improved Dung Beetle Optimization Algorithm

Abstract: Given the inherent volatility and intermittency of photovoltaic power generation, enhancing the precision of photovoltaic power predictions becomes imperative to ensure the stability of power systems and to elevate power quality. This article introduces an intelligent photovoltaic power prediction model based on the Extreme Learning Machine (ELM) with the Adaptive Spiral Dung Beetle Optimization (ASDBO) algorithm. The model aims to accurately predict photovoltaic power generation under multi-factor correlation conditions, including environmental temperature and solar irradiance. The computational efficiency in high-dimensional data feature conditions is enhanced by using the Pearson correlation analysis to determine the state input of the ELM. To address local optimization challenges in traditional Dung Beetle Optimization (DBO) algorithms, a spiral search strategy is implemented during the dung beetle reproduction and foraging stages, expanding the exploration capabilities. Additionally, during the dung beetle theft stage, dynamic adaptive weights update the optimal food competition position, and the levy flight strategy ensures search randomness. By balancing convergence accuracy and search diversity, the proposed algorithm achieves global optimization. Furthermore, eight benchmark functions are chosen for performance testing to validate the effectiveness of the ASDBO algorithm. By optimizing the input weights and implicit thresholds of the ELM through the ASDBO algorithm, a prediction model is established. Short-term prediction experiments for photovoltaic power generation are conducted under different weather conditions. The selected experimental results demonstrate an average prediction accuracy exceeding 93%, highlighting the effectiveness and superiority of the proposed methodology for photovoltaic power prediction.


Edge intelligence-assisted animation design with large models: a survey

Abstract: Abstract The integration of edge intelligence (EI) in animation design, particularly when dealing with large models, represents a significant advancement in the field of computer graphics and animation. This survey aims to provide a comprehensive overview of the current state and future prospects of EI-assisted animation design, focusing on the challenges and opportunities presented by large model implementations. Edge intelligence, characterized by its decentralized processing and real-time data analysis capabilities, offers a transformative approach to handling the computational and data-intensive demands of modern animation. This paper explores various aspects of EI in animation and then delves into the specifics of large models in animation, examining their evolution, current trends, and the inherent challenges in their implementation. Finally, the paper addresses the challenges and solutions in integrating EI with large models in animation, proposing future research directions. This survey serves as a valuable resource for researchers, animators, and technologists, offering insights into the potential of EI in revolutionizing animation design and opening new avenues for creative and efficient animation production.


Direct adaptive fractional-order non-singular terminal sliding mode control strategy using extreme learning machine for position control of 5-DOF upper-limb exoskeleton robot systems

Abstract: This paper presents an innovative adaptive fractional-order (FO) terminal sliding mode (TSM) controller designed for a 5-degree-of-freedom (5-DOF) upper-limb exoskeleton robot. The primary focus is on addressing the challenges posed by nonlinearities and uncertainties in parameter values. The proposed approach integrates several key elements, including a novel fractional-order non-singular terminal sliding mode (FONSTSM) surface, an exponential reaching law, and the utilization of the extreme learning machine (ELM). This comprehensive strategy guarantees not only finite-time convergence but also robust stability, effectively alleviating the well-known chattering phenomenon. Furthermore, it successfully overcomes the singularity issues typically observed in conventional TSM controllers. The incorporation of the ELM with rectified linear unit (ReLU) activation function enhances robustness by facilitating the estimation of parameters related to the exponential control law. Numerical simulations provide compelling evidence of improved tracking, increased robustness against uncertainties, achievement of finite-time convergence, and notable reductions in control signal oscillations and singularity problems.


Compact Data Learning for Machine Learning Classification

Abstract: This paper targets the area of optimizing machine learning (ML) training data by constructing compact data. The methods of optimizing ML training have improved and become a part of artificial intelligence (AI) system development. Compact data learning (CDL) is an alternative practical framework to optimize a classification system by reducing the size of the training dataset. CDL originated from compact data design, which provides the best assets without handling complex big data. CDL is a dedicated framework for improving the speed of the machine learning training phase without affecting the accuracy of the system. The performance of an ML-based arrhythmia detection system and its variants with CDL maintained the same statistical accuracy. ML training with CDL could be maximized by applying an 85% reduced input dataset, which indicated that a trained ML system could have the same statistical accuracy by only using 15% of the original training dataset.


Single image super-resolution with denoising diffusion GANS

Abstract: Abstract Single image super-resolution (SISR) refers to the reconstruction from the corresponding low-resolution (LR) image input to a high-resolution (HR) image. However, since a single low-resolution image corresponds to multiple high-resolution images, this is an ill-posed problem. In recent years, generative model-based SISR methods have outperformed conventional SISR methods in performance. However, the SISR methods based on GAN, VAE, and Flow have the problems of unstable training, low sampling quality, and expensive computational cost. These models also struggle to achieve the trifecta of diverse, high-quality, and fast sampling. In particular, denoising diffusion probabilistic models have shown impressive variety and high quality of samples, but their expensive sampling cost prevents them from being well applied in the real world. In this paper, we investigate the fundamental reason for the slow sampling speed of the SISR method based on the diffusion model lies in the Gaussian assumption used in the previous diffusion model, which is only applicable for small step sizes. We propose a new Single Image Super-Resolution with Denoising Diffusion GANS (SRDDGAN) to achieve large-step denoising, sample diversity, and training stability. Our approach combines denoising diffusion models with GANs to generate images conditionally, using a multimodal conditional GAN to model each denoising step. SRDDGAN outperforms existing diffusion model-based methods regarding PSNR and perceptual quality metrics, while the added latent variable Z solution explores the diversity of likely HR spatial domain. Notably, the SRDDGAN model infers nearly 11 times faster than diffusion-based SR3, making it a more practical solution for real-world applications.


Multi-view subspace clustering based on adaptive search

Abstract: Multi-view clustering has been widely applied to image classification, information retrieval, medical pathology analysis, and other fields. So far, many multi-view subspace clustering algorithms based on self-representation learning have been developed. However, most of them use the original data as a dictionary to construct affinity graphs, and their clustering performance depends largely on the quality of the original data features. If the raw data is corrupted by noise, directly using these noisy data as a self-representing dictionary may pose a series of research challenges. Secondly, in the process of self-representation learning, the internal relationship between data points is actually continuously updated and changed, and fixed data points not only affect the selection of view feature diversity, but also may lead to clustering results that are unusually sensitive to the input data. Finally, the coefficient matrix obtained by self-representation learning may contain noise or data structures that are not relevant to multi-view clustering. To remedy these shortcomings, this work proposes a new multi-view clustering algorithm, namely, multi-view subspace clustering based on adaptive search. Specifically, first, we find its similar data matrix for each view’s original data and perform dictionary learning by using the similar data matrix as a dictionary. This not only facilitates identifying the continuously varied internal relationships between data points, but also alleviates the errors that occur when the raw data is directly selected as a dictionary. Second, we introduce robust principal component analysis (RPCA) and rank constraints into the construction of affinity matrices to obtain cleaner and more robust affinity matrices that explore the same clustering properties among different views. In addition, we develop an augmented Lagrange multiplier (ALM) based method to solve the objective function of the model. Finally, we conducted substantial experiments on six real multi-view datasets to demonstrate that the MSC-AS algorithm is more robust and effective than some other advanced clustering algorithms.


Exploring Students’ Learning Experience and Engagement in Asynchronous Learning Using the Community of Inquiry Framework through Educational Design Research

Abstract: Students’ learning experience and their engagement in online learning environments are becoming increasingly important as blended learning grows more prevalent in tertiary education. In this study, asynchronous lectures for applied sciences courses, offered to polytechnic students of Applied Chemistry and Pharmaceutical Science at Nanyang Polytechnic, were designed using the Community of Inquiry framework. Students’ perceptions of their learning experience and cognitive, emotional, and behavioral engagement in asynchronous lectures were determined through a survey study. The results showed that students were engaged and had positive learning experiences. Through an educational design research methodology, this survey study also determined and provided design features important for designing asynchronous lectures. Further research could explore the possibility of expanding the scope of the research to other institutions with students of different cultural backgrounds, learning preferences, and learning abilities.


Dimensions of Text Complexity in the Spoken and Written Modes: A Comparison of Theory-Based Models

Abstract: In many studies, grammatical complexity has been treated as a single unified construct. However, other research contradicts that view, suggesting instead that the different structural types and syntactic functions of complexity features are distributed in texts in fundamentally different ways. These patterns have been documented in general corpora that include a wide range of spoken and written registers. One question that has not been fully addressed is whether grammatical complexity features are organized in the same ways in the spoken versus written modes. The present study tests the empirical adequacy of four competing models based on different theoretical conceptualizations of text complexity, comparing their goodness-of-fit in spoken versus written modes. The results show that text complexity must be treated as a multi-dimensional construct; dimensions that combine structural type and syntactic function provide the best account of the actual patterns of linguistic co-occurrence. To a large extent, the same complexity dimensions operate in both the spoken and written modes. Two of these dimensions—dependent phrases functioning as noun modifiers and finite dependent clauses functioning as clause-level constituents—represent the strongest co-occurrence patterns. In addition, these two dimensions operate in complementary distribution, in both the spoken and written modes. Overall, though, these two dimensions are shown to represent stronger co-occurrence patterns in the written mode than in the spoken mode.


Advantages of quantum communication revealed by the reexamination of hyperbit theory limitations

Abstract: Paw\l{}owski and Winter's hyperbit theory, proposed in 2012, presented itself as an alternative to quantum theory, suggesting novel ways of redefining entanglement and classical communication paradigms. This research undertakes a meticulous reevaluation of hyperbit theory, uncovering significant operational constraints that question its equivalence with quantum mechanics. Crucially, the supposition that hyperbit theory and quantum theory are equivalent relies on the receiver having unattainable additional knowledge about the sender's laboratory, indicating that the work by Paw\l{}owski and Winter is incorrect. This study accentuates the constraints of hyperbits in information processing and sheds light on the superiority of quantum communication, thereby advancing the investigation at the intersection of classical and quantum communication.


Autoencoder-based detector for distinguishing process anomaly and sensor failure

Abstract: Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring values may fail, leading to a biased detection result – false alarm. In such cases, replacing the sensor is necessary instead of performing equipment maintenance. This study proposes an effective framework embedded with autoencoder-based control limits that can dynamically distinguish sensor anomaly from process anomaly in real-time. We conduct a simulation numerical study and an empirical study of semiconductor assembling manufacturers to validate the proposed framework. The results show that the proposed model outperforms other benchmark methods and can successfully identify sensor failures, even under conditions of (1) large variations in process values or sensor values and (2) heteroscedasticity effect. This is particularly beneficial in various practical applications where sensors are used for numerical measurements and support equipment maintenance.


Preliminary report of field reconnaissance on the 6 February 2023 Kahramanmaras Earthquakes in Türkiye

Abstract: Abstract On February 6, a successive rupture of major faults in the Eastern Anatolian Fault Zone and Cardak-Surgu fault triggered a strong mainshock (M w 7.7) and a major aftershock (M w 7.6) in Kahramanmaras. The successive earthquake sequence hit southern provinces in Türkiye and northern regions in Syria, causing severe fatality and economic loss. After the earthquakes, the International Consortium on Geo-disaster Reduction (ICGdR) organized an investigation team, involving specialists from China, Japan and Türkiye, to conduct a primary field reconnaissance on seismic damage of infrastructure and ground failures. The 10-day reconnaissance, including a mini-symposium at the Istanbul Technical University (ITU), was conducted from 25 March to 3 April and specifically focused on fault ruptures, liquefaction, landslide, rockfall and lateral spreading along the major ruptured faults from Antakya in Hatay to Goksun in Kahramanmaras, passing through provinces of Gaziantep, Adıyaman and Malatya. By this reconnaissance, a large amount of original seismic data was collected and a primary understanding was established for further steps on mitigation and reduction of seismic damages and its secondary geohazards.


TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models

Abstract:
Summary
 In cancer research, pathology report text is a largely untapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly available datasets for benchmarking report-based models. Two recent advances suggest the urgent need for a benchmark dataset. First, improved optical character recognition (OCR) techniques will make it possible to access older pathology reports in an automated way, increasing the data available for analysis. Second, recent improvements in natural language processing (NLP) techniques using artificial intelligence (AI) allow more accurate prediction of clinical targets from text. We apply state-of-the-art OCR and customized post-processing to report PDFs from The Cancer Genome Atlas, generating a machine-readable corpus of 9,523 reports. Finally, we perform a proof-of-principle cancer-type classification across 32 tissues, achieving 0.992 average AU-ROC. This dataset will be useful to researchers across specialties, including research clinicians, clinical trial investigators, and clinical NLP researchers.


Data encoding for healthcare data democratization and information leakage prevention

Abstract: Abstract The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.


Non-radiative configurations of a few quantum emitters ensembles: Evolutionary optimization approach

Abstract: Suppressing the spontaneous emission in quantum emitters ensembles (atoms) is one of the topical problems in quantum optics and quantum technology. While many approaches are based on utilizing the subradiance effect in ordered quantum emitters arrays, the ensemble configurations providing the minimal spontaneous emission rate are yet unknown. In this work, we employ the differential evolution algorithm to identify the optimal configurations of a few atomic ensembles that support quantum states with maximal radiative lifetime. We demonstrate that atoms tend to assemble mostly in quasi-regular structures with specific geometry, which strongly depends on the minimally allowed interatomic distance rmin. While the discovered specific non-radiative realizations of small ensembles cannot be immediately predicted, there is particular correspondence to the non-radiative states in the atomic lattices. In particular, we have found that states inheriting their properties either from the bound states in the continuum or band edge states of infinite lattices dominate across a wide range of rmin values. Additionally, we show that for small interatomic distances, the linear arrays with modulated spacing have the smallest radiative losses exponentially decreasing as the size of the ensemble increases.


Experimental Demonstration of Inequivalent Mutually Unbiased Bases

Abstract: Quantum measurements based on mutually unbiased bases (MUBs) play crucial roles in foundational studies and quantum information processing. It is known that there exist inequivalent MUBs, but little is known about their operational distinctions, not to say experimental demonstration. In this Letter, by virtue of a simple estimation problem, we experimentally demonstrate the operational distinctions between inequivalent triples of MUBs in dimension 4 based on high-precision photonic systems. The experimental estimation fidelities coincide well with the theoretical predictions with only 0.16% average deviation, which is 25 times less than the difference (4.1%) between the maximum estimation fidelity and the minimum estimation fidelity. Our experiments clearly demonstrate that inequivalent MUBs have different information extraction capabilities and different merits for quantum information processing.


Learning within fiber-crafted algorithms: Posthumanist perspectives for capturing human-material collaboration

Abstract: Abstract A key commitment of computer-supported collaborative learning research is to study how people learn in collaborative settings to guide development of methods for capture and design for learning. Computer-supported collaborative learning research has a tradition of studying how the physical world plays a part in collaborative learning. Within the field, a material turn is emerging that considers how digital and tangible technologies actively contribute to collaborative learning processes. Studying how tangible materials produce collaborative learning visibly and algorithmically is particularly important at a time when advanced algorithms are integrated into educational contexts in ways that are not always transparent. However, the needed methodologies for capturing how non-human agents take part in collaborative learning remains underdeveloped. The present study builds on current CSCL research that investigates materials in collaborative learning and introduces posthumanist perspectives with the aim to decenter humans methodologically and to probe empirically whether and how these perspectives contribute to empirical understanding of collaborative learning processes. Taking fiber crafts (e.g., weaving and fabric manipulation) as a context for computational learning, the present study conducted a posthumanist analysis of differences among human and non-human participants in collaboration using video data to investigate how middle school youths and fiber craft components performed algorithms over time. The findings show how both youths and craft materials actively contributed to the performance of algorithms. In weaving, algorithms became repeated youth-material movements one dimension at a time. In fabric manipulation, algorithms became a repeated confluence of component parts. Decentering humans through an analysis of differences among human and non-human introduced human-material collaboration as a productive unit of analysis for understanding how materials and people together contribute to producing what can be recognized as computational performance. The findings of this research contribute to ongoing conversations in CSCL research on how computational materials can be considered in collaborative learning and present a new approach to capture collaborative learning as physical expansion over time. The study has implications for future research on capturing collaborative computational learning and designing physical computational learning opportunities that show technology as evolving.


Multigene and Improved Anti-Collision RRT* Algorithms for Unmanned Aerial Vehicle Task Allocation and Route Planning in an Urban Air Mobility Scenario

Abstract: Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks’ completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed.


Motion-guided and occlusion-aware multi-object tracking with hierarchical matching

Abstract: In the field of multi-target tracking, the widely embraced tracking-by-detection paradigm has rapidly progressed with the refinement of detectors and matching techniques. However, the paradigm of joint detection and tracking is relatively limited, and it is difficult to model complex scenes, such as the complexities introduced by camera motion and occlusion. In this work, a hierarchical joint detection and tracking framework is proposed, namely MSPNet. From a temporal concern, a motion-guided feature aggregation module is proposed to address the complexities of multi-frame variations. From a spatial concern, an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion. Extensive experiments on MOT challenging benchmarks demonstrate that the MSPNet can effectively reduce false negatives and improve the accuracy of tracking while outperforming a wide range of existing methods.


Improved moth-flame algorithm based on cat chaotic and dynamic cosine factor

Abstract: The moth-flame algorithm shows some shortcomings in solving the complex problem of optimization, such as insufficient population diversity and unbalanced search ability. In this paper, an IMFO (Improved Moth-Flame Optimization) algorithm is proposed to be applied in solving the optimization problem of function. First, cat chaotic mapping is used to generate the initial position of moth to improve the population diversity. Second, cosine inertia weight is introduced to balance the global and local search abilities of the algorithm. Third, the memory information in the particle swarm algorithm is introduced into the iterative process of the algorithm to speed up the convergence of the population. Finally, Gaussian mutation strategy is used in the current optimal solution to avoid the algorithm from falling into the local optimum. Simulation experiments are conducted on 11 benchmark test functions, compared with other improved MFO (Moth-Flame Optimization) algorithms and classical optimization algorithms. The results show that the IMFO has higher accuracy and stability in solving the above-mentioned test functions. The proposed algorithm is experimented and verified by optimizing the KELM (Kernel Extreme Learning Machine) in an engineering example and exhibits a better optimization performance.


Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning

Abstract: An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.


A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Abstract: With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease’s influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue’s nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework’s higher accuracy and precision rate.


How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway

Abstract: To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative.In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative.All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative.AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting.In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score.• All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.