Understanding students’ adoption of the ChatGPT chatbot in higher education: the role of anthropomorphism, trust, design novelty and institutional policy

Abstract: The present research aims to highlight the underlying factors that drive students’ adoption of the ChatGPT chatbot in higher education. This study extends the meta-UTAUT framework by including additional exogenous factors of anthropomorphism, trust, design novelty, and institutional policy. Empirical examination with Structural Equation Modelling among 355 students in Dutch higher education institutions revealed attitude and behavioural intention as significant positive predictors of students’ ChatGPT use behaviour. Institutional policy negatively moderated the effect of behavioural intention on use behaviour. Behavioural intention was significantly and positively influenced by attitude, performance expectancy, social influence, and facilitating conditions. Anthropomorphism, design novelty, trust, performance expectancy, and effort expectancy were unveiled as significant positive antecedents of attitude. The central theoretical contributions of this research include investigating students’ use behaviour instead of behavioural intention, establishing attitude as a core construct, underlining additional antecedents of attitude, and highlighting the importance of institutional policy. The present study contributes to prior research on technology adoption, especially in the area of artificial intelligence in education. The findings yield valuable insights for chatbot designers, product managers, and higher education policy writers.


GNNCL: A Graph Neural Network Recommendation Model Based on Contrastive Learning

Abstract: Abstract In the field of recommendation algorithms, the representation learning for users and items has evolved from using single IDs or historical interactions to utilizing higher-order neighbors. This can be achieved by modeling the user–item interaction graph to capture user preferences for items. Despite the promising results achieved by these algorithms, they still suffer from the issue of data sparsity. In order to mitigate the impact of data sparsity, contrastive learning has been adopted in graph collaborative filtering to enhance performance. However, current recommendation algorithms using contrastive learning yield uneven representations after data augmentation and do not consider the potential relationships among users (or items). To address these challenges, we propose a graph neural network-based recommendation model that integrates contrastive learning (GNNCL). This model combines data augmentation with added noise and the exploration of semantic neighbors for nodes. For the structural neighbors on the interaction graph, we introduce a novel and straightforward contrastive learning approach, abandoning previous graph augmentation methods, and introducing uniform noise into the embedding space to create contrastive views. To unearth potential semantic neighbor relationships in the semantic space, we assume that users with similar representations possess semantic neighbor relationships and merge these semantic neighbors into the prototype contrastive learning. We utilize a clustering algorithm to obtain prototypes for users and items and employ the EM algorithm for prototype contrastive learning. Experimental results validate the effectiveness of our approach. Particularly, on the Yelp2018 and Amazon-book datasets, our method exhibits significant performance improvements compared to basic graph collaborative filtering models.


A Modular Framework for Domain-Specific Conversational Systems Powered by Never-Ending Learning

Abstract: Complex and long interactions (e.g., a change of topic during a conversation) justify the use of dialog systems to develop task-oriented chatbots and intelligent virtual assistants. The development of dialog systems requires considerable effort and takes more time to deliver when compared to regular BotBuilder tools because of time-consuming tasks such as training machine learning models and low module reusability. We propose a framework for building scalable dialog systems for specific domains using the semi-automatic methods of corpus, ontology, and code development. By separating the dialog application logic from domain knowledge in the form of an ontology, we were able to create a dialog system for the banking domain in the Portuguese language and quickly change the domain of the conversation by changing the ontology. Moreover, by using the principles of never-ending learning, unsupported operations or unanswered questions create triggers for system knowledge demand that can be gathered from external sources and added to the ontology, augmenting the system’s ability to respond to more questions over time.


Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems

Abstract: Real-time applications increasingly rely on context information to provide relevant and dependable features. Context queries require large-scale retrieval, inferencing, aggregation, and delivery of context using only limited computing resources, especially in a distributed environment. If this is slow, inconsistent, and too expensive to access context information, the dependability and relevancy of real-time applications may fail to exist. This paper argues, transiency of context (i.e., the limited validity period), variations in the features of context query loads (e.g., the request rate, different Quality of Service (QoS), and Quality of Context (QoC) requirements), and lack of prior knowledge about context to make near real-time adaptations as fundamental challenges that need to be addressed to overcome these shortcomings. Hence, we propose a performance metric driven reinforcement learning based adaptive context caching approach aiming to maximize both cost- and performance-efficiency for middleware-based Context Management Systems (CMSs). Although context-aware caching has been thoroughly investigated in the literature, our approach is novel because existing techniques are not fully applicable to caching context due to (i) the underlying fundamental challenges and (ii) not addressing the limitations hindering dependability and consistency of context. Unlike previously tested modes of CMS operations and traditional data caching techniques, our approach can provide real-time pervasive applications with lower cost, faster, and fresher high quality context information. Compared to existing context-aware data caching algorithms, our technique is bespoken for caching context information, which is different from traditional data. We also show that our full-cycle context lifecycle-based approach can maximize both cost- and performance-efficiency while maintaining adequate QoC solely based on real-time performance metrics and our heuristic techniques without depending on any previous knowledge about the context, variations in query features, or quality demands, unlike any previous work. We demonstrate using a real world inspired scenario and a prototype middleware based CMS integrated with our adaptive context caching approach that we have implemented, how realtime applications that are 85% faster can be more relevant and dependable to users, while costing 60.22% less than using existing techniques to access context information. Our model is also at least twice as fast and more flexible to adapt compared to existing benchmarks even under uncertainty and lack of prior knowledge about context, transiency, and variable context query loads.


Unmasking the Power of Play Through TUI Designs

Abstract: Research on the potential benefits of technology for autistic children is an emergent field in Human-Computer Interaction (HCI), especially within the Child-Computer Interaction Community. At the same time, there are concerns about what these interventions and technologies are for and who benefits. We present a research and design approach for Tangible User Interfaces (TUIs) for minimally verbal to nonverbal autistic children following a neurodiversity narrative through three field studies developed and evaluated with three groups of children within a semi-structured scholastic environment between 2018 and 2021 in the UK. We discuss our insights for research and TUI designs in the context of social play for nonverbal autistic children and critically reflect on the methods and approaches we used. We do this to disrupt the normalisation agenda that subtly permeates the field of HCI and to direct designers’ attention toward supporting autistic ways of being in the world.


The aesthetics of deconstruction: neural synthesis of transformation matrices using GANs on multichannel polyphonic MIDI data

Abstract: Deconstruction, a concept that originated from French philosopher Jacques Derrida's post-structuralist theories, has significantly influenced architecture and graphic design. It has evolved into a rhizomatic network of styles and concepts ranging from new architectural geometry to AI. Despite its limited explicit application in music, this paper explores deconstruction within musical aesthetics, especially in the post-1945 serial school. Computer-Aided Design (CAD) methods have become an important part in the deconstructivist architectural design process; similarly, in music, Computer-Aided Composition (CAC) techniques have become important from generative algorithms to AI techniques. In graphic design, such systems (i.e. GANs, VAEs, CNNs, RNNs, Stable Diffusion, Transformers, etc.) have been experimented with. Google's Imagen or OpenAI's DALL-E 2, which automatically generate images from text prompts given by users, use, for example, diffusion models. Also, Google LM is a music equivalent to these. This paper delves into the novel aesthetics brought about by the application of GANs on multichannel polyphonic MIDI data in music, discussing the conceptual grounding of such usage and overcoming the challenges associated with it. Effectively, using our concept of distance matrices rather than, for example, Hidden Markov Models, is more suitable for GAN generation.


Asymptotics in the Bradley-Terry model for networks with a differentially private degree sequence

Abstract: The Bradley-Terry model is a common model for analyzing paired comparison data. Under differential private mechanism, there is a lack of asymptotic properties for the parameter estimator of parameters in this model. In this article, we show that the moment estimators of the parameters based on the differential private degree sequence with Laplace noise is uniformly consistent and asymptotically normal. Simulations are provided to illustrate asymptotic results.


ReFID: Reciprocal Frequency-aware Generalizable Person Re-identification via Decomposition and Filtering

Abstract: Domain generalization (DG) of person re-identification (ReID) aims to conduct testing across domains that have not been previously encountered, without utilizing target domain data during the training stage. As the number of source domains increases, the relationships between training samples become more complex. This can lead to domain-invariant features that include certain instance-level spurious correlations, which can impact the model’s ability to generalize further. To overcome this limitation, the Reciprocal Frequency-aware Generalizable Person Re-identification (ReFID) method is proposed in this paper, which aims to utilize spectral feature correlation learning to transmit frequency component information and generate more discriminative hybrid features. A module called Bilateral Frequency Component-guided Attention (BFCA) is developed to help the network understand high-level semantic and texture information from various frequency features. Furthermore, to reduce the impact of noise from the frequency domain, this paper proposes an innovative module called Fourier Noise Masquerade Filtering (FNMF). This module enhances the portability of frequency domain components while simultaneously suppressing elements that do not contribute to generalization. Extensive experimental results on various datasets demonstrate that our method is effective and superior to the state-of-the-art methods.


Origins, Theoretical Foundations, and Economic Implications of Cryptocurrencies

Abstract: The theoretical foundations and indeed implications related to the emergence of digital assets, particularly cryptocurrencies, remain a scarcely explored area in recent academic literature. This chapter aims to analyze the presumed influences of various economic schools of thought on the genesis of Bitcoin, the monetary policies underlying several digital assets, and the evolution of their market capitalization since 2009. The study also addresses the case of several cryptocurrency exchange platforms, commonly known as crypto exchanges, and focuses on industrial entities, such as Ledger and Bitmain. An overview of use cases involving “smart contracts” and Blockchain technology is provided. This chapter seeks to revisit the emergence of different approaches to national regulation on a global scale and to establish international comparisons. The examples of Japan, long trapped in a deflationary spiral, and Argentina, facing hyperinflation, are particularly enlightening for understanding the legal treatment of digital assets and their perception by local authorities. Two central questions guide this exploration: what are the economic and ideological foundations underlying the conception of Bitcoin, and to what extent does the current development of the digital asset ecosystem remain true to the initial ideas of Satoshi Nakamoto?


Web-based simulation and motion planning for human-robot and multi-robot applications

Abstract: This paper introduces the human-robot and multi-robot programming and simulation capabilities of a web-based, open-source robot programming and simulation tool called Assembly. The tool supports scenarios with up to three robots and provides support for simulating human arms working closely with a robot. To facilitate the simulation of scenarios involving human-robot and robot-robot collaboration, the paper proposes an improved version of the neural network-based Adaptive Simplex Motion Planner, which is capable of performing motion planning at a rate of over 3500 Hz. The improved toolpath planner uses an obstacle avoidance approach that leverages ray casting and shadow volume teachniques. The new planner is evaluated on the basis of two human-robot and multi-robot simulation scenarios and the results are compared to those of the original algorithm.


Enhancing Facial Recognition in Surveillance Systems through Embedded Super-resolution

Abstract: This document details the implementation of a sub-pixel convolutional neural network designed to enhance the resolution of face images. The model uses a series of filters to progressively increase the number of pixels, estimating the necessary information for new pixels from the original image and training derived from 22000 synthetic images produced by adversarial neural networks. Within the context of surveillance and related applications, the trained convolutional network exhibits beneficial characteristics. For instance, it can be deployed within a device to achieve higher-resolution images than those the physical camera can produce. This research underscores the feasibility of such a device through the implementation and evaluation of the network on the NVIDIA Jetson TX2 embedded system. The findings demonstrate the model's practicality for real-time surveillance applications and its ability to produce superior-quality images compared to several interpolation methods, as determined by an exhaustive testing process measuring various attributes of the generated images.


A Study to Know Impact of AI on CRM

Abstract: This research paper delves into the dynamic intersection of Artificial Intelligence (AI) and Customer Relationship Management (CRM), exploring the profound effects of AI technologies on modern business practices. As AI continues to evolve, it reshapes how organizations manage and nurture their relationships with customers, presenting new opportunities and challenges. The study investigates the role of AI in personalizing customer experiences, emphasizing the utilization of advanced algorithms to analyze vast datasets and derive actionable insights. AI's influence on data management, analysis, and the subsequent enhancement of customer insights are scrutinized, providing a comprehensive understanding of its impact on informed decision-making. Furthermore, the paper examines the integration of AI-powered chatbots and virtual assistants in CRM systems, evaluating their effectiveness in providing real-time support, streamlining interactions, and improving overall customer satisfaction. The automation of repetitive CRM tasks through AI technologies is also explored, highlighting the resulting efficiencies and the potential for human resources to engage in more strategic aspects of customer relationship management. Sales forecasting, customer segmentation, and sentiment analysis emerge as key focal points, illustrating how AI contributes to more accurate predictions, targeted marketing strategies, and proactive reputation management. The impact of AI on cross-selling, upselling, and customer retention strategies is scrutinized, offering insights into how businesses can leverage AI to optimize revenue and foster enduring customer loyalty. As businesses navigate the rapidly evolving landscape of AI in CRM, this research paper aims to provide a comprehensive overview of the transformative dynamics at play. By understanding the nuanced influence of AI on customer relationships, organizations can adapt their strategies to align with the evolving expectations and demands of the contemporary market.


Curriculum in the digital age: Intensifying the work of teachers, the remix

Abstract: This research focuses on the Teacher-to-Teacher Online Marketplace of Ideas (TOMI), the popular ecosystem of participatory online platforms for teachers seeking to share and/or curate classroom materials and curricular ideas. Teachers engage with online platforms for curricular planning through multiple, complex labor practices. In this manuscript, interview data is used to describe the process teachers engage in when using the TOMI: Identifying, Remixing and Teaching, Reflecting, and Reviewing. We then consider how these new labor practices constitute a new type of work intensification that is both richly complex and concerning due to their market orientation. We trouble these findings, considering teachers' labor practices as examples of unpaid, casual digital labor in light of efforts to defund public education. We argue for structural shifts in public education that allow teachers more time and space for productive, creative, and fulfilling work remixing their curriculum and collaborating with colleagues in the digital space.


Survey Paper on Hospital Management System using Blockchain

Abstract: Electronic health records are health information of patients that are saved digitally in a network. The information of the patient are stored in the blockchain and these details are stored in the block chain as a blocks of data. The data is encrypted by the algorithm known as AES which is used to encrypt all the data of the patients. A Blockchain network is used in the healthcare system to exchange patient data improve the performance, security, and transparency of sharing medical data in the health care system. The three main feature of blockchain technology – Security, Decentralization Transparency make any application secure and not accessible by unauthorized parties. Key Words: Health records, Blockchain, Encryption of data, Decryption of data, Security.


Active Learning for Discrete Latent Variable Models

Abstract: Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of linear regressions (MLR). While it is well known that active learning confers no advantage for linear-gaussian regression models, we use Fisher information to show analytically that active learning can nevertheless achieve large gains for mixtures of such models, and we validate this improvement using both simulations and real-world data. We then consider a powerful class of temporally structured latent variable models given by a hidden Markov model (HMM) with generalized linear model (GLM) observations, which has recently been used to identify discrete states from animal decision-making data. We show that our method substantially reduces the amount of data needed to fit GLM-HMMs and outperforms a variety of approximate methods based on variational and amortized inference. Infomax learning for latent variable models thus offers a powerful approach for characterizing temporally structured latent states, with a wide variety of applications in neuroscience and beyond.


Criminal Investigation with the Help of Face Recognition

Abstract: The Criminal Investigation and Face Recognition System (CIFRS) streamlines investigations with an intuitive interface and automated face recognition. Investigators effortlessly manage cases, set parameters, and upload facial images for analysis against a database. Real-time notifications alert investigators to potential matches, contributing to investigative efficiency. CIFRS supports diverse law enforcement management systems, allowing the download of case reports and data in various formats. The system also offers practice scenarios to familiarize users with face recognition technology. Keywords – Criminal Investigation, Face Recognition, Database Management, Law Enforcement.


Aspect-based sentiment analysis with metaphorical information

Abstract: Aspect-Based Sentiment Analysis (ABSA) has been the focus of increasing study in recent years. Previous research has demonstrated that incorporating syntactic information, such as dependency trees, can enhance ABSA performance. Despite the widespread use of metaphors in daily life to express emotions more vividly, few studies have integrated this literary device into ABSA. In this paper, we propose a novel ABSA model that utilizes Metaphor Identification Procedure (MIP) to encode both the sentence and aspect word as a single unit, thereby overcoming these limitations. Our experimental results demonstrate that our model achieves competitive performance in ABSA.


Integrating learning factors and Bayesian network for interpretable knowledge tracing

Abstract: Knowledge tracing aims to model learners’ knowledge mastery based on their historical interaction records and predict their future performance. Due to its great potential in enabling personalized learning in intelligent tutoring systems, it has received extensive attention. However, most deep learning-based knowledge tracing methods have significant predictive performance. It is difficult to extract meaningful interpretations from the thousands of parameters in neural networks. The interpretability of knowledge tracing refers to the ability of learners to easily understand the predicted results.To address this problem, based on learning factors that influence the learner’s exercise performance, this paper proposes a novel knowledge tracing model which is named Integrating Learning factors and Bayesian network for interpretable Knowledge Tracing (LBKT). Firstly, meaningful learning factors, including knowledge mastery, learning ability, and exercise difficulty, are calculated from learners’ historical interaction records using deep learning and statistical methods. Then, Bayesian network is constructed to capture the causal relation between the three learning factors and exercise response. Finally, the Bayesian network is generated through structure and parameter learning to obtain interpretable prediction of future exercise performance. The proposed model named LBKT is evaluated on three public real-world educational datasets. The experiment results demonstrate that our approach achieves better predictive performance compared to baseline knowledge tracing methods, while also exhibiting significant superiority in model interpretability.


Movie Recommendation System: Real-time Evaluation

Abstract: In today's e-commerce time, it has become very troublesome task to find any content of online movies, songs, books , restaurants etc. A practical recommendation scheme now very helpful and important. Nowadays, movie recommendation system are also available( Netflix, Movie lens, Amazon movie recommendation system etc.) But using a method of combination of content based and collaborative filtering, a recommender system worked and finding a customized output to user. Key Words: Content-based, Collaborative Filtering, Recommender System, Machine Learning.


Suppression of chaos in a partially driven recurrent neural network

Abstract: The dynamics of recurrent neural networks (RNNs), and particularly their response to inputs, play a critical role in information processing. In many applications of RNNs, only a specific subset of the neurons generally receive inputs. However, it remains to be theoretically clarified how the restriction of the input to a specific subset of neurons affects the network dynamics. Considering RNNs with such restricted input, we investigate how the proportion, $p$, of the neurons receiving inputs (the ``input neurons'') and the strength of the input signals affect the dynamics by analytically deriving the conditional maximum Lyapunov exponent. Our results show that for sufficiently large $p$, the maximum Lyapunov exponent decreases monotonically as a function of the input strength, indicating the suppression of chaos, but if $p$ is smaller than a critical threshold, ${p}_{c}$, even significantly amplified inputs cannot suppress spontaneous chaotic dynamics. Furthermore, although the value of ${p}_{c}$ is seemingly dependent on several model parameters, such as the sparseness and strength of recurrent connections, it is proved to be intrinsically determined solely by the strength of chaos in spontaneous activity of the RNN. This is to say, despite changes in these model parameters, it is possible to represent the value of ${p}_{c}$ as a common invariant function by appropriately scaling these parameters to yield the same strength of spontaneous chaos. Our study suggests that if $p$ is above ${p}_{c}$, we can bring the neural network to the edge of chaos, thereby maximizing its information processing capacity, by amplifying inputs.


Optimizing feature selection and parameter tuning for breast cancer detection using hybrid GAHBA-DNN framework

Abstract: Breast cancer has been life-threatening for many years as it is the common cause of fatality among women. The challenges of screening such tumors through manual approaches can be overcome by computer-aided diagnosis, which aids radiologists in making precise decisions. The selection of significant features is crucial for the estimation of prediction accuracy. This work proposes a hybrid Genetic Algorithm (GA) and Honey Badger Algorithm (HBA) based Deep Neural Network (DNN), HGAHBA-DNN for the concurrent optimal features selection and parameter optimization; further, the optimal features and parameters extracted are fed into the DNN for the prediction of the breast cancer. It fuses the benefits of HBA with parallel processing and efficient feedback with GA’s excellent global convergent rate during the processing stages. The aforementioned method is evaluated on the Wisconsin Original Breast Cancer (WOBC), Wisconsin Diagnostic Breast Cancer (WDBC), and the Surveillance, Epidemiology, and End Results (SEER) datasets. Subsequently, the performance is validated using several metrics like accuracy, precision, Recall, and F1-score. The experimental result shows that HGAHBA-DNN obtains accuracy of 99.42%, 99.84%, and 92.44% for the WOBC, WDBC, and SEER datasets respectively, which is much superior to the other state-of-the-art methods.


Probing fractal spatiotemporal inhomogeneity in a quantum walk

Abstract: We investigate the transport and entanglement properties exhibited by quantum walks with coin operators concatenated in a space-time fractal structure. Inspired by recent developments in photonics, we choose the paradigmatic Sierpinski gasket. The 0-1 pattern of the fractal is mapped into an alternation of the generalized Hadamard-Fourier operators. This two-state coin-operator approach overcomes the intricacies caused by the utilization of high-dimensional coin operators required in prior studies of discrete-time quantum walks on fractals. In fulfilling the blank space on the analysis of the impact of inhomogeneity in quantum walk properties, specifically, fractal deterministic inhomogeneity, our results show a robust effect of entanglement enhancement as well as an interesting road to superdiffusive spreading with a tunable scaling exponent attaining robust superdiffusion, subballistic though. Explicitly, with this fractal approach it is possible to obtain an increase in quantum entanglement with reduced impact on standard quantum walk theoretical spreading. Alongside those features, we analyze further properties such as the degree of interference and visibility. The present model corresponds to an application of fractals in an experimentally viable setting, namely, the building block for the construction of photonic patterned structures.


Maximal information leakage from quantum encoding of classical data

Abstract: An alternative measure of information leakage for quantum encoding of classical data is defined. An adversary can access a single copy of the state of a quantum system that encodes some classical data and is interested in correctly guessing a general randomized or deterministic function of the data (e.g., a specific feature or attribute of the data in quantum machine learning) that is unknown to the security analyst. The resulting measure of information leakage, referred to as maximal quantum leakage, is the multiplicative increase of the probability of correctly guessing any function of the classical data upon observing measurements of the quantum state. Maximal quantum leakage is shown to satisfy the postprocessing inequality (i.e., applying a quantum channel reduces information leakage) and independence property (i.e., leakage is zero if the quantum state is independent of the classical data), which are fundamental properties required for privacy and security analysis. It also bounds accessible information. Effects of global and local depolarizing noise models on the maximal quantum leakage are established.


Noisy Demkov-Kunike model

Abstract: The Demkov-Kunike (DK) model, characterized by a time-dependent Rabi coupling $J\phantom{\rule{0.16em}{0ex}}\text{sech}(t/T)$ and on-site detuning ${\mathrm{\ensuremath{\Delta}}}_{0}+{\mathrm{\ensuremath{\Delta}}}_{1}tanh(t/T)$, has one of the most general forms of an exactly solvable two-state quantum system and therefore it provides a paradigm for coherent manipulations of a qubit's quantum state. Despite its extensive application in noise-free cases, the exploration of the noisy DK model remains limited. Here we extend the coherent DK model to take into account a noisy coupling term $J\ensuremath{\rightarrow}{J}_{\text{noisy}}(t)$. We consider colored Markovian noise sources represented by telegraph noise and Gaussian noise. We present exact solutions for the survival probability ${Q}_{\text{DK}}^{\text{noisy}}$ of the noisy DK model, namely, the probability of the system to remain in its initial state. For slow telegraph noise, we identify parameter regimes where the survival probability ${Q}_{\text{DK}}^{\text{noisy}}$ is suppressed rather than enhanced by noise. In contrast, for slow Gaussian noise, the noise always enhances the survival probability ${Q}_{\text{DK}}^{\text{noisy}}$, due to the absorption of noise quanta across the energy gap. This study not only complements the existing research on the noisy Landau-Zener model but also provides valuable insights into the control of two-level quantum systems.


Multi-modal sarcasm detection based on emotion perception and cross-modality attention fusion

Abstract: Sarcasm is a rhetorical device commonly used in social media and is prevalent on some social platforms, such as Twitter and Reddit, to dismiss, criticize or ridicule people or events using metaphors and exaggeration. With the rapid growth of social media and internet technology, the way people express their emotions and feelings is not limited to text. Therefore, a multi-modal sarcasm detection task is crucial to understanding people’s real feelings and beliefs. However, most existing models often use implicit fusion and do not significantly align the emotions between modalities explicitly, neglecting the significant role of emotional words in sarcasm detection. In this paper, a model was proposed based on emotion perception and cross-modality attention fusion for multi-modal sarcasm detection. Specifically, an external emotional knowledge was introduced for emotional information enhancement. In addition, the dual-channel BERT-based module and cross-modality interaction fusion were proposed based on an attention mechanism. The experimental results on a public multi-modal sarcasm detection dataset based on Twitter demonstrate the superiority of the proposed model.


Echo State Property upon Noisy Driving Input

Abstract: The echo state property (ESP) is a key concept for understanding the working principle of the most widely used reservoir computing model, the echo state network (ESN). The ESP is achieved most of the operation time under general conditions, yet the property is lost when a combination of driving input signals and intrinsic reservoir dynamics causes unfavorable conditions for forgetting the initial transient state. A widely used treatment, setting the spectral radius of the weight matrix below the unity, is not sufficient as it may not properly account for the nature of driving inputs. Here, we characterize how noisy driving inputs affect the dynamical properties of an ESN and the empirical evaluation of the ESP. The standard ESN with a hyperbolic tangent activation function is tested using the MNIST handwritten digit datasets at different additive white Gaussian noise levels. The correlations among the neurons, input mapping, and memory capacity of the reservoir nonlinearly decrease with the noise level. These trends agree with the deterioration of the MNIST classification accuracy against noise. In addition, the ESP index for noisy driving input is developed as a tool to help easily assess ESPs in practical applications. Bifurcation analysis explicates how the noise destroys an asymptotical convergence in an ESN and confirms that the proposed index successfully captures the ESP against noise. These results pave the way for developing noise-robust reservoir computing systems, which may promote the validity and utility of reservoir computing for real-world machine learning applications.


Machine translation: Turkish–English bilingual speakers’ accuracy detection of evidentiality and preference of MT

Abstract: Machine translation (MT) is the automated process of translating text between different languages, encompassing a wide range of language pairs. This study focuses on non-professional bilingual speakers of Turkish and English, aiming to assess their ability to discern accuracy in machine translations and their preferences regarding MT. A particular emphasis is placed on the linguistically subtle yet semantically meaningful concept of evidentiality. In this experimental investigation, 36 Turkish-English bilinguals, comprising both early and late bilinguals, were presented with simple declarative sentences. These sentences varied in their evidential meaning, distinguishing between firsthand and non-firsthand evidence. The participants were then provided with MT of these sentences in both translation directions (Turkish to English and English to Turkish) and asked to identify the accuracy of these translations. Additionally, participants were queried about their preference for MT in four crucial domains: medical, legal, academic, and daily contexts. The findings of this study indicated that late bilinguals exhibited a superior ability to detect translation accuracy, particularly in the case of firsthand evidence translations, compared to their early bilingual counterparts. Concerning the preference for MT, age of acquisition and the accuracy detection of non-firsthand sentence translations emerged as significant predictors.


COMPARISON OF THE SPEECH TEXTS OF INDONESIAN PRESIDENT JOKO WIDODO AND PRESIDENT SUSILO BAMBANG YUDHOYONO: STUDY USING A CORPUS LINGUISTIC APPROACH

Abstract: Purpose: This research has several objectives. First, determine lexical density and compare the lexical density. Second, to determine the key lexical density and compare the key lexical density. Third, to test the independence of the relationship between lexical variations and the text of President Joko Widodo's and President Susilo Bambang Yudhoyono's speeches. Theoretical Reference: The theoretical basis used in this research is the lexical analysis approach in linguistics. The application of lexical perspective analysis is expected to be able to review the communication used by each individual. The theoretical lexical discussion will also use a statistical independence analysis approach. The application of a statistical independence analysis approach is used to review a person's individual language abilities. Method: This research uses a qualitative and quantitative corpus linguistics approach. The corpus linguistic application used in this research is the KORTARA application (Korpus Nusantara). The research data is a corpus of 9 texts of President Joko Widodo's speeches and a corpus of 9 texts of President Susilo Bambang Yudhoyono which are official speeches every 16 August before the DPR of the Republic of Indonesia. Results and Conclusion: The results of this research reveal that the text corpus of President Joko Widodo's speech is richer and more varied than the text corpus of President Susilo Bambang Yudhoyono's speech in lexical use. This research also revealed that there is a relationship between lexical variation and the type of text of the President of the Republic of Indonesia's speech with a confidence level of 95%. The difference in lexical variation and frequency between the text corpus of President Joko Widodo's speech and the text corpus of Susilo Bambang Yudhoyono's speech is statistically significant at p < 0.05. Implication of Research: The implication of this research is the realization of the KORTARA corpus linguistic approach (Korpus Nusantara) which can facilitate research for small and large scale data. This research also reveals that the application of a statistical approach provides maximum results in the analysis of large-scale linguistic phenomena. Originality/value: The current study makes a valuable empirical contribution by combining statistical analysis using corpus and qualititative analysis to give comprehensive conclusion. This study is the answer toward the question about the reliability and validity of linguistic studies.


Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Abstract:
Human Action Recognition (HAR) is a progressive research area in the field of computer vision and machine learning. Earlier methods on HAR were based on single sensor modality, either vision-based sensor or wearable inertial sensor. Both of these modalities have some limitations that prevent widespread adoption of HAR; e.g. visual sensors typically require elaborate hardware setup and are limited to a small operating area, where inertial sensors are prone to drift. The solution is to fuse the information from different modalities. In this dissertation, we present novel multimodal sensor fusion frameworks that overcome the limitations of single sensor modality. In these frameworks, we convert all data streams to images through innovative signal to image conversion schemes and feed them to Convolutional Neural Networks (CNN), thus enabling extraction of higher-level features that CNNs are proven to be capable of especially from images. Moreover, we propose fast and robust multilevel fusion schemes by extracting features from multiple layers of the CNNs and employing statistical methods such as Canonical Correlation Analysis and gated fusion, instead of the more popular single stage fusion. We applied these fusion frameworks for HAR using depth and inertial sensors. At the input of each fusion framework, we transform depth and inertial sensor data into images called Sequential Front view Images (SFI) and Signal Images (SI). The SFI and SI images are then fused through our proposed multilevel frameworks for more accurate HAR while maintaining computational speed. We evaluate the proposed frameworks on three public multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTDMHAD Kinect V2 and achieved accuracies of 99.3%, 99.85% and 99.8% respectively.

While the proposed frameworks were developed with HAR as a target application area, they can be applied to other fusion problems as well. We show the generalizability of frameworks by applying them to a different domain, where ECG (1D time series) data is converted to multimodal images and fed through our fusion frameworks for arrythmia classification and stress assessment. Preliminary results in these applications are encouraging, further strengthening the significance of the proposed frameworks.


The impact of digital media on the education of different digital generations: contributions and challenges for accelerating information and promoting the learning of connected individuals

Abstract: The present work is the result of a bibliographic research, based on the discussion about the effectiveness of the use of digital media in the classroom, promoting comparative relations with the current reality and exposing some primordial media resources to enhance the school teaching process of the Digital Generation. The purpose was the analog realization of how learning was transmitted to the students of the past, establishing a reflection of how it is given to the transmission of knowledge in contemporary times, evidencing that the education of the XXI century, has advanced significantly with the advent and influences related to the use of the New Digital Technologies based on the challenge of educating the generation born in the Digital Age. The methodological procedure covered the writings and analysis of scientific articles through a critical reflection on the generation of Screenagers and Education.The bibliographic investigation provided the expansion of knowledge on the subject, supported by authors who address the teaching-learning process mediated by the New Digital Technologies, demonstrating the possibilities and limits of using digital technologies in school as a pedagogical resource in the generation of the Digital Age, contributing to a quality and transformative teaching..


Recoverability of quantum channels via hypothesis testing

Abstract: Abstract A quantum channel is sufficient with respect to a set of input states if it can be reversed on this set. In the approximate version, the input states can be recovered within an error bounded by the decrease of the relative entropy under the channel. Using a new integral representation of the relative entropy in Frenkel (Integral formula for quantum relative entropy implies data processing inequality, Quantum 7 , 1102 (2023)), we present an easy proof of a characterization of sufficient quantum channels and recoverability by preservation of optimal success probabilities in hypothesis testing problems, equivalently, by preservation of $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -distance.


Resource analysis and modifications of quantum computing with noisy qubits for elliptic curve discrete logarithms

Abstract: We estimate the number of physical qubits and execution time by decomposing an implementation of Shor's algorithm for elliptic curve discrete logarithms into universal gate units at the logical level when surface codes are used. We herein also present modified quantum circuits for elliptic curve discrete logarithms and compare our results with those of the original quantum circuit implementations at the physical level. Through the analysis, we show that the use of more logical qubits in quantum algorithms does not always lead to the use of more physical qubits. We assumed using rotated surface code and logical qubits with all-to-all connectivity. The number of physical qubits and execution time are expressed in terms of bit length, physical gate error rate, and probability of algorithm failure. In addition, we compare our results with the number of physical qubits and execution time of Shor's factoring algorithm to assess the risk of attack by quantum computers in RSA and elliptic curve cryptography.


Analysing environmental opinion using highly customisable visualisation tools to understand citizens’ attitudes and barriers

Abstract: Abstract Knowledge of public opinion is key to understanding citizens' attitudes towards environmental policies. However, large polls and surveys generate complex datasets from which it is not always easy to draw conclusions. In addition, tailor-made solutions for analysing public opinion face the challenge of handling too many layers of information, which can easily lead to an overwhelming user experience and impair decision-making. Service design methodologies can support the design of ad hoc visualisation tools focused on user needs. We present Op-e-nion , a case study of a visualisation tool for the analysis of public opinion regarding environmental issues, aimed at administrations and public institutions. The involvement of experts from different fields allowed for the identification of the main metrics necessary to target the least engaged socio-demographic groups as well as the barriers that limited their environmental actions. Experts also highlighted useful aspects of the design process and the final prototype to help them define more effective campaigns and policies to address social challenges and promote citizen action. An innovative step was introduced in the methodology by involving non-state actors in the evaluation of the tool, ensuring problem detection and enhancing the sustainability of the final product. Important aspects for the visualisation of multi-categorical data included simplifying the interaction with the tool while prioritising relevant information, and using highly customizable visualisations to answer specific user requirements and changing needs (i.e. analytical vs. managerial tasks). Improved visualisations of public opinion data will, in turn, better support the development of policies shaped by citizens’ concerns.


Thin-film transistor for temporal self-adaptive reservoir computing with closed-loop architecture

Abstract: Reservoir computing is a powerful neural network-based computing paradigm for spatiotemporal signal processing. Recently, physical reservoirs have been explored based on various electronic devices with outstanding efficiency. However, the inflexible temporal dynamics of these reservoirs have posed fundamental restrictions in processing spatiotemporal signals with various timescales. Here, we fabricated thin-film transistors with controllable temporal dynamics, which can be easily tuned with electrical operation signals and showed excellent cycle-to-cycle uniformity. Based on this, we constructed a temporal adaptive reservoir capable of extracting temporal information of multiple timescales, thereby achieving improved accuracy in the human-activity-recognition task. Moreover, by leveraging the former computing output to modify the hyperparameters, we constructed a closed-loop architecture that equips the reservoir computing system with temporal self-adaptability according to the current input. The adaptability is demonstrated by accurate real-time recognition of objects moving at diverse speed levels. This work provides an approach for reservoir computing systems to achieve real-time processing of spatiotemporal signals with compound temporal characteristics.


Category-Aware Saliency Enhance Learning Based on CLIP for Weakly Supervised Salient Object Detection

Abstract: Abstract Weakly supervised salient object detection (SOD) using image-level category labels has been proposed to reduce the annotation cost of pixel-level labels. However, existing methods mostly train a classification network to generate a class activation map, which suffers from coarse localization and difficult pseudo-label updating. To address these issues, we propose a novel Category-aware Saliency Enhance Learning (CSEL) method based on contrastive vision-language pre-training (CLIP), which can perform image-text classification and pseudo-label updating simultaneously. Our proposed method transforms image-text classification into pixel-text matching and generates a category-aware saliency map, which is evaluated by the classification accuracy. Moreover, CSEL assesses the quality of the category-aware saliency map and the pseudo saliency map, and uses the quality confidence scores as weights to update the pseudo labels. The two maps mutually enhance each other to guide the pseudo saliency map in the correct direction. Our SOD network can be trained jointly under the supervision of the updated pseudo saliency maps. We test our model on various well-known RGB-D and RGB SOD datasets. Our model achieves an S-measure of 87.6 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> on the RGB-D NLPR dataset and 84.3 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> on the RGB ECSSD dataset. Additionally, we obtain satisfactory performance on the weakly supervised E-measure, F-measure, and mean absolute error metrics for other datasets. These results demonstrate the effectiveness of our model.


A Novel Procedure for the AHP Method for the Site Selection of Solar PV Farms

Abstract: This study proposes a novel approach to enhance the analytic hierarchy process (AHP) for the selection of suitable sites for solar photovoltaic (PV) farms. This approach is particularly beneficial when it is possible to establish a predefined objective relation in the final weights of the AHP method. The methodology focuses on achieving this predefined relation introducing a systematic revision of the constants of related constraints. In this study, the costs of constructing a unit transmission line and road in the Kayseri Province are objectively related, and the initial constant matrix of the AHP method is iteratively revised until the relation of the final weights converges to the predefined one. The suitability of solar PV farm locations is classified into five classes, revealing approximately 28% (40-100% of suitability) of the province as favorably suitable and designating about 67% as restricted zones. The findings reveal notable distinctions between the revised weights and those derived from the conventional AHP method. The disparity in weights for various constraints varies from 13.5% to 7.2%. Consequently, the alterations in the area of suitability regions range from 3.4% to 50%. The revision of AHP weights results in a reduction in higher-suitability areas, coupled with a significant expansion in the region exhibiting lower suitability. Notably, the extent of change in the suitability map increases when the difference in ratios between two criteria obtained from the AHP and the predefined objective relation is high. The proposed method demonstrates its applicability in regions like Kayseri where an objective relation between criteria can be established. Given the inherent subjectivity of the AHP method, the proposed procedure becomes essential to attain more objective weights. Since the methodology objectively adjusts weights based on known ratios, it increases the accuracy and reliability of site selection studies.


Factors Influencing the Positive Thinking of the Future Teacher

Abstract: The article is devoted to the analysis of scientific literature on the problem under study, positive psychology and socio-psychological problems of the formation of a positive mood in professional activity. Research objectives: to determine factors influencing positive thinking among future teachers and to analyze psychological problems. Research materials and methods: analysis of statements by scientist psychologists about positive thinking and psychological problems such as tension, hopelessness, lack of self-esteem, conflict at work, self-flagellation, painful emotions, poor health, dissatisfaction with oneself as a subject of education, the presence of constant negative thoughts, lack of optimism. Research results: the basis for positive thinking of a future teacher is the ability to evaluate negativity through the prism of optimism and constructiveness. Conclusions: the development of positive thinking of a future teacher during the period of study at a higher educational institution depends on the dynamics of certain characteristics.


Scientometric visualization of data on artificial intelligence and marketing: Analysis of trends and themes

Abstract:
Abstract
 Artificial Intelligence (AI) technologies are being used in marketing to make automated judgments based on data gathering, data analysis, and further insights into audience or market indicators that may influence marketing activities. The study utilized Biblioshiny, a web-based interface, to do a bibliometric analysis of AI and marketing and its associated studies found in the Scopus database. The time span of the analyzed data is 40 years (1984–2024).The results were analyzed based on many categories, including data set, sources, authors, documents, conceptual structure, intellectual structure, and social structure. The findings revealed satisfactory literature in the selected research domain. The annual scientific production of work related to AI and marketing elevated after 2016. The disciplines of Natural Language Processing, Decision Support Systems etc. garnered the most substantial focus in marketing and were positioned at the core of the subject matter. Based on the investigation of research in AI and marketing, this study will be useful for future researchers and policymakers as a preliminary step to locate pertinent studies.


Analysis of Forced Aligner Performance on L2 English Speech

Abstract: There is growing interest in how speech technologies perform on L2 speech. Largely omitted from this discussion are tools used in the early data processing steps, such as forced aligners, that can introduce errors and biases. This study adds to the conversation and tests how well a model pre-trained for the alignment of L1 American English speech performs on L2 English speech. We test and discuss the impact of language variety, demographic factors, and segment type on the performance of the forced aligner. We also examine systematic errors encountered. Forty-five speakers representing nine L2 varieties were selected from the Speech Accent Archive and force aligned using the Montreal Forced Aligner. The phoneme-level boundary placements were manually corrected in order to assess differences between the automatic and manual alignments. Results show marked variation in the performance across language groups and segment types for the two metrics used to assess accuracy: Onset Boundary Displacement, a distance metric between the automatic and manual boundary placements, and Overlap Rate, which indicates to what extent the automatically aligned segment overlaps with the manually aligned segment. The highest accuracy on both measures was obtained for German and French, and lowest accuracy for Russian. The aligner's performance on all varieties was comparable to that on conversational American English and non-standard varieties of English. Furthermore, the percentage of boundary placements within 10 and 20ms of the corrected boundary was similar to that observed between transcribers. Apart from errors due to variety mismatch, most issues encountered in the alignment were due to issues not exclusive to L2 speech such as inaccurate orthographic transcriptions, hesitations, specific voice qualities, and background noise. The results of this study can inform the use of automatic aligners on L2 English speech and provide a baseline of potential errors and information to help the development of more robust alignment tools for further development of automatic systems using L2 English.


The role of lifestyle factors, biological sex, and racial identity for (Visually Induced) Motion Sickness Susceptibility: Insights from an online survey

Abstract: Motion sickness (MS) and visually induced motion sickness (VIMS) are common side-effects when travelling or when using visual devices, respectively. A variety of individual factors may determine one’s susceptibility to MS/VIMS. The role of lifestyle factors including video-game usage, physical activity, diet, and substance use on self-reported susceptibility to MS/VIMS was investigated. Additionally, the role of biological sex and racial identity on the overall prevalence of MS/VIMS were analyzed. A total of 711 responses to an online survey were collected from adults between the ages of 17-49 years. Method of analyses included correlations, comparison tests, and multiple regressions. The results showed that overall, VIMS is a prevalent issue for most participants, with approximately 24% of users reporting nausea at least sometimes when using visual displays. Although some lifestyle factors (video game usage, assorted dietary elements) were identified as potentially relevant for MS/VIMS susceptibility, these factors only explained a minimal amount of the variance in MS/VIMS susceptibility. Significant sex differences were found, with female participants reporting higher susceptibility for MS/VIMS than male participants. Additionally, East Asians participants reported higher MS susceptibility. Our findings do not suggest that lifestyle factors are prominent predictors for the susceptibility of VIMS and MS.


Shape-constrained deformable brain segmentation: Methods and quantitative validation

Abstract: MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.


A framework for efficient cross-chain token transfers in blockchain networks

Abstract: The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.


Cross-validated tree-based models for multi-target learning

Abstract: Multi-target learning (MTL) is a popular machine learning technique which considers simultaneous prediction of multiple targets. MTL schemes utilize a variety of methods, from traditional linear models to more contemporary deep neural networks. In this work we introduce a novel, highly interpretable, tree-based MTL scheme which exploits the correlation between the targets to obtain improved prediction accuracy. Our suggested scheme applies cross-validated splitting criterion to identify correlated targets at every node of the tree. This allows us to benefit from the correlation among the targets while avoiding overfitting. We demonstrate the performance of our proposed scheme in a variety of synthetic and real-world experiments, showing a significant improvement over alternative methods. An implementation of the proposed method is publicly available at the first author's webpage.


Cenozoic morphotectonic evolution of the eastern edge of Paraná Sedimentary Basin, southern Brazil

Abstract: The Paraná Sedimentary Basin (PSB) is one of the most important geotectonic structures on the South American continent and its tectono-sedimentary evolution has been widely investigated, especially during the Paleozoic and Mesozoic. However, the post-rift Cenozoic dynamics need further studies to fully understand its evolution. Hence, this research aims to add data and interpretations regarding the morphostructural features inherent in the Cenozoic tectonic framework, especially in the south/southeast region of Brazil. Here, we explore the Cenozoic processes of tectonic reactivation of preexisting structural features, as well as the neoformation of structural features, considering that Mesozoic sedimentary and volcanic strata of the Serra Geral Group in the Paraná Basin were deformed by Cenozoic joints and faults. For this purpose, structural and geomorphological (morphometric) analyses were used in the Itararé and Cinzas rivers catchments, on the eastern edge of the PSB. The study area is part of the Ponta Grossa Arch domain, in the northeast of Paraná state and southwest of São Paulo state. The structural data collected, when associated with the morphometric analyses (qualitative and quantitative), were compatible, showing the influence of tectonic structuring on the geomorphological framework of the investigated watersheds, resulting in the morphotectonic compartmentalization of the surveyed area. Alterations to the topographic surface, asymmetry patterns in the watersheds, disturbances in the longitudinal river profiles and anomalies in the drainage network patterns are the result of reactivation of ancient NE-SW and NW-SE faults in the crystalline basement and present in the units that fill the sedimentary basin, as well as new families of faults, mainly in N–S and E-W direction. The structural analysis supported the determination of the Cenozoic paleostress fields, defined from the analysis of the fault population, spatial arrangement and through statistical and mechanical treatment of the kinematic structural data. The results of the paleostress tensors indicate a landscape evolution associated with at least four tectonic pulses, which compressive SHmax values are oriented in NE-SW, N–S, NW-SE and E-W directions, respectively. These are in agreement with the main regional scientific studies in southern and southeastern Brazil, which involve tectonic events from the Upper Cretaceous and Paleogene, advancing into the Neogene and up to at least the Pleistocene.


Graph Neural Network contextual embedding for Deep Learning on tabular data

Abstract: All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on seven public datasets, also achieving competitive results when compared to boosted-tree solutions.


Data-driven model selections of second-order particle dynamics via integrating Gaussian processes with low-dimensional interacting structures

Abstract: In this paper, we focus on the data-driven discovery of a general second-order particle-based model that contains many state-of-the-art models for modeling the aggregation and collective behavior of interacting agents of similar size and body type. This model takes the form of a high-dimensional system of ordinary differential equations parameterized by two interaction kernels that appraise the alignment of positions and velocities. We propose a Gaussian Process-based approach to this problem, where the unknown model parameters are marginalized by using two independent Gaussian Process (GP) priors on latent interaction kernels constrained to dynamics and observational data. This results in a nonparametric model for interacting dynamical systems that accounts for uncertainty quantification. We also show that our estimators can be efficiently derived through the use of preconditioned techniques, ensuring scalability. Moreover, we perform a theoretical analysis to interpret the methodology and investigate the conditions under which the kernels can be recovered. We demonstrate the effectiveness of the proposed approach on various prototype systems, including the selection of the order of the systems and the types of interactions. In particular, we present applications to modeling two real-world fish motion datasets that display flocking and milling patterns up to 248 dimensions. Despite the use of small data sets, the GP-based approach learns an effective representation of the nonlinear dynamics in these spaces and outperforms competitor methods.


Pedagogy and technology: the transformative impact of digital tools in education

Abstract: This bibliographic review addressed the transformative impact of digital tools on education, focusing on the interplay between pedagogy and technology. The central problem was to understand how technology reshapes education, both in pedagogical practices and in the development of 21st-century skills. The main objective was to explore the nature and extent of this impact, considering opportunities and challenges for students and teachers. The methodology adopted was a literature review, allowing a critical and comprehensive analysis of relevant publications. The results showed that digital technologies offer significant opportunities to innovate in education, but also present challenges such as unequal access, the need for teacher training, and the adaptation of methodologies. Successful experiences highlighted how effective integration of technology can promote critical skills and educational inclusion. It was concluded that technology is a transformative element in education, capable of improving the quality and effectiveness of teaching. However, overcoming existing barriers is essential to maximize its potential. The research underlined the continuous need for innovation and investigation in educational technology.


Few-Shot Semantic Segmentation via Mask Aggregation

Abstract: Abstract Few-shot semantic segmentation aims to recognize novel classes with only very few labelled data. This challenging task requires mining of the correlation between the query image and the support images. Previous works have typically regarded it as a pixel-wise classification problem. Therefore, various models have been designed to explore the correlation of pixels between the query image and the support images. However, they focus only on pixel-wise correspondence and ignore the overall correlation of objects. In this paper, we introduce a mask-based classification method for addressing this problem. The mask aggregation network, which is a simple mask classification model, is proposed to simultaneously generate a fixed number of masks and their probabilities of being targets. Then, the final segmentation result is obtained by aggregating all the masks according to their locations. Experiments on both the PASCAL- $$5^i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> and COCO- $$20^i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>20</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> datasets show that our method performs comparably to the state-of-the-art pixel-based methods. This competitive performance demonstrates the potential of mask classification as an alternative baseline method for few-shot semantic segmentation.


Service Level Agreement in cloud computing: Taxonomy, prospects, and challenges

Abstract: Cloud computing represents a distributed computing paradigm, offering hosted services via the internet on a pay-as-you-go basis. The Service Level Agreement (SLA) serves as a pivotal element of communication between Cloud Service Providers (CSPs) and Cloud Service Users (CSUs), detailing Service Level Objectives (SLOs) pertinent to service functionalities and Quality of Service (QoS). Within an SLA, service terms are formally documented in a contract between the CSP and CSU, ensuring that outsourcing customers establish an SLA with their vendors, thereby holding vendors accountable for financial consequences or payments should the set objectives not be achieved. In recent years, SLA methodologies in cloud computing have attracted considerable attention from the research community, with numerous strategies being developed to tackle challenges that may impede the efficient provisioning and management of QoS. This survey paper offers a comprehensive review of SLA techniques, presenting a detailed taxonomy based on their distinctive attributes. It discusses the evaluation parameters and platforms utilized in analyzing SLA approaches. Furthermore, the paper outlines design objectives and highlights open research issues that should be addressed when proposing new SLA techniques.


Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes with Application to Brain Signals

Abstract: One of neuroscience's goals is to study the interactions between different brain regions during rest and while performing specific cognitive tasks. Multivariate Bayesian autoregressive decomposition (MBMARD) is proposed as an intuitive and novel Bayesian non-parametric model to represent high-dimensional signals as a low-dimensional mixture of univariate uncorrelated latent oscillations. Each latent oscillation captures a specific underlying oscillatory activity and, hence, is modeled as a unique second-order autoregressive process due to a compelling property, namely, that its spectral density's shape is characterized by a unique frequency peak and bandwidth, parameterized by a location and a scale parameter. The posterior distributions of the latent oscillation parameters are computed using a Metropolis-within-Gibbs algorithm. One of the advantages of the MBMARD model is its higher robustness against misspecification, compared with standard models. The main scientific questions addressed by the MBMARD model relate to the effects of long-term alcohol abuse on memory. These effects were examined by analyzing the electroencephalogram records of alcoholic and non-alcoholic subjects performing a visual recognition experiment. The MBMARD model yielded novel and interesting findings, including the identification of subject-specific clusters of low- and high-frequency oscillations in different brain regions.


Average consensus of whole-process privacy protection: A scale parameter method

Abstract: Privacy-preserving average consensus is a distributed control/estimation framework where a group of agents cooperatively compute the average of initial state. Meanwhile, the initial state and real-time state (such as initial opinion, initial location, real-time location, real-time active power ratio) in the communication and computation process may contain some sensitive information, which do not want other nodes of the network to obtain. However, existing privacy protection results mainly focused on protecting the initial state, and rarely took into account the privacy effect from the beginning to the end. To deal with this problem, a novel and interesting scale parameter method is proposed. First, a group of privacy coefficients is designed and assigned in advance for each node, which ensures that only their own parameters can be known and has no knowledge of others. Correspondingly, the initial state and real-time state can be hidden in the false value after multiplying these scale parameters. Then, the correctness analysis and privacy analysis of this method are provided respectively. Later, the generalization on directed networks is discussed. Finally, numerical simulations are conducted to show the effectiveness of obtained theoretical results.


Network traffic classification based on federated semi-supervised learning

Abstract: Traffic Classification (TC) has been applied to a wide range of applications, from security monitoring to quality of service (QoS) provisioning in network Internet Service Providers (ISPs). In recent years, many researchers have applied Machine Learning (ML) or Deep Learning (DL) to TC, namely AI-TC. However, AI-TC methods face significant challenges, including high data dependency, exhaustively costly traffic labeling, and network subscribers’ privacy. This paper proposes a TC framework for smart home networks using Federated Learning (FL) that protects traffic data privacy by performing local training and inference of TC models. Firstly, we design a DPI-based traffic labeling method on edge home gateways as FL nodes, which enables these nodes to have data labeling capability while protecting data privacy. Then, a semi-supervised TC model based on an autoencoder (AE) is proposed to reduce the dependence of the model on labeled traffic samples. Finally, an XAI-based method is utilized to interpret the model to ensure its explainability. We validate the proposed method on public and real datasets using benchmarking methods. The experimental results show that the method can achieve high performance using a small number of samples while protecting data privacy and improving the model’s credibility. Experimental code can be found in the following url: https://github.com/PrinceXuan12138/HGW-TC-Experimental-code.


Institute for Artificial Intelligence and Fundamental Interactions (IAIFI): Infusing physics intelligence into artificial intelligence

Abstract: Abstract The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /aI‐faI/) is one of the inaugural NSF AI research institutes ( https://iaifi.org ). The IAIFI is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles from fundamental physics. By combining state‐of‐the‐art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community. Since trustworthy AI is as important for physics discovery as it is for other applications of AI in society, IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies. To cultivate human intelligence, the IAIFI promotes training, education, and public engagement at the intersection of physics and AI. In these ways, the IAIFI is fusing deep learning with deep thinking to gain a deeper understanding of our universe and AI.


Toward Useful Quantum Kernels

Abstract: Abstract Supervised machine learning is a popular approach to the solution of many real‐life problems. This approach is characterized by the use of labeled datasets to train algorithms for classifying data or predicting outcomes accurately. The question of the extent to which quantum computation can help improve existing classical supervised learning methods is the subject of intense research in the area of quantum machine learning. The debate centers on whether an advantage can be achieved already with current noisy quantum computer prototypes or it is strictly dependent on the full power of a fault‐tolerant quantum computer. The current proposals can be classified into methods that can be suitably implemented on near‐term quantum computers but are essentially empirical, and methods that use quantum algorithms with a provable advantage over their classical counterparts but only when implemented on the still unavailable fault‐tolerant quantum computer. It turns out that, for the latter class, the benefit offered by quantum computation can be shown rigorously using quantum kernels, whereas the approach based on near‐term quantum computers is very unlikely to bring any advantage if implemented in the form of hybrid algorithms that delegate the hard part (optimization) to the far more powerful classical computers.


Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Abstract: Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.


Question answering systems for health professionals at the point of care—a systematic review

Abstract: Abstract Objectives Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. Materials and methods We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems. Results We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians’ questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. Discussion While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.


BLOCKBOX: Blockchain based black box designing and modeling

Abstract: Summary With the development of technology, data has become more accessible. The storage of critical and valuable information is getting harder and harder with the increase of cyber attacks and vulnerabilities of network and internet. The centralized storage of data causes security and privacy problems. As a remedy to these problems, blockchain has occurred. The reliability, transparency, integrity and confidentiality of the data have been increased with the blockchain technology. Blockchain maintains the immutability of the data with cryptographic hash algorithms. The data is kept in distributed structures with the consensus algorithms so that it is not lost. Projects and studies using blockchain technology have shown that data stored in centralized structures is not as reliable as data that is distributed and secured by cryptographic algorithms, smart contracts, and consensus. The data of flights, which is a critical data, has a great place in world transportation. The parameters inside the black boxes guide us on why the aircrafts have crashed: whether the responsibility belongs to the manufacturer, to the pilot, or to the aircraft technicians. In this way, the real reasons of the accident may be determined and measures can be taken for the accidents that may occur afterwards. In addition, the safe delivery of all black box data to the interested parties and its storage in a way that is immutable will not only benefit to the manufacturer's self‐development by processing the data, but also help flight companies to find optimum flight data. Both the flight companies and the manufacturer will be able to read the black box data, thus the suspicion of data manipulation will be eliminated and the data will become more transparent. In this study, a system is proposed in which an aircraft's black box data can be saved safely and securely with the help of blockchain technology.


Angell and McCall Meet Wansing

Abstract: Abstract In this paper, we introduce a new logic, which we call AM3 . It is a connexive logic that has several interesting properties, among them being strongly connexive and validating the Converse Boethius Thesis . These two properties are rather characteristic of the difference between, on the one hand, Angell and McCall’s CC1 and, on the other, Wansing’s C . We will show that in other aspects, as well, AM3 combines what are, arguably, the strengths of both CC1 and C . It also allows us an interesting look at how connexivity and the intuitionistic understanding of negation relate to each other. However, some problems remain, and we end by pointing to a large family of weaker logics that AM3 invites us to further explore.


What characterizes LIS as a fragmenting discipline?

Abstract: Purpose The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley’s (1984) theory about the organization of sciences and Fuchs’ (1993) theory about scientific change. Design/methodology/approach The study combines historical source analysis with conceptual and theoretical analysis for characterizing LIS. An attempt is made to empirically validate the distinction between LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Findings The origin of fragmentation in LIS due the contributions of other disciplines can be traced in the 1960s and 1970s for solving the problems produced by the growth of scientific literature. Computer science and business established academic programs and started research relevant to LIS community focusing on information retrieval and bibliometrics. This has led to differing research interests between LIS and other disciplines concerning research topics and methods. LIS has been characterized as fragmented adhocracy as a whole, but we make a distinction between research topics LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Originality/value The paper provides an elaborated historical perspective on the fragmentation of LIS in the pressure of other disciplines. It also characterizes LIS as discipline in a fresh way by applying Whitley’s (1984) theory.


The architecture of visual narrative: Can text-to-image algorithms enhance the power of stylistic narrative for architecture

Abstract: Architecture has always been a means of communicating stories through its design, with its structures and spaces serving as visual narratives. However, recent advancements in technology have created opportunities for architects to enhance their storytelling capabilities through the use of text-to-image algorithms. These algorithms have the potential to improve visual narratives by enabling architects to translate written descriptions into tangible visual representations. This article explores the architecture of visual narrative and how text-to-image algorithms can enhance it in diverse styles. This inquiry aims to help architectural epistemology understand and foresee the potential impact of this technology on the field of architecture. To understand the limits of AI in generating styles to enhance architectural narrative, six distinct styles were chosen for experimentation. The styles were selected based on their unique features, including an architect’s style, movement, or era. These styles include Zaha Hadid, Brutalist, modern-minimalistic, Peter Zumthor, Gothic, and Gaudi. The narrative was kept the same for each style while observing the changes in AI-generated visuals. The results were evaluated by comparing AI’s interpretations in terms of stylistic, environmental, material, form-based, and atmospheric features. While the results showed promise in terms of variations in each category, AI was not successful in implementing all stylistic features while keeping the narrative stable. In particular, after the second environment layer, the modern-minimalistic, Zumthor, and Brutalist styles lost their distinct features, while Gothic and Gaudi-inspired visuals were hardly generated even in the second environment layer. As a result, AI performed well in generating detailed environmental features without any given narrative and creating an atmospheric environment with enlightening the environment for the last layer.


Combining Swin Transformer and Attention-Weighted Fusion for Scene Text Detection

Abstract: Abstract The existing text detection algorithms based on Convolutional Neural Networks (CNN) commonly have the problems of insufficient receptive fields and inadequate extraction of spatial positional information, which limit their ability to detect large-scale variation text instances, long-distance and wide-spaced text instances as well as effectively distinguish complex background textures. To address the above problems, in this paper, a scene text detection algorithm combining Swin Transformer and attention-weighted fusion is proposed. Firstly, an attention-weighted fusion (AWF) module is proposed, which embeds a modified coordinate attention module (CAM) in the feature pyramid network (FPN). This module learns spatial positional weights of foreground information in different-scale features while suppressing redundant background information. As a result, the fused features are more focused on the text regions, enhancing the localization ability for text regions and boundaries. Secondly, the window-based self-attention mechanism of the Swin Transformer is utilized to achieve global feature perception on the fused features of the pyramid network. This compensates for the insufficient receptive fields of CNN and enhances the representation capability of global contextual features, thereby further improving the performance of text detection. Experimental results demonstrate that the proposed algorithm achieves competitive performance on three public datasets, namely ICDAR2015, MSRA-TD500, and Total-Text, with F-measure reaching 87.9%, 91.4%, and 86.7%, respectively. Code is available at: https://github.com/xgli411/ST-AWFNet .


Escaping chronic poverty: long-term impacts of an agricultural development program

Abstract: Abstract There is a need for groundbreaking initiatives to come to the rescue of the world’s rain-fed regions, especially in the face of the current climate and food crises and the associated reversal of previous advances in poverty reduction. However, there is little evidence of successful programs, which can inform the development of proactive policies to meet these urgent challenges. Can sustainable agricultural intensification enable marginalized, smallholder farmers to break out of chronic poverty? In this study, we scrutinize the Wadi concept, an integrated farming system that includes water resource development, soil conservation, cultivation of fruit trees, and intercropping on degraded land. We use a mixed-methods approach that involves surveying 1860 households, which were randomly sampled from a population of 188,231 participants across four Indian states, encompassing a variety of geographical regions. This is the first investigation into the effects of integrated farming systems on livelihoods on such a large scale and spanning 23 years. We compare the household participants of the program with the non-participants and run a linear regression analysis, ceteris paribus , comparing farmers who established integrated farming systems at different points in time over the period 1990–2017. Significant progress in several socioeconomic factors can be observed, such as higher incomes (the annual income of a typical participant has increased from INR 30,000 to 100,000 or even more), diversified income sources, positive life changes, higher life satisfaction and better perspectives for children, as well as environmental benefits. The Wadi program’s effectiveness in providing sustainable development pathways implies benefits to be gained from scaling it up. By thoroughly assessing its potential to help farmers escape chronic poverty and build resilience, this study provides evidence of the Wadi concept’s success that can enrich the science-policy dialog on development programs.


The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer

Abstract: Human activity Recognition (HAR) using smartphone sensors suffers from two major problems: sensor orientation and placement. Sensor orientation and sensor placement problems refer to the variation in sensor signal for a particular activity due to sensors' altering orientation and placement. Extracting orientation and position invariant features from raw sensor signals is a simple solution for tackling these problems. Using few heuristic features rather than numerous time-domain and frequency-domain features offers more simplicity in this approach. The heuristic features are features which have very minimal effects of sensor orientation and placement. In this study, we evaluated the effectiveness of four simple heuristic features in solving the sensor orientation and placement problems using a 1D-CNN-LSTM model for a data set consisting of over 12 million samples.We accumulated data from 42 participants for six common daily activities: Lying, Sitting, Walking, and Running at 3-Metabolic Equivalent of Tasks (METs), 5-METs and 7-METs from a single accelerometer sensor of a smartphone. We conducted our study for three smartphone positions: Pocket, Backpack and Hand. We extracted simple heuristic features from the accelerometer data and used them to train and test a 1D-CNN-LSTM model to evaluate their effectiveness in solving sensor orientation and placement problems.We performed intra-position and inter-position evaluations. In intra-position evaluation, we trained and tested the model using data from the same smartphone position, whereas, in inter-position evaluation, the training and test data was from different smartphone positions. For intra-position evaluation, we acquired 70-73% accuracy; for inter-position cases, the accuracies ranged between 59 and 69%. Moreover, we performed participant-specific and activity-specific analyses.We found that the simple heuristic features are considerably effective in solving orientation problems. With further development, such as fusing the heuristic features with other methods that eliminate placement issues, we can also achieve a better result than the outcome we achieved using the heuristic features for the sensor placement problem. In addition, we found the heuristic features to be more effective in recognizing high-intensity activities.


Is ChatGPT taking over the language classroom?

Abstract: ChatGPT generated much dialogue on the implications of large language models (LLMs) for language teaching and learning. Since language teachers are uniquely positioned to teach metalinguistic awareness, they can support their learners’ understanding of how LLMs are shaped by language ideologies and how their outputs are indexical of social power. This awareness would help learners be more conscientious in using LLMs, deciding how to interact with them and adapt their outputs for their purposes. This article introduces LLMs as statistical systems that predict linguistic forms. It surfaces two language ideologies that have shaped their development: the belief in the separability of language from its social contexts and the belief in the value of larger text corpora. It also highlights some ideological effects including uneven language performance, text outputs that reflect biases, privacy violations, circulation of copyrighted materials, misinformation, and hallucinations. Some suggestions for mitigating these effects are offered.