Reviewerly/exHarmony huggingface dataset:
FL-FD: Federated learning-based fall detection with multimodal data fusion

Multimodal data fusion is a critical element of fall detection systems, as it provides more comprehensive information than single-modal data. Yet, data heterogeneity between sources has posed a challenge for the effective fusion of such data. This paper proposes a novel multimodal data fusion method under a federated learning (FL) framework that addresses the privacy concerns of users while exploiting the complementarity of such data. Specifically, we fuse time-series data from wearable sensors and visual data from cameras at the input level, where the data is first transformed into images using the Gramian Angular Field (GAF) method. Moreover, each user is treated as a private client in the FL system whereby the fall detection model is trained without requiring the sharing of user data. The proposed method is evaluated using the UP-Fall dataset, where we perform different fall detection tasks: binary classification for fall and non-fall detection yields a remarkable accuracy of 99.927%, while multi-classification for different fall activity recognition attains an accurate result of 89.769%.
How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare

Medical use cases for machine learning (ML) are growing exponentially. The first hospitals are already using ML systems as decision support systems in their daily routine. At the same time, most ML systems are still opaque and it is not clear how these systems arrive at their predictions.In this paper, we provide a brief overview of the taxonomy of explainability methods and review popular methods. In addition, we conduct a systematic literature search on PubMed to investigate which explainable artificial intelligence (XAI) methods are used in 450 specific medical supervised ML use cases, how the use of XAI methods has emerged recently, and how the precision of describing ML pipelines has evolved over the past 20 years.A large fraction of publications with ML use cases do not use XAI methods at all to explain ML predictions. However, when XAI methods are used, open-source and model-agnostic explanation methods are more commonly used, with SHapley Additive exPlanations (SHAP) and Gradient Class Activation Mapping (Grad-CAM) for tabular and image data leading the way. ML pipelines have been described in increasing detail and uniformity in recent years. However, the willingness to share data and code has stagnated at about one-quarter.XAI methods are mainly used when their application requires little effort. The homogenization of reports in ML use cases facilitates the comparability of work and should be advanced in the coming years. Experts who can mediate between the worlds of informatics and medicine will become more and more in demand when using ML systems due to the high complexity of the domain.
Classification of tumor in one single ultrasound image via a novel multi-view learning strategy

Computer-aided diagnosis (CAD) technology has been widely used in the early diagnosis of breast cancer. Nowadays, most of the existing breast ultrasound classification methods need to crop a tumor-centered image (TCI) on each image as the input of the system. These methods ignore the fact that the tumor as well as its surrounding tissues can actually be viewed from multiple aspects, and it is difficult to extract multi-resolution information applying only a single view image. In addition, the current methods do not effectively extract fine-grained features, and subtle details play an important role in breast classification. In our research, we propose a novel strategy to generate multi-resolution TCIs in a single ultrasound image, resulting in a multi-data-input learning task. Hence, a conventional single image based learning task is converted into a multi-view learning task, and an improved combined style fusion method suitable for a deep network is proposed, which integrates the advantage of the decision-based and feature-based methods to fuse the information of different views. At the same time, we first attempt to introduce the fine-grained classification method into breast classifications and capture the pairwise correlation between feature channels at each position to extract subtle information. The comparative experimental results show that our method can effectively improve the classification performance and achieves the best results in five metrics.
Accelerating accurate simulations of calorimeter showers with normalizing flows and probability density distillation

Recently, we introduced caloflow, a high-fidelity generative model for geant4 calorimeter shower emulation based on normalizing flows. Here, we present caloflow v2, an improvement on our original framework that speeds up shower generation by a further factor of 500 relative to the original. The improvement is based on a technique called probability density distillation, originally developed for speech synthesis in the machine learning literature, and which we develop further by introducing a set of powerful new loss terms. We demonstrate that caloflow v2 preserves the same high fidelity as the original using qualitative (average images, histograms of high-level features) and quantitative (classifier metric between geant4 and generated samples) measures. The result is a generative model for calorimeter showers that matches the state of the art in speed (a factor of ${10}^{4}$ faster than geant4) and greatly surpasses the previous state of the art in fidelity.
Metaverse in Education: Contributors, Cooperations, and Research Themes

Research on Educational Metaverse (Edu-Metaverse) has developed into an active research field. Based on 310 academic papers published from 2004 to 2022, this study identifies contributors, scientific cooperations, and research themes using bibliometrics, social network analysis, topic modeling, and keyword analysis. Results suggest that Edu-Metaverse has gained increasing attention in academia since 2019. Countries/affiliations located in the same regions are close partners in scientific cooperation. By jointly interpreting topic modeling and keyword analysis results, this study reveals eight main themes in the field of Edu-Metaverse: 1) metaverse-based physical education, 2) metaverses-supported simulations for collaborative problem-based learning (PBL) in health/medical education, 3) three-dimensional virtual learning environment-supported art appreciation and creation in art education, 4) metaverse-enabled laboratories for STEM education, 5) language and 21st-century skill development through metaverse-supported immersive language learning, 6) metaverse for developing autism children' social communication abilities, 7) virtual world metaverse-supported gameful experience-based education, and 8) quantitative research on Edu-Metaverse focusing on learners' experience. We also identified challenges and directions needing further attention: 1) data security and privacy protection, 2) balance between the real world and virtual world identities, 3) preparing instructors for Edu-Metaverse, and 4) assessment of higher-order thinking competencies in Edu-Metaverse-based PBL. This work helps facilitate researchers' and practitioners' understanding of Edu-Metaverse research and raises their awareness of research frontiers and future directions.
ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations

The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting for the effective integration of solar energy into the energy system. Reliable solar radiation forecasting has become crucial for the design, planning, and operational management of energy systems, especially in the context of ambitious greenhouse gas emission goals. This paper presents a study on the application of auto-regressive integrated moving average (ARIMA) models for the seasonal forecasting of solar radiation in different climatic conditions. The performance and prediction capacity of ARIMA models are evaluated using data from Jordan and Poland. The essence of ARIMA modeling and analysis of the use of ARIMA models both as a reference model for evaluating other approaches and as a basic forecasting model for forecasting renewable energy generation are presented. The current state of renewable energy source utilization in selected countries and the adopted transition strategies to a more sustainable energy system are investigated. ARIMA models of two time series (for monthly and hourly data) are built for two locations and a forecast is developed. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can contribute to the stable long-term integration of solar energy into countries’ systems. However, it is crucial to develop location-specific models due to the variability of solar radiation characteristics. This study provides insights into the use of ARIMA models for solar radiation forecasting and highlights their potential for supporting the planning and operation of energy systems.
Deep learning with coherent VCSEL neural networks

Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs) are emerging to process DNN tasks in the optical domain with high clock rates, parallelism and low-loss data transmission. However, to explore the potential of ONNs, it is necessary to investigate the full-system performance incorporating the major DNN elements, including matrix algebra and nonlinear activation. Existing challenges to ONNs are high energy consumption due to low electro-optic (EO) conversion efficiency, low compute density due to large device footprint and channel crosstalk, and long latency due to the lack of inline nonlinearity. Here we experimentally demonstrate an ONN system that simultaneously overcomes all these challenges. We exploit neuron encoding with volume-manufactured micron-scale vertical-cavity surface-emitting laser (VCSEL) transmitter arrays that exhibit high EO conversion (<5 attojoule/symbol with $V_\pi$=4 mV), high operation bandwidth (up to 25 GS/s), and compact footprint (<0.01 mm$^2$ per device). Photoelectric multiplication allows low-energy matrix operations at the shot-noise quantum limit. Homodyne detection-based nonlinearity enables nonlinear activation with instantaneous response. The full-system energy efficiency and compute density reach 7 femtojoules per operation (fJ/OP) and 25 TeraOP/(mm$^2\cdot$ s), both representing a >100-fold improvement over state-of-the-art digital computers, with substantially several more orders of magnitude for future improvement. Beyond neural network inference, its feature of rapid weight updating is crucial for training deep learning models. Our technique opens an avenue to large-scale optoelectronic processors to accelerate machine learning tasks from data centers to decentralized edge devices.
Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence

Few studies have examined user motivations to use generative artificial intelligence (AI). This research aims to address this gap by examining how user motivations for ChatGPT usage affect perceived creepiness, trust, and the intention to continue using AI chatbot technology. The findings of an online survey (N = 421) reveal a negative relationship between personalization and creepiness, while task efficiency and social interaction are positively associated with creepiness. Increased levels of creepiness, in turn, result in decreased continuance intention. Furthermore, task efficiency and personalization have a positive impact on trust, leading to increased continuance intention. The results contribute to the field of human–computer interaction by investigating the motivations for utilizing generative AI chatbots and advancing our comprehension of AI creepiness, trust, and continuance intention. The practical ramifications of this research can inform the design of user interfaces and the development of features for generative AI chatbots.
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments

In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.
SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing

The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-occluded regions. Our approach consists of an attention generator network and a scene reconstruction network. We use the luminance differences of images to restrict the attention map and introduce a self-paced semi-curricular learning strategy to reduce learning ambiguity in the early stages of training. Extensive quantitative and qualitative experiments demonstrate that our SCANet outperforms many state-of-the-art methods. The code is publicly available at https://github.com/gy65896/SCANet.
EGCrypto: A Low-Complexity Elliptic Galois Cryptography Model for Secure Data Transmission in IoT

In recent years, data security has been a challenging endeavor, especially when the data is being transmitted every second.Internet of Things (IoT) involves continuously sending data over public networks, making the data vulnerable to various security threats.Therefore, ensuring the secure end-to-end communication of IoT data is critical.Cryptography and steganography have proven effective in providing secure connectivity for IoT devices.However, challenges in existing approaches include scalability, computational complexity, implementation, key management, trade-offs, evolving threats, and hyperparameter tuning.Therefore, in this paper, we propose EGCrypto, an efficient and secure model for IoT networks.EGCrypto utilizes a low-complexity elliptic Galois cryptography approach along with matrix XOR steganography to enhance security.To optimize its performance, we employ the zoning evolution of control attributes and adaptive mutation based self-adaptive differential evolution with fitness and diversity ranking.These techniques are utilized to fine-tune the hyperparameters of EGCrypto, enhancing its effectiveness and efficiency.The confidential IoT data is encrypted using low-complexity elliptic Galois cryptography.Following encryption, the encrypted data is embedded or hidden into cover blocks of an image, which are selected using the optimization algorithm.This ensures secure data communication in IoT architectures, as the encrypted data is transferred safely and can be easily recovered and decrypted at the receiving end.The experimental results demonstrate that EGCrypto outperforms competitive models with improvements of 1.8473% in peak signal to noise ratio (PSNR), 1.5490% in strutural similarity index metric (SSIM), 1.7682% in normalized root mean square error (NRMSE), 1.3829% in carrier capacity, and 1.9372% in embedding efficiency.
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning

In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the same output sequence. Such a unified model requires large-scale training data, which is not available in current annotated datasets. We show that it is possible to leverage unlabeled narrated videos for dense video captioning, by reformulating sentence boundaries of transcribed speech as pseudo event boundaries, and using the transcribed speech sentences as pseudo event captions. The resulting Vid2Seq model pretrained on the YT-Temporal-1B dataset improves the state of the art on a variety of dense video captioning benchmarks including YouCook2, ViTT and ActivityNet Captions. Vid2Seq also generalizes well to the tasks of video paragraph captioning and video clip captioning, and to few-shot settings. Our code is publicly available at [1].
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-based Active Learning

Recent research on video moment retrieval has mostly focused on enhancing the performance of accuracy, efficiency, and robustness, all of which largely rely on the abundance of high-quality annotations. While the precise frame-level annotations are time-consuming and cost-expensive, few attentions have been paid to the labeling process. In this work, we explore a new interactive manner to stimulate the process of human-in-the-loop annotation in video moment retrieval task. The key challenge is to select “ambiguous” frames and videos for binary annotations to facilitate the network training. To be specific, we propose a new hierarchical uncertainty-based modeling that explicitly considers modeling the uncertainty of each frame within the entire video sequence corresponding to the query description, and selecting the frame with the highest uncertainty. Only selected frame will be annotated by the human experts, which can largely reduce the workload. After obtaining a small number of labels provided by the expert, we show that it is sufficient to learn a competitive video moment retrieval model in such a harsh environment. Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection. In general, our active learning strategy for video moment retrieval works not only at the frame level but also at the sequence level. Experiments on two public datasets validate the effectiveness of our proposed method. Our code is released at https://github.com/renjie-liang/HUAL.
Dynamic Intelligence-Driven Engineering Flooding Attack Prediction Using Ensemble Learning

The rapid evolution of the Internet and communication technologies has fueled the proliferation of wireless sensor network (WSN) technology, which is increasingly important in today's interconnected world. For a broad variety of industries and applications, an enormous number of sensing devices continuously create and/or gather copious amounts of sensory data. However, it has been shown that WSN is susceptible to security flaws. These networks' abrasive and unmanaged deployment, along with their limited resources and the amount of data produced, raise serious security issues. The development of trustworthy solutions that include quick and continuous processes for live data stream analysis allowing the identification of flooding assaults is crucial since WSN applications are of the utmost importance. To put it in plain words: The assault is carried out by repeatedly sending pointless requests to the target computer in an effort to overwhelm it, cause the systems to fail, and prevent people from accessing the network or machine.
Generative AI

The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other. In this article, we provide a conceptualization of generative AI as an entity in socio-technical systems and provide examples of models, systems, and applications. Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research. Different from previous works, we focus on generative AI in the context of information systems, and, to this end, we discuss several opportunities and challenges that are unique to the BISE community and make suggestions for impactful directions for BISE research.
An Exploratory Study of EFL Learners’ Use of ChatGPT for Language Learning Tasks: Experience and Perceptions

ChatGPT, a general-purpose intelligent chatbot developed by OpenAI, has introduced numerous opportunities and challenges in the field of language education. With its remarkable ability to generate diverse forms of text, answer questions, and provide translations within minutes, ChatGPT has become an influential tool in the era of advanced AI technology. However, to what extent ChatGPT can be used to assist students in completing language learning tasks remains largely unexplored. Against this background, this study aimed to investigate students’ experiences with ChatGPT and their perceptions of its role in language learning through a small-scale qualitative study. The data were collected through semi-structured interviews with five students at a top-tier international university in China. Students’ responses revealed that ChatGPT has the potential to serve as a valuable learning partner and aid students in completing language-related tasks. Furthermore, participants exhibited critical judgment in evaluating the quality of ideas and outputs generated by ChatGPT, as well as the ability to modify prompts to maximize learning benefits. Such critical judgment offsets the potential threats to academic integrity posed by ChatGPT. Our findings contribute to the understanding of the potential of ChatGPT in language education by adding empirical evidence from students’ perspectives. This study supports the idea that ChatGPT can work as an effective tool for providing students with immediate feedback and personalized learning experiences. Such findings generate implications for future pedagogical practices in the new era by providing students with personalized guidance, designing technology-embedded language support, and developing students’ lifelong learning skills (e.g., autonomy and evaluative judgment) with the support of ChatGPT.