Reviewerly/exHarmony huggingface dataset:
Unified Named Entity Recognition as Multi-Label Sequence Generation

Currently, named entity recognition (NER) task can be mainly divided into three types: flat, nested, and discontinuous. In previous work, different types of NER tasks were usually studied separately, which as a result ignored the correlation between different NER tasks and brought inconvenience to their practical applications. Therefore, recently some researchers have begun to focus on how to handle three different types of NER tasks in a unified model by mainly using sequence-to-sequence (Seq2Seq) methods. However, the recognition performance of these Seq2Seq models still lags behind the best models corresponding to various NER tasks. To further improve the recognition performance of Seq2Seq models, this paper proposes a NER unified model based on Multi-Label Sequence Generation (MLSG). Firstly, to avoid the problem of information confusion caused by concatenating multiple unrelated entities, we propose a multi-label and multi-sequence methods in MLSG to output each entity as an independent sequence. Secondly, to address the issue of not utilizing non-entity information in existing Seq2Seq models, MLSG enumerates each word in the sentence and generates its corresponding target sequence, which allows to utilize non-entity information to strengthen the model's local attention on each starting word and thus reduce the learning difficulty of the task. Thirdly, to further take interactions between multiple labels for specific states into consideration, MLSG designs a multi-label attention module to learn the relationships between candidate labels for different states. Experiments conduced on eight popular NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets, demonstrate that our MLSG model achieves current best or near best performance.
Optimized NLP Models for Digital Twins in Metaverse

Digital Twins (DTs) in Metaverse face many challenges such as the lack of optimized AI models to allow the interaction between the user and the virtual environment. In this paper, we propose an optimized model for human language processing based on Convolutional Neural Networks (CCNs) and we present an input processing strategy to meet the real-time requirements of smart applications that integrate DTs oriented to speech-based functionalities for user interaction and Metaverse. In our solution, CNNs are applied for the processing and classification of the human voice, while structured data and MFCC coefficients are used to train the neural networks and generate interference in the models. Similarly, the MFCC algorithm is provided to extract the unique characteristics that specify each generated audio file and to reduce the complexity of the neural network model in order to obtain better performance. Starting from an approach to the problem available in the literature, we have optimized a specific CNN model for Natural Language Processing (NLP) in order to increase effective results. The proposed model has demonstrated excellent performance and can be used as a basis for the implementation of software that allows the interaction of DTs with voice commands issued by a user.
A Survey on Blockchain-Based Federated Learning and Data Privacy

Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the model’s transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.
A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks

Numerous sampling strategies have been proposed to reduce the visual clutter of large-scale geographical point data visualization, which focus on the preservation of original data features, such as randomness, spatial distribution, and associated relationship. However, user preferences and demands are not taken into account in the course of sampling, which will lead to the sampled results deviating from user requirements and impede personalized geospatial analysis in specific application scenarios. In this article, we propose a user-driven sampling model for the visual abstraction of the large-scale geographical point data based on convolutional neural networks (CNN). First, a blue noise sampling model is applied to partition the geographical space into local areas, and a set of visual interfaces are designed to present the data features of those points in the local areas, enabling users to visually select representative points according to their requirements. Then, user preferences are quantified with a CNN model based on the eigenvectors of the representative points, which are further utilized to guide the sampling courses of the other local areas. Thus, all the sampled points will retain the spatial distribution of original data points and fulfill the user preference as far as possible. In addition, we implement a visualization framework to integrate manual point selection, CNN training, automatic point sampling, and visual comparison, allowing users to easily obtain and evaluate the sampled points from the perspectives of data analysis and user requirements. Quantitative comparisons and case studies based on real-world datasets are conducted to demonstrate the effectiveness of our sampling model in the preservation of user preferences and visual exploration of large-scale geospatial point data.
Can AI have a personality?

In this paper, we evaluated several large language models (including ChatGPT, GPT3 and LLAMA) by running standardized personality tests on their results. Generally, we found that each large language model has an internal consistent personality. We further found that LLama tends to score more highly on Neuroticism than other models, whereas ChatGPT/GPT3 tends to score more highly on Conscientiousness and Agreeableness.
A Morphable Model For The Synthesis Of 3D Faces

In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an ''unlikely'' appearance
Edge device-based real-time implementation of CycleGAN for the colorization of infrared video

Edge computing has gained significant attention as a pivotal technology for practical implementation of machine learning. Nevertheless, resource-constrained edge devices face challenges in meeting the performance requirements of latency-sensitive applications. Moreover, computationally intensive applications such as CycleGAN pose further hindrances to the practical utilization of machine learning. To overcome these challenges, we proposed a hybrid method that combines the implementation of a lightweight model with optimization in deployment. Initially, a lightweight sub-network is derived from a student network obtained through distillation while considering the trade-off between performance and computational complexity. Subsequently, a quantization model is deployed for low-precision inference, resulting in a substantial reduction of inference time. Finally, we apply this approach to a real-life application: a real-time headset for color night vision from infrared videos. For this purpose, three datasets of image pairs comprising long-wave infrared, visible light RGB, and up to near-infrared images are collected. Using these datasets, a lightweight implementation of the CycleGAN model is trained to translate infrared images to RGB images. To ensure efficiency, the model is deployed in a low-precision inference manner using C＋＋ on three different hardware platforms: Jetson Xavier NX, RK3399 Pro, and Raspberry Pi, each with distinct hardware architectures. Image quality and model efficiency are thoroughly analyzed. Experimental results demonstrate that our hybrid method drastically reduces inference time from 112 ms to 17 ms per frame at a resolution of 256 × 256 on the Jetson Xavier NX platform. This improvement is accompanied by only a slight degradation in image quality, enabling a real-time video frame rate close to 60 fps, thereby meeting the requirements for a real-world real-time headset for color night vision applications.
Interactive Feedback Loop with Counterfactual Data Modification for Serendipity in a Recommendation System

AbstractUsers often encounter tedious recommendations as they are continuously exposed to the recommendation system. In response to this issue, serendipity in a recommendation system has been introduced to generate novel and unexpected recommendations while keeping them relevant to users' previous preferences. This study proposes an interactive feedback loop for a serendipity in a recommendation system that allows users to directly explore content via counterfactual manipulation of features. Specifically, users indicate their preferences through the "what-if" based customization of content meta-information, and these modifications influence their usage history, thereby enabling the elicitation of serendipitous items. To validate the proposed feedback loop, we conducted a scenario-based experiment and compared system-initiated and user-intervened recommendations. The results reveal that counterfactual exploration can help to generate serendipitous recommendations. This study contributes to providing a user-friendly recommendation system that can retrieve preference-reflected recommendations through user interaction.Keywords: Recommendation systemserendipityinteractive machine learningcounterfactual data modificationhuman intervention Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2022-0-00078, Explainable Logical Reasoning for Medical Knowledge Generation).Notes on contributorsGyewon JeonGyewon Jeon is a graduate student in Department of Industrial and Management Engineering at Korea University. His academic interests lie in Serendipitous Recommender System, Interactive Machine Learning, and Human Artificial Intelligence Interaction.Sangyeon KimSangyeon Kim is a visiting scholar in North Carolina State University. He has obtained his PhD degree from Sungkyunkwan University in 2022. His academic interests lie in HCI, Intelligent user interface, and accessible computing.Sangwon LeeSangwon Lee is a Professor in School of Industrial and Management Engineering at Korea University. He has obtained his PhD and Master degrees from the Pennsylvania State University in 2010 and 2006, respectively. Also, he has graduated as B.S. from Korea University in 2004. His academic interests lie in HCI, UX, XAI, and affective computing.
Graph Fourier transform based on singular value decomposition of the directed Laplacian

The Graph Fourier transform (GFT) is a fundamental tool in graph signal processing. In this paper, based on singular value decomposition of the Laplacian, we introduce a novel definition of GFT on directed graphs, and use the singular values of the Laplacian to carry the notion of graph frequencies. We show that the proposed GFT has its frequencies and frequency components evaluated by solving some constrained minimization problems with low computational cost, and it could represent graph signals with different modes of variation efficiently. Moreover, the proposed GFT is consistent with the conventional GFT in the undirected graph setting, and on directed circulant graphs, it is the classical discrete Fourier transform, up to some rotation, permutation and phase adjustment.
Hacking Nuclear Stability: Wargaming Technology, Uncertainty, and Escalation

Abstract How do emerging technologies affect nuclear stability? In this paper, we use a quasi-experimental cyber-nuclear wargame with 580 players to explore three hypotheses about emerging technologies and nuclear stability: (1) technological uncertainty leads to preemption and escalation; (2) technological uncertainty leads to restraint; and (3) technological certainty leads to escalation through aggressive counterforce campaigns. The wargames suggest that uncertainty and fear about cyber vulnerabilities create no immediate incentives for preemptive nuclear use. The greater danger to strategic stability lies in how overconfidence in cyber exploits incentivizes more aggressive counterforce campaigns and, secondarily, how vulnerabilities encourage predelegation or automation. Both of these effects suggest worrisome relationships between cyber exploits and inadvertent nuclear use on one hand and cyber vulnerabilities and accidental nuclear use on the other hand. Together, these findings reveal the complicated relationship between pathways to escalation and strategic stability, highlighting the role that confidence and perhaps-misplaced certainty—versus uncertainty and fear—play in strategic stability.
Counting-based visual question answering with serial cascaded attention deep learning

The counting-based questions play a major part in Visual Question Answering (VQA), the most challenging factor is counting the different objects present in the images. Recently more attention is paid to design a model of count-aided VQA. Based on the questions, the VQA system responds with appropriate answers. Yet, the complex questions are necessitating in the system with answers. The earlier models are still facing the challenging problems of counting the various objects within the images as the models become futile to select the features and lack fine-grained representation. In order to sustain the image representation, this paper proposes a new model for VQA using the heuristic approach of serial cascaded deep learning methods. Initially, the standard data regarding images and text data are gathered and fed to the pre-processing process. Consequently, the feature extraction is done on both the image and the text data. Here, the deep features from images are taken using Visual Geometry Group 16 (VGG16) and the text features are extracted using Text Convolutional Neural Network (TCNN). Then, the optimal weighted fused features are obtained, where the weights used for getting the necessary features are tuned via the Improved Tuna Swarm Optimization (ITSO) algorithm. Finally, the counting answers are retrieved based on the given queries, which is carried out via Serial Cascaded Recurrent Neural Network with Attention Mechanism-based Long Short-Term Memory (SCRAM-LSTM). The performance is examined with divergent metrics compared with conventional models. Hence, the findings reveal that it offers superior performance in estimating the appropriate answers. Therefore, the proposed work is widely used for such potential applications as helping blind or visually impaired people to get information, integrating with image retrieval systems, and also for search engines. Especially, it is utilized for the vision and language systems.
Explainable and programmable hypergraph convolutional network for imaging genetics data fusion

Integrating multi-view information to gain a new understanding of complex disease like Alzheimer’s disease (AD) has great clinical value. Hypergraphs have unique advantages in modeling high-order associations, but current deep learning methods cannot fully utilize the structural information in hypergraphs and have limited application value due to the black-box nature. This paper improves the hypergraph learning in interpretability and programmability. Firstly, we fuse multi-view information by constructing brain region-gene hypergraphs. Secondly, a characteristic information aggregation model is constructed based on hypergraph structure, Finally, a characteristic information aggregation hypergraph convolutional network (CIA-HGCN) is proposed based on the idea of graph neural networks. Evaluated by clinical imaging genetics data, CIA-HGCN obtained accuracy of 88.3% in AD identification task and showed superior performance in characteristic extraction. This paper provides a practical and flexible deep learning method for AD research and clinical applications.
Assessing Australia’s future solar power ramps with climate projections

Abstract Increasing levels of photovoltaic (PV) penetration to the electricity grid brings challenges to both design and operation of the grid due to its vulnerability to climate change. A crucial aspect of PV operation is power ramps leading to variability and instability in the grid. With notable large-scale PV deployment planned, including the world’s largest planned solar energy infrastructure in Powell Creek Australia, characterising future ramps is crucial for ensuring stable power generation to support large-scale economic development. Using CORDEX-Australasia projections under RCP8.5 and RCP4.5 emission scenarios, future solar ramps across Australia have been characterised up to 2100. Results predict a reduction in ramp magnitude across Australia, with changes in frequency and period length varying with the location. This work highlights the importance of considering future changes in climate when designing large-scale solar farms to ensure the incorporation of frequency control devices and storage plans for a reliable power supply.
Evaluating Spintronics-Compatible Implementations of Ising Machines

The commercial and industrial demand for the solution of hard combinatorial optimization problems push forward the development of efficient solvers. One of them is the Ising machine which can solve combinatorial problems mapped to Ising Hamiltonians. In particular, spintronic hardware implementations of Ising machines can be very efficient in terms of area and performance, and are relatively low-cost considering the potential to create hybrid CMOS-spintronic technology. Here, we perform a comparison of coherent and probabilistic paradigms of Ising machines on several hard Max-Cut instances, analyzing their scalability and performance at software level. We show that probabilistic Ising machines outperform coherent Ising machines in terms of the number of iterations required to achieve the problem s solution. Nevertheless, high frequency spintronic oscillators with sub-nanosecond synchronization times could be very promising as ultrafast Ising machines. In addition, considering that a coherent Ising machine acts better for Max-Cut problems because of the absence of the linear term in the Ising Hamiltonian, we introduce a procedure to encode Max-3SAT to Max-Cut. We foresee potential synergic interplays between the two paradigms.
IoTBChain: Adopting Blockchain Technology to Increase PLC Resilience in an IoT Environment

The networks on a centralized cloud architecture that interconnect Internet of Things (IoT) gadgets are not limited by national or jurisdictional borders. To ensure the secure sharing of sensitive user data among IoT gadgets, it is imperative to maintain security, resilience and trustless authentication. As a result, blockchain technology has become a viable option to provide such noteworthy characteristics. Blockchain technology is foundational for resolving many IoT security and privacy issues. Blockchain’s safe decentralization can solve the IoT ecosystem’s security, authentication and maintenance constraints. However, blockchain, like any innovation, has drawbacks, mainly when used in crucial IoT systems such as programmable logic controller (PLC) networks. This paper addresses the most recent security and privacy issues relating to the IoT, including the perception, network and application layers of the IoT’s tiered architecture. The key focus is to review the existing IoT security and privacy concerns and how blockchain might be used to deal with these problems. This paper proposes a novel approach focusing on IoT capabilities and PLC device security. The new model will incorporate a proof-of-work-based blockchain into the (PLC) IoT ecosystem. This blockchain enables the transmission of binary data and the data logging of the (PLC) networks’ signals. This novel technique uses fewer resources than other sophisticated methods in that PLC devices communicate data while maintaining a high transmission, encryption and decoding speed. In addition to ensuring repeatability, our new model addresses the memory and tracing problems that different PLC manufacturers encounter.
Exploring the user experience (UX) of a multi-window augmented reality environment

Augmented reality is an emergent form of technology that allows users to interact with and manipulate virtual objects and information integrated into the physical environment. Whether it is replying to browser-based emails or playing a game, completing such tasks in augmented reality requires the use of hand-tracking gestures or interactions. With the anticipated growth of this technology, future users may experience it for extended periods with a variety of applications (e.g., metaverse). This study explores the perceptions and user experience of individuals when interacting with and maneuvering in a multi-window augmented reality environment, using a range of hand-tracking interactions. The results provide both qualitative and quantitative insights into these interactions, highlighting the impact of perceived usability, subjective user experience, perceived difficulty, and perceived workload on task completion.