Reviewerly/exHarmony huggingface dataset:
Interaction-Enhanced and Time-Aware Graph Convolutional Network for Successive Point-of-Interest Recommendation in Traveling Enterprises

Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nteraction-enhanced and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ime-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onvolution <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods.
A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution

Recently, a number of CNN based methods have made great progress in single image super-resolution. However, these existing architectures commonly build massive number of network layers, bringing high computational complexity and heavy memory consumption, which is inappropriate to be applied on embedded terminals such as mobile platforms. In order to solve this problem, we propose a hybrid network of CNN and Transformer (HNCT) for lightweight image super-resolution. In general, HNCT consists of four parts, which are shallow feature extraction module, Hybrid Blocks of CNN and Transformer (HBCTs), dense feature fusion module and up-sampling module, respectively. By combining CNN and Transformer, HBCT extracts deep features beneficial for super-resolution reconstruction in consideration of both local and non-local priors, while being lightweight and flexible enough. Enhanced spatial attention is introduced in HBCT to further improve performance. Extensive experimental results show our HNCT is superior to the state-of-the-art methods in terms of super-resolution performance and model complexity. Moreover, we won the second best PSNR and the least activation operations in NTIRE 2022 Efficient SR Challenge. Code is available at https://github.com/lhjthp/HNCT.
A manifesto on explainability for artificial intelligence in medicine

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.
AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach

Chatbot implementation for assisting customers as a virtual agent can be seen as a tool in helping an organisation to serve better customer service. Malaysia is among the countries forging ahead with the Fourth Industrial Revolution. One of the core technologies mentioned is adopting artificial intelligence tools such as chatbots. In the last few years, there has been a growing interest in AI-based chatbot adoption in the non-HEI context. However, most higher-education institutions (HEIs) are reported not ready to adopt AI-based chatbots as one of the solutions for virtual student services support. The research of chatbot adoption in the HEI context is still new and is a less explored and examined topic in the information systems domain. Moreover, most of the existing research regarding chatbot adoption in the HEI context focuses more on the benefit of chatbot usage and is not specialised in a student services solution perspective. Furthermore, most of the studies were not guided by the information systems (IS) theories. Therefore, this study aims to identify factors that influence the effectiveness of chatbot adoption in the HEI context by adapting the UTAUT2 model as the IS theory reference. A survey method was applied using the purposive sampling technique. For 3 months, data were collected online from 302 users of Malaysia’s HEI postgraduate students from various public and private universities. A two-stage analytical procedure (SEM-ANN) was used to validate the research model and assess the presented research hypotheses. This research reveals that perceived trust is influenced by interactivity, design, and ethics. Meanwhile, behavioural intention is influenced by perceived trust, performance expectancy, and habit towards the use of chatbot applications in the HEI context. Lastly, the findings of this study can be helpful to the HEI student services unit and can be a guide towards productivity and marketing strategy in serving the students better.
A systematic review of conversational AI in language education: focusing on the collaboration with human teachers

Despite the increasing use of conversational artificial intelligence (AI) in language learning, few studies explored how to develop collaborative partnership between AIs and humans. This systematic review examines empirical evidence of human-computer collaboration from 24 studies conducted in an AI-integrated language learning environment and published between 2015 and 2021. The roles of conversational AIs and teachers in each language learning phase with challenges of and suggestions for conversational AI-integrated language learning were identified. Although limited evidence for collaboration between conversational AIs and human teachers was found, future language education should integrate conversational AIs to promote intelligence amplification and decrease human teachers’ workload through classroom orchestration. The study concludes with guidelines and recommendations for teachers and AI researchers.
Patch Slimming for Efficient Vision Transformers

This paper studies the efficiency problem for visual transformers by excavating redundant calculation in given networks. The recent transformer architecture has demonstrated its effectiveness for achieving excellent performance on a series of computer vision tasks. However, similar to that of convolutional neural networks, the huge computational cost of vision transformers is still a severe issue. Considering that the attention mechanism aggregates different patches layer-by-layer, we present a novel patch slimming approach that discards useless patches in a topdown paradigm. We first identify the effective patches in the last layer and then use them to guide the patch selection process of previous layers. For each layer, the impact of a patch on the final output feature is approximated and patches with less impacts will be removed. Experimental results on benchmark datasets demonstrate that the proposed method can significantly reduce the computational costs of vision transformers without affecting their performances. For example, over 45% FLOPs of the ViT-Ti model can be reduced with only 0.2% top-1 accuracy drop on the ImageNet dataset.
Everything at Once – Multi-modal Fusion Transformer for Video Retrieval

Multi-modal learning from video data has seen increased attention recently as it allows training of semantically meaningful embeddings without human annotation, enabling tasks like zero-shot retrieval and action localization. In this work, we present a multi-modal, modality agnostic fusion transformer that learns to exchange information between multiple modalities, such as video, audio, and text, and integrate them into a fused representation in a joined multi-modal embedding space. We propose to train the system with a combinatorial loss on everything at once – any combination of input modalities, such as single modalities as well as pairs of modalities, explicitly leaving out any add-ons such as position or modality encoding. At test time, the resulting model can process and fuse any number of input modalities. Moreover, the implicit properties of the transformer allow to process inputs of different lengths. To evaluate the proposed approach, we train the model on the large scale HowTo100M dataset and evaluate the resulting embedding space on four challenging benchmark datasets obtaining state-of-the-art results in zero-shot video retrieval and zero-shot video action localization. Our code for this work is also available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/ninatu/everything_at_once
Image Segmentation Using Text and Image Prompts

Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. This approach enables us to create a unified model (trained once) for three common segmentation tasks, which come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense prediction. After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail. This novel hybrid input allows for dynamic adaptation not only to the three segmentation tasks mentioned above, but to any binary segmentation task where a text or image query can be formulated. Finally, we find our system to adapt well to generalized queries involving affordances or properties. Code is available at https://eckerlab.org/code/CLIPSeg
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation

We present Panoptic Neural Fields (PNF), an object-aware neural scene representation that decomposes a scene into a set of objects (things) and background (stuff). Each object is represented by an oriented 3D bounding box and a multi-layer perceptron (MLP) that takes position, direction, and time and outputs density and radiance. The background stuff is represented by a similar MLP that additionally outputs semantic labels. Each object MLPs are instance-specific and thus can be smaller and faster than previous object-aware approaches, while still leveraging category-specific priors incorporated via meta-learned initialization. Our model builds a panoptic radiance field representation of any scene from just color images. We use off-the-shelf algorithms to predict camera poses, object tracks, and 2D image semantic segmentations. Then we jointly optimize the MLP weights and bounding box parameters using analysis-by-synthesis with self-supervision from color images and pseudo-supervision from predicted semantic segmentations. During experiments with real-world dynamic scenes, we find that our model can be used effectively for several tasks like novel view synthesis, 2D panoptic segmentation, 3D scene editing, and multiview depth prediction.
Internet of Things (IoT) Security With Blockchain Technology: A State-of-the-Art Review

With the rapid enhancement in the design and development of the Internet of Things creates a new research interest in the adaptation in industrial domains. It is due to the impact of distributed emerging technology and topology of industrial Internet of Things and the security-related resource constraints of industrial 5.0. This conducts new paradigm along with critical challenges to the existing information preservation, node transactions and communication, transmission, trust and privacy, and security protection related problems. These critical aspects pose serious limitations and issues for the industry to provide industrial data integrity, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects. In addition, the intersection of blockchain and industrial IoT has gained more consideration and research interest. However, there is an emerging limitation between the inadequate performance of industrial IoT and connected nodes, and the high resource requirement of permissioned private blockchain ledger has not yet been tackled with the complete solution. Due to the introductions of NuCypher Re-Encryption infrastructure, hashing tree and allocation, and deployment of blockchain proof-of-work required more computational power as well. This paper is divided into three different folds; first, we studied various related literature of blockchain-enabling industrial Internet of Things and its critical implementation challenging aspects along with the solution. Secondly, we proposed a blockchain hyperledger sawtooth-enabled framework. This framework provides a secure and trusted execution environment, in which service delivery mechanisms and protocols are designed with an acknowledgment, including the immutable ledger storage security, along with the peer-to-peer network on-chain and off-chain communication of industrial activities. Thirdly, we design pseudo-chain codes and consensus protocols to provide smooth industrial node streamline transactions and broadcast content. The proposed multiple proof-of-work investigated and simulated using Hyperledger Sawtooth-enabled docker for testing to exchange information between connected devices of industrial Internet of Things within the limited usage of resource constraints.
Video Frame Interpolation with Transformer

Video frame interpolation (VFI), which aims to synthesize intermediate frames of a video, has made remarkable progress with development of deep convolutional networks over past years. Existing methods built upon convolutional networks generally face challenges of handling large motion due to the locality of convolution operations. To overcome this limitation, we introduce a novel framework, which takes advantage of Transformer to model long-range pixel correlation among video frames. Further, our network is equipped with a novel cross-scale window-based attention mechanism, where cross-scale windows interact with each other. This design effectively enlarges the receptive field and aggregates multi-scale information. Extensive quantitative and qualitative experiments demonstrate that our method achieves new state-of-the-art results on various benchmarks.
FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification

For Visible-Infrared person ReIDentification (VI-ReID), existing modality-specific information compensation based models try to generate the images of missing modality from existing ones for reducing cross-modality discrepancy. However, because of the large modality discrepancy between visible and infrared images, the generated images usually have low qualities and introduce much more interfering information (e.g., color inconsistency). This greatly degrades the subsequent VI-ReID performance. Alternatively, we present a novel Feature-level Modality Compensation Network (FMCNet) for VI-ReID in this paper, which aims to compensate the missing modality-specific information in the feature level rather than in the image level, i.e., directly generating those missing modality-specific features of one modality from existing modality-shared features of the other modality. This will enable our model to mainly generate some discriminative person related modality-specific features and discard those non-discriminative ones for benefiting VI-ReID. For that, a single-modality feature decomposition module is first designed to decompose single-modality features into modality-specific ones and modality-shared ones. Then, a feature-level modality compensation module is present to generate those missing modality-specific features from existing modality-shared ones. Finally, a shared-specific feature fusion module is proposed to combine the existing and generated features for VI-ReID. The effectiveness of our proposed model is verified on two benchmark datasets.
Federated learning for smart cities: A comprehensive survey

With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.
Consensus mechanism for software-defined blockchain in internet of things

This article aims to discuss the consensus mechanism of software-defined blockchain in the Internet of Things, analyze the characteristics of the traditional consensus mechanism algorithms, on the basis of comparing the advantages of each model, the traditional consensus mechanism algorithm is improved. Later, a supervisable consensus scheme based on improved DPOS-PBFT (Delegated proof of stake-Practical Byzantine Fault Tolerance) was proposed. In the context of the development of the Internet of Things technology, the decentralized distributed computing paradigm is used to improve the blockchain smart contract technology, and the DPOS blockchain consensus mechanism is optimized based on the DPOS protocol of the credit model. In addition, through the dynamic grouping algorithm of credibility, the research ranks the credit level of the consensus nodes of the blockchain network, thus further realizing the supervision of the Internet of Things system. The results of the case analysis show that the success rate of the mechanism algorithm can still be maintained at about 97% after 3000 user requests, the maximum delay remains below 8s after 3000 user requests, the minimum delay is always around 3s, the average delay is 2.38s, the overall performance of the algorithm is superior. It can ensure the final consistency of data transmission of each node in the Internet of Things, the research on the blockchain consensus mechanism in the Internet of Things has practical reference value.
ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions

ABSTRACTABSTRACTSince its launch, ChatGPT, an artificial intelligence chatbot developed by Open AI based on the premises of generative pre-trained transformer autoregressive language models, has gained widespread popularity and is making significant impact on society with its unique features, such as natural language processing and contextual awareness. ChatGPT is viewed as a major disruptive innovation that is likely to revolutionize the operations in many industries including the hospitality and tourism industry. The adoption of ChatGPT will result in substantial changes throughout the hospitality and tourism industry by disrupting how customer search for information, make decisions, and how businesses produce, create, and deliver customized services and experiences. This conceptual paper provides a comprehensive discussion on generative pre-trained transformers’ (GPTs) benefits, and potential challenges and threats they pose to the hospitality and tourism industry. The feasibility of integrating GPT into different travel stages and decision-making processes is also discussed. The article concludes by proposing a potential future research agenda on using GPT in creating and delivering hospitality and tourism experiences, which can guide further advancements in the field.ChatGPT 是一款由 Open AI 开发出来的人工智能聊天机器人,它基于生成预训练转换器的自回归语言模型。自其发布以来, ChatGPT 因其独特的功能特性,如自然语言处理和上下文感知等,备受欢迎,并对社会产生了重要的影响。ChatGPT 被认为是一种重大的颠覆性创新,这种创新可能会彻底改变许多行业领域的运行方式,包括旅游和酒店业。把ChatGPT应用在酒店和旅游业中,将会在该行业中产生重大变革,这将改变顾客搜索信息和做出决策的方式,转变企业生产创造以及提供个性化服务和体验的方式。这篇概念性论文全面探讨了生成预训练转换器 (GPT) 的优点,以及它将对旅游与酒店业带来的潜在威胁和挑战。本文还讨论了将 GPT 应用到不同的旅行阶段和决策过程中的可行性。在文章最后,提出了使用 GPT为旅游和酒店业创造和传递体验的未来研究方向,这将对该领域的进一步发展起到导向作用。KEYWORDS: ChatGPTcustomer behaviorservice experience creation and deliverybenefitschallenges and threatsresearch agenda Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by 2023 PaiChai University research grant.
Transfer Learning in Deep Reinforcement Learning: A Survey

Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.