Reviewerly/exHarmony huggingface dataset:
Non-fungible tokens: The missing ingredient for sustainable supply chains in the metaverse age?

Authors:
raw_author_name: Jennifer Davies
raw_affiliation_string: University of Liverpool Management School, Chatham Street, Liverpool, England L69 7ZH, United Kingdom

Abstract: Non-fungible tokens (NFTs), unique digital assets on a blockchain that can represent ownership of digital or physical items, are potentially ground-breaking in their capacity for transforming the sustainability of supply chains. This paper explores and discusses how NFTs, analysed through the lens of the Technology Organisation Environment (TOE) framework, can drive supply chain sustainability and overcome the barriers to Blockchain Technology (BCT) adoption. We elucidate the unique value proposition of NFTs and explore the potential of 'phygital products' for linking digital NFTs to physical goods. Based on the existing literature and a review of real-world case studies, we propose that NFTs can (1) incentivise supply chain stakeholders to engage in more sustainable practices; (2) enhance customer willingness to pay for sustainable products; (3) provide anti-counterfeit measures; (4) support circular business model growth. A sustainable 'Mint-to-Order' production strategy starting with the creation (minting) of an NFT is proposed based on a synthesis of the findings. The use of NFTs has significant implications for both digital and physical product offerings and sustainable supply chain management (SSCM) practice making a compelling case for adoption. This paper is one of the first to explore the potential of using NFTs to drive sustainability in supply chains forming an essential component of a metaverse nexus and delineating a future research agenda.
A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

Authors:
raw_author_name: Sadik Kamel Gharghan
raw_affiliation_string: Middle Technical University, Electrical Engineering Technical College-Baghdad, Iraq

Abstract: Falls among older adults substantially affect mobility, health, and mortality. However, advancements in wireless and internet-of-things technologies have led to the development of fall detection and rescue systems. These systems offer a solution to mitigate the impact of falls by swiftly delivering emergency services to individuals, thereby reducing the risk of loss of life, injuries, and associated healthcare expenses. This paper aims to review current elderly fall detection systems (FDSs) comprehensively. The assessment examines FDSs explicitly designed for older individuals, considering fall detection methods, system architecture, wireless communications, sensor types, performance metrics, challenges, limitations, and more. In addition, a taxonomy and comprehensive review, accompanied by comparative analysis, have been conducted to categorize FDSs into traditional and artificial intelligence-based methods. The artificial intelligence techniques containing machine learning and deep learning methods for detecting elderly falls were critically reviewed and compared. Moreover, the deep learning-based systems have shown high accuracy in fall detection during the review. By conducting a comparative analysis among various FDSs, this review aims to aid researchers in identifying the most accurate and appropriate method for detecting falls in elderly individuals. In conclusion, this review assists researchers in making informed decisions and enhances the reliability and usability of elderly fall detection systems by addressing significant challenges and limitations.
CPT: Color-based Prompt Tuning for pre-trained vision-language models

Authors:
raw_author_name: Yuan Yao
raw_affiliation_string: Department of Computer Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing, China
raw_author_name: Ao Zhang
raw_affiliation_string: Sea-NExT Joint Lab, Singapore, School of Computing, National University of Singapore, Singapore
raw_author_name: Zhengyan Zhang
raw_affiliation_string: Department of Computer Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing, China
raw_author_name: Zhiyuan Liu
raw_affiliation_string: Department of Computer Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing, China
raw_author_name: Tat-Seng Chua
raw_affiliation_string: Sea-NExT Joint Lab, Singapore, School of Computing, National University of Singapore, Singapore
raw_author_name: Maosong Sun
raw_affiliation_string: Department of Computer Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing, China

Abstract: Vision-Language Pre-training (VLP) models have shown promising capabilities in grounding natural language in image data, facilitating a broad range of cross-modal tasks. However, we note that there exists a significant gap between the objective forms of model pre-training and fine-tuning, resulting in a need for large amounts of labeled data to stimulate the visual grounding capability of VLP models for downstream tasks. To address the challenge, we present Color-based Prompt Tuning (CPT), a novel paradigm for tuning VLP models, which reformulates visual grounding into a fill-in-the-blank problem with color-based co-referential markers in image and text, maximally mitigating the gap. In this way, CPT enables strong few-shot and even zero-shot visual grounding capabilities of VLP models. Comprehensive experimental results show that CPT achieves state-of-the-art performance on zero/few-shot visual grounding (e.g., 75.1 zero-shot accuracy in RefCOCO evaluation), outperforming fine-tuned and other prompt-tuned models by a large margin. Moreover, CPT can also be easily extended to achieve promising zero/few-shot performance on other vision-language tasks, such as visual relation detection, visual commonsense reasoning and visual question answering. We make the data and codes publicly available at https://github.com/thunlp/CPT.
Surface codes, quantum circuits, and entanglement phases

Authors:
raw_author_name: Florian Venn
raw_affiliation_string: DAMTP, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
raw_author_name: Benjamin Béri
raw_affiliation_string: T.C.M. Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom

Abstract: Surface codes---leading candidates for quantum error correction (QEC)---and entanglement phases---a key notion for many-body quantum dynamics---have heretofore been unrelated. Here we establish a link between the two. We map two-dimensional (2D) surface codes under a class of incoherent or coherent errors (bit flips or uniaxial rotations) to $(1+1)\mathrm{D}$ free-fermion quantum circuits via Ising models. We show that the error-correcting phase implies a topologically nontrivial area law for the circuit's 1D long-time state $|{\mathrm{\ensuremath{\Psi}}}_{\ensuremath{\infty}}\ensuremath{\rangle}$. Above the error threshold, we find a topologically trivial area law for incoherent errors and logarithmic entanglement in the coherent case. In establishing our results, we formulate 1D parent Hamiltonians for $|{\mathrm{\ensuremath{\Psi}}}_{\ensuremath{\infty}}\ensuremath{\rangle}$ via linking Ising models and 2D scattering networks, the latter displaying respective insulating and metallic phases and setting the 1D fermion gap and topology via their localization length and topological invariant. We expect our results to generalize to a duality between the error-correcting phase of ($d+1$)D topological codes and $d$-dimensional area laws; this can facilitate assessing code performance under various errors. The approach of combining Ising models, scattering networks, and parent Hamiltonians can be generalized to other fermionic circuits and may be of independent interest.
Securing Cloud Infrastructure in IaaS and PaaS Environments

Authors:
raw_author_name: Ashok Kumar Nanda
raw_affiliation_string: Department of Computer Science and Engineering, B.V. Raju Institute of Technology, India
raw_author_name: Abhishek Sharma
raw_affiliation_string: Department of Computer Science Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya, India
raw_author_name: P. John Augustine
raw_affiliation_string: Department of Information Technology, Sri Eshwar College of Engineering, India
raw_author_name: B. Rex Cyril
raw_affiliation_string: Department of Computer Science, St. Joseph's College (Autonomous), India
raw_author_name: Boopathi Sampath
raw_affiliation_string: Mechanical Engineering, Mythayammal Engineering College (Autonomous), India

Abstract: Cloud computing has revolutionized IT infrastructure deployment and management, but it also presents security and resilience challenges. The study delves into the principles and strategies of cloud security to safeguard cloud environments and guarantee business continuity. It explains the concepts of infrastructure as a service (IaaS) and platform as a service (PaaS), their benefits and challenges, and the complex web of security principles within the cloud, including the shared responsibility model, best practices, and identity and access management. The guide explores cloud threats, focusing on common threats and emerging trends. It covers data security, network security measures, and security monitoring. It emphasizes integrating security into DevOps, securing CI/CD pipelines, and infrastructure as code (IaC) security. It covers disaster recovery, business continuity, cloud backup strategies, high availability, and cloud-based solutions, enabling organizations to effectively manage cloud security and resilience.
Non-Uniform Illumination Underwater Image Restoration via Illumination Channel Sparsity Prior

Authors:
raw_author_name: Guojia Hou
raw_affiliation_string: College of Computer Science and Technology, Qingdao University, Qingdao, China
raw_author_name: Nan Li
raw_affiliation_string: College of Computer Science and Technology, Qingdao University, Qingdao, China
raw_author_name: Peixian Zhuang
raw_affiliation_string: Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, and the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
raw_author_name: Kunqian Li
raw_affiliation_string: College of Engineering, Ocean University of China, Qingdao, China
raw_author_name: Haihan Sun
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Jurong West, Singapore
raw_author_name: Chongyi Li
raw_affiliation_string: College of Computer Science, Nankai University, Tianjin, China

Abstract: Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon. To this end, we develop an illumination channel sparsity prior (ICSP) guided variational framework for non-uniform Illumination underwater image restoration. Technically, the illumination channel sparsity prior is built on the observation that the illumination channel of a uniform-light underwater image in HSI color space contains few pixels whose intensity is very low. Then according to the Retinex theory, we design a variational model with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm term, constraint term, and gradient term, by integrating the proposed ICSP into an extended underwater image formation model. Such three regularizations are effective in enhancing the brightness, correcting color distortion, and revealing structures and fine-scale details. Meanwhile, we exploit a fast numerical algorithm on the base of the alternating direction method of multipliers (ADMM) to accelerate solving this optimization problem. We also collect a benchmark dataset, namely NUID that contains 925 real underwater images of different non-uniform illumination. Extensive experiments demonstrate that our proposed method is effective in terms of quantitative and qualitative comparisons, ablation studies, convergence analysis, and applications. The code and dataset are available at https://github.com/Hou-Guojia/ICSP.
A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection

Authors:
raw_author_name: Bach Hoai Nguyen
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Bing Xue
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Mengjie Zhang
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

Abstract: Feature selection (FS) is an important data preprocessing technique that selects a small subset of relevant features to improve learning performance. However, it is also challenging due to its large search space. Recently, a competitive swarm optimizer (CSO) has shown promising results in FS because of its potential global search ability. The main idea of CSO is to select two solutions randomly and then let the loser (worse fitness) learn from the winner (better fitness). Although such a search mechanism provides a high population diversity, it is at risk of generating unqualified solutions since the winner’s quality is not guaranteed. In this work, we propose a constrained evolutionary mechanism for CSO, which verifies the quality of all the particles and lets the infeasible (unqualified) solutions learn from the feasible (qualified) ones. We also propose a novel local search and a size-change operator that guide the population to search for smaller feature subsets with similar or better classification performance. A surrogate model, based on support vector machines, is proposed to assist both local search and the size-change operator to explore a massive number of potential feature subsets without requiring excessive computational resource. Results on 24 real-world datasets show that the proposed algorithm can select smaller feature subsets with higher classification performance than state-of-the-art evolutionary computation (EC) and non-EC benchmark algorithms.
Exploration of challenges to deployment of blockchain in small construction projects

Authors:
raw_author_name: Ahsan Waqar
raw_affiliation_string: Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
raw_author_name: Abdul Hannan Qureshi
raw_affiliation_string: Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
raw_author_name: Idris Othman
raw_affiliation_string: Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia

Abstract: Blockchain technology provides a decentralized way of handling the data and supports the construction project's sustainability and performance. Small construction projects in Malaysia always need help adopting new technology like blockchain. This research aimed to investigate the challenges affecting the implementation of blockchain technology in small construction projects in Malaysia. The study examined Malaysian minor construction project blockchain implementation issues. A total of 17 challenges were found significant in affecting the implementation of Blockchain technology in small construction projects in Malaysia. Five constructs were seen: technological barriers, work environment barriers, economic and planning barriers, operational barriers, and privacy and regulation barriers. Structural equation modeling and these structures create a blockchain implementation model for modest building projects. The model's validity, dependability, and forecast accuracy for all obstacles and their potential influence on blockchain deployment in small construction projects in Malaysia are substantial. The model also has good convergent and discriminant validity, supporting it. Blockchain use in Malaysian small building projects could be improved mainly through economic and planning issues. The present study needs to highlight the Malaysian small construction sector's essential obstacles in integrating blocks and technology that are the basis for more creative technologies. This research will help the Malaysian construction sector concentrate on improvements and assess the effect of blockchain on modest projects.
Perceived conversational ability of task-based chatbots – Which conversational elements influence the success of text-based dialogues?

Authors:
raw_author_name: Alexandra Rese
raw_affiliation_string: Chair of Marketing & Innovation, University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany

Abstract: The use of text-based chatbots offering individual support to customers has increased steadily in recent years. However, thus far, research has focused on comparing text-based chatbots with either each other or with humans, whilst the investigation of task-based dialogues has been scarce. This paper aims to identify the characteristics of dialogues – that is, conversational elements – that lead to a successful task-based conversation. For this purpose, the chatbot, KIM, by MAGGI Kochstudio was used. It was designed to help customers find a recipe tailored to their individual needs. In order to investigate which conversational elements contribute to successful communication between the user and the chatbot KIM, a usability study collecting 123 unstructured dialogues and a scenario-based experiment using four dialogues with 627 respondents was conducted. The quantitative analysis demonstrates that task completion is characterized by a higher perception of the chatbot's conversational ability and user satisfaction. The chatbot should propose correct recipe suggestions following a short dialogue, without the user needing to provide too much input. Based on these findings, we recommended equipping the skillset of task-based chatbots with elements that will complement their assistive qualities – for example, improved use of standard phrases, and reactions to similar domains and non-requests. Gender-specific differences in task completion should be considered.
JND-aware Two-pass Per-title Encoding Scheme for Adaptive Live Streaming

Authors:
raw_author_name: Vignesh V Menon
raw_affiliation_string: Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universit&#x00E4;t Klagenfurt, Klagenfurt, Austria
raw_author_name: Prajit T Rajendran
raw_affiliation_string: CEA-List, Palaiseau, France
raw_author_name: Christian Feldmann
raw_affiliation_string: Bitmovin, Klagenfurt, Austria
raw_author_name: Klaus Schoeffmann
raw_affiliation_string: Institute of Information Technology, Alpen-Adria-Universit&#x00E4;t Klagenfurt, Klagenfurt, Austria
raw_author_name: Mohammad Ghanbari
raw_affiliation_string: School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K
raw_author_name: Christian Timmerer
raw_affiliation_string: Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universit&#x00E4;t Klagenfurt, Klagenfurt, Austria

Abstract: Adaptive live video streaming applications utilize a predefined collection of bitrate-resolution pairs, known as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bitrate ladder</i> , for simplicity and efficiency, eliminating the need for additional run-time to determine the optimal pairs during the live streaming session. These applications do not incorporate two-pass encoding methods due to increased latency. However, an optimized bitrate ladder could result in lower storage and delivery costs and improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quality of Experience</i> (QoE). This paper presents a Just Noticeable Difference (JND)-aware constrained Variable Bitrate (cVBR) Two-pass Per-title encoding Scheme (JTPS) designed specifically for live video streaming. JTPS predicts a content- and JND-aware bitrate ladder using low-complexity features based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discrete Cosine Transform</i> (DCT) energy and optimizes the constant rate factor (CRF) for each representation using random forest-based models. The effectiveness of JTPS is demonstrated using the open source video encoder x265, with an average bitrate reduction of 18.80% and 32.59% for the same PSNR and VMAF, respectively, compared to the standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HTTP Live Streaming</i> (HLS) bitrate ladder using Constant Bitrate (CBR) encoding. The implementation of JTPS also resulted in a 68.96% reduction in storage space and an 18.58% reduction in encoding time for a JND of six VMAF points.
When Deep Learning-Based Soft Sensors Encounter Reliability Challenges: A Practical Knowledge-Guided Adversarial Attack and Its Defense

Authors:
raw_author_name: Han Liu
raw_affiliation_string: School of Automation and Information Engineering, Xi&#x0027;an University of Technology, Xi&#x0027;an, China
raw_author_name: Ding Liu
raw_affiliation_string: School of Automation and Information Engineering, Xi&#x0027;an University of Technology, Xi&#x0027;an, China

Abstract: Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly improved sensing accuracy; however, their vulnerability to adversarial attacks affects their reliability, thus hindering their widespread application. To improve the reliability of DLSSs, in this article, we conducted a systematic investigation of the adversarial attack and defense of DLSSs. By considering the task requirements of DLSSs and the actual scenarios that attackers may encounter, a framework based on black-box attack and proactive defense was proposed to realize the adversarial attack and defense of soft sensors. The adversarial attack was implemented through the proposed knowledge-guided adversarial attack (KGAA) method. By reconstructing the optimization model and introducing the mechanism knowledge into the objective function, the KGAA method could overcome the ill-posed problem of adversarial attack optimization when attacking a regression model. Moreover, based on the KGAA, a corresponding KGAA adversarial training defense method was proposed to achieve proactive defense. The attack and defense methods were verified in terms of the thermal deformation sensing of an air preheater rotor. Compared to other attacks, the KGAA exhibited higher imperceptibility, rationality, and stability; it can thus be considered a practical attack. The implementation of KGAA adversarial training enhances the adversarial robustness of DLSSs, thus aiding the defense of DLSSs to various attacks and improving their reliability.