Reviewerly/exHarmony huggingface dataset:
Continual Test-Time Domain Adaptation

Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time. Existing methods, which are mostly based on self-training and entropy regularization, can suffer from these non-stationary environments. Due to the distribution shift over time in the target domain, pseudo-labels become unreliable. The noisy pseudo-labels can further lead to error accumulation and catastrophic forgetting. To tackle these issues, we propose a continual test-time adaptation approach (CoTTA) which comprises two parts. Firstly, we propose to reduce the error accumulation by using weight-averaged and augmentation-averaged predictions which are often more accurate. On the other hand, to avoid catastrophic forgetting, we propose to stochastically restore a small part of the neurons to the source pre-trained weights during each iteration to help preserve source knowledge in the longterm. The proposed method enables the longterm adaptation for all parameters in the network. CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models. We demonstrate the effectiveness of our approach on four classification tasks and a segmentation task for continual test-time adaptation, on which we outperform existing methods. Our code is available at https://gin.ee/cotta.
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly- Throughs

We use neural radiance fields (NeRFs) to build interac-tive 3D environments from large-scale visual captures spanning buildings or even multiple city blocks collected pri-marily from drones. In contrast to single object scenes (on which NeRFs are traditionally evaluated), our scale poses multiple challenges including (1) the need to model thou-sands of images with varying lighting conditions, each of which capture only a small subset of the scene, (2) pro-hibitively large model capacities that make it infeasible to train on a single GPU, and (3) significant challenges for fast rendering that would enable interactive fly-throughs. To address these challenges, we begin by analyzing visi-bility statistics for large-scale scenes, motivating a sparse network structure where parameters are specialized to dif-ferent regions of the scene. We introduce a simple geomet-ric clustering algorithm for data parallelism that partitions training images (or rather pixels) into different NeRF sub-modules that can be trained in parallel. We evaluate our approach on existing datasets (Quad 6k and UrbanScene3D) as well as against our own drone footage, improving training speed by 3x and PSNR by 12%. We also evaluate re-cent NeRF fast renderers on top of Mega-NeRF and intro-duce a novel method that exploits temporal coherence. Our technique achieves a 40x speedup over conventional NeRF rendering while remaining within 0.8 db in PSNR quality, exceeding the fidelity of existing fast renderers.
Security and Privacy in Metaverse: A Comprehensive Survey

Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021. There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse. However, these definitions could hardly reach universal acceptance. Rather than providing a formal definition of the Metaverse, we list four must-have characteristics of the Metaverse: socialization, immersive interaction, real world-building, and expandability. These characteristics not only carve the Metaverse into a novel and fantastic digital world, but also make it suffer from all security/privacy risks, such as personal information leakage, eavesdropping, unauthorized access, phishing, data injection, broken authentication, insecure design, and more. This paper first introduces the four characteristics, then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors. Based on the four characteristics and the findings of the current progress, the security and privacy issues in the Metaverse are investigated. We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics. Lastly, the paper also raises some other concerns regarding society and humanity.
Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion

This study presents a novel physics-based metaheuristic algorithm called Kepler optimization algorithm (KOA), inspired by Kepler’s laws of planetary motion to predict the position and velocity of planets at any given time. In KOA, each planet with its position acts as a candidate solution, which is randomly updated through the optimization process with respect to the best-so-far solution (Sun). KOA allows for a more effective exploration and exploitation of the search space because the candidate solutions (planets) exhibit different situations from the Sun at different times. Four challengeable benchmarks, namely CEC 2014, CEC 2017, CEC 2020, and CEC2022, and eight constrained engineering design problems, in addition to the parameter estimation problem of photovoltaic modules, were used to assess the performance of KOA. To observe its effectiveness, it was compared with three classes of stochastic optimization algorithms, including: (i) the latest published algorithms, including Snake Optimizer (SO), Fick’s Law Algorithm (FLA), Coati Optimization Algorithm (COA), Pelican Optimization Algorithm (POA), Dandelion Optimizer (DO), Mountain Gazelle Optimizer (MGO), Artificial Gorilla Troops Optimizer (GTO), and Slime Mold Algorithm (SMA); (ii) well-studied and highly cited algorithms, such as Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO); and (iii) two highly performing optimizers: LSHADE-cnEpSin and LSHADE-SPACMA. Results of the convergence curve and statistical information indicated that KOA is more promising than all the compared optimizers. The source code of KOA is publicly accessible at https://www.mathworks.com/matlabcentral/fileexchange/126175-kepler-optimization-algorithm-koa
Game of algorithms: ChatGPT implications for the future of tourism education and research

Purpose The paper aims to evaluate the ways ChatGPT is going to disrupt tourism education and research. Design/methodology/approach This is a conceptual paper. Findings ChatGPT has the potential to revolutionize tourism education and research because it can do what students and researchers should do, namely, generate text (assignments and research papers). Universities will need to reevaluate their teaching and assessment strategies and incorporate generative language models in teaching. Publishers will need to be more receptive toward manuscripts that are partially generated by artificial intelligence. In the future, digital teachers and research assistants will take over many of the cognitive tasks of tourism educators and researchers. Originality/value To the authors’ best knowledge, this is one of the first academic papers that investigates the implications of ChatGPT to tourism education and research.
Leakage and the reproducibility crisis in machine-learning-based science

Machine-learning (ML) methods have gained prominence in the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. We systematically investigate reproducibility issues in ML-based science. Through a survey of literature in fields that have adopted ML methods, we find 17 fields where leakage has been found, collectively affecting 294 papers and, in some cases, leading to wildly overoptimistic conclusions. Based on our survey, we introduce a detailed taxonomy of eight types of leakage, ranging from textbook errors to open research problems. We propose that researchers test for each type of leakage by filling out model info sheets, which we introduce. Finally, we conduct a reproducibility study of civil war prediction, where complex ML models are believed to vastly outperform traditional statistical models such as logistic regression (LR). When the errors are corrected, complex ML models do not perform substantively better than decades-old LR models.
Recognition and Repetition Counting for Complex&#x0D; Physical Exercises with Deep Learning

Activity recognition using off-the-shelf smartwatches is an important problem in humanactivity recognition. In this paper, we present an end-to-end deep learning approach, able to provideprobability distributions over activities from raw sensor data. We apply our methods to 10 complexfull-body exercises typical in CrossFit, and achieve a classification accuracy of 99.96%. We additionallyshow that the same neural network used for exercise recognition can also be used in repetitioncounting. To the best of our knowledge, our approach to repetition counting is novel and performswell, counting correctly within an error of 1 repetitions in 91% of the performed sets.
Learning by Cheating

Vision-based urban driving is hard. The autonomous system needs to learn to perceive the world and act in it. We show that this challenging learning problem can be simplified by decomposing it into two stages. We first train an agent that has access to privileged information. This privileged agent cheats by observing the ground-truth layout of the environment and the positions of all traffic participants. In the second stage, the privileged agent acts as a teacher that trains a purely vision-based sensorimotor agent. The resulting sensorimotor agent does not have access to any privileged information and does not cheat. This two-stage training procedure is counter-intuitive at first, but has a number of important advantages that we analyze and empirically demonstrate. We use the presented approach to train a vision-based autonomous driving system that substantially outperforms the state of the art on the CARLA benchmark and the recent NoCrash benchmark. Our approach achieves, for the first time, 100% success rate on all tasks in the original CARLA benchmark, sets a new record on the NoCrash benchmark, and reduces the frequency of infractions by an order of magnitude compared to the prior state of the art. For the video that summarizes this work, see this https URL
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU

Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks.Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors.In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer.We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus.Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time.We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.
Improved Grey Wolf Optimization Algorithm and Application

This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a meta-heuristic algorithm with strong optimal search capability in the path planning for mobile robots. We improved chaotic tent mapping to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor based on the Gaussian distribution change curve to balance the global and local searchability. In addition, an improved dynamic proportional weighting strategy is proposed that can update the positions of grey wolves so that the convergence of this algorithm can be accelerated. The proposed improved GWO algorithm results are compared with the other eight algorithms through several benchmark function test experiments and path planning experiments. The experimental results show that the improved GWO has higher accuracy and faster convergence speed.
The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co .
Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience

Abstract Artificial intelligence (AI) continues to transform firm-customer interactions. However, current AI marketing agents are often perceived as cold and uncaring and can be poor substitutes for human-based interactions. Addressing this issue, this article argues that artificial empathy needs to become an important design consideration in the next generation of AI marketing applications. Drawing from research in diverse disciplines, we develop a systematic framework for integrating artificial empathy into AI-enabled marketing interactions. We elaborate on the key components of artificial empathy and how each component can be implemented in AI marketing agents. We further explicate and test how artificial empathy generates value for both customers and firms by bridging the AI-human gap in affective and social customer experience. Recognizing that artificial empathy may not always be desirable or relevant, we identify the requirements for artificial empathy to create value and deduce situations where it is unnecessary and, in some cases, harmful.
A conceptual framework for blockchain‐based sustainable supply chain and evaluating implementation barriers: A case of the tea supply chain

Abstract The increasing population and income inequality in the last decades have made it necessary to focus on the concept of sustainability. In the changing world order, with economic crises, instabilities, pandemics, and social media, sustainability awareness differs significantly from past years. In addition, developing new technologies and concepts (big data, blockchain, IoT, robotic, etc.) plays a crucial role in meeting social awareness in terms of sustainability. Food sustainability is also one of the most important pillars in this concept. The integration of new technologies in agriculture and food chains will enable the current world population to use resources more efficiently and sustainably. Blockchain, one of the technologies that emerged with the arrival of Industry 4.0, is a technology that can be used effectively in many sectors. Particularly in supply chain networks, it is seen as a technology that supports the sustainability concept due to its features such as decentralization, reliability, transparency, consensus standards, and traceability. However, since blockchain is an immature and new technology, there are some challenges with integrating it into existing conventional systems. This study aims to present a conceptual framework for the integration of blockchain technology to establish a sustainable tea supply chain, define possible actions, and prioritize the possible risks that may arise in this integration process; this will be done through the Spherical Fuzzy Analytic Hierarchy Process (SF‐AHP) approach. In this context, initially, the current tea supply chain will be considered and the activities carried out will be defined in terms of technological, environmental, and strategic sustainability. Then, the design that includes the integration of all activities with blockchain technology is presented. The proposed design covers the entire tea supply chain from end to end and is considered with regard to all sustainability dimensions. In the proposed framework, barriers that may be encountered and risks that may arise at each stage of the tea supply chain process are identified; managerial implications are then presented to eliminate these risks. To enhance the use of the recommendations made, risks and barriers are prioritized with SF‐AHP management. Thus, the problems that need to be solved primarily in the technological transformation process can be evaluated more clearly. The proposed theoretical framework is expected to extend to all agricultural practices and support technological advances throughout the agricultural sector.
A study of college students' intention to use metaverse technology for basketball learning based on UTAUT2

Recent advancements in virtual reality technology have attracted increasing attention from enterprises and scholars, and many new related products have been launched. Due to the current COVID-19 epidemic, the non-face-to-face teaching environment will seriously affect students' basketball learning. We therefore combined basketball learning with metaverse technology, discussed basketball teaching in a virtual reality environment, and examined the influencing factors of college students' intentions to use metaverse technology. In the light of UTAUT2, a new research model was proposed, and quantitative research was carried out. The results of a survey of 1074 valid samples revealed that habits and attitudes are crucial factors in the success of basketball learning using a metaverse. The findings also indicate that grade and gender are moderator variables.
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges.
Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference. Finally, this paper thoroughly discusses the diversified application scenarios of virtual collection, hoping to provide helpful information for the development of the DPVS industry.
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization

Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. With the assumption of Gaussian feature distribution, conventional feature distribution matching methods usually match the mean and standard deviation of features. However, the feature distributions of real-world data are usually much more complicated than Gaussian, which cannot be accurately matched by using only the first-order and second-order statistics, while it is computationally prohibitive to use high-order statistics for distribution matching. In this work, we, for the first time to our best knowledge, propose to perform Exact Feature Distribution Matching (EFDM) by exactly matching the empirical Cumulative Distribution Functions (eCDFs) of image features, which could be implemented by applying the Exact Histogram Matching (EHM) in the image feature space. Particularly, a fast EHM algorithm, named Sort-Matching, is employed to perform EFDM in a plug-and-play manner with minimal cost. The effectiveness of our proposed EFDM method is verified on a variety of AST and DG tasks, demonstrating new state-of-the-art results. Codes are available at https://github.com/YBZh/EFDM.