Reviewerly/exHarmony huggingface dataset:
Avatar design in Metaverse: the effect of avatar-user similarity in procedural and creative tasks

Purpose Through the lens of self-perception theory, this paper investigates how avatar design (i.e. avatar user similarity) affects users' self-awareness and shapes their task engagement and performance in the Metaverse. Design/methodology/approach The authors conducted a 2 (avatar user similarity: high vs low) × 2 (task type: procedural vs creative) lab experiment and collected data from questionnaires, the recording of users' behavior during tasks and their actual task performance. Findings The results show that higher avatar user similarity leads to higher task engagement in general. Furthermore, while a similar avatar promotes users to regulate their behaviors and achieve better performance in a procedural task, high similarity also inhibits users' creativity by invoking habitual thinking, resulting in worse performance in generating original ideas in a creative task. Originality/value This study is expected to contribute to the information systems literature by revealing the value of avatar design and providing new perspectives on improving users' experiences in the Metaverse.
An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell and Module Models

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases.
An Orthogonal Dual-Polarization and Time Multiplexing Scheme of Continuous-Variable Quantum Key Distribution

In this article, we propose a time and orthogonal polarization division multiplexing scheme for local local oscillator continuous-variable quantum key distribution (LLO CV-QKD) system. According to the influence mechanism of time-domain diffusion and polarization disturbance on the key parameters, such as the channel transmittance and excess noise, of the studied system, we established a dual-polarization theoretical model and verified its validity by simulations. Here, the pulse width at the nanosecond level and polarization extinction ratio (PER) are considered for optimizing the performance of secure key rate (SIKR) quantitatively. For transmitting over 25 km optical fiber, the required PER should be above 22 dB when the pulse width is fixed to lns. Moreover, the optimal repetition frequency is obtained. In this case, 5.25 Mbps SKR can be achieved under the lns pulse width and 25dB PER, considering the $10^{11}$ finite-size block. Our work provides a systematic and theoretical framework for the study of improving the SKR of the dual-polarization CV-QKD system.
Hourly global solar radiation prediction based on seasonal and stochastic feature

Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Co</b></u> llaborative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>M</b></u> ulti- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>e</b></u> xpert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines.
A Rejoinder to Garfinkel (2023) – Legacy Statistical Disclosure Limitation Techniques for Protecting 2020 Decennial US Census: Still a Viable Option

Abstract In our article “Database Reconstruction Is Not So Easy and Is Different from Reidentification”, we show that reconstruction can be averted by properly using traditional statistical disclosure control (SDC) techniques, also sometimes called legacy statistical disclosure limitation (SDL) techniques. Furthermore, we also point out that, even if reconstruction can be performed, it does not imply reidentification. Hence, the risk of reconstruction does not seem to warrant replacing traditional SDC techniques with differential privacy (DP) based protection. In “Legacy Statistical Disclosure Limitation Techniques Were Not an Option for the 2020 US Census of Population and Housing”, by Simson Garfinkel, the author insists that the 2020 Census move to DP was justified. In our view, this latter article contains some misconceptions that we identify and discuss in some detail below. Consequently, we stand by the arguments given in “Database Reconstruction Is Not So Easy:: :”.
CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection

Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. Recently, weakly supervised VAED (WVAED) has become a popular VAED technical route of research. WVAED methods do not depend on a supplementary self-supervised substitute task, yet they can assess anomaly scores straightway. However, the performance of WVAED methods depends on pretrained feature extractors. In this paper, we first address taking advantage of two pretrained feature extractors for CNN (e.g., C3D and I3D) and ViT (e.g., CLIP), for effectively extracting discerning representations. We then consider long-range and short-range temporal dependencies and put forward video snippets of interest by leveraging our proposed temporal self-attention network (TSAN). We design a multiple instance learning (MIL)-based generalized architecture named CNN-ViT-TSAN, by using CNN- and/or ViT-extracted features and TSAN to specify a series of models for the WVAED problem. Experimental results on publicly available popular crowd datasets demonstrated the effectiveness of our CNN-ViT-TSAN.
Deep Reinforcement Learning for Robotic Approaching Behavior Influenced by User Activity and Disengagement

Abstract A robot intended to monitor human behavior must account for the user’s reactions to minimize his/her perceived discomfort. The possibility of learning user interaction preferences and changing the robot’s behavior accordingly may positively impact the perceived quality of the interaction with the robot. The robot should approach the user without causing any discomfort or interference. In this work, we contribute and implement a novel Reinforcement Learning (RL) approach for robot navigation toward a human user. Our implementation is a proof-of-concept that uses data gathered from real-world experiments to show that our algorithm works on the kind of data that it would run on in a realistic scenario. To the best of our knowledge, our work is one of the first attempts to provide an adaptive navigation algorithm that uses RL to account for non-deterministic phenomena.
Discovering Interesting Patterns from Hypergraphs

A hypergraph is a complex data structure capable of expressing associations among any number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are useful to model real-life problems. Frequent pattern mining is one of the most popular problems in data mining with a lot of applications. To the best of our knowledge, there exists no flexible pattern mining framework for hypergraph databases decomposing associations among data entities. In this article, we propose a flexible and complete framework for mining frequent patterns from a collection of hypergraphs. To discover more interesting patterns beyond the traditional frequent patterns, we propose frameworks for weighted and uncertain hypergraph mining also. We develop three algorithms for mining frequent, weighted, and uncertain hypergraph patterns efficiently by introducing a canonical labeling technique for isomorphic hypergraphs. Extensive experiments have been conducted on real-life hypergraph databases to show both the effectiveness and efficiency of our proposed frameworks and algorithms.
Rise of digital fashion and metaverse: influence on sustainability

Abstract The fashion industry is a lucrative market that generates revenue in the trillions through the production and distribution of clothing, footwear, and various accessories. The apparel industry has experienced a remarkable expansion in the past few years, evidenced by a 100% increase in output between 2000 and 2023. The apparel industry makes a substantial contribution to the negative impacts of biodiversity loss and climate change. The fashion industry’s expansion and social and environmental sustainability concerns, catalyzed by digitalization, are propelling it toward a sustainable trajectory. The present review article centers on the primary issues faced by the fashion industry and the utilization of metaverse technologies to tackle sustainability challenges within this sector. The metaverse refers to the amalgamation of physical and digital space within a virtual world, commonly referred to as the meta-universe. The metaverse is a strategy that is anticipated to be employed in fashion e-commerce and retailing, aiming to facilitate consumer decision-making processes, ranging from user experience to customer experience. This approach is expected to confer considerable competitive benefits. This review paper aims to analyze the sustainable impact of the metaverse on the fashion industry and foster discourse among researchers and industry professionals.
Domain and Histopathology Adaptations–Based Classification for Malignancy Grading System

Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual evaluation may lead to high costs and be ineffective because diagnosis depends on the facility and the pathologists' insights and experiences. Convolutional neural network acts as a computer-based observer to improve clinicians' capacity in grading breast cancer. Therefore, this study proposes a novel scheme for automatic breast cancer malignancy grading from invasive ductal carcinoma. The proposed classifiers implement multistage transfer learning incorporating domain and histopathologic transformations. Domain adaptation using pretrained models, such as InceptionResNetV2, InceptionV3, NASNet-Large, ResNet50, ResNet101, VGG19, and Xception, was applied to classify the ×40 magnification BreaKHis data set into eight classes. Subsequently, InceptionV3 and Xception, which contain the domain and histopathology pretrained weights, were determined to be the best for this study and used to categorize the Databiox database into grades 1, 2, or 3. To provide a comprehensive report, this study offered a patchless automated grading system for magnification-dependent and magnification-independent classifications. With an overall accuracy (means ± SD) of 90.17% ± 3.08% to 97.67% ± 1.09% and an F1 score of 0.9013 to 0.9760 for magnification-dependent classification, the classifiers in this work achieved outstanding performance. The proposed approach could be used for breast cancer grading systems in clinical settings.
Uncovering Trauma in Genocide Tribunals

This paper applies Natural Language Processing (NLP) methods to analyze the exposure to trauma experienced by witnesses in international criminal tribunals when testifying in court. One major contribution of this study is the creation of a substantially extended version of the Genocide Transcript Corpus (GTC) that includes 52,845 text segments of transcripts from three different genocide tribunals. Based on this data, we first examine the prevalence of trauma-related content in witness statements. Second, we are implementing a binary classification algorithm to automatically detect potentially traumatic content. Therefore, in a preparatory step, an Active Learning (AL) approach is applied to establish the ideal size for the training data set. Subsequently, this data is used to train a transformer model. In this case, the two models BERTbase and HateBERT are used for both steps, allowing for a comparison of a base-level model with a model that has already been pre-trained on data more relevant in the context of harmful vocabulary. In a third step, the study employs an Explainable Artificial Intelligence (XAI) model to gain a deeper understanding of the reasoning behind the model's classifications. Our results suggest that both BERTbase and HateBERT perform comparatively well on this classification task, with no model clearly outperforming the other. The classification outcomes further suggest that a reduced data set size can achieve equally high performance metrics and might be a preferable choice in certain use cases. The results can be used to establish more trauma-informed legal procedures in genocide-related tribunals, including the identification of potentially re-traumatizing examination approaches at an early stage.
A certificateless authenticated searchable encryption with dynamic multi-receiver for cloud storage

The searchable encryption schemes (PEKS, IBEKS, and CLPEKS) based on public-key cryptography (i.e., PKI-based, Identity-based, and Certificateless) enable the data users to accomplish searches on encrypted data stored in cloud storage by a cloud server. However, these schemes have three fundamental challenges: (1) Impervious to Keyword Guessing Attacks (KGAs) and File Injection Attacks (FIA), (2) Keyword privacy in query-trapdoor and encryption-keywords algorithms, and (3) Support for conjunctive keyword search in dynamic multi-user environments. Consequently, the authors produced Certificateless Authenticated Searchable Encryption with Dynamic Multi-receiver (CLASEDM). It also provides two security models that enable keyword privacy in encryption-keyword and query-trapdoor: Chosen keyword to Encryption-keyword attack (CKE) and Chosen keyword to Query-trapdoor attack (CKQ). And the authentication in the encryption of the keywords method also defends against KGA and FIA attacks. Afterward, the authors perform a conjunctive keyword search and add or remove the data recipient from the document’s access list. Finally, the performance analysis shows the CLASEDM scheme outperforming the related systems in comparative and experimental studies.
A novel collaborative path planning algorithm for 3-wheel omnidirectional Autonomous Mobile Robot

Collaboration of multiple mobile robots becomes important in situations where collaborative tasks are required. For this purpose, a method including obstacle detection based on collaborative path planning of multiple Autonomous Mobile Robots (AMRs) has been developed. This study ensures the usability of collaborative omnidirectional AMRs technologies in various fields. In the study, novel path planning and obstacle avoidance algorithms are developed for collaborative mobile robots with omnidirectional mobility. Essentially, these algorithms include motion planning performed by obstacle avoidance with two identical 3-wheel omnidirectional mobile robots (TWOMR). Numerical calculations of the collaborative algorithm have been performed after the kinematic calculations, and tested algorithms developed for the proposed model. As a result, it has been observed that the path planning and obstacle avoidance algorithms developed for collaborative omnidirectional AMRs successfully follow the Master robot in the most efficient trajectory without collision and it has been observed that the developed method works with high accuracy.
Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China

Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data.
Are LSTMs good few-shot learners?

Abstract Deep learning requires large amounts of data to learn new tasks well, limiting its applicability to domains where such data is available. Meta-learning overcomes this limitation by learning how to learn. Hochreiter et al. (International conference on artificial neural networks, Springer, 2001) showed that an LSTM trained with backpropagation across different tasks is capable of meta-learning. Despite promising results of this approach on small problems, and more recently, also on reinforcement learning problems, the approach has received little attention in the supervised few-shot learning setting. We revisit this approach and test it on modern few-shot learning benchmarks. We find that LSTM, surprisingly, outperform the popular meta-learning technique MAML on a simple few-shot sine wave regression benchmark, but that LSTM, expectedly, fall short on more complex few-shot image classification benchmarks. We identify two potential causes and propose a new method called Outer Product LSTM (OP-LSTM) that resolves these issues and displays substantial performance gains over the plain LSTM. Compared to popular meta-learning baselines, OP-LSTM yields competitive performance on within-domain few-shot image classification, and performs better in cross-domain settings by 0.5–1.9% in accuracy score. While these results alone do not set a new state-of-the-art, the advances of OP-LSTM are orthogonal to other advances in the field of meta-learning, yield new insights in how LSTM work in image classification, allowing for a whole range of new research directions. For reproducibility purposes, we publish all our research code publicly.