Reviewerly/exHarmony huggingface dataset:
Angle Tokenization Guided Multi-Scale Vision Transformer for Oriented Object Detection in Remote Sensing Imagery

In this paper, an angle tokenization guided multi-scale Trans-former framework is proposed for oriented object detection in remote sensing images. Different from existing detectors that are based on convolutional neural networks (CNNs), our proposed method is based on a pyramid Transformer architecture with a compact and flexible angle tokenization module (ATM) to efficiently learn the orientation knowledge for rotated geospatial objects. The Transformer structure can progressively render long-range dependencies and multi-scale spatial details required for accurate localization, while the ATM provides robust guidance on feature refinement for angle prediction, jointly achieving end-to-end orientation de-tection. To the best of our knowledge, this is the first work to adapt Vision Transformers to remote sensing oriented object detection. Experimental results demonstrate the effectiveness and superiority of our method.
Experiences of Depersonalization/Derealization Among Users of Virtual Reality Applications: A Cross-Sectional Survey

Previous research shows that virtual reality (VR) users may experience symptoms of depersonalization/derealization (DPDR) immediately after use. However, the impact of long-term VR use on these symptoms has not been analyzed so far. In a preregistered study, we conducted an online survey among a bigger sample of VR users (N = 754) to investigate the relationship between time of use during the past 6 months and the presence of DPDR symptoms. The results support the absence of a linear association between time of VR use and the presence of symptoms, when controlling for other factors. DPDR symptoms are more frequent among younger female users and in those who experience higher levels of embodiment during use. Secondary analyses show that symptoms are more common among newer users and among those who engage in longer sessions. These findings suggest that current common VR experiences are not a cause of long-term DPDR symptoms for the majority of users, yet also encourage further research about specific cases where VR use might trigger DPDR experiences in the long term.
Education Beats at the Heart of the Sustainability in Thailand: The Role of Institutional Awareness, Image, Experience, and Student Volunteer Behavior

The grim effects of COVID-19 have widely obstructed every sector of life, and the education sector is not an exception. The physical meeting restrictions, lockdowns, social distancing, and movement control orders increased the dropouts of students in educational institutes. These dropouts compromised the contribution of the educational sector to the gross domestic product (GDP) and worsened the economic situation. The spike of COVID-19 shaped a curve of fears, and students are clueless as to how to mitigate its affects. Therefore, this study proposes an implication of a rare phenomenon termed as student volunteering behavior. Student volunteering behavior has the capacity to turn existing students into advocates of the institute, a great assistance to attract potential students and bring back the dropout students. This study suggests that educational institutes in Thailand should provide a better experience to students, strengthen the institute’s image, and make students aware of their services. Their memorable experiences of institutional services will enhance students’ loyalty and urge them to exhibit volunteering behavior. The result of this study indicates significant fallout of institute experience and image to student loyalty and to the student volunteering behavior. However, the study unveiled insignificant results of the relationship between institute awareness to the student loyalty and student volunteering behavior. Furthermore, the study proposes directions for further studies based on the rarer limitations.
Graph neural networks for decentralized multi-agent perimeter defense

In this work, we study the problem of decentralized multi-agent perimeter defense that asks for computing actions for defenders with local perceptions and communications to maximize the capture of intruders. One major challenge for practical implementations is to make perimeter defense strategies scalable for large-scale problem instances. To this end, we leverage graph neural networks (GNNs) to develop an imitation learning framework that learns a mapping from defenders’ local perceptions and their communication graph to their actions. The proposed GNN-based learning network is trained by imitating a centralized expert algorithm such that the learned actions are close to that generated by the expert algorithm. We demonstrate that our proposed network performs closer to the expert algorithm and is superior to other baseline algorithms by capturing more intruders. Our GNN-based network is trained at a small scale and can be generalized to large-scale cases. We run perimeter defense games in scenarios with different team sizes and configurations to demonstrate the performance of the learned network.
Characterizing Job Microarchitectural Profiles at Scale: Dataset and Analysis

Understanding the microarchitectural resource characteristics of datacenter jobs has become increasingly critical to guarantee the performance of jobs while improving resource utilization. Prior work studied the resource characteristics of datacenter jobs at the OS level, little reveals the deep and detailed characteristics at the microarchitecture level due to the lack of related open traces. In this paper, we provide a new open trace, AMTrace (Alibaba Microarchitecture Trace) 1, which is profiled from 8,577 high-end physical hosts from Alibaba's datacenter by a hardware/software co-design monitoring method. AMTrace provides the microarchitectural metrics of 9.8 × 105 Linux containers with "Per-Container-Per-Logic CPU" granularity. Different from existing open traces, AMTrace provides a new perspective to analyze the microarchitectural resource characteristics of datacenter jobs. Based on AMTrace, we first reveal the uneven resource usage of jobs among multiple logic CPUs. Then, we analyze the impact of resource contention of CPU and memory bandwidth on job performance. Finally, we analyze the job performance under different CPU provisioning modes from microarchitecture perspective. These analyses lead to constructive insights for datacenter resource management and optimization. Furthermore, we discuss possible research opportunities on AMTrace and we believe that AMTrace will inspire more exciting research on microarchitecture and resource management.
Sentiment Analysis of Twitter User Opinions Related to Metaverse Technology Using Lexicon Based Method

The technology of the metaverse has been a topic of discussion in the tech sector recently, and the implementation of the metaverse in the future is one of the most anticipated things for internet users around the world, allowing users to become the avatar they want in the virtual universe. To see how someone approaches metaverse technology, we can analyze it by looking at various opinions that emerge from the sentiment of internet users, one of which is from the social media Twitter. This study aims to analyze the sentiment of Twitter users' opinions, which is one of the most popular internet social media platforms. The researcher collects tweet data using the snscrape library in the Python programming language, a tool for mass data collection from Twitter. Then, the analysis of the sentiment of Twitter user opinions is carried out using the Lexicon Based method, which is a method that uses a dictionary of words to categorize the sentiment of a text. The results of this study show that the Lexicon Based method can classify the sentiment of Twitter user opinions related to metaverse technology with an accuracy value of 84%. This shows that the results of sentiment analysis using the Lexicon Based method have a good level of accuracy in performance in analyzing the sentiment of Twitter user opinions related to metaverse technology.
Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network

Taking smartphone-made videos for photogrammetry is a convenient approach because of the easy image collection process for the object being reconstructed. However, the video may contain a lot of relatively similar frames. Additionally, frames may be of different quality. The primary source of quality variation in the same video is varying motion blur. Splitting the sequence of the frames into chunks and choosing the least motion-blurred frame in every chunk would reduce data redundancy and improve image data quality. Such reduction will lead to faster and more accurate reconstruction of the 3D objects. In this research, we investigated image quality evaluation in the case of human 3D head modeling. Suppose a head modeling workflow already uses a convolutional neural network for the head detection task in order to remove non-static background. In that case, features from the neural network may be reused for the quality evaluation of the same image. We proposed a motion blur evaluation method based on the LightGBM ranker model. The method was evaluated and compared with other blind image quality evaluation methods using videos of a mannequin head and real faces. Evaluation results show that the developed method in both cases outperformed sharpness-based, BRISQUE, NIQUE, and PIQUE methods in finding the least motion-blurred image.
COVID-19 Precautionary Measures and Practices for Delivering Modular Distance Learning

The purpose of this descriptive study is to investigate the safety precautions and delivery methods for the modular distance learning modality during the COVID-19 pandemic. This empirical study employed a quantitative approach and descriptive research design to address the research problem and questions. The respondents for primary data collection were the junior high school teachers who were identified through a referral sampling technique. The frequency count, percentage, ranking, and Chi-square test for homogeneity and independence were the statistical tools employed in the study. The alpha threshold for all inferential statistics was set at 0.05. The outcomes showed that the safety precautions in the modular distance learning delivery provided equitable and inclusive access to favorable learning environments. Teachers' evaluations of the methods employed in the modular distance learning program revealed that parents were urged to play a significant role as home facilitators. The data analysis revealed that there were significant differences in how the modular distance learning was carried out. According to the findings, there is no conclusive link between the prevention measures and the delivery of the distance learning mode. To strengthen this investigation, additional research is required with a larger focus and new factors.
A parametric similarity method: Comparative experiments based on semantically annotated large datasets

We present the parametric method SemSimp aimed at measuring semantic similarity of digital resources. SemSimp is based on the notion of information content, and it leverages a reference ontology and taxonomic reasoning, encompassing different approaches for weighting the concepts of the ontology. In particular, weights can be computed by considering either the available digital resources or the structure of the reference ontology of a given domain. SemSimp is assessed against six representative semantic similarity methods for comparing sets of concepts proposed in the literature, by carrying out an experimentation that includes both a statistical analysis and an expert judgement evaluation. To the purpose of achieving a reliable assessment, we used a real-world large dataset based on the Digital Library of the Association for Computing Machinery (ACM), and a reference ontology derived from the ACM Computing Classification System (ACM-CCS). For each method, we considered two indicators. The first concerns the degree of confidence to identify the similarity among the papers belonging to some special issues selected from the ACM Transactions on Information Systems journal, the second the Pearson correlation with human judgement. The results reveal that one of the configurations of SemSimp outperforms the other assessed methods. An additional experiment performed in the domain of physics shows that, in general, SemSimp provides better results than the other similarity methods.
An ensemble oversampling method for imbalanced classification with prior knowledge via generative adversarial network

Currently, an increasing number of real-world applications show characteristics of class-imbalance classification suffering from severe class distribution skewing, thus requiring brand new algorithms to learn from imbalanced datasets. In this paper, a novel oversampling method using GAN framework is proposed for numerical imbalanced data, namely G-GAN. In the method, a Gaussian distribution of minority samples is estimated to get prior knowledge of minority class for the latent space of GAN. In order to increase the randomness of the generated samples, noises are obtained by a mixed strategy, that is, some noises of generator obey Gaussian distribution and others obey random distribution. Then G-GAN is trained to generate dispersive positive samples with the idea of Bagging, which could avoid the occurrence of overfitting. G-GAN is different from other literatures in that GAN does not directly generate minority samples, but adds the distribution information of minority samples to the latent space of GAN, and then generates minority samples. Compared with 11 commonly used oversampling methods, G-GAN obtains promising results in terms of G-mean, AUC, F-measure and ROC utilizing three classifiers on 11 benchmark imbalanced datasets. Furthermore, G-GAN is also validated on AUC metrics of a real Diabetes imbalanced dataset. The results demonstrate that G-GAN can provide great potential for imbalanced classification in the two numerical experiments.
A scale and region-enhanced decoding network for nuclei classification in histology image

Accurate classification of nuclei in histology images is essential for clinical diagnosis, prognosis, and therapeutic response prediction of cancer. However, this is still a challenging task due to (1) nuclei exhibiting a high level of heterogeneity within different types and (2) large intra-class variability including complex morphology and large variations of scale. To solve these problems, we propose a novel scale and region-enhanced decoding network based on the traditional U-shape structure for nuclei classification. We employ a nuclei detection head as region enhancement module in the decoding branch, which can enhance the nuclear regional information by locating the approximate bounding regions and provide more distinguish information for producing better feature maps of subsequent classification. Then, we propose a scale-aware feature fusion module, which fuses stage-wise feature maps generated from the decoder branch, to effectively learn multi-scale features. Finally, we utilize a scale attention module to calibrate the features and adapt to the most suitable scale in the hybrid multi-scale feature maps. In comparison with several state-of-the-art methods on two publicly available colonic cancer nuclei classification datasets, namely ConSep and Lizard, the proposed method obtains the highest accuracy of 0.860 and 0.927, respectively. It also achieves the highest accuracy of 0.838 on the PanNuke dataset collected from different tissues at different magnitudes. The independent validation on two subsets of the Lizard dataset indicates the proposed method obtains the highest accuracy. In conclusion, the proposed method can greatly improve classification performance, particularly for challenging nuclei with complex contexts and large-scale variations.
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection

Designing robust algorithms capable of training accurate neural networks on uncurated datasets from the web has been the subject of much research as it reduces the need for time consuming human labor. The focus of many previous research contributions has been on the detection of different types of label noise; however, this paper proposes to improve the correction accuracy of noisy samples once they have been detected. In many state-of-the-art contributions, a two phase approach is adopted where the noisy samples are detected before guessing a corrected pseudo-label in a semi-supervised fashion. The guessed pseudo-labels are then used in the supervised objective without ensuring that the label guess is likely to be correct. This can lead to confirmation bias, which reduces the noise robustness. Here we propose the pseudo-loss, a simple metric that we find to be strongly correlated with pseudo-label correctness on noisy samples. Using the pseudo-loss, we dynamically down weight under-confident pseudo-labels throughout training to avoid confirmation bias and improve the network accuracy. We additionally propose to use a confidence guided contrastive objective that learns robust representation on an interpolated objective between class bound (supervised) for confidently corrected samples and unsupervised representation for under-confident label corrections. Experiments demonstrate the state-of-the-art performance of our Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets including curated data synthetically corrupted with in-distribution and out-of-distribution noise, and two real world web noise datasets. Our experiments are fully reproducible github.com/PaulAlbert31/PLS.
Error-resilient Monte Carlo quantum simulation of imaginary time

Computing the ground-state properties of quantum many-body systems is a promising application of near-term quantum hardware with a potential impact in many fields. The conventional algorithm quantum phase estimation uses deep circuits and requires fault-tolerant technologies. Many quantum simulation algorithms developed recently work in an inexact and variational manner to exploit shallow circuits. In this work, we combine quantum Monte Carlo with quantum computing and propose an algorithm for simulating the imaginary-time evolution and solving the ground-state problem. By sampling the real-time evolution operator with a random evolution time according to a modified Cauchy-Lorentz distribution, we can compute the expected value of an observable in imaginary-time evolution. Our algorithm approaches the exact solution given a circuit depth increasing polylogarithmically with the desired accuracy. Compared with quantum phase estimation, the Trotter step number, i.e. the circuit depth, can be thousands of times smaller to achieve the same accuracy in the ground-state energy. We verify the resilience to Trotterisation errors caused by the finite circuit depth in the numerical simulation of various models. The results show that Monte Carlo quantum simulation is promising even without a fully fault-tolerant quantum computer.
Emphasizing unseen words: New vocabulary acquisition for end-to-end speech recognition

Due to the dynamic nature of human language, automatic speech recognition (ASR) systems need to continuously acquire new vocabulary. Out-Of-Vocabulary (OOV) words, such as trending words and new named entities, pose problems to modern ASR systems that require long training times to adapt their large numbers of parameters. Different from most previous research focusing on language model post-processing, we tackle this problem on an earlier processing level and eliminate the bias in acoustic modeling to recognize OOV words acoustically. We propose to generate OOV words using text-to-speech systems and to rescale losses to encourage neural networks to pay more attention to OOV words. Specifically, we enlarge the classification loss used for training neural networks' parameters of utterances containing OOV words (sentence-level), or rescale the gradient used for back-propagation for OOV words (word-level), when fine-tuning a previously trained model on synthetic audio. To overcome catastrophic forgetting, we also explore the combination of loss rescaling and model regularization, i.e. L2 regularization and elastic weight consolidation (EWC). Compared with previous methods that just fine-tune synthetic audio with EWC, the experimental results on the LibriSpeech benchmark reveal that our proposed loss rescaling approach can achieve significant improvement on the recall rate with only a slight decrease on word error rate. Moreover, word-level rescaling is more stable than utterance-level rescaling and leads to higher recall rates and precision rates on OOV word recognition. Furthermore, our proposed combined loss rescaling and weight consolidation methods can support continual learning of an ASR system.
Quantum Algorithm for Higher-Order Unconstrained Binary Optimization and MIMO Maximum Likelihood Detection

In this paper, we propose a quantum algorithm that supports a real-valued higher-order unconstrained binary optimization (HUBO) problem. This algorithm is based on the Grover adaptive search that originally supported HUBO with integer coefficients. Next, as an application example, we formulate multiple-input multiple-output maximum likelihood detection as a HUBO problem with real-valued coefficients, where we use the Gray-coded bit-to-symbol mapping specified in the 5G standard. The proposed approach allows us to construct an efficient quantum circuit for the detection problem and to analyze specific numbers of required qubits and quantum gates, whereas other conventional studies have assumed that such a circuit is feasible as a quantum oracle. To further accelerate the quantum algorithm, we also derive a probability distribution of the objective function value and determine a unique threshold to sample better states. Assuming a future fault-tolerant quantum computing, our proposed algorithm has the potential for significantly reducing query complexity in the classical domain and providing a quadratic speedup in the quantum domain.
A comparative analysis of deep learning algorithms in eye gaze estimation

Eye tracking is the procedure of keeping track of one’s point of gaze. Researchers have proposed a range of algorithms and methods to automatically track the gaze direction and position, which can be beneficial in a number of applications. Eye tracking is growing in popularity due to its ability to make a range of activities simpler, especially for the elderly or people with special needs. This research intends to investigate and review eye tracking methodologies by focusing on modern approaches such as machine learning and deep learning for gaze detection. This paper provides a quick overview of gaze tracking based upon appearance of eye images and model based approaches. Different mapping functions are mostly used for calculating gaze points. The findings of this study indicates that when compared with machine learning algorithms for estimating gaze points, deep learning techniques produce more accurate results in gaze detection from images captured using camera. Finally, some potential applications of gaze tracking are discussed.