Reviewerly/exHarmony huggingface dataset:
Experimental quantum simulation of superradiant phase transition beyond no-go theorem via antisqueezing

Superradiant phase transition (SPT) in thermal equilibrium, as a fundamental concept bridging the statistical physics and electrodynamics, can offer the key resources for quantum information science. Notwithstanding its fundamental and practical significances, equilibrium SPT has never been observed in experiments since the first proposal in the 1970s. Furthermore, the existence of equilibrium SPT in the cavity quantum electrodynamics (QED) systems is still subject of ongoing debates, due to the no-go theorem induced by the so-called A2 term. Based on the platform of nuclear magnetic resonance (NMR), here we experimentally demonstrate the occurrence of equilibrium SPT beyond no-go theorem by introducing the antisqueezing effect. The mechanism relies on the antisqueezing that recovers the singularity of the ground state via exponentially enhancing the zero point fluctuation (ZPF) of system. The strong entanglement and squeezed Schrodinger cat states of spins are achieved experimentally in the superradiant phase, which may play an important role in fundamental tests of quantum theory, implementing quantum metrology and high-efficient quantum information processing. Our experiment also shows the antisqueezing-enhanced signal-to-noise rate (SNR) of NMR spectrum, providing a general method for implementing high-precision NMR experiments.
Student’s Satisfaction of the Quality of Online Learning in Higher Education: An Empirical Study

Higher education institutions (HEIs) have been facing a digital transformation in online learning as a result of the restrictions generated by COVID-19. Therefore, identifying which are the elements that influence student satisfaction will allow HEIs to establish strategies to ensure the quality of the digital transformation. This study proposes a model that measures student satisfaction considering three dimensions: teacher quality, technical service quality and service quality. Then, the impact of each dimension on student satisfaction is estimated using a Partial Least Squares Structural Equation Model (PLS-SEM), and finally an Importance-Performance Map Analysis (IPMA) was performed to identify the improvements that should be made to increase student satisfaction. When analyzing the responses of 1430 students at the School of Medicine, it was found that the quality of technical service should be improved, specifically training, and encouraging teachers to use strategies that allow student participation.
A Hybrid Deep Learning Technique for Personality Trait Classification From Text

Recently, Cognitive-based Sentiment Analysis with emphasis on automatic detection of user behaviour, such as personality traits, based on online social media text has gained a lot of attention. However, most of the existing works are based on conventional techniques, which are not sufficient to get promising results. In this research work, we propose a hybrid Deep Learning-based model, namely Convolutional Neural Network concatenated with Long Short-Term Memory, to show the effectiveness of the proposed model for 8 important personality traits (Introversion-Extroversion, Intuition-Sensing, Thinking-Feeling, Judging-Perceiving). We implemented our experimental evaluations on the benchmark dataset to accomplish the personality trait classification task. Evaluations of the datasets have shown better results, which demonstrates that the proposed model can effectively classify the user’s personality traits as compared to the state-of-the-art techniques. Finally, we evaluate the effectiveness of our approach through statistical analysis. With the knowledge obtained from this research, organizations are capable of making their decisions regarding the recruitment of personals in an efficient way. Moreover, they can implement the information obtained from this research as best practices for the selection, management, and optimization of their policies, services, and products.
An Efficient Deep-Sea Debris Detection Method Using Deep Neural Networks

Marine debris impacts negatively upon the marine environment and the survival of marine life, because they are some difficult-to-degrade substances, and most of them will sink into the deep sea and continue to exist in the ocean. Autonomous underwater vehicles can clean up the deep-sea debris to some extent. However, the efficient detection method plays a critical role in the collection rate. This paper establishes an efficient deep-sea debris detection method with high speed using deep learning methods. First, a real deep-sea debris detection dataset (3D dataset) is established for further research. The dataset contains 7 types of debris: cloth, fishing net &rope, glass, metal, natural debris, rubber, and plastic. Second, the one-stage deep-sea debris detection network ResNet50-YOLOV3 is proposed. In addition, eight advanced detection models are also involved in the detection process of deep-sea debris. Finally, the performance of ResNet50-YOLOV3 is verified by experiments. Furthermore, the applicability and effectiveness of ResNet50-YOLOV3 in deep-sea debris detection are proved by the experimental results.
Blockchain systems and ethical sourcing in the mineral and metal industry: a multiple case study

Purpose The purpose of this paper is to examine blockchain's roles in promoting ethical sourcing in the mineral and metal industry. Design/methodology/approach It analyzes multiple case studies of blockchain projects in the mineral and metal industry. Findings It gives detailed descriptions of how blockchain-based supply chain networks' higher density of information flow and high degree of authenticity of information can increase supply chain participants' compliance with sustainability standards. It gives special consideration to blockchain systems' roles in overcoming the deficits in the second party and the third-party trust. It also demonstrates how blockchain-based supply chain networks include outside actors and configure the supply chain networks in a way that enhances the empowerment of marginalized groups. Practical implications It suggests various mechanisms by which blockchain-based supply chain networks can give a voice to marginalized groups. Originality/value It demonstrates how blockchain is likely to force mineral and metal supply chains to become more traceable and transparent.
Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm

Osteosarcoma is one of the most common malignant bone tumors mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and it is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify medical images, which reduces the dependency on experts and leads to faster processing. In this paper, an automatic detection system: Integrated Features-Feature Selection Model for Classification (IF-FSM-C) to detect osteosarcoma from the high-resolution whole slide images (WSIs) is proposed. The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted (HC) feature extraction techniques and deep learning models (DLMs) namely EfficientNet-B0 and Xception. To further improve the performance of the proposed system, feature selection (FS) is performed. Here, two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V are proposed to perform FS. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed to compare the performance of proposed IF-FSM-C to the classifiers which use HC or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection. The best overall accuracy of 96.08% is obtained when integrated features extracted using HC techniques and Xception are used. The overall accuracy is enhanced to 99.54% after applying BAOA-S for FS. Further, the application of BAOA-S for FS reduces the number of features with the best model having only 188 features compared to 2118 features if no FS is applied.
Graph Pattern Matching in GQL and SQL/PGQ

As graph databases become widespread, the International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) have approved a project to create GQL, a standard property graph query language. This complements the SQL/PGQ project, which specifies how to define graph views over a SQL tabular schema, and to run read-only queries against them.
The AdaBoost Approach Tuned by Firefly Metaheuristics for Fraud Detection

The use of powerful classifiers is broad and the problem of fraud detection tends to benefit from similar solutions as well. The problem in the digital age cannot be disregarded as the number of cases is worrisome. The use of machine learning has been beneficial to many real-world problems, as the classification ability of such solutions is high. Furthermore, these solutions are not without shortcomings, and possibilities of hybrid methods are explored for the reasons of further enhancements. Therefore, in the research proposed in this manuscript, the adaptive boosting algorithm is optimized by the firefly metaheuristics and validated against the imbalanced credit card fraud detection dataset. Moreover, the synthetic minority over-sampling technique is applied for addressing the class imbalance. According to experimental findings, the proposed method shows substantially better performance than other state-of-the-art machine learning models for tackling the same problem in terms of standard classification metrics.
High-Resolution Image Harmonization via Collaborative Dual Transformations

Given a composite image, image harmonization aims to adjust the foreground to make it compatible with the background. High-resolution image harmonization is in high demand, but still remains unexplored. Conventional image harmonization methods learn global RGB-to-RGB transformation which could effortlessly scale to high resolution, but ignore diverse local context. Recent deep learning methods learn the dense pixel-to-pixel transformation which could generate harmonious outputs, but are highly constrained in low resolution. In this work, we propose a high-resolution image harmonization network with Collaborative Dual Transformation (CDTNet) to combine pixel-to-pixel transformation and RGB-to-RGB transformation coherently in an end-to-end network. Our CDTNet consists of a low-resolution generator for pixel-to-pixel transformation, a color mapping module for RGB-to-RGB transformation, and a refinement module to take advantage of both. Extensive experiments on high-resolution bench-mark dataset and our created high-resolution real composite images demonstrate that our CDTNet strikes a good balance between efficiency and effectiveness. Our used datasets can be found in https://github.com/bcmi/CDTNet-High-Resolution-Image-Harmonization.
Inferring the Meaning of Verbs from Context

This paper describes a cross-disciplinary extension of previous work on inferring the meanings of unknown verbs from context. In earlier work, a computational model was developed to incrementally infer meanings while processing texts in an information extraction task setting. In order to explore the space of possible predictors that the system could use to infer verb meanings, we perf ormed a statistical analysis of the corpus that had been used to test the computational system. There were various syntactic and semantic features of the verbs that were significantly diagnostic in determining verb meaning. We also evaluated human performance at inferring the verb in the same set of sentences. The overall number of correct predictions for humans was quite similar to that of the computational system, but humans had higher precision scores. The paper concludes with a discussion of the implications of these statistical and experimental findings for future computational work.
The perceived value of digital fashion product and purchase intention: the mediating role of the flow experience in metaverse platforms

Purpose This paper aims to explore digital fashion products in the metaverse platform contexts and empirically examine the effect of the metaverse platform characteristics on the purchase intention of digital fashion products through users' flow experience and perceived value of the products. Design/methodology/approach A survey method was used in this study. Answers from 314 metaverse users were analyzed, and the hypotheses were tested using the structural equations modeling and bootstrapping analysis. Findings The analyses showed that telepresence, social interaction and economic flow had significant effects on users' flow experience among the metaverse platform characteristics, while the continuity and content creation of the metaverse platform did not have significant effects. The flow experience also appeared to have significant effects on multiple consumption values, including pleasure value, self-expression value and economic value. Last, the perceived pleasure value and economic value of digital fashion products had a positive effect on purchase intention. Originality/value The main contribution of this research is that it is one of the first empirical attempts to investigate individual consumers' perceptions and experiences of digital fashion products in the context of metaverse platforms.
Diffraction Neural Network for Multi‐Source Information of Arrival Sensing

Abstract All‐optical diffractive neural networks (DNNs) based on passive structures have been shown to perform complicated functions by optical acceleration, thus reforming in situ applications requiring digital computing processing. Its stable, compact, and passive features make it suitable for many conventional applications in complex electromagnetic environments. Here, a DNN based on a multilayer passive metasurface array is presented to estimate the information of arrival (IOA) over a wide range of frequency bands and incident angles. Unlike traditional methods, multiple correlated or coherent electromagnetic signals interfere with each other, resulting in difficult separation which is a fundamental obstacle in multi‐source IOA detection. Here, the proposed diffraction system creates separate virtual channels to isolate and process different incoming waves. It recognizes and classifies the beams, focuses them on the output plane divided by the arrival angle and working frequency, and displays the IOA and number of sources in real time at the speed of light. Furthermore, the massive‐parallelism and data‐post‐processing‐free strategy allows for broader applications in harsh environments, such as seismic detection and underwater circumstances.
The assessment of presence and performance in an AR environment for motor imitation learning: A case-study on violinists

The acquisition of advanced gestures is a challenge in various domains of proficient sensorimotor performance. For example, orchestral violinists must move in sync with the lead violinist's gestures. To help train these gestures, an educational music play-back system was developed using a HoloLens 2 simulated AR environment and an avatar representation of the lead violinist. This study aimed to investigate the impact of using a 2D or 3D representation of the lead violinist's avatar on students' learning experience in the AR environment. To assess the learning outcome, the study employed a longitudinal experiment design, in which eleven participants practiced two pieces of music in four trials, evenly spaced over a month. Participants were asked to mimic the avatar's gestures as closely as possible when it came to using the bow, including bowing, articulations, and dynamics. The study compared the similarities between the avatar's gestures and those of the participants at the biomechanical level, using motion capture measurements, as well as the smoothness of the participants' movements. Additionally, presence and perceived difficulty were assessed using questionnaires. The results suggest that using a 3D representation of the avatar leads to better gesture resemblance and a higher experience of presence compared to a 2D representation. The 2D representation, however, showed a learning effect, but this was not observed in the 3D condition. The findings suggest that the 3D condition benefits from stereoscopic information that enhances spatial cognition, making it more effective in relation to sensorimotor performance. Overall, the 3D condition had a greater impact on performance than on learning. This work concludes with recommendations for future efforts directed towards AR-based advanced gesture training to address the challenges related to measurement methodology and participants' feedback on the AR application.
Multiheaded deep learning chatbot for increasing production and marketing

Some businesses on product development prefer to use a chatbot for judging the customer's view. Today, the ability of a chatbot to consider the context is challenging due to its technical nature. Sometimes, it may misjudge the context, making the wrong decision in predicting the product's originality in the market. This task of chatbot helps the enterprise make huge profits from accurate predictions. However, chatbots may commit errors in dialogs and bring inappropriate responses to users, reducing the confidentiality of product and marketing information. This, in turn, reduces the enterprise gain and imposes cost complications on businesses. To improve the performance of chatbots, AI models are used based on deep learning concepts. This research proposes a multi-headed deep neural network (MH-DNN) model for addressing the logical and fuzzy errors caused by retrieval chatbot models. This model cuts down on the error raised from the information loss. Our experiments extensively trained the model on a large Ubuntu dialog corpus. The recall evaluation scores showed that the MH-DNN approach slightly outperformed selected state-of-the-art retrieval-based chatbot approaches. The results obtained from the MHDNN augmentation approach were pretty impressive. In our proposed work, the MHDNN algorithm exhibited accuracy rates of 94% and 92%, respectively, with and without the help of the Seq2Seq technique.
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter

Pre-trained language models (PLMs) are fundamental for natural language processing applications. Most existing PLMs are not tailored to the noisy user-generated text on social media, and the pre-training does not factor in the valuable social engagement logs available in a social network. We present TwHIN-BERT, a multilingual language model productionized at Twitter, trained on in-domain data from the popular social network. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision but also with a social objective based on the rich social engagements within a Twitter heterogeneous information network (TwHIN). Our model is trained on 7 billion tweets covering over 100 distinct languages, providing a valuable representation to model short, noisy, user-generated text. We evaluate our model on various multilingual social recommendation and semantic understanding tasks and demonstrate significant metric improvement over established pre-trained language models. We open-source TwHIN-BERT and our curated hashtag prediction and social engagement benchmark datasets to the research community.
The Role of Digital Literacy to Promote the Gender Equality

Digital Technology is crucial part of everyone’s life. This research is based on the digital literacy role to promote the gender equality in every field. According to UNICEF, 2017, Digital literacy is becoming more and more recognized as a necessary skill for employability and has been associated with increased earning potential and new business opportunities. This research found that various obstacles faced by female during the usages of digital technology. There are various measures introduced to overcome this problem. Education policy should place a strong emphasis on digital literacy for both boys and girls. To reach girls at a young age and provide them the opportunity to develop their talents over time, digital skill training should be incorporated into formal school curriculum starting in the basic grades.
The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share &amp; Requests

The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the Emotion Share Sub-Challenge, a regression on speech has to be made; and in the Requests Sub-Challenges, requests and complaints need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComPaRE features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectRum toolkit; in addition, wav2vec2 models are used.
Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots

Subterranean robot exploration is difficult, with many mobility, communications, and navigation challenges that require an approach with a diverse set of systems, and reliable autonomy. While prior work has demonstrated partial successes in addressing the problem, here we convey a comprehensive approach to address the problem of subterranean exploration in a wide range of tunnel, urban, and cave environments. Our approach is driven by the themes of resiliency and modularity, and we show examples of how these themes influence the design of the different modules. In particular, we detail our approach to artifact detection, pose estimation, coordination, planning, control, and autonomy, and we discuss our performance in the tunnel, urban, and self-organized cave circuits of the DARPA Subterranean Challenge. Our approach led to a winning result in the tunnel circuit, and placing second in the urban circuit event. We convey lessons learned in designing and testing a resilient system for subterranean exploration that can generalize to a large range of operating conditions, and potential improvements for the future.