Reviewerly/exHarmony huggingface dataset:
Investor intention, investor behavior and crypto assets in the framework of decomposed theory of planned behavior

The interest in crypto assets, known as an extremely volatile, risky and digital currency that first emerged in 2009, continued to increase day by day. Crypto assets, especially Bitcoin, have become very valuable and are now seen as an investment tool. Online survey data of 1,222 individuals were used in the study. The data were analyzed through the structural equation model. With the Decomposed Theory of Planned Behavior used in the research, the dimensions affecting Attitude, Subjective Norms, Perceived Behavioral Control variables and the effect of Intention on the behavior of investors, namely crypto asset investment, were investigated. According to the Standardized Regression Weights, a one-unit change in attitude affects intention 0.822, a one-unit change in subjective norms 0.048, and a one-unit change in perceived behavioral control affects intention 0.117. In addition, it has been concluded that the most important determinant of the actual behavior that reveals the investment is the intention with 0.754, while the PBC effect is 0.144. The study is a comprehensive study on crypto asset investments in Turkey, a developing country. It is aimed that the results obtained will contribute to researchers, crypto asset companies, policy makers and researchers who want to increase their market share in the sector.
A framework for designing interactive mobile training course content using augmented reality

Because mobile technology and the widespread usage of mobile devices have swiftly and radically evolved, several training centers have started to offer mobile training (m-training) via mobile devices. Thus, designing suitable m-training course content for training employees via mobile device applications has become an important professional development issue to allow employees to obtain knowledge and improve their skills in the rapidly changing mobile environment. Previous studies have identified challenges in this domain. One important challenge is that no solid theoretical framework serves as a foundation to provide instructional design guidelines for interactive m-training course content that motivates and attracts trainees to the training process via mobile devices. This study proposes a framework for designing interactive m-training course content using mobile augmented reality (MAR). A mixed-methods approach was adopted. Key elements were extracted from the literature to create an initial framework. Then, the framework was validated by interviewing experts, and it was tested by trainees. This integration led us to evaluate and prove the validity of the proposed framework. The framework follows a systematic approach guided by six key elements and offers a clear instructional design guideline checklist to ensure the design quality of interactive m-training course content. This study contributes to the knowledge by establishing a framework as a theoretical foundation for designing interactive m-training course content. Additionally, it supports the m-training domain by assisting trainers and designers in creating interactive m-training courses to train employees, thus increasing their engagement in m-training. Recommendations for future studies are proposed.
EVJVQA CHALLENGE: MULTILINGUAL VISUAL QUESTION ANSWERING

Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV), attracting significant attention from researchers. English is a resource-rich language that has witnessed various developments in datasets and models for visual question answering. Visual question answering in other languages also would be developed for resources and models. In addition, there is no multilingual dataset targeting the visual content of a particular country with its own objects and cultural characteristics. To address the weakness, we provide the research community with a benchmark dataset named EVJVQA, including 33,000+ pairs of question-answer over three languages: Vietnamese, English, and Japanese, on approximately 5,000 images taken from Vietnam for evaluating multilingual VQA systems or models. EVJVQA is used as a benchmark dataset for the challenge of multilingual visual question answering at the 9th Workshop on Vietnamese Language and Speech Processing (VLSP 2022). This task attracted 62 participant teams from various universities and organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 0.4392 in F1-score and 0.4009 in BLUE on the private test set. The multilingual QA systems proposed by the top 2 teams use ViT for the pre-trained vision model and mT5 for the pre-trained language model, a powerful pre-trained language model based on the transformer architecture. EVJVQA is a challenging dataset that motivates NLP and CV researchers to further explore the multilingual models or systems for visual question answering systems.
Bridging Depth Estimation and Completion for Mobile Robots Reliable 3D Perception

Autonomous mobile robots rely on knowing the 3D structure of their environment in order to plan and operate safely. This paper addresses the problem of estimating depth maps from monocular RGB images and any potentially available sparse depth measurements—as would be the case of using inexpensive RGBD or LIDAR sensors. Our approach bridges depth estimation and depth completion by introducing a novel lightweight two-stream encoder-decoder neural network that exploits late fusion and additional output refinements. The novelty of our network is that it can benefit from any available depth data, while not making any assumptions about their actual availability, density or distribution. Our proposed architecture outperforms models with equivalent number of operations in terms of depth accuracy and performs on par with much larger models.
Criticality-enhanced quantum sensing in the anisotropic quantum Rabi model

Quantum systems that undergo quantum phase transitions exhibit divergent susceptibility and can be exploited as probes to estimate physical parameters. We generalize the dynamic framework for criticality-enhanced quantum sensing by the quantum Rabi model (QRM) to its anisotropic counterpart and derive the correspondingly analytical expressions for the quantum Fisher information (QFI). We find that the contributions of the rotating-wave and counterrotating-wave interaction terms are symmetric at the limit of the infinite ratio of qubit frequency to field frequency, with the QFI reaching a maximum for the isotropic QRM. At finite frequency scaling, we analytically derive the inverted variance of higher-order correction and find that it is more affected by the rotating-wave coupling than by the counterrotating-wave coupling.
Designed an Inactive Archives Management Information System Using Visual Basic 2010 at The Department of Community Empowerment and Village West Java Province

In the field of archive depots, the West Java Province Village and Community Empowerment Service offers inactive archive management as one of its services. The West Java Province Village and Community Empowerment Service is responsible for keeping, organizing, and safeguarding archives that have been in the processing unit for more than two years. The goal of this research is to help archivists manage inactive archives more effectively and efficiently. Material gathering tactics included interviews and the addition of inactive archive data based on data collected in each field. Software is designed using the waterfall process. According to the findings, there are a number of issues that impede the archive management system that still employs the manual method. As a result, in this study, an inactive records management information system was designed using the Unified Modeling Language (UML) and implemented using the Visual Basic programming language with the.NET framework 4.0 and Ms. Access as the database. An approach for testing applications is called black box testing. The goal of this application is to make archive administration more effective and efficient.
YOLOSA: Object detection based on 2D local feature superimposed self-attention

We analyzed the network structure of real-time object detection models and found that the features in the feature concatenation stage are very rich. Applying an attention module here can effectively improve the detection accuracy of the model. However, the commonly used attention module or self-attention module shows poor performance in detection accuracy and inference efficiency. Therefore, we propose a novel self-attention module, called 2D local feature superimposed self-attention, for the feature concatenation stage of the neck network. This self-attention module reflects global features through local features and local receptive fields. We also propose and optimize an efficient decoupled head and AB-OTA, and achieve SOTA results. Average precisions of 49.0% (71FPS, 14ms), 46.1% (85FPS, 11.7ms), and 39.1% (107FPS, 9.3ms) were obtained for large, medium, and small-scale models built using our proposed improvements. Our models exceeded YOLOv5 by 0.8% – 3.1% in average precision.
An Image Turing Test on Realistic Gastroscopy Images Generated by Using the Progressive Growing of Generative Adversarial Networks

Generative adversarial networks (GAN) in medicine are valuable techniques for augmenting unbalanced rare data, anomaly detection, and avoiding patient privacy issues. However, there were limits to generating high-quality endoscopic images with various characteristics, such as peristalsis, viewpoints, light sources, and mucous patterns. This study used the progressive growing of GAN (PGGAN) within the normal distribution dataset to confirm the ability to generate high-quality gastrointestinal images and investigated what barriers PGGAN has to generate endoscopic images. We trained the PGGAN with 107,060 gastroscopy images from 4165 normal patients to generate highly realistic 5122 pixel-sized images. For the evaluation, visual Turing tests were conducted on 100 real and 100 synthetic images to distinguish the authenticity of images by 19 endoscopists. The endoscopists were divided into three groups based on their years of clinical experience for subgroup analysis. The overall accuracy, sensitivity, and specificity of the 19 endoscopist groups were 61.3%, 70.3%, and 52.4%, respectively. The mean accuracy of the three endoscopist groups was 62.4 [Group I], 59.8 [Group II], and 59.1% [Group III], which was not considered a significant difference. There were no statistically significant differences in the location of the stomach. However, the real images with the anatomical landmark pylorus had higher detection sensitivity. The images generated by PGGAN showed highly realistic depictions that were difficult to distinguish, regardless of their expertise as endoscopists. However, it was necessary to establish GANs that could better represent the rugal folds and mucous membrane texture.
An Analysis of Representative Works of Japanese Literature Based on Emotions and Topics

In literary research, subjective analysis by researchers and critics using artisanal analog methods is still the mainstream approach. In contrast, text-processing techniques that make full use of machine learning are already being actively applied to the analysis of Internet public opinion and various types of reviews. In this study, we analyzed the works of representative writers of modern Japanese literature in terms of emotions and topics. For emotion-based analysis, we combined a dictionary-based emotional analysis technique with the RoBERTa machine learning model. For topic-based analysis, we used a method for clustering distributed representations generated by Sentence-BERT. The method proposed in this study for analyzing the characteristics of literary works using machine learning techniques represents one of the most advanced and objective digital approaches to literary research.
Entanglement of the Ising–Heisenberg diamond spin- 1/2 cluster in evolution

Abstract In the last two decades, magnetic, thermodynamic properties and bipartite thermal entanglement in diamond spin clusters and chains have been studied. Such spin structures are presented in various compounds. The ions of Cu 2+ in the natural mineral azurite are arranged in a diamond spin chain. There are no studies of the entanglement behaviour during the quantum evolution of such systems. Herein, we consider the evolution of entanglement in the diamond spin-1/2 cluster. This cluster consists of two central spins described by the anisotropic Heisenberg model, which interact with two side spins via Ising interaction. The influence of the interaction coupling with side spins on the entanglement of central spins is investigated. It is shown that choosing the value of this coupling allows us to control the behaviour of entanglement between central spins. As a result, we find conditions for achieving the maximal values of entanglement. In addition, the entanglement behaviour between the side spins, central and side spins, and between a certain spin and the rest of the system is studied. In these cases, the conditions for achieving maximal entanglement are also obtained.
VAI-B: a multicenter platform for the external validation of artificial intelligence algorithms in breast imaging

PurposeMultiple vendors are currently offering artificial intelligence (AI) computer-aided systems for triage detection, diagnosis, and risk prediction of breast cancer based on screening mammography. There is an imminent need to establish validation platforms that enable fair and transparent testing of these systems against external data.ApproachWe developed validation of artificial intelligence for breast imaging (VAI-B), a platform for independent validation of AI algorithms in breast imaging. The platform is a hybrid solution, with one part implemented in the cloud and another in an on-premises environment at Karolinska Institute. Cloud services provide the flexibility of scaling the computing power during inference time, while secure on-premises clinical data storage preserves their privacy. A MongoDB database and a python package were developed to store and manage the data on-premises. VAI-B requires four data components: radiological images, AI inferences, radiologist assessments, and cancer outcomes.ResultsTo pilot test VAI-B, we defined a case-control population based on 8080 patients diagnosed with breast cancer and 36,339 healthy women based on the Swedish national quality registry for breast cancer. Images and radiological assessments from more than 100,000 mammography examinations were extracted from hospitals in three regions of Sweden. The images were processed by AI systems from three vendors in a virtual private cloud to produce abnormality scores related to signs of cancer in the images. A total of 105,706 examinations have been processed and stored in the database.ConclusionsWe have created a platform that will allow downstream evaluation of AI systems for breast cancer detection, which enables faster development cycles for participating vendors and safer AI adoption for participating hospitals. The platform was designed to be scalable and ready to be expanded should a new vendor want to evaluate their system or should a new hospital wish to obtain an evaluation of different AI systems on their images.
VV-YOLO: A Vehicle View Object Detection Model Based on Improved YOLOv4

Vehicle view object detection technology is the key to the environment perception modules of autonomous vehicles, which is crucial for driving safety. In view of the characteristics of complex scenes, such as dim light, occlusion, and long distance, an improved YOLOv4-based vehicle view object detection model, VV-YOLO, is proposed in this paper. The VV-YOLO model adopts the implementation mode based on anchor frames. In the anchor frame clustering, the improved K-means++ algorithm is used to reduce the possibility of instability in anchor frame clustering results caused by the random selection of a cluster center, so that the model can obtain a reasonable original anchor frame. Firstly, the CA-PAN network was designed by adding a coordinate attention mechanism, which was used in the neck network of the VV-YOLO model; the multidimensional modeling of image feature channel relationships was realized; and the extraction effect of complex image features was improved. Secondly, in order to ensure the sufficiency of model training, the loss function of the VV-YOLO model was reconstructed based on the focus function, which alleviated the problem of training imbalance caused by the unbalanced distribution of training data. Finally, the KITTI dataset was selected as the test set to conduct the index quantification experiment. The results showed that the precision and average precision of the VV-YOLO model were 90.68% and 80.01%, respectively, which were 6.88% and 3.44% higher than those of the YOLOv4 model, and the model’s calculation time on the same hardware platform did not increase significantly. In addition to testing on the KITTI dataset, we also selected the BDD100K dataset and typical complex traffic scene data collected in the field to conduct a visual comparison test of the results, and then the validity and robustness of the VV-YOLO model were verified.
Understanding public discourse surrounding the impact of bitcoin on the environment in social media

Abstract Increasing public concerns about the environment have led to many studies that have explored current issues and approaches towards its protection. Much less studied, however, is topic of public opinion surrounding the impact that cryptocurrencies are having on the environment. The cryptocurrency market, in particular, bitcoin, currently rivals other top well-known assets such as precious metals and exchanged traded funds in market value, and its growing. This work examines public opinion expressed about the environmental impacts of bitcoin derived from Twitter feeds. Three primary research questions were addressed in this work related to topics of public interest, their location, and people and places involved. Our findings show that factions of of the public are interest in protecting the environment, with topics that resonate mainly related to energy. This discourse was also taking place at few similar locations with a mix of different people and places of interest.
Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

As a major public health emergency, the COVID-19 pandemic has some uncertainties.Coupling of the uncertainties and anti-epidemic policies easily leads to the spread of negative emotions.It is challenging to maintain the sustainability of anti-epidemic measures.Therefore, this paper aimed to analyze the challenges that the sustainability of anti-epidemic measures is faced with.A topic clustering extension method was proposed, which integrated Latent Dirichlet Allocation (LDA) topic information with Bidirectional Encoder Representations from Transformers (BERT) contextual information through aspect-based sentiment analysis.In addition, this paper constructed a thesaurus of aspect words from the two stages of dynamic zero-COVID and orderly relaxation of epidemic control.This paper established the BERT-pair-ABSA model for semantic expansion of auxiliary sentences and calculated sentiment polarity to gain insight into the changes in netizens' concerns, emotional states and evolution trends at different stages.The research results showed: (1) Compared with the benchmark model, the proposed sentiment analysis model had better classification accuracy and was applicable to the sentiment classification of short texts in the epidemic situation; (2) During the dynamic zero-COVID stage, netizens paid attention to grassroots epidemic management and the scope of lockdown and epidemic control, which were closely related to both specific lockdown and control management, and the implementation of regional epidemic management; and (3) in the orderly relaxation stage of epidemic control, netizens were concerned about drug guarantee, medical care guarantee, personal health protection and health protection of special population groups, and negative emotions always dominated in drug guarantee, medical care guarantee and health protection of special groups.The negative sentiment of drug guarantee, medical care guarantee and health protection of special groups always dominated.The results provided an empirical basis for the optimization and adjustment of the anti-epidemic policies.
Identification of encrypted and malicious network traffic based on one-dimensional convolutional neural network

Abstract The rapid advancement of the Internet has brought a exponential growth in network traffic. At present, devices deployed at edge nodes process huge amount of data, extract key features of network traffic and then forward them to the cloud server/data center. However, since the efficiency of mobile terminal devices in identifying and classifying encrypted and malicious traffic lags behind, how to identify network traffic more efficiently and accurately remains a challenging problem. We design a convolutional neural network model: One-dimensional convolutional neural network with hexadecimal data (HexCNN-1D) that combines normalized processing and attention mechanisms. By adding the attention mechanism modules Global Attention Block (GAB) and Category Attention Block (CAB), network traffic is classified and identified. By extracting effective load information from hexadecimal network traffic, our model can identify most categories of network traffic including encrypted and malicious traffic data. The experimental results show that the average accuracy is 98.8%. Our model can greatly improve the accuracy of network traffic data recognition.
Separability criteria based on a class of symmetric measurements

Abstract Highly symmetric quantum measurements, such as mutually unbiased measurements (MUMs) and general symmetric informationally complete positive-operator-valued measures (GSIC-POVMs), play an important role in both foundational and practical aspects of quantum information theory. Recently, a broad class of symmetric measurements were introduced [K Siudzińska, (2022) Phys. Rev. A 105 , 042209], which can be viewed as a common generalization of MUMs and GSIC-POVMs. In this work, the role of these symmetric measurements in entanglement detection is studied. More specifically, based on the correlation matrices defined via (informationally complete) symmetric measurements, a separability criterion for arbitrary dimensional bipartite systems is proposed. It is shown that the criterion is stronger than the method provided by Siudzińska, meanwhile, it can unify several popular separability criteria based on MUMs or GSIC-POVMs. Furthermore, using these (informationally complete) symmetric measurements, two efficient criteria are presented to detect the entanglement of tripartite quantum states. The detection power and advantages of these criteria are illustrated through several examples.