Reviewerly/exHarmony huggingface dataset:
A Survey of the Accounting Industry on Holdings of Cryptocurrencies in Xiamen City, China

This is the first survey conducted in China on the holding of cryptocurrencies. Although cryptocurrencies have existed in the world for more than a decade, because the exchange of cryptocurrencies is banned in China, there is no guidance on the holding of cryptocurrencies in China’s accounting standards. Moreover, although the exchange of cryptocurrencies is prohibited by the Chinese government, holdings of cryptocurrencies by Chinese entities and individuals cannot be prevented. Thus, we conducted a survey in investors’ attitudes towards cryptocurrencies in Xiamen City, a special economic zone (SEZ) and a pilot free trade zone (FTZ) in China. The survey respondents commonly defined cryptocurrencies as investments (45%), inventories (19%), and intangible assets (36%). A total of 84% of respondents stated that the value of a cryptocurrency should be represented by a fair value. These results are similar to those obtained in a survey by The Digital Assets Accounting Consortium (DAAC), but different to the tentative agenda decision of the International Financial Reporting Standards Interpretations Committee (IFRSIC). Additionally, 65% of respondents stated that they prefer to accept cryptocurrencies as cash equivalent currencies, and these cash equivalent currencies were considered to have two main functions: a medium of exchange (56%) and a monetary unit for pricing goods and services (52%).
Image Segmentation Techniques for Intelligent Monitoring of Putonghua Examinations

Image recognition and image processing usually contain the technique of image segmentation. Excellent segmentation results can directly affect the accuracy of image recognition and processing. The essence of image segmentation is to segment each frame of a certain image or a video into multiple specific objects or regions and represent them with different labels. This paper focuses on the segmentation results obtained in image segmentation of images used for intelligent monitoring of Mandarin exams are usually visualized for image analysis. In this paper, we first investigate the performance improvement techniques for semantic segmentation in the image segmentation task for intelligent monitoring of Mandarin exams, improve the pixel classification capability by performing semantic migration, and, for the first time, extend the dataset substantially by style transformation to improve the model’s recognition of advanced features. In addition, to further address the shortcomings of the dataset, this paper improves the performance of image segmentation using synthetic datasets by investigating synthetic dataset image segmentation improvement techniques that reduce the reliance on manually annotated datasets. Image segmentation techniques continue to advance, and there are even thousands of commonly used segmentation methods for image segmentation development to date. Among them, they can be broadly classified as region-based segmentation methods, threshold-based segmentation methods, edge-based segmentation methods, specific theory-based segmentation methods, and deep learning-based segmentation methods. However, the methods used in this paper have all been experimentally demonstrated to improve the effectiveness of the techniques and proved to outperform other existing methods in the same field in the publicly available datasets LSUN, Cityscapes, and GTA5 datasets, respectively.
Object detection based on deep learning techniques in resource-constrained environment for healthcare industry

Advanced technologies and algorithms such as the Internet of Things (IoT), computer vision (CV), and deep learning are widely used in the healthcare industry to enhance global med-ical care. Internet of Things (IoT) has the potential to be limitless due to increased network agility, integrated artificial intelligence (AI), and the ability to deploy and automate systems. Embedded systems playa vital role in IoT due to real-time computing, low power consumption, and low maintenance cost. Object detection is a computer vision technique that aims to process and identify certain objects such as people, cars, animals, or buildings in digital images or videos. The goal of object detection is to develop computational models for computer vision applications. Recently, rapid advancement in deep learning techniques accelerated the momentum of object detection. In this study, we proposed a mechanism to perform object detection based on deep learning techniques in resource-constrained IoT devices. Due to limited computational powers in embedded systems, the performance of deep learning algorithms is not good enough. To achieve this, we compressed the video using a codec and streamed it to the amazon cloud for object detection. Video codec was used to uncompress the video in its original format so that there is no loss of video quality. A pre-trained YOLO model was deployed for object detection in medical images. The output is sent to the client using a lightweight protocol for data communication. Results indicate that the proposed mechanism worked well in a resource-constrained environment without compromising accuracy and time.
An Empirical Study on the Impact of Python Dynamic Typing on the Project Maintenance

Python is a popular typical dynamic programming language. In Python, dynamic typing is one of the most critical dynamic features. The lack of type information is likely to hinder the maintenance of Python projects. However, existing work has seldom focused on studying the impact of Python dynamic typing on project maintenance. This paper focuses on the two most common practices of Python dynamic typing, i.e. inconsistent-type assignments (ITA) and inconsistent variable types (IVT). Two approaches are proposed to identify ITA and IVT, i.e. identifying ITA by analyzing Abstract Syntax Trees and comparing identifiers types and identifying IVT by constructing a type dependency graph. In empirical experiments, we first locate the usage of ITA and IVT in 10 open-source Python projects. Then, we investigate the relations between the occurrence of ITA and IVT and the results of maintenance tasks. The study results show that projects are more prone to change as the number of dynamic typing identifiers increases. There is a weak connection between change-proneness and variable dynamic typing. There is a high probability that maintenance time and the acceptance of commits decrease as dynamic typing identifiers increase in projects. These results implicate that dynamic and static variables should be divided while developing new programming languages. Dynamic typing identifiers may not be the direct root causes for most software bugs. The categories of these bugs are worth exploring.
Image-to-Image Translation for Near-Infrared Image Colorization

In the existing near-infrared image colorization methods, the main obstacles are distortion of color consistency and texture. We propose a feature fusion module using multi-scale channel attention module (MS-CAM) that extracts edges by Canny filter and fuses two colorized images with feature maps. The proposed method improves the PSNR of NIR2VC compared to the baseline on VSIAD datasets and output has consistency to the exterior wall of the building. Moreover, the proposed method well captures textures in objects. However, when predicting gray color like roads and cars, artifacts are occurred and the output tends to be slightly brighter. This method can also be applied to the field of monitoring such as CCTV and military facilities, as well as thermal imaging cameras with properties similar to near-infrared rays.
A general soft-balanced clustering framework based on a novel balance regularizer

In the past few decades, clustering algorithms have been extensively explored. Conventional clustering methods such as k-means and spectral clustering have achieved excellent clustering performance, however they pay less attention on the balanced distribution of many real-world data, which is significant to some practical applications. In this paper, we present a soft-balanced clustering framework, and the degree of balance can be flexibly adjusted by setting the parameter. The core of the framework is a novel regularizer, by optimizing which the clustering becomes more balanced. The framework can be combined with many clustering methods in a concise way. Taking k-means as an example, we extend k-means to a Balanced k-means with a Novel Constraint (BKNC) model with the help of this framework. Then an alternative iterative optimization algorithm is proposed to solve it. Comprehensive experiments on several benchmark datasets demonstrate the superior balance and clustering performance of the BKNC method than other k-means based hard- and soft-balanced methods.
Robust Face Detection and Recognition using Image Processing and OpenCV

An enormous progress in the fields of Artificial Intelligence, Machine Learning, and Computer Vision is witnessed over the last decade, owing to a doubling in processing capacity per year, as well as a massive rise in data internationally. Facial identity, biometrics, and face identification have gone from a rarefied issue to a prominent topic for academics in computer science these days. Due to the basic nature of the topic, breakthroughs in computer image processing and understanding studies will give insights into how the human brain operates, according to the common belief. Face detection and recognition are often employed in national defense, CCTV surveillance, criminal investigation, personal protection, law enforcement, biometrics, and a variety of other applications. For this project, OpenCV, Haar cascade, NumPy, and Matplotlib Python packages are utilized. Haar-Cascades will be utilized for face detection, and OpenCV provides three built-in face recognizers: Face recognition algorithms such as Eigenfaces, Fisher faces, and Local binary pattern histograms are used. The approach employed in this research is literature review, which entails reading and examining numerous books or literature relevant to mathematical ideas that underpin the development of algorithms to detect a human's face, which are subsequently followed by implementation in code, particularly Python code.
A Novel Fusion Pruning Algorithm Based on Information Entropy Stratification and IoT Application

To further reduce the size of the neural network model and enable the network to be deployed on mobile devices, a novel fusion pruning algorithm based on information entropy stratification is proposed in this paper. Firstly, the method finds similar filters and removes redundant parts by Affinity Propagation Clustering, then secondly further prunes the channels by using information entropy stratification and batch normalization (BN) layer scaling factor, and finally restores the accuracy training by fine-tuning to achieve a reduced network model size without losing network accuracy. Experiments are conducted on the vgg16 and Resnet56 network using the cifar10 dataset. On vgg16, the results show that, compared with the original model, the parametric amount of the algorithm proposed in this paper is reduced by 90.69% and the computation is reduced to 24.46% of the original one. In ResNet56, we achieve a 63.82%-FLOPs reduction by removing 63.53% parameters. The memory occupation and computation speed of the new model are better than the baseline model while maintaining a high network accuracy. Compared with similar algorithms, the algorithm has obvious advantages in the dimensions of computational speed and model size. The pruned model is also deployed to the Internet of Things (IoT) as a target detection system. In addition, experiments show that the proposed model is able to detect targets accurately with low reasoning time and memory. It takes only 252.84 ms on embedded devices, thus matching the limited resources of IoT.
SCU-Net: Semantic Segmentation Network for Learning Channel Information on Remote Sensing Images

Extracting detailed information from remote sensing images is an important direction in semantic segmentation. Not only the amounts of parameters and calculations of the network model in the learning process but also the prediction effect after learning must be considered. This paper designs a new module, the upsampling convolution-deconvolution module (CDeConv). On the basis of CDeConv, a convolutional neural network (CNN) with a channel attention mechanism for semantic segmentation is proposed as a channel upsampling network (SCU-Net). SCU-Net has been verified by experiments. The mean intersection-over-union (MIOU) of the SCU-Net-102-A model reaches 55.84%, the pixel accuracy is 91.53%, and the frequency weighted intersection-over-union (FWIU) is 85.83%. Compared with some of the state-of-the-art methods, SCU-Net can learn more detailed information in the channel and has better generalization capabilities.
Testing Communication Concepts on COVID-19 Contact Tracing Among Black and Latinx/Hispanic People in the United States

Black and Latinx/Hispanic people were more than twice as likely to die from COVID-19 than White people, but because of legacies of discrimination and maltreatment in health care, were less likely to participate in some public health responses to COVID-19, including contact tracing. This study aimed to test three communication campaign concepts to engage Black and Latinx/Hispanic people in contact tracing efforts.Twelve focus group discussions with 5 to 10 participants each were conducted online among participants from Black and Latinx/Hispanic urban populations in Philadelphia and New York state. Participants provided sociodemographic information and were presented with potential campaign concepts and prompted to rate the concepts and engage in open-ended discussion. For rating and sociodemographic data, chi-square tests were performed. For open-ended discussion data, a thematic analysis approach was used.Across groups, the campaign concept that was rated most likely to encourage cooperation with contact tracing efforts was "Be the One," with 45% of total first-place votes. Participants expressed that the campaign caught their attention (79%), motivated them to engage with contact tracers (71%) and to talk to others about contact tracing (77%). Discussions also elucidated: the importance of community engagement; the need for clearer explanations of contact tracing; the preference for already trusted, community-based contact tracers; the need to reassure people about confidentiality; and for contact tracing to be culturally competent and empathetic.This study highlights how strategic, culturally sensitive communication can buttress current and future contact tracing efforts, especially among Black and Latinx/Hispanic people.
Violation Detection Technique for COVID-19 Self-Isolation and Control Measures using Wireless and Geofencing Technologies

The spread of COVID-19 pandemic forced the governments to apply severe control measures such as lockdowns and quarantines. This lead to the need for innovative digital health technological solutions to increase the effectiveness of the necessary precautionary. In this regard, a smart geofencing solution is proposed in this paper to effectively support the implementation of COVID-19 self-isolation and control measures in different areas where each intended area is defined as a geofence with specific control actions. The proposed solution is based on a mobile client application that exploits specific wireless metrics for Wi-Fi and cellular networks to generate a digital signature for the defined geofence and detects any violation of its virtual borders. A detailed description along with a mathematical model of how the geofence digital signature can be created is presented. Then, a geofence matching criterion based on a weighted score is proposed to detect the geofence violation by comparing the periodically measured wireless metrics with the created geofence digital signature. The proposed solution can be used for detecting violation in different cases such as distributed home quarantine and controlled areas. The performance of the proposed solution is studied on different scenarios, and the results show the significance of the proposed solution compared to others. Moreover, the effect of the decision time window parameter on the performance of the proposed geofence matching criterion is illustrated.
Retention of Qualified Cybersecurity Professionals: A Qualitative Study

The current endeavors to retain cybersecurity professionals are not enough to sustain the needs of the U.S. workforce. The researchers aim to explore retention strategies for qualified cybersecurity professionals supporting U.S. government contracts at a leading global security company. The researchers interviewed a sample of qualified cybersecurity professionals supporting U.S. government contracts to get essential information on retention in the cybersecurity profession. The researchers proposed four distinct pillars to make up the strategic framework for retaining qualified cybersecurity professionals.
Improving Phishing Detection with the Grey Wolf Optimizer

With the recent epidemic of COVID-19-themed scam and phishing, the efficient automated detection of such attacks is crucial. Although many anti-phishing solutions, such as lists and similarity and heuristic-based approaches detect attacks, methods still can be improved. Classification accuracy is highly dependent on the feature selection method used to select appropriate features for classification. In this article, a multi-objective grey wolf optimizer is used to select proper features for classifying phishing websites through a variational autoencoder. Our results indicate the superiority of the classification rate compared with related work: A classification rate of 97.49%, is obtained, thereby suggesting the feasibility of evaluating our work.
Link technology for all-optical satellite-based quantum key distribution system in C-/L-band

Satellite based quantum key distribution (QKD) enables the delivery of keys for quantum secure communications over long distances. Maturity of the technology as well as industrial interest keep increasing. So does the technology readiness of satellite free-space optical communications. A satellite QKD system comprises a quantum communication subsystem and a classical communication subsystem (public channel). Both are implemented with free-space optics. Thus, in satellite QKD system design, there are strong synergies that should be exploited as much as possible and lead to an all-optical satellite QKD system. In this paper, we present a system like this locating all optical channels in ITU DWDM C-band. We focus on the overall conceptual design and the setup of the optical channels for quantum and classical signal transmission. The system description addresses the breadboards of a transmitter laser terminal (Alice terminal), a receiver laser terminal, (Bob terminal), the public channel implementation, the interfaced QKD system and the deployed encryption system. The design basis for the Alice terminal is the laser terminal development OSIRISv3. The design basis for the Bob terminal is the ground station development THRUST. The later contains an adaptive optics correction to enable single mode fiber coupling. This enables the interfacing to almost arbitrary quantum receivers such as the Bob modules used in the described experiment. The public channel is composed of a bi-directional 1 Gbps IM/DD system and a MODEM that implements a proprietary waveform optimized for free-space channels. The system was experimentally analyzed in a field test in the framework of the German initiative QuNET which addresses the use case of quantum secure communication for authorities. The results of the experiment are used to model a feasible LEO satellite-ground link. Performance indicators such as quantum bit error rate and secure key rate of a potential mission are estimated analytically.
Deep learning based object tracking for 3D microstructure reconstruction

In medical and material science, 3D reconstruction is of great importance for quantitative analysis of microstructures. After the image segmentation process of serial slices, in order to reconstruct each local structure in volume data, it needs to use precise object tracking algorithm to recognize the same object region in adjacent slice. Suffering from weak representative hand-crafted features, traditional object tracking methods always draw out under-segmentation results. In this work, we have proposed an adjacent similarity based deep learning tracking method (ASDLTrack) to reconstruct 3D microstructure. By transferring object tracking problem to classification problem, it can utilize powerful representative ability of convolutional neural network in pattern recognition. Experiments in three datasets with three metrics demonstrate that our algorithm achieves the promising performance compared to traditional methods.
(<i>k</i>, <i>m</i>, <i>t</i>)‐anonymity: Enhanced privacy for transactional data

Abstract Recent years have witnessed the wide availability of an array of transactional datasets for mining and other research activities. A primary concern related to the public sharing of transactional datasets is identifying individuals whose data is being published. Data anonymization is a commonly utilized privacy preservation method for preventing user identification. However, the existing anonymization models such as ‐anonymity, ‐uncertainty, and ( h, k, p )‐coherence for privacy preservation of transactional data do not provide complete protection from the various types of possible privacy attacks. Therefore, this article proposes a novel privacy model called ( k, m, t )‐anonymity to effectively prevent identity and attribute disclosure as well as skewness attack on transactional data. A genetic algorithm‐based implementation of the model is also presented. The genetic algorithm clusters transactional data based on the similarity among the transactions for effective ‐anonymization with low information loss. The clustering algorithm simultaneously aims to minimize the skewness of data distribution in the obtained clusters for preventing skewness attack on anonymized data. Experimental results have verified that the ( k, m, t )‐anonymity model ensures transactional data anonymization without significant information loss. The proposed privacy model is implemented using the proposed approach on two real‐world datasets (health domain and click‐stream data) and an enormous dataset generated synthetically (health domain consisting of 5,00,000 records). The relative error is less as compared to the relative privacy and disassociation technique for all test case scenarios. Hence, the proposed anonymization model maintains the data utility.
ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer

Image narrative generation is a task to create a story from an image with a subjective viewpoint. Given the importance of the subjective feelings of writers, readers, and characters in storytelling, an image narrative generation method should consider human emotion. In this study, we propose a novel method of image narrative generation called ViNTER (Visual Narrative Transformer with Emotion arc Representation), which takes "emotion arc" as input to capture a sequence of emotional changes. Since emotion arcs represent the trajectory of emotional change, it is expected that we can include detailed information about the emotional changes in the story to the model. We present experimental results of both automatic and manual evaluations on the Image Narrative dataset and demonstrate the effectiveness of the proposed approach.