Reviewerly/exHarmony huggingface dataset:
Bitcoin threat and the emergence of Central Bank Digital Currency: Regulatory and valuation challenges

This study examined the possibility of a Central Bank Digital Currency (CBDC) replacing bitcoin. CBDCs could emerge as a new form of digital currency embodying the characteristics of bitcoin (digital nature) and fiat currency (value based on the public’s faith in the central bank). Unlike bitcoin with no research consensus on having a fundamental value, a CBDC could have a fundamental value as it is proposed to have the properties of a fiat currency. Considering that bitcoin could become private money in tandem with CBCD due to its properties of scarcity, secure storage and immutability, the study estimated an imputed value for bitcoin using the daily trading data for a sample period from I January 2013 to 10August 2021. Fitting a Vector Auto Regression (VAR) model to the bitcoin data and conducting the Granger causality test, the study shows that the model value implies the observed market value. The network effect appears to be the main determinant of the bitcoin value. In practical terms, the results demonstrate that despite the bubbles in the market, bitcoin prices tend to show a long-term trend.
3D scanning technology application in the imperfect cultural relic

Data extraction of cultural relic is helpful for their records and protection.During field excavations, some vulnerable and dilapidated cultural relics may be more seriously damaged by manual drawing and measuring.Meanwhile, records of the two-dimensional data and graphic image cannot accurately reflect the situation of the unearthed relics.At the same time, due to the irregular shape of the imperfect cultural relics, the precision of the measurement can hardly meet the requirement by traditional artificial method.In this article, taking Yanxiadu deer vein residue brick during the warring states period as an example, the usage of the 3D handheld scanner has been introduced, in order to record three-dimensional data of residue brick accurately.The three-dimensional model of residual brick was measured and analyzed by 3D software, which provides a new way for protecting the imperfect cultural relics.
A Real-time Super-Resolution for Surveillance Thermal Cameras using optimized pipeline on Embedded Edge Device

The avenue of deep learning is scarcely explored in the domain of thermal imaging. Recovering a high-resolution output from images and videos is a classical problem in many computer vision applications. In this paper, we propose an optimized pipeline for a real-time video super-resolution task using thermal camera on embedded edge device. To tackle the challenges, we make contributions in the following several aspects: 1) comparative study of selected deep learning super-resolution models; 2) constructing and optimizing an end-to-end inference pipeline; 3) using cutting edge technology to integrate the whole workflow; 4) a real-time performance was achieved using less data; 5) we have also experimented the entire pipeline on our custom thermal dataset. As a consequence, the chosen model was able to achieve a real-time speed of over 29, 36 and 45 high FPS; 32.9dB/0.889, 31.86dB/0.801 and 30.94dB/0.728 PSNR/SSIM values for 2x, 3x and 4x scaling factors respectively.
A hyper-chaotic system and adaptive substitution box (S-Box) for image encryption

the inclusion of chaos to encrypt multimedia information has been a remarkable feature of nonlinear dynamics for the last three decades. A hyper-chaotic system and a Substitution S-Box for image encryption are included in this article. Substitution is built by adapting the Rijndael S-Box into one dimension to minimize converting between digital systems using the same safety key for safe communication during the encryption process. This concept of chaos-based encryption and substitution box provides comparatively enhanced results by better CPU encryption time for images colored 256x256 to 0.2485 seconds, and 512x512 to 0.3665 seconds. According to the results of the testing, the proposed image encryption algorithm is highly safe, resistant against image processing attacks, and fast for the image encryption application as depicted in security analysis.
Design and Analysis of Single-Phase Inverter for Avionics System

In this paper, a single-phase inverter for avionics application is designed. Usually, the load in the aircraft works on high frequency and with a specific power supply. In the proposed work, a single-phase inverter of 115 V, 400 Hz is designed with an input DC power of 28 V. For the proposed system, DC-DC SEPIC converter is used to step up the input voltage to achieve high gain and an H-bridge inverter followed by a filter to get sinusoidal output. To improve power quality, high efficiency and low total harmonic distortion value closed-loop implementation are validated through detailed simulation in MATLAB Simulink and experimentally verified using a prototype hardware model to get high frequency.
Loudspeaker Array to Maximize Acoustic Contrast Using Proximal Splitting Method

We propose a method of creating acoustically bright and dark zones using a circular loudspeaker array. Practical reproduction must maximize acoustic contrast with a limited range of loudspeaker weights to ensure the quality of reproduced sounds. Therefore, we consider a constrained pressure-matching method that uses Tikhonov regularization. Furthermore, we impose additional sparse regularization to describe the ideal reproduced sound field as a simple model with a sparse structure. We obtain optimal loudspeaker weights by using an acoustic maximization algorithm based on a proximal splitting method. Numerical simulation results show that our method achieves a higher acoustic contrast than existing pressure-matching-based methods with a constraint of limited loudspeaker weights.
Facilitating Students’ Speaking Task Using YouTube During Covid-19 Pandemic

This study investigates students' perception of the use of YouTube to facilitate undergraduate students' speaking activities. The method used in this research is descriptive quantitative research. The sampling system is done randomly and takes 40 students as a sample. The questionnaire has two parts, followed by ten questions with five answer choices using a Likert scale covering strongly disagree to strongly agree. The result of the study shows that 80% of students access YouTube because YouTube is very interesting, 75% of students say YouTube is an easy media to access, 80% of students say YouTube can be used as a learning resource (80%), and 85% of students use YouTube as a medium for doing speaking tasks. The student response to the use of YouTube as the media of facilitating students' speaking tasks is very positive, where 72% of students stated they strongly agreed if the practice of speaking through YouTube was applied, and 20% of students agreed. In general, student responses in using YouTube to facilitate students speaking activities in distance learning during the Covid-19 pandemic are very positive.
Linear and Nonlinear Optical Properties in Spherical Quantum Dots: Modified Möbius Squared Potential

In this paper, the optical properties of quantum dots confined in modified Möbius squared potential are studied. To obtain the energy expressions and the wave functions, we solved the Schrödinger equation by using Nikiforov-Uvarov (NU) method. We investigated the linear, third-order nonlinear and total absorption coefficients and refractive index changes by applying the density matrix formalism. The numerical results show that the structure parameters and optical intensity have a strong influence on absorption coefficients and refractive index changes.
Configuration Method of AWS Security Architecture That Is Applicable to the Cloud Lifecycle for Sustainable Social Network

Recently, due to the many features and advantages of cloud computing, “cloud service” is being introduced to countless industries around the world at an unbelievably rapid pace. However, with the rapid increase in the introduction of cloud computing services, security vulnerabilities are increasing and the risk of technology leakage from cloud computing services is also expected to increase in social network service. Therefore, this study will propose an AWS-based (Amazon Web Services) security architecture configuration method that can be applied for the entire life cycle (planning, establishment, and operation) of cloud services for better security in AWS Cloud Services, which is the most used cloud service in the world. The proposed AWS security guide consists of five different areas, Security Solution Selection Guide, Personal Information Safeguard Guide, Security Architecture Design Guide, Security Configuration Guide, and Operational Security Checklist, for a safe social network. The AWS Security Architecture has been designed with three reference models: Standard Security Architecture, Basic Security Architecture, and Essential Security Architecture. The AWS Security Guide and AWS Security Architecture proposed in this paper are expected to help many businesses and institutions that are hoping to establish and operate a safe and reliable AWS cloud system in the social network environment.
Keyphrase Extraction using Markov Decision Process from Social Media Data

In the last decade, academic researchers have adapted traditional random-walk based keyphrase extraction techniques for short text. However, the language and the structure of Microblog Word Co-occurrence Network evolved from social media text are different from that of the well-formed text. The social media data is informal and user-generated. The nature of data is repetitive, ill-formed, short text and domain specific. To deal with this challenge, a graph-based keyphrase extraction technique, KEST: keyphrase Extraction for Short Text is proposed using Markov Decision Process (MDP). In KEST, unlike the existing techniques where words were visited in iteration, a path is generated using dynamic chain of highly interconnected words. The robustness of KEST is tested and validated over two different dataset.
RF Fingerprint Recognition in Resource-Constrained Edge Computing

RF fingerprinting is a method to identify different wireless devices based on hardware differences in the communication devices, aiming to assist in solving the problem of secure access to wireless networks. Traditional RF fingerprinting relies on feature extraction and the training and prediction of data under a single node. However, in real-world application scenarios, it is impractical to send all data to a centralized location, and these approaches do not take into account the limited resources such as bandwidth and storage in edge networks. In this paper, we use distributed machine learning to solve the problem of learning models from data from multiple edge nodes. And the high communication cost in distributed machine learning is optimized for the problem of resource constraint of edge devices. Simulation results show that the method can not only cope with the training problem of a large amount of data but also reduce the communication cost on the edge devices, which can reduce the training time by about 9%.
A Novel Power-Saving Reversing Camera System with Artificial Intelligence Object Detection

According to a study by the Insurance Institute for Highway Safety (IIHS), the driving collision rate of using only the reversing camera system is lower than that of using both the reversing camera system and the reversing radar. In this article, we implemented a reversing camera system with artificial intelligence object detection to increase the information of the reversing image. Our system consists of an image processing chip (IPC) with wide-angle image distortion correction and an image buffer controller, a low-power KL520 chip and an optimized artificial intelligence model MobileNetV2-YOLOV3-Optimized (MNYLO). The results of the experiment show the three advantages of our system. Firstly, through the image distortion correction of IPC, we can restore the distorted reversing image. Secondly, by using a public dataset and collected images of various weathers for artificial intelligence model training, our system does not need to use image algorithms that eliminate bad weathers such as rain, fog, and snow to restore polluted images. Objects can still be detected by our system in images contaminated by weather. Thirdly, compared with the AI model Tiny_YOLOV3, not only the parameters of our MNYLO have been reduced by 72.3%, the amount of calculation has been reduced by 86.4%, but the object detection rate has also been maintained and avoided sharp drops.
Real-time Sinhala Writing Assistant for Kids

This paper presents a system that provides self-learning aids for children aged 5 and 7 years old to practice writing the Sinhala alphabet correctly. The system determines the correctness of the written letters based on the correctness of the shape of the letter, pen up-down movements, and writing the letter in the correct regions of the five lines provided, through the real-time letter writing correction module that utilizes the principles of online handwriting recognition approaches. Users are allowed to write the letters on a touch-enabled device and the real-time evaluation of the written letters is conducted using the template-matching. The proposed system assists the children to correct the errors in handwriting through a graphical representation. Furthermore, using a Convolution Neural Network (CNN), offline handwriting recognition is used to determine whether the child has written the same letter displayed by the system. The real-time letter writing correction module has an average accuracy of 94.28%, while the offline handwriting recognition module has an average accuracy of 95.0%.
Sampling Graph Signals with Sparse Dictionary Representation

Graph sampling strategies require the signal to be relatively sparse in an alternative domain, e.g. bandlimitedness for reconstructing the signal. When such a condition is violated or its approximation demands a large bandwidth, the reconstruction often comes with unsatisfactory results even with large samples. In this paper, we propose an alternative sampling strategy based on a type of overcomplete graph-based dictionary. The dictionary is built from graph filters and has demonstrated excellent sparse representations for graph signals. We recognize the proposed sampling problem as a coupling between support recovery of sparse signals and node selection. Thus, to approach the problem we propose a sampling procedure that alternates between these two. The former estimates the sparse support via orthogonal matching pursuit (OMP), which in turn enables the latter to build the sampling set selection through greedy algorithms. Numerical results corroborate the role of key parameters and the effectiveness of the proposed method.
Self-supervised Compressed Video Action Recognition via Temporal-Consistent Sampling

Compressed video action recognition targets at classifying action class in compressed video, instead of decoded/standard video. It benefits from fast training and inference by reducing the utilization of redundant information. However, off-the-shelf methods still rely on heavy-cost labels for training. In this paper, we propose self-supervised compressed video action recognition method via Momentum contrast (MoCo) and temporal-consistent sampling. We leverage temporal-consistent sampling into MoCo to improve the ability of feature presentation on each input modality of compressed video. Modality-oriented fine-tuning is introduced to applying into the downstream compressed video action recognition.Extensive experiments demonstrate the effectiveness of our method on different datasets with different backbones. Compared to SOTA self-supervised learning methods for decoded videos on HMDB51 dataset, our method achieves the highest accuracy of 57.8%.
Architectures and technologies for stream processing

In this chapter we present and analyze stream processing platforms from a practical perspective. Since Big Data and Big Data streams entered the arena of large scale processing, a number of platforms have emerged that enable processing the Big Data and Big Data streams. Platform, such as Kafka, analyzed in this chapter, offer several characters tics and support to processing and meeting the demanding requirements (for processing and storage) of Big Data and Big Data streams. It should be noted that large companies (Amazon, Google, Microsoft, IBM, and others) do have their own platforms for Big Data and Big Data streams. The platforms analyzed here are from open source.
Object Tracking

This chapter gives information on object detection in digital video. Also, the chapter provides Kalman filtering-based object detection algorithm for the video surveillance system.
Program Synthesis: Does Feedback Help?

Computers are devices that execute precise instructions provided to them using various programming languages. However, the idea of delivering instructions to a computer through natural language could vastly simplify the act of programming as a specific task. Generating code from high-level descriptions for a given program is a significantly challenging task and has been an active area of research in the natural language processing domain. In this paper, we present a novel feedback-based deep learning approach for synthesizing code from human-specified descriptions. Inspired by the dual-learning mechanism, our framework uses a feedback loss to produce more consistent and robust predictions. We show how our approach fares empirically on standard code generation datasets and achieves state-of-the-art results on the NAPS (Natural Program Synthesis) dataset.
WannaCry Data Collection System Design and Tutorial

The proliferation of malware-based attacks, particularly those of the ransomware variety, poses a serious threat to business operations. In order to prevent these types of attacks, existing forms of malware must be analyzed and studied in controlled settings. Unfortunately, implementing a test network to study the effects of ransomware can be difficult for several different reasons including the pedigree of the target ransomware. One example of this struggle is illustrated by the WannaCry ransomware that caused significant damage in 2017. In this paper, we document the importance of malware pedigree in the malware analysis process. We also present the first tutorial creating a test network incorporating both physical and virtual elements in which the WannaCry ransomware was released and analyzed. Finally, we make available our captured data for analysis.
Fuzzy C-Medoids Clustering Based on Interval Type-2 Inituitionistic Fuzzy Sets

For clustering problems, each data sample has the potential to belong to many different clusters depending on the similarity. However, besides the degree of similarity and non-similarity, there is a degree of hesitation in determining whether or not a data sample belongs to a defined cluster. Besides the fuzzy c-means algorithm (FCM), another popular algorithm is fuzzy C-medoids clustering (FCMdd). FCMdd chooses several existing objects as the cluster centroids, while FCM considers the samples’ weighted average to be the cluster centroid. This subtle difference causes the FCMdd is more resistant to interference than FCM. Since noise samples will more easily affect the center of centroids of the FCM, it is easier to create clustering results with great accuracy. In this study, we proposed a method for extending the fuzzy c-medoids clustering based on interval type-2 intuitionistic fuzzy sets, named the interval type-2 intuitionistic fuzzy c-medoids clustering algorithm (IT2IFCMdd). With this combination, the proposed algorithm can take advantage of both the fuzzy c-medoids clustering (FCMdd) method and the interval type-2 intuitionistic fuzzy sets applied to the clustering problem. Experiments performed on data sets commonly used in machine learning show that the proposed method gives better clustering results in most experimental cases.
Future of Scholarly Publishing: A Perspective

The academic publication takes on an increasingly relevant place to shape, on the one hand, the scholar’s prestige, and on the other, the prestige of the institution to which he or she is attached. In addition, academic publishing is vital for the development of scientific knowledge and the contribution to the community. This paper analyzes several dimensions that may be central to academic publishing in the near future. To this end, in methodological terms, a qualitative approach was favored, namely through the documentary analysis of scientific writings that analyze this topic. The results of this analysis reveal that this is a process in constant and accelerated development, but there will have to be criteria and processes for selecting what is scientifically relevant from what is called “noise” in scientific publishing. Increasing quality will have to be a fundamental element in this process.
ST-NAS: Efficient Optimization of Joint Neural Architecture and Hyperparameter

Deep learning models often require intensive efforts in neural architecture search and hyperparameter optimization. Conventional hyperparameter optimization methods are inefficient because they refer to multi-trial: different configurations are undertaken in parallel to find the best one. In this paper, we propose ST-NAS, an efficient optimization framework of joint neural architecture and hyperparameter. ST-NAS generalizes the efficient weight sharing strategy from architecture search to hyperparameter optimization. Hence, ST-NAS can jointly optimize both architecture and hyperparameter in a single training phrase. Fundamentally, we design a new module, ST-layer, based on STN, then further extend it to ST-super-net. With the ST-layer, each sub-model’s hyperparameter configurations can simultaneously update using the shared weight while training. Thus, with these designs, ST-NAS can efficiently couple with its architecture searching and hyperparameter schedulers. Extensive experiments show ST-NAS can collaborate well with three different NAS algorithms and consistently improves performance across search spaces, datasets, and tasks.
Convergence Media Related Information Production Platform Based on Kalman Filter Video Target Tracking

This paper summarizes the current status and needs of the development of media convergence, adopts a Kalman filter-based video target tracking. Moreover, this paper studies the in-depth information of the video content, designs a specification for related information, and uses this specification to develop a related information production platform. Target tracking, as a key module of the related information production platform, detects and tracks a single target selection hot spot in the video screen, and can update the selected target template. The design and module implementation of the platform are introduced here. Through test data analysis, the platform can automatically and all-timely add the related information of the video screen; the stored related information conforms to the media metadata format. The platform takes practicability and innovation as the standard, effectively setting up an application demonstration for the radio and television industry to explore new models.