Reviewerly/exHarmony huggingface dataset:
ACCOUNTING ANALYSIS FOR CRYPTO-ASSETS BASED ON IFRS

Crypto-asset has begun to develop and is starting to be widely owned by people and institution around the world. Since 2020, government of Indonesia throught Bappebti regulate Crypto-asset transaction as future contract. This is a phenomenon in view of the need for accounting treatment for Crypto-asset transactions. This research attempts to explore transaction characteristic of Crypto-asset with the approach and review of IAS and focus on the accounting treatment for cryptocurrency. The purpose of this study is to conduct a study of accounting for Crypto-asset based on the applicable IAS. This study uses a review literature model to find out accounting for Crypto-asset. The results of the study indicate that the most common treatment for Crypto-asset is as Intangible Asset and Inventory since the used of Crypto-asset until now mostly as investment and trading in contract futures.
Resilient distributed estimation against FDI attacks: A correntropy-based approach

In this paper, we consider the resilient distributed estimation in adversarial networks, compromised by false data injection (FDI) attacks. We develop a two-step distributed approach to detect the compromised nodes of the network due to FDI attacks. Specifically, we develop a distributed correntropy-based discrimination scheme followed by a distributed state perception scheme to distinguish the compromised nodes from the secure, such that the reliable neighbors could be determined by each node in a distributed manner. We further theoretically analyze the mean and mean-square performance of the resilient distributed estimation, and propose an optimal reference neighbor selection scheme to enhance the network estimation performance. Illustrative simulations validate the superior performance of both FDI attack detection and parameter estimation of the proposed distributed algorithms for adversarial networks, especially under heavy and time-varying FDI attacks. The theoretical results well predict the performance of the proposed resilient distributed estimation algorithm.
Techno-Economic Analysis of Rooftop Photovoltaic System under Different Scenarios in China University Campuses

The expansively unutilized rooftop spaces in the university campuses can provide an excellent opportunity for the installation of solar photovoltaic systems to achieve renewable electricity generation and carbon dioxide reduction. Based on available rooftop areas and local solar radiation situations, technical potential and economic benefits of rooftop photovoltaic system under seven scenarios were carried out for three university campuses located in different solar zones in China. The potential capacity of photovoltaic installations on building’s flat rooftops in Tibet University, Qinghai University, and Qilu University of Technology reaches 11,291 kW, 9102 kW, and 3821 kW, corresponding to the maximum annual power generation of 28.19 GWh, 18.03 GWh, and 5.36 GWh, respectively. From the perspective of economic analysis, PV systems installed in “full self-consumption” mode are superior to those installed in “full-feed-into-grid” mode for all three study cases. The highest return on investment of PV systems installed on flat and pitched rooftops can be achieved at 208% and 204%, respectively, in Tibet University. The payback period for PV systems installed on flat rooftops is 1 year in Tibet University, and less than 8 years for both Qinghai University and Qilu University of Technology, respectively. Results reveal that rooftop photovoltaic systems can significantly help the universities to move towards sustainability.
Virtual Reality Based on Machine Learning: State of the Art

The use of machine learning techniques has exploded in recent years as well as the use of virtual reality techniques it’s can be used in many fields such as medicine, entertainment, education, military, etc. Machine learning visualization can be extended to virtual reality. For machine learning, data visualization is important. In addition, virtual reality can use machine learning to visualize digital educational simulations in three dimensions. Virtual reality transforms the learning process from passive to active, allowing users to engage with the material and apply what they have learned in a real-world setting. Using machine learning, we can design motion interaction systems with motion examples rather than coding, we design interactions by moving. This can make it possible to design much more natural interactions. In this article, we will discuss the contribution of machine learning to virtual reality, and compare the different uses of these two great technologies in some fields.
Dark Channel Prior based Single Image Dehazing of Daylight Captures

Real-time photographs are frequently distorted for a variety of reasons. Atmospheric factors like haze are the key factor that affects them. Every real-time image has to deal with the major problem of haze, but there are several ways to get rid of it now. The primary focus of this research is on real-time and synthetic daylight photographs. One of the most effective and popular methods for image dehazing is dark channel prior. In this study, we use a Guided Variance Weighted Average filter to improvise the transmission map and to produce a clear image. The quality of the image captured improves when the haze is removed, boosting the image's visibility. Dehazing is being used right now in many different domains, including satellite image dehazing, underwater image dehazing, nighttime image dehazing, and so on. In our work we mainly concentrated daylight images. We measured two important parameters of Image Processing ie, SSIM and PSNR. We compared our results with normal guided filter transmission map refinement and we get an improvement of approximately 10% and 40% in the parameters respectively.
Block Chain Technology for Limiting the Impact of Pandemic: Open Issues and Challenges

Time stamp digital documents so that they cannot be backdated or tampered with. A two-day block chain hackathon will be organized in Bangalore on the 5th and 6th of next month. This hackathon has been named as Building Future Cities. This hackathon is being organized by crypto investing app Coin Switch. It will be organized by Karnataka government's initiative ‘Startup Karnataka’, Bangalore South MP Tejashwi Surya and Sequoia India. The organizers of the event have said that the purpose of this hackathon is to provide a platform to those working on it along with discussing the use and possibilities of block chain in India. During this hackathon, the winners will also be awarded up to three lakh rupees. Block chain has been the topic of discussion all over the world in recent times. Ever since the crypto currency era began, block chain has come into the limelight more and more, because crypto currency works on block chain technology itself. As the use of crypto currencies or NFT (Non-Fungible Tokens) is increasing all over the world, the importance of block chain technology is also increasing. Block chain technology is now starting to have its impact on the life of the common man. Many of the services we use in our daily lives may be based on block chain technology, but we are not aware of it. In this way, in today's time, it is important to know about what block chain is and how our lives are connected to it.
SSQLi: A Black-Box Adversarial Attack Method for SQL Injection Based on Reinforcement Learning

SQL injection is a highly detrimental web attack technique that can result in significant data leakage and compromise system integrity. To counteract the harm caused by such attacks, researchers have devoted much attention to the examination of SQL injection detection techniques, which have progressed from traditional signature-based detection methods to machine- and deep-learning-based detection models. These detection techniques have demonstrated promising results on existing datasets; however, most studies have overlooked the impact of adversarial attacks, particularly black-box adversarial attacks, on detection methods. This study addressed the shortcomings of current SQL injection detection techniques and proposed a reinforcement-learning-based black-box adversarial attack method. The proposal included an innovative vector transformation approach for the original SQL injection payload, a comprehensive attack-rule matrix, and a reinforcement-learning-based method for the adaptive generation of adversarial examples. Our approach was evaluated on existing web application firewalls (WAF) and detection models based on machine- and deep-learning methods, and the generated adversarial examples successfully bypassed the detection method at a rate of up to 97.39%. Furthermore, there was a substantial decrease in the detection accuracy of the model after multiple attacks had been carried out on the detection model via the adversarial examples.
BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings.
A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been a topic of interest for several years. The ability of computers to recognize CAPTCHA has significantly increased due to the development of deep learning techniques. To prevent this ability from being utilised, adversarial machine learning has recently been proposed by perturbing CAPTCHA images. As a result of the introduction of various removal methods, this perturbation mechanism can be removed. This paper, thus, presents the first comprehensive survey on adversarial perturbations and attacks on CAPTCHAs. In particular, the art of utilizing deep learning techniques with the aim of breaking CAPTCHAs are reviewed, and the effectiveness of adversarial CAPTCHAs is discussed. Drawing on the reviewed literature, several observations are provided as part of a broader outlook of this research direction. To emphasise adversarial CAPTCHAs as a potential solution for current attacks, a set of perturbation techniques have been suggested for application in adversarial CAPTCHAs.
The Study of Failure Mode Data Development and Feature Parameter’s Reliability Verification Using LSTM Algorithm for 2-Stroke Low Speed Engine for Ship’s Propulsion

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.
YoPose: Yoga Posture Recognition Using Deep Pose Estimation

Originated in India, yoga is considered a spiritual practice as it brings flexibility, balance, and harmony to both physical and mental health. It becomes the art of healthy living. A variety of positions (also known as asanas) are exercised. Each of them is designed to provide a particular benefit to the body. In contrast, any incorrect action during a yoga session can be harmful to muscles and ligaments. As people are more comfortable with the home workout, the need for an instructor to assess the accuracy of a movement or posture is turned into a need for an auto-guiding framework. Human pose estimation is an important field of research in computer vision. It serves several applications, extending from health monitoring to public safety. As it tackles multiple challenges related to the human posture, it can be used to identify yoga asanas. In this study, we develop a deep-learning self-instruction yoga classifier named YoPose. Based on the pose information, the proposed framework helps individuals to improve their yoga postures by providing personalized feedback. A public dataset including six asanas is used to train and evaluate the model. The introduction of transfer learning and a data augmentation scheme helped to achieve promising results with more than a 98% accuracy.
Wavefront measurement of a multilens optical system based on phase measuring deflectometry

Usually, a multilens optical system is composed of multiple undetectable sublenses. Wavefront of a multilens optical system cannot be measured when classical transmitted phase measuring deflectometry (PMD) is used. In this study, a wavefront measuring method for an optical system with multiple optics is presented based on PMD. A paraxial plane is used to represent the test multilens optical system. We introduce the calibration strategy and mathematical deduction of gradient equations. Systematic errors are suppressed with an N-rotation test. Simulations have been performed to demonstrate our method. The results showing the use of our method in multilens optical systems, such as the collimator and single-lens reflex camera lenses show that the measurement accuracy is comparable with those of interferometric tests.
Approximation via gradients on the ball. The Zernike case

In this work, we deal in a d dimensional unit ball equipped with an inner product constructed by adding a mass point at zero to the classical ball inner product applied to the gradients of the functions. Apart from determining an explicit orthogonal polynomial basis, we study approximation properties of Fourier expansions in terms of this basis. In particular, we deduce relations between the partial Fourier sums in terms of the new orthogonal polynomials and the partial Fourier sums in terms of the classical ball polynomials. We also give an estimate of the approximation error by polynomials of degree at most n in the corresponding Sobolev space, proving that we can approximate a function by using its gradient. Numerical examples are given to illustrate the approximation behavior of the Sobolev basis.
Towards a Truly Decentralized Blockchain Framework for Remittance

Blockchain is a revolutionary technology that is constructively transforming many traditional industries, including financial services. Blockchain demonstrates immense potential in bringing substantial benefits to the remittance industry. Although the remittance industry has crossed the mark of USD 600 billion in 2021, remittance cost is still substantially high, around 6% on average, indirectly limiting financial inclusion and promoting de-risking. The involvement of multiple intermediaries in global remittances makes cross-border payments more expensive. Many projects, including Ripple and Stellar, employ blockchain technology to provide alternative infrastructure for cross-border payments. However, the decentralization of blockchain networks in both solutions is debatable. This paper examines the market characteristics impacting remittance cost, a prominent factor driving the evolution of the remittance industry. A truly decentralized blockchain framework viz. LayerOneX, which provides remittance services at a reduced cost, is proposed in this paper. Devices with low computation and memory capacity can act as transaction validators in this solution. A universal wallet across homogeneous and heterogeneous blockchains is proposed to facilitate fast and inexpensive remittance services. Thus, a novel framework for true decentralization of blockchain-based remittance services, resulting in reduced cost and, therefore, better financial inclusion, is proposed in this paper.
Research on user experience evaluation model of VR museum

A model was established to evaluate users' experiences when visiting the virtual reality museum. By using the shadowing survey method (N=6), context mapping (N=10), and expert meetings (N=4), 19 comprehensive experiences and 24 interactions were obtained, and an initial questionnaire model was obtained after summarizing and organizing them with correspondence analysis and cluster analysis. After the experimental evaluation and factor analysis (N=75), a questionnaire containing 14 relevant questions was derived to measure the user's experience in the virtual reality museum. With the development of information networks and virtual reality technology, virtual museums have been created to enable people to break the time and space constraints. While previous research on the museum experience has focused on real museums and virtual reality museum environments and technologies, this questionnaire brings a user perspective to the evaluation that can be used to promote and apply virtual reality museums in the future. The developed questionnaire allows for a comprehensive assessment that will help inform the future VR museum experience.
Integrating Visual and Network Data with Deep Learning for Streaming Video Quality Assessment

Existing video Quality-of-Experience (QoE) metrics rely on the decoded video for the estimation. In this work, we explore how the overall viewer experience, quantified via the QoE score, can be automatically derived using only information available before and during the transmission of videos, on the server side. To validate the merits of the proposed scheme, we consider a dataset of videos encoded and streamed under different conditions and train a novel deep learning architecture for estimating the QoE of the decoded video. The major novelty of our work is the exploitation and demonstration of cutting-edge deep learning techniques in automatically estimating video QoE scores. Our work significantly extends the existing approach for estimating the QoE in video streaming services by combining visual information and network conditions.
Exploiting the microarchitectural leakage of prefetching activities for side-channel attacks

Microarchitectural optimizations are designed to maximize CPU usage from all aspects of instruction execution. While they effectively shorten the overall timing of execution by executing instructions or load data speculatively, observable traces, which can be used to infer sensitive information about programs on the fly, are left behind. The Instruction Pointer (IP) based stride prefetcher is implemented by Intel since Sandy Bridge, which uses sequential load history to determine whether to fetch additional lines in advance. Shin et al. (2018) discovered that lines near the lookup table are prefetched by the IP-based stride prefetcher, and they conducted side-channel attack on ECDH in face of constant-time algorithm. Their exploitation of prefetching leakage leverage secret bits which directly relate to prefetching themselves, hence such attacks cannot be applied to algorithms without bit-dependent control/data flow, e.g., AES. @ We believe the potential of prefetching leakage is not fully explored as many details of prefetcher implementation are unrevealed. Understanding how the prefetching works will help us to construct more powerful attacks. Motivated by this, we reverse-engineer three prefetching rules of IP-based stride prefetcher. A novel side-channel attack to recover the secret key of AES-128 is proposed to exemplify the exploitation, in which 3 or 4 consecutive bytes of the secret key are inferred according to observed prefetching activities combined with our concluded rules. Other unknown bytes can be complemented one at a time iteratively with our complement method. Besides, we verify that little interference is introduced by delaying the probing phase. In that case, it is rather reasonable and practical to schedule probing after the entire encryption is completed, instead of the first round where attackers need to interrupt the execution of encryption. Based on the inference, our side-channel attack successfully recovered all bytes of the secret key of AES-128. The microarchitectural leakage of prefetching activities is proved to contribute in terms of direct sensitive information recovery when detailed prefetching mechanisms are offered.
Energy based denoising convolutional neural network for image enhancement

Deep learning technologies like convolutional neural networks have recently become popular in the field of image denoising. A combined discrete wavelet transform (DWT) and denoising CNN (DnCNN)-based technique suggested in this paper can be used to adaptively boost image contrast. Images were captured in a wide variety of lighting circumstances, which means that not all of them are of the highest quality. As a result of the limited dynamic range of the pixel values, the overall image quality suffers when a shot is taken in low light. We employ a lossy method based on a structural similarity index to keep as much of the original texture in the images as feasible. Current advancements in image denoising, such as energy band analysis, very deep architecture, learning algorithms, dense-sparse-dense training, and regularization approaches, were applied in the hybrid DWT-Denoising convolutional neural networks. DnCNN eliminates the latent information in the hidden layers by design, resulting in a clean image. The DWT-DnCNN delivers SSIM values that are 2% higher than those of the standard RIDNet. Our hybrid DWT-DnCNN-based contrast enhancement strategy surpasses existing methods, according to simulation outcomes.