Reviewerly/exHarmony huggingface dataset:
Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance.To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied.Time-series analysis can predict the future ups and downs in the price of Bitcoin.For this purpose we have used ARIMA, FBProphet, XG Boosting for time series analysis as a machine learning techniques.The parameters on the basis of which we have evaluated these models are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R 2 .We conduct experiments on these three techniques but after conducting time series analysis, ARIMA considered as the best model for forecasting Bitcoin price in the crypto-market with RMSE score of 322.4 and MAE score of 227.3.Additionally, this research can be helpful for investors of crypto-market.
Virtual Reality Exposure Therapy for Fear of Heights: Clinicians’ Attitudes Become More Positive After Trying VRET

Background Virtual reality exposure therapy (VRET) has the potential to solve logistic challenges when treating specific phobias. However, VRET has yet to see a large-scale implementation in clinical settings despite positive findings in treatment trials. This may partly be due to attitudes and lack of experience among clinicians, but also because of expensive and stationary VR solutions. Objective This study tested whether modern, wireless, commercially available VR equipment with controller-free hand tracking could induce and reduce discomfort using scenarios designed for fear of heights. Also, the study tested if clinicians’ attitudes toward using VR in therapy changed after trying it themselves. Method Attitudes to using VR in therapy and discomfort ratings were assessed for 74 clinicians before and after completing two VR scenarios. In addition, 54 non-clinicians completed the same scenarios. Participants were not diagnosed with acrophobia. Results The VR scenarios induced discomfort comparable to participants’ reported fear of heights in real life. Repeated training reduced discomfort. Positive attitudes toward use of VR in therapy was predicted by previous experience with VR, as well as positive attitudes toward novel technology and exposure therapy. Clinicians’ attitudes became more favorable after trying VRET themselves. Clinicians reported a range of possible advantages and disadvantages of using VR in therapy. Conclusion VRET for fear of heights was able to induce and reduce discomfort in clinicians and non-clinicians, and clinicians’ attitudes toward using VRET become more positive after trying VRET for themselves. The latest generation of VR solutions has potential to improve clinical availability and treatment options. Future research should explore how VRET can be implemented in clinical settings.
Personalized Trajectory Prediction via Distribution Discrimination

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy. In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions. Motivated by that the motion pattern of each person is personalized due to his/her habit, our DisDis learns the latent distribution to represent different motion patterns and optimize it by the contrastive discrimination. This distribution discrimination encourages latent distributions to be more discriminative. Our method can be integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution. To evaluate the latent distribution, we further propose a new metric, probability cumulative minimum distance (PCMD) curve, which cumulatively calculates the minimum distance on the sorted probabilities. Experimental results on the ETH and UCY datasets show the effectiveness of our method. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Daily Human Activity Recognition Using Non-Intrusive Sensors

In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.
An efficient approach for no-reference image quality assessment based on statistical texture and structural features

This paper presents a rotation-invariant and computationally efficient no-reference image quality assessment (NR-IQA) model. It estimates the image quality based on texture and structural information associated with the images. The human visual system (HVS) uses perceptual features such as texture and structure as primary information to understand the visual scene and the image content. Moreover, the texture and structural information capture the loss of naturalness due to distortions in the image. Therefore, in this work, the important texture features are extracted using the local binary patterns (LBP). The modified LBP, also known as the hyper-smoothing LBP (H-LBP) and Laplacian of H-LBP (LH-LBP), represents the image structure. Further, the image quality prediction model computes the quality of the image based on the statistical feature measures of the texture and structural information. In the proposed approach, the image quality prediction model uses support vector regression (SVR) to measure image quality. Various experimentations are carried out on the LIVE and TID2013 database to test the effectiveness of the proposed NR-IQA model. The performance metrics such as Spearman rank-ordered correlation coefficient, Pearson linear correlation coefficient, and root mean square error is computed to show the efficiency of the presented approach. The experimental results illustrate a high correlation between the predicted quality score and the human visual perceptions. It is also found to be competitive with the best-performing full-reference and no-reference IQA models.
Adapting the coordination of eyes and head to differences in task and environment during fully-mobile visual exploration

How are eyes and head adapted to meet the demands of visual exploration in different tasks and environments? In two studies, we measured the horizontal movements of the eyes (using mobile eye tracking in Studies 1 and 2) and the head (using inertial sensors in Study 2) while participants completed a walking task and a search and retrieval task in a large, outdoor environment. We found that the spread of visual exploration was greater while searching compared with walking, and this was primarily driven by increased movement of the head as opposed to the eyes. The contributions of the head to gaze shifts of different eccentricities was greater when searching compared to when walking. Findings are discussed with respect to understanding visual exploration as a motor action with multiple degrees of freedom.
RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform

In recent years, synthetic speech generated by advanced text-to-speech (TTS) and voice conversion (VC) systems has caused great harms to automatic speaker verification (ASV) systems, urging us to design a synthetic speech detection system to protect ASV systems. In this paper, we propose a new speech anti-spoofing model named ResWavegram-Resnet (RW-Resnet). The model contains two parts, Conv1D Resblocks and backbone Resnet34. The Conv1D Resblock is based on the Conv1D block with a residual connection. For the first part, we use the raw waveform as input and feed it to the stacked Conv1D Resblocks to get the ResWavegram. Compared with traditional methods, ResWavegram keeps all the information from the audio signal and has a stronger ability in extracting features. For the second part, the extracted features are fed to the backbone Resnet34 for the spoofed or bonafide decision. The ASVspoof2019 logical access (LA) corpus is used to evaluate our proposed RW-Resnet. Experimental results show that the RW-Resnet achieves better performance than other state-of-the-art anti-spoofing models, which illustrates its effectiveness in detecting synthetic speech attacks.
GOOD, BAD AND DARK BITCOIN: A Systematic Literature Review

Crypto currency investments have been in demand since the popularity of bitcoin. Crypto currency (cryptocurrency) is a currency based on cryptography or digital currency. The birth of bitcoin as one of the important foundations in the formation of the reputation of the crypto currency. Investing in crypto currencies is considered to provide more security and large profits for investors, with a cryptographic system the crypto currency is difficult to hack and fake. This cryptocurrency is also a digital key with encryption capabilities as security and validity, as well as real value that can be exchanged as an option. The most popular type of virtual currency is known as Bitcoin, and its popularity and strength continue to increase. At first glance, Bitcoin is very confusing. But here are ten facts that can be explained in simple terms, exactly what that little coin is and what happens.
Nine Years of Mobile Healthcare Research: A Bibliometric Analysis

&lt;p class="0abstractCxSpFirst"&gt;&lt;span lang="EN-US"&gt;Purpose: The purpose of the paper was to explore the central keyword searched (&lt;em&gt;e.g., mobile healthcare&lt;/em&gt;). It also aimed at identifying the valuable contributions made by authors, journals, countries, and institutions and their associations in ‘&lt;em&gt;mobile healthcare&lt;/em&gt;’ search around the world. &lt;/span&gt;&lt;/p&gt;&lt;p class="0abstractCxSpMiddle"&gt;&lt;span lang="EN-US"&gt;Methodology: Data was extracted from 2012 to 2020 by using Scopus database and analysed through VOSviewer software and MS Excel. PRISMA guidelines were used to screen the records. &lt;/span&gt;&lt;/p&gt;&lt;p class="0abstractCxSpMiddle"&gt;&lt;span lang="EN-US"&gt;Analysis: Co-authorship, Co-occurrence, Bibliographic Coupling and Co-citation analysis were executed to identify the links and collaborations among the authors, countries, author keywords and documents globally. &lt;/span&gt;&lt;/p&gt;&lt;p class="0abstractCxSpMiddle"&gt;&lt;span lang="EN-US"&gt;Findings: Results showed that &lt;em&gt;Yang X&lt;/em&gt;. had the highest association with other authors and &lt;em&gt;Sood, S.K.&lt;/em&gt; had published more documents than others. &lt;em&gt;Australia&lt;/em&gt; was found to have the highest association with other countries, and &lt;em&gt;India&lt;/em&gt; was leading other countries in publications. &lt;em&gt;Computers and Electrical Engineering&lt;/em&gt; was found to be the leading journal in publication of documents. &lt;/span&gt;&lt;/p&gt;&lt;p class="0abstractCxSpLast"&gt;&lt;span lang="EN-US"&gt;Originality:&lt;em&gt; &lt;/em&gt;This study, to best of our knowledge, was the first of its kind in mapping the ‘&lt;em&gt;mobile healthcare&lt;/em&gt;’ search which was designed till 2020. This will aid in shaping and understanding the central theme and set the future research directions for the researchers.&lt;/span&gt;&lt;/p&gt;
Blockchain for Electric Vehicles Energy Trading: Requirements, Opportunities, and Challenges

Today's technologies, techniques, and systems leveraged for managing energy trading operations in electric vehicles fall short in providing operational transparency, immutability, fault tolerance, traceability, and trusted data provenance features.They are centralized and vulnerable to the single point of failure problem, and less trustworthy as they are prone to the data modifications and deletion by adversaries.In this paper, we present the potential advantages of blockchain technology to manage energy trading operations between electric vehicles as it can offer data traceability, immutability, transparency, audit, security, and confidentiality in a fully decentralized manner.We identify and discuss the essential requirements for the successful implementation of blockchain technology to secure energy trading operations among electric vehicles.We present a detailed discussion on the potential opportunities offered by blockchain technology to secure the energy trading operations of electric vehicles.We discuss several blockchain-based research projects and case studies to highlight the practicability of blockchain technology in electric vehicles energy trading.Finally, we identify and discuss open challenges in fulfilling the requirements of electric vehicles energy trading applications.
SAR Image Despeckling Using Continuous Attention Module

Speckle removal process is inevitable in the restoration of synthetic aperture radar (SAR) images. Several variant methods have been proposed for enhancing SAR images over the past decades. However, in recent studies, convolutional neural networks (CNNs) have been widely applied in SAR image despeckling because of their versatility in representation learning. Nonetheless, a fair number of textures of the images are still lost when despeckling using simple CNN structures. To solve this problem, an encoder–decoder architecture was previously proposed. Although this architecture extracts features on different scales and has been shown to yield state-of-the-art performance, it still learns representation locally, resulting in missing overall information of convolutional features. Therefore, we herein introduce a new method for SAR image despeckling (SAR-CAM), which improves the performance of an encoder–decoder CNN architecture by using various attention modules. Moreover, a context block is introduced at the minimum scale to capture multiscale information. The model is trained via a data-driven approach using the gradient descent algorithm with a combination of modified despeckling gain and total variation loss function. Experiments performed on simulated and real SAR data demonstrate that the proposed method achieves significant improvements over state-of-the-art methodologies.
OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments

The indispensable collaboration of cloud computing in every digital service has raised its resource usage exponentially. The ever-growing demand of cloud resources evades service availability leading to critical challenges such as cloud outages, SLA violation, and excessive power consumption. Previous approaches have addressed this problem by utilizing multiple cloud platforms or running multiple replicas of a Virtual Machine (VM) resulting into high operational cost. This paper has addressed this alarming problem from a different perspective by proposing a novel $$\mathbb {O}$$ nline virtual machine $$\mathbb {F}$$ ailure $$\mathbb {P}$$ rediction and $$\mathbb {T}$$ olerance $$\mathbb {M}$$ odel (OFP-TM) with high availability awareness embedded in physical machines as well as virtual machines. The failure-prone VMs are estimated in real-time based on their future resource usage by developing an ensemble approach-based resource predictor. These VMs are assigned to a failure tolerance unit comprising of a resource provision matrix and Selection Box (S-Box) mechanism which triggers the migration of failure-prone VMs and handle any outage beforehand while maintaining the desired level of availability for cloud users. The proposed model is evaluated and compared against existing related approaches by simulating cloud environment and executing several experiments using a real-world workload Google Cluster dataset. Consequently, it has been concluded that OFP-TM improves availability and scales down the number of live VM migrations up to 33.5% and 83.3%, respectively, over without OFP-TM.
A High-Efficiency FPGA-Based Multimode SHA-2 Accelerator

The secure hash algorithm 2 (SHA-2) family, including the SHA-224/256/384/512 hash functions, is widely adopted in many modern domains, ranging from Internet of Things devices to cryptocurrency. SHA-2 functions are often implemented on hardware to optimize performance and power. In addition to the high-performance and low-cost requirements, the hardware for SHA-2 must be highly flexible for many applications. This paper proposes an SHA-2 hardware architecture named the multimode SHA-2 accelerator (MSA), which has high performance and flexibility at the system-on-chip level. To achieve high performance and flexibility, our accelerator applies three optimal techniques. First, a multimode processing element architecture is proposed to enable the accelerator to compute various SHA-2 functions for many applications. Second, a three-stage arithmetic logic unit pipeline architecture is proposed to reduce the critical paths and hardware resources. Finally, nonce generator and nonce validator architectures are proposed to reduce memory access and maximize the performance of the proposed MSA for blockchain mining applications. The MSA accuracy is tested on a real hardware platform (the Xilinx Alveo U280 FPGA). The experimental results on the field programmable gate array (FPGA) prove that the proposed MSA achieves significantly better performance, hardware efficiency, and flexibility than previous works. The evaluation results for energy efficiency show that the proposed MSA achieves up to 38.05 Mhps/W, which is 543.6 and 29 times better than the state-of-the-art Intel i9-10940X CPU and RTX 3090 GPU, respectively.
ID-YOLO: Real-Time Salient Object Detection Based on the Driver’s Fixation Region

Object detection is an important task for self-driving vehicles or advanced driver assistant systems (ADASs). Additionally, visual selective attention is a crucial neural mechanism in a driver’s vision system that can rapidly filter out unnecessary visual information in a driving scene. Some existing models detect all objects in driving scenes from the aspect of computer vision. However, in a rapidly changing driving environment, detecting salient or critical objects appearing in drivers’ interested or safety-relevant areas is more useful for ADASs. In this paper, we managed to detect salient and critical objects based on drivers’ fixation regions. To this end, we built an augmented eye tracking object detection (ETOD) dataset based on driving videos with multiple drivers’ eye movement collected by Deng <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> Furthermore, we proposed a real-time salient object detection network named increase-decrease YOLO (ID-YOLO) to discriminate the critical objects within the drivers’ fixation region. The proposed ID-YOLO shows excellent detection of major objects that drivers are concerned about during driving. Compared with the present object detection models in autonomous and assisted driving systems, our object detection framework simulates the selective attention mechanism of drivers. Thus, it does not detect all of the objects appearing in the driving scenes but only detects the most relevant ones for driving safety. It can largely reduce the interference of irrelevant scene information, showing potential practical applications in intelligent or assisted driving systems.
KLGCN: Knowledge graph-aware Light Graph Convolutional Network for recommender systems

Most popular recommender systems learn the embedding of users and items through capturing valuable information from user–item interactions or item knowledge graph (KG) with Graph Convolutional Network. However, only a few methods capture information from both source data, and they introduce many trainable parameters that increase training difficulty. In this work, we aim to aggregate information from both the user–item interaction graph and the item KG in a light and effective manner. We first experimentally verify the effectiveness of removing feature transformation and nonlinear activation in KG-aware recommendation, which has been proven to greatly reduce parameters while improving performance in the collaborative filtering-based recommendation. Then we propose a new Knowledge graph-aware Light Graph Convolutional Network (KLGCN), which can learn partial embeddings of users and items by aggregating features on the source graphs for recommendation and introduces no extra parameters. Extensive experiments on three public datasets demonstrate that KLGCN achieves substantial improvement over several state-of-the-art models and maintains satisfactory performance on cold-start scenarios.
Research Into improved Distance Learning Using VR Technology

Practical skill-based education requires exemplary face-to-face operational teaching, and VR can enhance online distance learning, facilitating an alternative form of “face-to-face” teaching, which results in better teacher–student communication and learner self-efficacy. It also constitutes as a useful substitute for in-person teaching, and it also has a positive impact on learning effectiveness. In this study, a mixed-method approach was used, which utilized the following methodologies: a combination of quantitative and qualitative measures, document collection, case and comparative analysis, and VR teaching that utilizes “You, Calligrapher” as a survey tool. Teachers and students of art were selected, who then used an educational VR-based calligraphy game application for teaching activities. We investigated the impact of virtual time, space, and technical availability on learners’ understanding, imagination, and interactivity in VR education, and then we evaluated the positive impact via learner feedback. Research tools that we utilized consist of comprehension, imagination, and how feedback motivation scales with effective learning; we have also used Chinese calligraphy performance tests. The SPSS statistical analysis software was used for related statistical processing, and α was set to 0.05. The results of this study indicated that Chinese calligraphy studies in VR time and space affect students’ understanding and imagination but not their operational abilities. According to our research, a fundamental difference between traditional and modern teaching methods is a shift toward the use of VR (and the internet) in education. Therefore, the focus of this study is on understanding the impact on practical skills during distance learning and investing the impacts in order to form an effective approach to the use of VR in education.