Reviewerly/exHarmony huggingface dataset:
Where to look? Sustaining Presence While Directing Attention in Virtual Reality Stories.

Humans have been fascinated with the notion of submerging themselves in other spaces since antiquity, or even before. As the contemporary media landscape seeks to employ newer immersive practices, particularly in today’s Metaverse environment, storytelling in immersive media such as virtual reality (VR) is still developing and being described. The notion of scale and (newfound) viewer agency afforded by VR has challenged VR film-makers, particularly in directing attention towards story beats and handling user interaction. The video gaming industry, as a platform that is built with immersion as the core of the experience, leads this development. The advent of virtual reality (VR) video games is developing an amalgamation of immersive strategies to induce the sense of presence within virtual worlds. In this article I look at current approaches and challenges faced when directing attention in films and storytelling in VR. I then consider strides taken in VR video games, namely Half-Life: Alyx (2020), to propose that directing techniques and immersive strategies employed in VR video games can be used to develop new strategies in VR films and storytelling.
Developing Augmented Reality Lontar Prasi Bali as an E-learning Material to Preserve Balinese Culture

Preservation of cultural heritage is one of the efforts that can be made to maintain and pass it on to the next generation, one of which is Lontar Prasi Bali. Thousands of prasi lontars are recorded in poorly maintained and damaged conditions in terms of form and physical appearance. This article aims to implement Augmented Reality in digitizing the Balinese lontar prasi so that it can preserve, promote, and develop the arts and ancestral heritage so that they have appeal and understanding in the present and even into the future. The application development method uses research and development with the stages of making 3D objects and the process of character animation with Blender, building applications with Unity software and making markers consisting of 3D objects and marker configurations on the Prasi Lontar Sheet. Application testing using a loading response time technique with higher smartphone specification results can provide a faster response time needed to run the application, as well as a User Experience Questionnaire (UEQ) test with 86 respondents who showed pragmatic quality, herdonic quality, and overall results above the average (Excellent), so that it shows that this AR application is good and as expected.
Generating Random SAT Instances: Multiple Solutions could be Predefined and Deeply Hidden

The generation of SAT instances is an important issue in computer science, and it is useful for researchers to verify the effectiveness of SAT solvers. Addressing this issue could inspire researchers to propose new search strategies. SAT problems exist in various real-world applications, some of which have more than one solution. However, although several algorithms for generating random SAT instances have been proposed, few can be used to generate hard instances that have multiple predefined solutions. In this paper, we propose the KHidden-M algorithm to generate SAT instances with multiple predefined solutions that could be hard to solve by the local search strategy when the number of predefined solutions is small enough and the Hamming distance between them is not less than half of the solution length. Specifically, first, we generate an SAT instance that is satisfied by all of the predefined solutions. Next, if the generated SAT instance does not satisfy the hardness condition, then a strategy will be conducted to adjust clauses through multiple iterations to improve the hardness of the whole instance. We propose three strategies to generate the SAT instance in the first part. The first strategy is called the random strategy, which randomly generates clauses that are satisfied by all of the predefined solutions. The other two strategies are called the estimating strategy and greedy strategy, and using them, we attempt to generate an instance that directly satisfies or is closer to the hardness condition for the local search strategy. We employ two SAT solvers (i.e., WalkSAT and Kissat) to investigate the hardness of the SAT instances generated by our algorithm in the experiments. The experimental results show the effectiveness of the random, estimating and greedy strategies. Compared to the state-of-the-art algorithm for generating SAT instances with predefined solutions, namely, M-hidden, our algorithm could be more effective in generating hard SAT instances.
A Four-Tier Smart Contract Model with On-Chain Upgrade

When there are loopholes in smart contracts or changes in demand, the existing three-tier model can only implement partial on-chain upgrades and the security of on-chain upgrades cannot be guaranteed. In this study, we optimized the three-tier smart contract model and proposed a four-tier smart contract model that includes the proxy, verification, business, and storage layers. The proxy layer is used to link contracts with other layers, the verification layer is used to check the integrity, boundary values, and abnormal processes of contracts, the business layer is used to execute business logic, and the storage layer is used to store data uniformly. On the basis of the proposed model, an on-chain upgrade and verification algorithm is proposed, which implements on-chain upgrade, on-chain verification, and version compatibility of contracts. We then design an information exchange system based on the proposed model and algorithm and test it based on the FISCO BCOS platform. Experiments show that, compared to the three-tier model, the proposed four-tier model and algorithm can implement the on-chain upgrade and reduce the contract complexity and data migration cost at the cost of some overall deployment.
Technical Study of Deep Learning in Cloud Computing for Accurate Workload Prediction

Proactive resource management in Cloud Services not only maximizes cost effectiveness but also enables issues such as Service Level Agreement (SLA) violations and the provisioning of resources to be overcome. Workload prediction using Deep Learning (DL) is a popular method of inferring complicated multidimensional data of cloud environments to meet this requirement. The overall quality of the model depends on the quality of the data as much as the architecture. Therefore, the data sourced to train the model must be of good quality. However, existing works in this domain have either used a singular data source or have not taken into account the importance of uniformity for unbiased and accurate analysis. This results in the efficacy of DL models suffering. In this paper, we provide a technical analysis of using DL models such as Recurrent Neural Networks (RNN), Multilayer Perception (MLP), Long Short-Term Memory (LSTM), and, Convolutional Neural Networks (CNN) to exploit the time series characteristics of real-world workloads from the Parallel Workloads Archive of the Standard Workload Format (SWF) with the aim of conducting an unbiased analysis. The robustness of these models is evaluated using the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error metrics. The findings of these highlight that the LSTM model exhibits the best performance compared to the other models. Additionally, to the best of our knowledge, insights of DL in workload prediction of cloud computing environments is insufficient in the literature. To address these challenges, we provide a comprehensive background on resource management and load prediction using DL. Then, we break down the models, error metrics, and data sources across different bodies of work.
FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes

Quantum Approximate Optimization Algorithm (QAOA) is one of the leading candidates for demonstrating the quantum advantage using near-term quantum computers. Unfortunately, high device error rates limit us from reliably running QAOA circuits for problems with more than a few qubits. In QAOA, the problem graph is translated into a quantum circuit such that every edge corresponds to two 2-qubit CNOT operations in each layer of the circuit. As CNOTs are extremely error-prone, the fidelity of QAOA circuits is dictated by the number of edges in the problem graph.
Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

Anomaly detection is a ubiquitous and challenging task, relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for the smooth functioning of society. To this end, we propose a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs contrastive learning in an adversarial setup to learn effective representations of normal and anomalous behavior in wireless networks. We conduct rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques and verify that CAAD yields a mean performance improvement of 92.84%. Additionally, to adapt to the dynamic shifts in benign and anomalous data distributions, we also augment CAAD enabling it to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). We view CAADEF as a novel, holistic, and widely applicable solution to anomaly detection. Our source code and data are available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
The Model Architecture of AI Technology in Higher Vocational English Classroom Teaching

This paper mainly analyzes the establishment of system model of artificial intelligence technology in higher vocational English classroom teaching. Artificial intelligence technology gives full play to its intelligent and efficient application advantages in higher vocational English classroom teaching. Through the construction of artificial intelligence expert system model, the paper designs and improves the teaching system of English course in vocational colleges, and realizes the comprehensive application of artificial intelligence expert system in English teaching by using artificial intelligence data processing model. In order to further integrate vocational English classroom and realize the technical guarantee of vocational English individualized teaching. In view of the relationship between artificial intelligence technology and English teaching in vocational colleges, this paper deeply studies the shortcomings of artificial intelligence data processing model in English teaching in vocational colleges, and puts forward solutions.
Research on patent classification based on hierarchical label semantics

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model.
Archive-based Differential Learning Incorporated Sparrow Search Algorithm

Sparrow search algorithm (SSA) is a new evolutionary algorithm that has the advantage of good exploration ability. Benefiting from its search strategy, SSA can effectively alleviate the local optima. However, it still suffers from the issue of low solution quality because of its weak exploitation ability. Therefore, we propose an archive-based differential learning incorporated sparrow search algorithm (ASSA), which introduces a local search strategy for enhancing the exploitation ability of SSA. The local search strategy searches for a superior solution in the neighborhood region of the optimal solution in each iteration and uses it to replace the original optimal solution so that SSA can find a better solution. ASSA is compared with four state-of-the-art algorithms on 29 benchmark functions of IEEE CEC2017. The experiment results demonstrate that ASSA has superior performance to its competitors.
Analisis bibliometrik publikasi ilmiah bidang digital asset management berbasis data Scopus 2011-2020

Digital asset management (DAM) is a series of systems or processes for managing content, e.g.text and images.The concept of DAM began to move into the library.With an integrated library system, libraries must be ready to develop DAMs according to their size and type.Bibliometric can be done in a certain time period (3-5 years).The research aims to determine trends in DAM scientific publications, core journals of scientific publications DAM, number of productive researchers, number of scientific publications by affiliation/institution, number of scientific publications by country, number of publications by type of document, number of scientific publications by subject, number of scientific publications based on funding sponsors, and frequency of occurrence of the most keywords.Data collection was carried out by Scopus searches with the keyword 'digital asset management' in 2011-2020.The research used a qualitative descriptive method with a bibliometric approach.The results showed the growth of scientific publications peaked in 2019 with 374 publications (22.09%).The highest publication came from Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics is most common (37 publications/7.97%).The most productive writer is Love, P.E.D from Curtin University with 6 publications (9,230 citations).The most productive institutions are Schlumberger Limited with 26 publications.The research publications are published by 81 countries, of which the United States has 456 publications.The common document type is conference paper (997 publications/58.89%).Computer science is the highest subject of publication (689 publications).The results of research publications received the most funding sponsors, namely the European Commission (19 publications).The results of VOSviewer keyword mapping show the research trend is divided into 9 clusters.The conclusion is scientific publication development have positive growth and the visualization was divided into 9 clusters.
An Optimal Differential Vector Incorporated Whale Optimizer

The whale optimization algorithm (WOA) is one of the more classic optimization algorithms, which derived from the hunting actions of whale. While WOA is well suited to exploitation, this also leads to a low level of exploration capability, making it difficult to break out of the local optimum. In this study, by incorporating the optimal differential vector in an effective whale optimizer, we propose a differential perturbation operation to break out of the local optimum namely differential vector whale optimization algorithm (DVWOA). According to experimental results based on IEEE CEC2017 benchmark functions, DVWOA is beneficial in the aspects of global search, convergence speed, and population diversity.
Double Image Encryption System Using a Nonlinear Joint Transform Correlator in the Fourier Domain

In this work, we present a new nonlinear joint transform correlator (JTC) architecture in the Fourier domain (FD) for the encryption and decryption of two simultaneous images. The main features of the proposed system are its increased level of security, the obtention of a single real-valued encrypted signal that contains the ciphered information of the two primary images and, additionally, a high image quality for the two final decrypted signals. The two images to be encrypted can be either related to each other, or independent signals. The encryption system is based on the double random phase encoding (DRPE), which is implemented by using a nonlinear JTC in the FD. The input plane of the JTC has four non-overlapping data distributions placed side-by-side with no blank spaces between them. The four data distributions are phase-only functions defined by the two images to encrypt and four random phase masks (RPMs). The joint power spectrum (JPS) is produced by the intensity of the Fourier transform (FT) of the input plane of the JTC. One of the main novelties of the proposal consists of the determination of the appropriate two nonlinear operations that modify the JPS distribution with a twofold purpose: to obtain a single real-valued encrypted image with a high level of security and to improve the quality of the decrypted images. The security keys of the encryption system are represented by the four RPMs, which are all necessary for a satisfactory decryption. The decryption system is implemented using a 4f-processor where the encrypted image and the security keys given by the four RPMs are introduced in the proper plane of the processor. The double image encryption system based on a nonlinear JTC in the FD increases the security of the system because there is a larger key space, and we can simultaneously validate two independent information signals (original images to encrypt) in comparison to previous similar proposals. The feasibility and performance of the proposed double image encryption and decryption system based on a nonlinear JTC are validated through computational simulations. Finally, we additionally comment on the proposed security system resistance against different attacks based on brute force, plaintext and deep learning.
AeRChain: An Anonymous and Efficient Redactable Blockchain Scheme Based on Proof-of-Work

Redactable Blockchain aims to ensure the immutability of the data of most applications and provide authorized mutability for some specific applications, such as for removing illegal content from blockchains. However, the existing Redactable Blockchains lack redacting efficiency and protection of the identity information of voters participating in the redacting consensus. To fill this gap, this paper presents an anonymous and efficient redactable blockchain scheme based on Proof-of-Work (PoW) in the permissionless setting, called "AeRChain". Specifically, the paper first presents an improved Back's Linkable Spontaneous Anonymous Group (bLSAG) signatures scheme and uses the improved scheme to hide the identity of blockchain voters. Then, in order to accelerate the achievement of redacting consensus, it introduces a moderate puzzle with variable target values for selecting voters and a voting weight function for assigning different weights to puzzles with different target values. The experimental results show that the present scheme can achieve efficient anonymous redacting consensus with low overhead and reduce communication traffic.
UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set

Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.
Real-time multi-user 3D visualization software in medicine

Nowadays, the flow of information is paramount in order for everyone to have the knowledge they need to make decisions as soon as possible. It is no different in digital pathology either. There is still research and development today to make relevant medical information available to right holders as soon as possible. One possible direction of development is the use of real-time multi-user software. With these programs, users are able to collaborate in real time from around the world, set up a diagnosis together, and share it with others. By using this type of software, users can make decisions together by receiving the same data. These data can be, among other things, various measurement data, image data, morphological data. Implementing this software is difficult in several ways, such as processing huge amounts of data and ensuring smooth operation for multiple users. Some possible uses in the future include online medical training, preparation for and consultation on surgeries. In this paper, we present similar software solutions currently available on the market and under development, as well as our team’s solution to the problem described.
Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network

Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images.Fifty patients who underwent 1184 MBq 99mTc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed.All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet.Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.