Reviewerly/exHarmony huggingface dataset:
The Effects of Degrees of Freedom and Field of View on Motion Sickness in a Virtual Reality Context

AbstractWith a growing interest in head-mounted display (HMD)-based virtual reality (VR) environments, there have been lots of studies enhancing user experience (UX) in the context. In particular, the study of motion sickness (MS) symptoms, which are a major barrier to providing a positive UX, is of high importance. This study investigated the effects of degree of freedom (DOF) and field of view (FOV) of HMD on MS symptoms. A user experiment was designed based on a 2 × 2 mixed design with DOF as a between-subject design variable (3-DOF and 6-DOF) and FOV as a within-subject design variable (Narrow and Wide). Participants experienced VR game content in four conditions in random order. MS symptoms were captured using heart rate variability (HRV) and virtual reality sickness questionnaire (VRSQ) at the pre- and post-task moment. The results showed that MS symptoms occurred more in the 3-DOF condition than in the 6-DOF. Further, MS symptoms in DOF conditions were adjusted depending on the FOV. In the Wide condition, we found a significant difference in MS symptoms depending on the DOF, but in the Narrow condition, no significant differences were found. Through this finding, we were able to not only show the effectiveness of HRV as MS measures but also provide meaningful design insights into HMD-based VR practices.Keywords: Virtual Realitydegrees of freedomfield of viewmotion sicknesselectrocardiogramhear rate variability Disclosure statementThe authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.Data availability statementData available on reasonable request (due to privacy/ethical restriction).Additional informationFundingThis research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [2020R1I1A3067932].Notes on contributorsChae Heon LimChae Heon Lim is a master student in the Department of Industrial and Systems Engineering at Gyeongsang National University. His research interests include user experience in virtual reality environments.Seul Chan LeeSeul Chan Lee is an Assistant Professor in the Department of Industrial and Systems Engineering at Gyeongsang National University. His research goal is to explore users' needs and requirements, evaluate system artifacts, and improve systems and devices based on the theories and methodologies of Human Factors, Ergonomics, and Human-Computer Interaction.
Efficient Hybrid Exact/Relaxed Lattice Proofs and Applications to Rounding and VRFs

In this work, we study hybrid exact/relaxed zero-knowledge proofs from lattices, where the proved relation is exact in one part and relaxed in the other. Such proofs arise in important real-life applications such as those requiring verifiable PRF evaluation and have so far not received significant attention as a standalone problem. We first introduce a general framework, $$\mathsf {LANES^+}$$ , for realizing such hybrid proofs efficiently by combining standard relaxed proofs of knowledge $$\textsf{RPoK}$$ and the $$\textsf{LANES}$$ framework (due to a series of works in Crypto'20, Asiacrypt'20, ACM CCS'20). The latter framework is a powerful lattice-based proof system that can prove exact linear and multiplicative relations. The advantage of $$\mathsf {LANES^+}$$ is its ability to realize hybrid proofs more efficiently by exploiting $$\textsf{RPoK}$$ for the high-dimensional part of the secret witness while leaving a low-dimensional secret witness part for the exact proof that is proven at a significantly lower cost via $$\textsf{LANES}$$ . Thanks to the flexibility of $$\mathsf {LANES^+}$$ , other exact proof systems can also be supported. We apply our $$\mathsf {LANES^+}$$ framework to construct substantially shorter proofs of rounding, which is a central tool for verifiable deterministic lattice-based cryptography. Based on our rounding proof, we then design an efficient long-term verifiable random function (VRF), named $$\textsf{LaV}$$ . $$\textsf{LaV}$$ leads to the shortest VRF outputs among the proposals of standard (i.e., long-term and stateless) VRFs based on quantum-safe assumptions. Of independent interest, we also present generalized results for challenge difference invertibility, a fundamental soundness security requirement for many proof systems.
Correlation Intractability and SNARGs from Sub-exponential DDH

We provide the first constructions of SNARGs for Batch- $$\textsf{NP}$$ and $$\textsf{P}$$ based solely on the sub-exponential Decisional Diffie Hellman (DDH) assumption. Our schemes achieve poly-logarithmic proof sizes. We obtain our results by following the correlation-intractability framework for secure instantiation of the Fiat-Shamir paradigm. The centerpiece of our results and of independent interest is a new construction of correlation-intractable hash functions for "small input" product relations verifiable in $$\textsf{TC}^0$$ , based on sub-exponential DDH.
Fully Adaptive Schnorr Threshold Signatures

We prove adaptive security of a simple three-round threshold Schnorr signature scheme, which we call $$\textsf{Sparkle}$$ . The standard notion of security for threshold signatures considers a static adversary – one who must declare which parties are corrupt at the beginning of the protocol. The stronger adaptive adversary can at any time corrupt parties and learn their state. This notion is natural and practical, yet not proven to be met by most schemes in the literature. In this paper, we demonstrate that $$\textsf{Sparkle}$$ achieves several levels of security based on different corruption models and assumptions. To begin with, $$\textsf{Sparkle}$$ is statically secure under minimal assumptions: the discrete logarithm assumption (DL) and the random oracle model (ROM). If an adaptive adversary corrupts fewer than $$t/2$$ out of a threshold of $$t+1$$ signers, then $$\textsf{Sparkle}$$ is adaptively secure under a weaker variant of the one-more discrete logarithm assumption (AOMDL) in the ROM. Finally, we prove that $$\textsf{Sparkle}$$ achieves full adaptive security, with a corruption threshold of $$t$$ , under AOMDL in the algebraic group model (AGM) with random oracles. Importantly, we show adaptive security without requiring secure erasures. Ours is the first proof achieving full adaptive security without exponential tightness loss for any threshold Schnorr signature scheme; moreover, the reduction is tight.
Lattice-Based Succinct Arguments from Vanishing Polynomials

Succinct arguments allow a prover to convince a verifier of the validity of any statement in a language, with minimal communication and verifier's work. Among other approaches, lattice-based protocols offer solid theoretical foundations, post-quantum security, and a rich algebraic structure. In this work, we present some new approaches to constructing efficient lattice-based succinct arguments. Our main technical ingredient is a new commitment scheme based on vanishing polynomials, a notion borrowed from algebraic geometry. We analyse the security of such a commitment scheme, and show how to take advantage of the additional algebraic structure to build new lattice-based succinct arguments. A few highlights amongst our results are:
IPFS-Blockchain Smart Contracts Based Conceptual Framework to Reduce Certificate Frauds in the Academic Field

In the digital age, ensuring the authenticity and security of academic certificates is a critical challenge faced by educational institutions, employers, and individuals alike. Traditional methods for verifying academic credentials are often cumbersome, time-consuming, and susceptible to fraud. However, the emergence of blockchain technology offers a promising solution to address these issues. The proposed system utilizes a blockchain network, where each academic certificate is stored as a digital asset on the blockchain. These digital certificates are cryptographically secured, timestamped, and associated with unique identifiers, such as hashes or public keys, ensuring their integrity and immutability. Anyone with access to the blockchain network can verify a certificate’s authenticity, using the MetaMask extension and Ethereum network, eliminating the need for intermediaries and reducing the risk of fraudulent credentials. The main strength of the paper is that the data that are stored in the blockchain are unique identifiers of the encrypted data, which is encrypted by using an encryption technique that provides more security to the academic certificates. Furthermore, IPFS is also used to store large amounts of encrypted data.
EDUCATION FROM THE SYSTEMIC POINT OF VIEW: THE CONTEXT OF (NON) - CONSTANT TRANSFORMATIONS

It is generally undeniable that education is a complicated and complex system. Moreover, it is an integrated and integral system of teaching and learning at different levels and in different contexts. In the public academic educational discourse, the need to search for modern teaching and learning methods is constantly expressed so that educational institutions can better respond to the changing educational needs of learners and society. However, there is no unequivocal answer to what is “modern”. Complex knowledge of education systems (and education as a system) guarantees effective educational changes in them. If we are talking about scientific knowledge of educational phenomena, it cannot be otherwise. Based on this point of view, most educational phenomena and processes can be called systems.
A Systematic Comparison between the Ethereum and Hyperledger Fabric Blockchain Platforms for Attribute-Based Access Control in Smart Home IoT Environments

Despite the lack of blockchain systems being utilized in modern IoT environments, the prevalence of blockchain technology is increasing, due to its high level of security and accountability. The integration of blockchain technology and access control in a decentralized system for smart home networks is a promising solution to this issue. This paper compares the implementation of attribute-based access control (ABAC) with two popular blockchain platforms, Ethereum and Hyperledger Fabric, for a smart home internet of things (IoT) environment. We present a comprehensive summary of access-control and blockchain-access-control methods, to provide the necessary background for this study. Additionally, we present an original ABAC smart contract for Ethereum, and the modification of a pre-existing Hyperledger Fabric ABAC smart contract, for this comparison. Through the simulation of both implementations, the advantages and limitations will be considered, to determine which is better suited for a smart home IoT environment.
Enough hot air: the role of immersion cooling

Abstract Air cooling is the traditional solution to chill servers in data centers. However, the continuous increase in global data center energy consumption combined with the increase of the racks’ power dissipation calls for the use of more efficient alternatives. Immersion cooling is one such alternative. In this paper, we quantitatively examine and compare air cooling and immersion cooling solutions. The examined characteristics include power usage efficiency (PUE), computing and power density, cost, and maintenance overheads. A direct comparison shows a reduction of about 50% in energy consumption and a reduction of about two-thirds of the occupied space, by using immersion cooling. In addition, the higher heat capacity of used liquids in immersion cooling compared to air allows for much higher rack power densities. Moreover, immersion cooling requires less capital and operational expenditures. However, challenging maintenance procedures together with the increased number of IT failures are the main downsides. By selecting immersion cooling, cloud providers must trade-off the decrease in energy and cost and the increase in power density with its higher maintenance and reliability concerns. Finally, we argue that retrofitting an air-cooled data center with immersion cooling will result in high costs and is generally not recommended.
Bidirectional Attentional Interaction Networks for RGB-D salient object detection

Aiming at the issues of insufficient cross-modality feature interaction and ineffective utilization of cross-modality data in RGB-D saliency object detection (SOD) tasks, we propose a Bidirectional Attentional Interaction Network (BAINet) for RGB-D SOD, which employs an encoder-decoder structure for bidirectional interaction of cross-modality features through a dual-branch progressive fusion approach. To begin with, based on the fact that RGB and depth information streams can complement each other, the bidirectional attention interaction module accomplishes bidirectional interaction between cross-modality features by capturing complementary cues from different modality data. In order to enhance the expressiveness of the fused RGB-D features, the global feature perception module endows the features with rich multi-scale contextual semantic information by enlarging the field of perception. In addition, exploring the correlation of cross-level features is vital to achieve accurate salient inference. Specifically, We introduce a cross-level guidance aggregation module to capture inter-layer dependencies and complete the integration of cross-level features, which effectively suppresses shallow cross-modality features and refines the saliency map during decoding. To improve the model training speed, a hybrid loss function is employed to train multi-branch saliency inference maps simultaneously. Extensive experiments on five publicly available datasets clearly show that the proposed model outperforms 18 state-of-the-art methods.
Experimental demonstration of five-intensity measurement-device-independent quantum key distribution over 442 km

Decoy-state measurement-device-independent quantum key distributions (MDI-QKD) can remove all the loopholes of detection devices and establish secure keys using weak coherent sources. To further extend the secure distance and enhance the key rate, Chau proposed a novel framework for decoy-state MDI-QKD that supports an arbitrary number of intensities and investigated its performance in detail [H. F. Chau, Phys. Rev. A 102, 012611 (2020)]. Here, we experimentally demonstrate its one kind, i.e., the five-intensity MDI-QKD protocol, and achieve a positive key rate over 442 km of ultralow-loss optical fibers. Our results verify that the five-intensity protocol outperforms four-intensity protocols in terms of both distance and key rate, setting a record distance for typical MDI-QKD.
Energy‐efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads

Abstract Cloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription‐based computing services anytime, anywhere, on a pay‐as‐you‐go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number of emerging networked devices. However, the success of Cloud computing has created a new global energy challenge, as it comes at the cost of vast energy usage. Currently, data centres hosting Cloud services world‐wide consume more energy than most countries. Globally, by 2025, they are projected to consume 20% of global electricity and emit up to 5.5% of the world's carbon emissions. In addition, a significant part of the energy consumed is transformed into heat which leads to operational problems, including a reduction in system reliability and the life expectancy of devices, and escalation in cooling requirements. Therefore, for the future generations of Cloud computing to address the environmental and operational consequences of such significant energy usage, they must become energy‐efficient and environmentally sustainable while continuing to deliver high‐quality services. In this article, we propose a vision for learning‐centric approach for the integrated management of new generation Cloud computing environments to reduce their energy consumption and carbon footprint while delivering service quality guarantees. In this article, we identify the dimensions and key issues of integrated resource management and our envisioned approaches to address them. We present a conceptual architecture for energy‐efficient new generation Clouds and early results on the integrated management of resources and workloads that evidence its potential benefits towards energy efficiency and sustainability.
Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning

Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency. In recent years, the Gaussian error linear unit (GELU) activation function has emerged as a dominant method, surpassing traditional functions such as the rectified linear unit (ReLU) in various applications. This study presents a rigorous mathematical investigation of the GELU activation function, exploring its differentiability, boundedness, stationarity, and smoothness properties in detail. In addition, we conduct an extensive experimental comparison of the GELU function against a broad range of alternative activation functions, utilizing a residual convolutional network trained on the CIFAR-10, CIFAR-100, and STL-10 datasets as the empirical testbed. Our results demonstrate the superior performance of GELU compared to other activation functions, establishing its suitability for a wide range of deep learning applications. This comprehensive study contributes to a more profound understanding of the underlying mathematical properties of GELU and provides valuable insights for practitioners aiming to select activation functions that optimally align with their specific objectives and constraints in deep learning.
GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal

Deep-learning-based image inpainting methods have made remarkable advancements, particularly in object removal tasks. The removal of face masks has gained significant attention, especially in the wake of the COVID-19 pandemic, and while numerous methods have successfully addressed the removal of small objects, removing large and complex masks from faces remains demanding. This paper presents a novel two-stage network for unmasking faces considering the intricate facial features typically concealed by masks, such as noses, mouths, and chins. Additionally, the scarcity of paired datasets comprising masked and unmasked face images poses an additional challenge. In the first stage of our proposed model, we employ an autoencoder-based network for binary segmentation of the face mask. Subsequently, in the second stage, we introduce a generative adversarial network (GAN)-based network enhanced with attention and Masked-Unmasked Region Fusion (MURF) mechanisms to focus on the masked region. Our network generates realistic and accurate unmasked faces that resemble the original faces. We train our model on paired unmasked and masked face images sourced from CelebA, a large public dataset, and evaluate its performance on multi-scale masked faces. The experimental results illustrate that the proposed method surpasses the current state-of-the-art techniques in both qualitative and quantitative metrics. It achieves a Peak Signal-to-Noise Ratio (PSNR) improvement of 4.18 dB over the second-best method, with the PSNR reaching 30.96. Additionally, it exhibits a 1% increase in the Structural Similarity Index Measure (SSIM), achieving a value of 0.95.
Fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging

Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has low operational efficiency and a "Domino" effect. To solve these defects, we propose a fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging (KS-FDPC). The new algorithm adopts a partitioning-merging strategy. By dividing the data into multiple sub-clusters, the impact range of high-density points on subsequent allocation points can be reduced. And the fast nearest neighbors search improves the speed of DPC. In the experiment, KS-FDPC is used to compare with eight improved DPC algorithms on eight synthetic data and eight UCI data. The results indicate that the overall clustering performance of KS-FDPC is superior to other algorithms. Moreover, KS-FDPC runs faster than other algorithms. Therefore, KS-FDPC is an effective improvement of DPC.
A Novel Security Framework to Mitigate and Avoid Unexpected Security Threats in Saudi Arabia

Many organizations around the world suffer large losses due to unexpected risks which can have a profound impact on their survival. This paper presents a novel security framework to address the security needs of Saudi organizations. There are four stages in the security framework: risk assessment and management, security intelligence and analytics, security policies and procedures, and security monitoring. A comprehensive security solution was provided by combining common security frameworks, e.g. ISO/IEC 27001:2013, NIST Cybersecurity Framework, and COBIT. The developed framework was designed to help Saudi organizations identify, assess, and control risks and respond to unexpected events in a timely and effective manner. It is expected to help organizations develop and implement effective security measures to protect their critical assets and operations from security threats. The proposed framework is comprehensive and can cover most organizations' requirements.
A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm

Today, many people prefer online payment methods due to the rapid growth in cashless electronic transactions. Credit and debit cards are the most popular electronic payment methods but are prone to fraud due to the nature of their use and the tendency of fraudsters to access their details. This study proposes a theoretical credit fraud detection and prevention model using a Decision Tree Algorithm (DCA). Moreover, a survey questionnaire was used to investigate students' perceptions of credit card fraud incidents. Data were collected from 102 students from different universities and countries around the world. The results showed that 95.9% of the respondents knew how credit/debit card fraud occurs, while 4.1% of them did not. Finally, 81.6% expressed their willingness to use a tool based on the proposed model to prevent or detect credit/debit card fraud incidents.
A Faster DBSCAN Algorithm Based on Self-Adaptive Determination of Parameters

The DBSCAN algorithm is a well-known cluster method that is density-based and has the advantage of finding clusters of different shapes, but it also has certain shortcomings, one of which is that it cannot determine the two important parameters Eps (neighborhood of a point) and Mints (minimum number of points) by itself, and the other is that it takes a long time to traverse all points when dataset is large. In this paper, we propose an improved method which is named as K-DBSCAN to improve the running efficiency based on self-adaptive determination of parameters and this method changes the way of traversing and only deals with core points. Experiments show that it outperforms DBSCAN algorithms in terms of running time efficiency.