Reviewerly/exHarmony huggingface dataset:
Effects of Avatars on Street Crossing Tasks in Virtual Reality

Head-mounted displays (HMDs) are a commonly applied tool to analyze pedestrian behavior in virtual environments. However, compared to reality, one's own body can only be represented in the form of a virtual replica. The present study examined the effects of displaying different virtual self-representations, or avatars, in a street crossing task on presence, virtual body ownership, gap acceptance and virtual collisions. 29 participants were instructed to cross a one-lane street with varying gap sizes between vehicles ranging from 1 to 6 s. Two different avatar concepts (with or without hand and finger tracking) were compared to a baseline without any visual body self-representation. Crossing was repeated ten times in each avatar condition, resulting in a total of 30 trials per participant. There was no difference in presence scores between the conditions. The illusion of virtual body ownership was stronger for an avatar that featured hands and finger tracking compared to an avatar in which only the position of the hands was displayed based on two hand-held controllers. In trials in which any avatar was present, participants accepted significantly smaller gaps to cross the street. An equal number of virtual collisions was observed for both avatars and the baseline without an avatar.
A Method for Finding Quasi-identifier of Single Structured Relational Data

Quasi-identifier is an attribute combined with other attributes to identify specific tuples or partial tuples. Improper selection of quasi-identifiers will lead to the failure of current privacy protection anonymization technology. Therefore, in this paper, we propose a method to solve single structured relational data quasi-identifiers based on functional dependency and determines the attribute classification standard. Firstly, the solution scope of quasi-identifier is determined to be all attributes except identity attributes and critical attributes. Secondly, the real data set is used to evaluate the dependency relationship between the indefinite attribute subset and the identity attribute to solve the quasi-identifiers set. Finally, we propose an algorithm to find all quasi-identifiers and experiment on real data sets of different sizes. The results show that our method can achieve better performance on the same dataset.
Hope, Stress, Sketch & Kvetch: Emphasizing Caring Through Reflection in Online Teaching in the Pandemic

This paper presents two reflection activities that supported the creation of community and a sense of caring for students learning in an online environment in the COVID-19 pandemic. In the first activity, students indicated how they were feeling in terms of hope/despair, stress/calm and energy level in a “check-in” slide at the beginning of each online class. This enabled the educator to respond to how the class was doing in the moment. In the second activity, students met outside of the regular class structure in an online group called “Sketch & Kvetch” to talk about how we were all doing and create a sketch that was then shared with the group. Each of these activities had a positive impact on students in the pandemic. © 2020
Remarks on Particular Properties of Ternary Bent Functions and Construction Algorithms

Binary bent functions have a strictly specified number of non-zero values. In the same way, ternary bent functions satisfy certain requirements on the elements of their value vectors. These requirements can be used to specify six classes of ternary bent functions. Classes are mutually related by encoding of function values. Functions within a class are mutually related by permutation of elements in their function vectors. Given a basic ternary bent function, other functions in the same class can be constructed by permutation matrices having a block structure similar to that of the factor matrices appearing in the Good-Thomas decomposition of Cooley-Tukey Fast Fourier transform and related algorithms. Conversion of function vectors into matrices or equivalent matrix-valued vectors of smaller length leads to the redistribution of space complexity of related manipulation algorithms. In this matrix representations, construction of other bent functions from a given known bent function is performed by using either properties of bent functions or by manipulation of such representations in terms of FFT-like permutation matrices of dimensions smaller than the length of the function vector of the initial bent functions.
Security Analysis of Blockchain Smart Contract: Taking Reentrancy Vulnerability as an Example

As the blockchain enters the 2.0 era, the smart contract which is based on the blockchain platform has gradually entered people's field of vision. By its transparency, non-tampering, independence from third-party arbitration, and trustlessness, it is widely applied in equity crowdfunding, games, insurance, particularly the Internet of Things. However, the attack on the TheDAO smart contract alert public awareness of the security of a smart contract. The essence of the smart contract is an electronic contract written in code. Due to reasons like lacking standard libraries for its programming language, several loopholes will inevitably appear in the code. Once they are found by attackers, the interests of the main body using smart contracts will be damaged. There are many types of vulnerabilities in smart contracts, such as reentrancy, short address attacks, and timestamp dependence. This article mainly focuses on re-entry vulnerabilities as the research object and analyzes the principle of re-entry vulnerabilities. Featuring immutable after being chained, the smart contract must be checked before it is chained to make up for the vulnerability. This paper provides a detection method based on symbolic execution to detect reentrancy vulnerabilities. We hope to strengthen people's security awareness of smart contracts and boost the research of smart contracts in terms of security by our study of the smart contract reentrancy vulnerability in this article. Promote the research and development of smart contracts.
Review of Denoising Framework for Efficient Removal of Noise from 3D Images

An image is a distributed amplitude of colors on a plane. An image may be in the form of two-dimensional image or three-dimensional image. Such images are compiled using optical sensors like camera and are processed using various image processing tools for better visualization. Purpose of the image processing is not limited for better visualization, but it is extended to remove noise from the captured image. Noise is a random variation of brightness, contrast and color pallets in an image. In the present discussion through review of denoising framework for efficient removal of noise from 3D images, different filters which are used so far for removal of noise are discussed. The research work is further extended by designing novel denoising framework for efficient removal of noise from the 3D image.
Cluster-wise Hierarchical Generative Model for Deep Amortized Clustering

In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac). It provides an efficient neural clustering architecture by grouping data points in a cluster-wise view rather than point-wise view. CHiGac simultaneously learns what makes a cluster, how to group data points into clusters, and how to adaptively control the number of clusters. The dedicated cluster generative process is able to sufficiently exploit pair-wise or higher-order interactions between data points in both inter- and intra-cluster, which is useful to sufficiently mine the hidden structure among data. To efficiently minimize the generalized lower bound of CHiGac, we design an Ergodic Amortized Inference (EAI) strategy by considering the average behavior over sequence on an inner variational parameter trajectory, which is theoretically proven to reduce the amortization gap. A series of experiments have been conducted on both synthetic and real-world data. The experimental results demonstrated that CHiGac can efficiently and accurately cluster datasets in terms of both internal and external evaluation metrics (DBI and ACC).
RiftNet Reconstruction Model for Radio Frequency Domain Waveform Representation and Synthesis

Waveform representation, manipulation, and synthesis are challenging problems in the RF domain traditionally demanding expert knowledge to produce transparent and efficient solutions. In this work we present a low-complexity neural network architecture for waveform representation, manipulation, and synthesis. We demonstrate this architecture's performance by training it to represent Wi-Fi 802.11a/g waveforms and modify them with the objective of enhancing waveform distinguishability for RF fingerprint classification. We further present analysis of the network waveforms' latent representation to discover time and frequency properties of the learned transform. We discuss these properties in the context of traditional signals processing transforms to increase understanding and transparency of the algorithm and inspire future research into this domain. Although we target RF domain applications, we expect this architecture's performance and benefits to have high transferability to other domains.
Extended Guided Image Filtering For Contrast Enhancement

Guided image filtering (GIF) has been widely adopted in computer-vison tasks for properties of effective edge-preservation and low complexity. However, GIF is prone to suffer from halo artifacts near edges. Some new versions of GIF such as WGIF and GGIF employ various edge-aware regularizations to address this problem. But above local variance-based versions are sensitive to over-smoothing problem. The main reason is that local variance is not an adequate metric to discriminate the anisotropic image components. In this paper, we propose an extended GIF (EGIF) by incorporating a novel edge-aware factor into the regularization term. The new factor in entropy-variance domain not only can well discriminate details and edges but also have a good anti-noise ability. This helps EGIF to extend to industry applications. Experimental results of contrast enhancement show that EGIF can provide better visual quality as well as quantitative performance.
FlipOut: Uncovering Redundant Weights via Sign Flipping

We propose a novel pruning method which uses the oscillations around 0, i.e. sign flips, that a weight has undergone during training in order to determine its saliency. Our method can perform pruning before the network has converged, requires little tuning effort due to having good default values for its hyperparameters, and can directly target the level of sparsity desired by the user. Our experiments, performed on a variety of object classification architectures, show that it is competitive with existing methods and achieves state-of-the-art performance for levels of sparsity of \(99.6\%\) and above for 2 out of 3 of the architectures tested. For reproducibility, we release our code at https://github.com/AndreiXYZ/flipout.
A Hybrid Dynamical Systems Perspective on Reinforcement Learning for Cyber-Physical Systems: Vistas, Open Problems, and Challenges

The next generation of autonomous cyber-physical systems will integrate a variety of heterogeneous computation, communication, and control algorithms. This integration will lead to closed-loop systems with highly intertwined interactions between the digital world and the physical world. For these systems, designing robust and optimal data-driven control algorithms necessitates fundamental breakthroughs at the intersection of different areas such as adaptive and learning-based control, optimal and robust control, hybrid dynamical systems theory, and network control, to name just a few. Motivated by this necessity, control techniques inspired by ideas of reinforcement learning have emerged as a promising paradigm that could potentially integrate most of the key desirable features. However, while significant results in reinforcement learning have been developed during the last decades, the literature is still missing a systematic framework for the design and analysis of reinforcement learning-based controllers that can safely and systematically integrate the intrinsic continuous-time and discrete-time dynamics that emerge in cyber-physical systems. Motivated by this limitation, and by recent theoretical frameworks developed for the analysis of hybrid systems, in this chapter we explore some vistas and open problems that could potentially be addressed by merging tools from reinforcement learning and hybrid dynamical systems theory, and which could have significant implications for the development of the next generation of autonomous cyber-physical systems.
Improved Active Contour Model through Automatic Initialisation : Liver Segmentation

Liver segmentation from Computed Tomography (CT) images is a crucial step in computer aided diagnosis. In this paper, an efficient liver segmentation method based on a new active contour initialization is proposed. First, with Rayleigh distribution, the background noisy in CT image is estimated and removed. Then, Gaussian mixture model (GMM) is employed to compute the optimal threshold based on different values of component Gaussian distributions. To increase the segmentation accuracy, edge detection and morphological filter are employed to remove the minor regions. Finally, the obtained edge of the processed CT image is given as an initial edge map to the Gradient Vector Flow (GVF) model. The proposed method was evaluated on a public clinical database of 34 different people with normal liver and 51 people with abnormal liver with tumors.
Decentralized Factoring for Self-Sovereign Identities

Invoice factoring is a handy tool for developing businesses that face liquidity problems. The main property that a factoring system needs to fulfill is to prevent an invoice from being factored twice. Distributed ledger technology is suitable for implementing the platform to register invoice factoring agreements and prevent double-factoring. Several works have been proposed to use this technology for invoice factoring. However, current proposals lack in one or several aspects, such as decentralization and security against corruption, protecting business and personally identifiable information (PII), providing non-repudiation for handling disputes, Know-Your-Customer (KYC) compliance, easy user on-boarding, and being cost-efficient. In this article, a factoring registration protocol is proposed for invoice factoring registration based on a public distributed ledger which adheres to the aforementioned requirements. We include a relayer in our architecture to address the entry barrier that the users have due to the need of managing cryptocurrencies for interacting with the public ledger. Moreover, we leverage the concept of Verifiable Credentials (VCs) for KYC compliance, and allow parties to implement their self-sovereign identities by using decentralized identifiers (DIDs). DIDs enable us to relay on the DIDComm protocol for asynchronous and secure off-chain communications. We analyze our protocol from several security aspects, compare it to the related work, and study a possible business use case. Our evaluations demonstrate that our proposal is secure and efficient, as well as covers requirements not addressed by existing related work.
Blockchain and smart contract for donation traceability

It has been observed that an organization that administers donations has problems controlling the flow of incoming and outgoing donations, as it does not have an accurate record of how many donations come in, how many go out, and who donates what, which creates difficulties for traceability, as they cannot assure any donor that what was collected is true or that everything collected has been delivered to the beneficiary. In this paper, a private Ethereum-based blockchain system is proposed to ensure the traceability of donations from the time a donor gives them until the beneficiary receives them. For this, a private blockchain was created with proof of work (PoW) consensus protocol and a smart contract to develop, obtain, and send a donation to a final user. The implementation was validated using tools and expert judgment on the blockchain and the smart contract. Finally, it was demonstrated that it is feasible to implement, both technically and procedurally, a blockchain and a smart contract for the traceability of donations, since the smart contract, through its functions in the blockchain, allows the donor to track his donation until it reaches the hands of the beneficiary.
Investigating the Role of Trust Dimension as a Mediator on CC-SaaS Adoption

The public sector of Iraq has been struggling from poor management of resources and numerous difficulties that affect its governmental organization’s development, such as financial issues resulting from corruption, insecurity, and the lack of IT resources and infrastructure. Thus, cloud computing Software as a Service (CC-SaaS) can be a useful solution to help governmental organizations increase their service efficiency through the adoption of low-cost technology and provision of better services. The adoption of CC-SaaS remains limited in Iraqi public organizations due to numerous challenges, including privacy and protection, legal policy, and trust. Trust was found to be an effective facilitator in the adoption of innovation in cloud computing (CC) using the Technology Organization and Environment (TOE) model. However, from the original TOE, various relationships are not confirmed for the CC-SaaS extended model. Therefore, this study investigated the effect of the trust dimension as a mediator represented by (ability, integrity, and benevolence) on an organization that intends to adopt CC-SaaS using TOE. The study also aimed to investigate the effect of perceived intensity of civil conflict factors on adopting CC-SaaS, especially in the violence and conflict environment. To fill the existing knowledge gap, this study adopted the Diffusion of Innovation and TOE model to investigate the determinants of CC-SaaS among government organizations in Iraq. Thus, ten variables are categorized within four dimensions in the study model. Furthermore, trust is seen as a critical facilitator for successful adoption, which is particularly true as a user receives customized services provided by a third party. Hence, the study further modeled and empirically validated the mediating role of trust dimension. The data are collected empirically from a sample of 367 IT professionals. Obtained results disclosed that the effects of technology, organization, and environment variables were statistically significant and reveal that external support and compliance with regulation have no significant effect. Nonetheless, trust dimension was proven a substantial facilitator in CC-SaaS adoption.
Ultrasound Image Speckle Reduction Based on Adaptive Image Decomposition Algorithm

Speckle noise is intrinsic to medical ultrasound images and can cause misleading on image-based interpretation and diagnoses procedures. Therefore, in the processing of ultrasound medical images, speckle mitigation is an essential preprocessing step. In this article, a novel speckle noise mitigation technique using adaptive multi-resolution image decomposition algorithm (AMID) is proposed. The effectiveness of the introduced method is evaluated with synthetic images as well as real ultrasound images for further analysis and comparison of the results with other works. Using well known measure metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), simulation results evidence that the presented method outperforms other state-of-the-art techniques.
Curvelet-based image super-resolution via Res2Net multi-scale network

This paper presents a novel Res2Net multi-scale image super-resolution (SR) network in the curvelet transform domain based on deep learning, namely curvelet-Res2Net. Firstly, we integrate Res2Net block to adaptively increase the range of receptive fields for each network layer and exploit its potential multi-scale ability at a granular level from images. And then, a sequence of cascaded Res2Net blocks is formed into curvelet-Res2Net. In addition, the image SR problem is formulated as curvelet coefficient prediction to preserve richer structure details to overcome the drawbacks of oversmoothed outputs and poor texture details of most previous high resolution (HR) image predication methods in the spatial domain. Three standard image datasets are used qualitatively and quantitatively verify the performance of our method, and the results show its capability of achieving higher Peak Signal-to-Noise Ratios (PSNRs) and Structural SIMilarity (SSIM) while preserving much more useful information, comparing with other methods.
Adaptive Security of Multi-party Protocols, Revisited

The goal of secure multi-party computation (MPC) is to allow a set of parties to perform an arbitrary computation task, where the security guarantees depend on the set of parties that are corrupted. The more parties are corrupted, the less is guaranteed, and typically the guarantees are completely lost when the number of corrupted parties exceeds a certain corruption bound.Early and also many recent protocols are only statically secure in the sense that they provide no security guarantees if the adversary is allowed to choose adaptively which parties to corrupt. Security against an adversary with such a strong capability is often called adaptive security and a significant body of literature is devoted to achieving adaptive security, which is known as a difficult problem. In particular, a main technical obstacle in this context is the so-called “commitment problem”, where the simulator is unable to consistently explain the internal state of a party with respect to its pre-corruption outputs. As a result, protocols typically resort to the use of cryptographic primitives like non-committing encryption, incurring a substantial efficiency loss.This paper provides a new, clean-slate treatment of adaptive security in MPC, exploiting the specification concept of constructive cryptography (CC). A new natural security notion, called CC-adaptive security, is proposed, which is technically weaker than standard adaptive security but nevertheless captures security against a fully adaptive adversary. Known protocol examples separating between adaptive and static security are also insecure in our notion. Moreover, our notion avoids the commitment problem and thereby the need to use non-committing or equivocal tools. We exemplify this by showing that the protocols by Cramer, Damgard and Nielsen (EUROCRYPT’01) for the honest majority setting, and (the variant without non-committing encryption) by Canetti, Lindell, Ostrovsky and Sahai (STOC’02) for the dishonest majority setting, achieve CC-adaptive security. The latter example is of special interest since all UC-adaptive protocols in the dishonest majority setting require some form of non-committing or equivocal encryption.