Reviewerly/exHarmony huggingface dataset:
The Challenge of Cryptocurrency in the Era of the Digital Revolution: A Review of Systematic Literature

Money is clearly a primary need of every human being that cannot be avoided, human needs can be realized by using money. Seeing from the lack of systematic literature review papers discussing cryptocurrency, this is a challenge as well as the main purpose of this paper. Along with the development of modernization and globalization which has now entered the industrial era 4.0 revolution there is a blockchain based technology, Cryptocurrency. Cryptocurrency is one of the developments of the blockchain that is often used as a decentralized digital currency. The word Cryptocurrency means a virtual currency that has no physical form, and Cryptocurrency also means that the transaction currency cannot be seen and is safe. This digital currency has many types such as Bitcoin, Ethereum, Litecoin, Monero, and many other types. Although it has no physical form, this currency functions the same as conventional currencies in general and has an exchange rate. Exchange rates on Cryptocurrency fluctuate which means unexpected, this is often exploited by traders. Cryptocurrency transactions in the form of forwarding from one individual to another individual online, therefore they deal directly without a third party. Every technology has advantages and disadvantages aside from efficiency and convenience, Cryptocurrency has the disadvantage of not having the authority responsible for dealing with all problems that occur in all transactions, and money laundering crimes also often occur, this is a challenge for how to utilize Cryptocurrency and blockchain technology in the current era of globalization.
Controlled flexibility in blockchain-based collaborative business processes

Blockchain technology enables the execution of collaborative business processes involving mutually untrusted parties. Existing tools allow such processes to be modeled using high-level notations and compiled into smart contracts that can be deployed on blockchain platforms. However, these tools do not provide mechanisms to cope with the flexibility requirements inherent to open and dynamic collaboration environments. In particular, existing tools adopt a static role binding approach wherein roles are bound to actors upfront when a process instance is created. Also, these tools do not allow participants to collectively make choices regarding alternative sub-processes or branches in the process model, at runtime. This paper presents a model for dynamic binding of actors to roles in collaborative processes and an associated binding policy specification language. The proposed language is endowed with a Petri net semantics, thus enabling policy consistency verification. Furthermore, the paper introduces a model for consensus-based control-flow flexibility, wherein participants in a process can collectively agree on how to steer the business process within the boundaries defined by control-flow agreement policies. The paper also outlines an approach to compile policy specifications into smart contracts for enforcement. An experimental evaluation shows that the cost of policy enforcement increases linearly with the number of roles, control-flow elements, and policy constraints.
Do You Feel the Same as I Do? Differences in Virtual Reality Technology Experience and Acceptance Between Elderly Adults and College Students

Virtual reality (VR) has been widely applied in medical health areas and the entertainment and tourism industries, in which elderly people are a primary target. However, most studies on VR have focused on how people respond to this new technology and its effects on younger generations. Our study explored the differences in VR experience and acceptance between elderly adults and college students. A mixed method approach including both experiments and in-depth interviews was applied in this study. Our results show that elderly adults reported a higher level of telepresence and stronger emotional responses than young adults in VR viewing regardless of the VR device. The ways in which elderly people processed and evaluate VR content and devices were different from those of young people. Elderly people reported more positive attitudes toward the video they watched than young people, but this did not necessarily lead to more positive attitudes toward the viewing experience.
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering

We present a simple yet powerful implicit neural function that can represent and render arbitrarily complex 3D scenes in a single network only from 2D observations. The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle. The key to our approach is to explicitly integrate the principle of multi-view geometry to obtain the internal representations from observed 2D views, such that the learned implicit representations empirically remain multi-view consistent. In addition, we introduce an effective neural module to learn general features for each pixel in 2D images, allowing the constructed internal 3D representations to be general as well. Extensive experiments demonstrate the superiority of our approach.
Impact of employee empowerment on organizational commitment through job satisfaction in four and five stars hotel industry

The purpose of this study is to empirically test the impact of employee empowerment on organizational commitment through the mediating role of job satisfaction. The non-probability random sampling technique and time lag was used to collect data from 307 employees working at four and five Stars Hotels in two cities Rawalpindi, Islamabad of Pakistan. Smart Partial least squares-structural equation modeling (Smart PLS SEM v.3.2.8) was used to test the hypotheses. The result indicates that employee empowerment has a significant and positive impact on organizational commitment. Also, job satisfaction is considered as a potential mediator between employee empowerment and organizational commitment. Furthermore, to sup-port the results current study used the social exchange theory. Finally, some theoretical and practical contributions to employee empowerment and organizational commitment literature, and research limitations and future directions are presented.
Card-Based Cryptographic Logical Computations Using Private Operations

Abstract This paper proposes new card-based cryptographic protocols to calculate logic functions with the minimum number of cards using private operations under the semi-honest model. Though various card-based cryptographic protocols were shown, the minimum number of cards used in the protocol has not been achieved yet for many problems. Operations executed by a player where the other players cannot see are called private operations. Private operations have been introduced in some protocols to solve a particular problem or to input private values. However, the effectiveness of introducing private operations to the calculation of general logic functions has not been considered. This paper introduces three new private operations: private random bisection cuts, private reverse cuts, and private reveals. With these three new operations, we show that all of AND, XOR, and copy protocols are achieved with the minimum number of cards by simple three-round protocols. This paper then shows a protocol to calculate any logical functions using these private operations. Next, we consider protocols with malicious players.
MSRBot: Using bots to answer questions from software repositories

Software repositories contain a plethora of useful information that can be used to enhance software projects. Prior work has leveraged repository data to improve many aspects of the software development process, such as, help extract requirement decisions, identify potentially defective code and improve maintenance and evolution. However, in many cases, project stakeholders are not able to fully benefit from their software repositories due to the fact that they need special expertise to mine their repositories. Also, extracting and linking data from different types of repositories (e.g., source code control and bug repositories) requires dedicated effort and time, even if the stakeholder has the expertise to perform such a task. Therefore, in this paper, we use bots to automate and ease the process of extracting useful information from software repositories. Particularly, we lay out an approach of how bots, layered on top of software repositories, can be used to answer some of the most common software development/maintenance questions facing developers. We perform a preliminary study with 12 participants to validate the effectiveness of the bot. Our findings indicate that using bots achieves very promising results compared to not using the bot (baseline). Most of the participants (90.0%) find the bot to be either useful or very useful. Also, they completed 90.8% of the tasks correctly using the bot with a median time of 40 seconds per task. On the other hand, without the bot, the participants completed 25.2% of the tasks with a median time of 240 seconds per task. Our work has the potential to transform the MSR field by significantly lowering the barrier to entry, making the extraction of useful information from software repositories as easy as chatting with a bot.
On the interplay between physical and content priors in deep learning for computational imaging

Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two important questions remain largely unanswered: first, how well can the trained neural network generalize to objects very different from the ones in training? This is particularly important in practice, since large-scale annotated examples similar to those of interest are often not available during training. Second, has the trained neural network learnt the underlying (inverse) physics model, or has it merely done something trivial, such as memorizing the examples or point-wise pattern matching? This pertains to the interpretability of machine-learning based algorithms. In this work, we use the Phase Extraction Neural Network (PhENN) [Optica 4 , 1117-1125 (2017)], a deep neural network (DNN) for quantitative phase retrieval in a lensless phase imaging system as the standard platform and show that the two questions are related and share a common crux: the choice of the training examples. Moreover, we connect the strength of the regularization effect imposed by a training set to the training process with the Shannon entropy of images in the dataset. That is, the higher the entropy of the training images, the weaker the regularization effect can be imposed. We also discover that weaker regularization effect leads to better learning of the underlying propagation model, i.e. the weak object transfer function, applicable for weakly scattering objects under the weak object approximation. Finally, simulation and experimental results show that better cross-domain generalization performance can be achieved if DNN is trained on a higher-entropy database, e.g. the ImageNet, than if the same DNN is trained on a lower-entropy database, e.g. MNIST, as the former allows the underlying physics model be learned better than the latter.
Malaria Screener: a smartphone application for automated malaria screening

Abstract Background Light microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and involve sophisticated hardware components, which makes it hard to deploy them in resource-limited areas. Results We designed an Android mobile application called Malaria Screener, which makes smartphones an affordable yet effective solution for automated malaria light microscopy. The mobile app utilizes high-resolution cameras and computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, smear image analysis, and result visualization in its slide screening process, and is equipped with a database to provide easy access to the acquired data. Conclusion Malaria Screener makes the screening process faster, more consistent, and less dependent on human expertise. The app is modular, allowing other research groups to integrate their methods and models for image processing and machine learning, while acquiring and analyzing their data.
Quantum sensing networks for the estimation of linear functions

Abstract The theoretical framework for networked quantum sensing has been developed to a great extent in the past few years, but there are still a number of open questions. Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal. Using this we show that if the vectors are clustered around two special subspaces, then the optimum is achieved when the correlation strength approaches its extreme values, while there is a monotonic transition between such extremes for any other geometry. Furthermore, we demonstrate that entanglement can be detrimental for estimating non-trivial global properties, and that sometimes it is in fact irrelevant. Finally, we perform a non-asymptotic analysis of these results using a Bayesian approach, finding that the amount of correlations needed to enhance the precision crucially depends on the number of measurement data. Our results will serve as a basis to investigate how to harness correlations in networks of quantum sensors operating both in and out of the asymptotic regime.
Some binary BCH codes with length n = 2 + 1

Under research for near sixty years, Bose-$\!$Ray-$\!$Chaudhuri-$\!$Hocquenghem(BCH) codes have played increasingly important roles in many applications such as communication systems, data storage and information security. However, the dimension and minimum distance of BCH codes are seldom solved until now because of their intractable characteristics. The objective of this paper is to study the dimensions of some BCH codes of length $n=2^m+1$ with $m=2t+1$, $4t+2$, $8t+4$ and $m\geq 10$. Some new techniques are employed to investigate coset leaders modulo $n$. For each type of $m$ above, the first five largest coset leaders modulo $n$ are determined, the dimension of some BCH codes of length $n$ with designed distance $\delta>2^{\lceil \frac{m}{2} \rceil}$ is presented. These new techniques and results may be helpful to study other families of cyclic codes over finite fields.
Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods.
Improved Version of Graph-Cut Algorithm for CT Images of Lung Cancer With Clinical Property Condition

In a clinical evaluation, the detection of lung cancer is a challenging task. Segmentation methods are used to detect the extra growing nodule. Early diagnosis of lung cancer is significant in clinical research. The early stage of lung nodules is very soft tissues and tough to segment accurately. Generally, conservative graph cut methods are very weak to detect those soft edges in medical images. In this article, the proposed algorithm is improved to obtain the accuracy of the process to segment the edges than the conventional graph cut methods. This investigation is executed to shows the accuracy of lung segmentation.
Techniques for Inverted Index Compression

The data structure at the core of large-scale search engines is the inverted index , which is essentially a collection of sorted integer sequences called inverted lists . Because of the many documents indexed by such engines and stringent performance requirements imposed by the heavy load of queries, the inverted index stores billions of integers that must be searched efficiently. In this scenario, index compression is essential because it leads to a better exploitation of the computer memory hierarchy for faster query processing and, at the same time, allows reducing the number of storage machines. The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation.
Sentiment Analysis in English Texts

Social cues, such as eye gaze and pointing fingers, can increase the prioritisation of specific locations for cognitive processing.A previous study using a manual reaching task showed that, although both gaze and pointing cues altered target prioritisation (reaction times [RTs]), only pointing cues affected action execution (trajectory deviations).These differential effects of gaze and pointing cues on action execution could be because the gaze cue was conveyed through a disembodied head; hence, the model lacked the potential for a body part (i.e., hands) to interact with the target.In the present study, the image of a male gaze model, whose gaze direction coincided with two potential target locations, was centrally presented.The model either had his arms and hands extended underneath the potential target locations, indicating the potential to act on the targets (Experiment 1), or had his arms crossed in front of his chest, indicating the absence of potential to act (Experiment 2).Participants reached to a target that followed a nonpredictive gaze cue at one of three stimulus onset asynchronies.RTs and reach trajectories of the movements to cued and uncued targets were analysed.RTs showed a facilitation effect for both experiments, whereas trajectory analysis revealed facilitatory and inhibitory effects, but only in Experiment 1 when the model could potentially act on the targets.The results of this study suggested that when the gaze model had the potential to interact with the cued target location, the model's gaze affected not only target prioritisation but also movement execution.
Improved Denoising Diffusion Probabilistic Models

Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at this https URL
Oscillation-Bound Estimation of Perturbations Under Bandler–Kohout Subproduct

The Bandler-Kohout subproduct (BKS) method is one of the two widely acknowledged fuzzy relational inference (FRI) schemes. The previous works related to its stability and robustness mainly concentrated on how the output values were changed with perturbation parameters of input values. However, the works on estimating oscillation bounds of output values with regard to varying limits of input, are lacking. In this study, we investigate the oscillation-bound estimation of perturbations for BKS. First, the BKS output variation scopes are acquired for interval perturbation, where the R -implication, ( S, N )-implication, QL-implication, and t -norm implication are adopted. Second, in allusion to the more sophisticated problem of the fuzzy reasoning chain with BKS, the oscillation bounds of BKS output resulting from input interval perturbation are offered. Third, we construct the upper and lower bounds of BKS output deviation originated in the simple perturbation of the input fuzzy set, in which the situations of one rule and multiple rules are both dissected. Finally, the stable properties of all these BKS strategies are confirmed. It is emphasized that interval perturbation and simple perturbation are more general ways to give expression describing the robustness issue, and the obtained oscillation bounds also deliver more detailed characterization of the output deviation along with the input perturbation. This study further validates the sound properties of the BKS method.
"I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter

Over the past few years, there is a heated debate and serious public concerns regarding online content moderation, censorship, and the principle of free speech on the Web. To ease these concerns, social media platforms like Twitter and Facebook refined their content moderation systems to support soft moderation interventions. Soft moderation interventions refer to warning labels attached to potentially questionable or harmful content to inform other users about the content and its nature while the content remains accessible, hence alleviating concerns related to censorship and free speech. In this work, we perform one of the first empirical studies on soft moderation interventions on Twitter. Using a mixed-methods approach, we study the users who share tweets with warning labels on Twitter and their political leaning, the engagement that these tweets receive, and how users interact with tweets that have warning labels. Among other things, we find that 72% of the tweets with warning labels are shared by Republicans, while only 11% are shared by Democrats. By analyzing content engagement, we find that tweets with warning labels had more engagement compared to tweets without warning labels. Also, we qualitatively analyze how users interact with content that has warning labels finding that the most popular interactions are related to further debunking false claims, mocking the author or content of the disputed tweet, and further reinforcing or resharing false claims. Finally, we describe concrete examples of inconsistencies, such as warning labels that are incorrectly added or warning labels that are not added on tweets despite sharing questionable and potentially harmful information.