Reviewerly/exHarmony huggingface dataset:
Assessing the influential factors of cybersecurity awareness in Malaysia during the pandemic outbreak: a structural equation modeling

The advanced growth of communication technology today has brought great benefits to the people of Malaysia in many ways.One of those is the introduction of Society 5.0 concept, in which referring to smart cities which will solve future issues and challenges using digital approaches including artificial intelligence (AI), big data and Internet of Things (IoT).Big data collected by the various devices will be analysed and transformed into new type of innovation technologies by AI and will be beneficial for all levels of the community.The aim of society 5.0 is to change our lifestyle to becoming more comfortable with digital technology and its services whenever it is needed.Despite of its advantage, some problems have come into the picture.
Feature augmentation for the inversion of the Fourier transform with limited data

We investigate an interpolation/extrapolation method that, given scattered observations of the Fourier transform, approximates its inverse. The interpolation algorithm takes advantage of modelling the available data via a shape-driven interpolation based on Variably Scaled Kernels (VSKs), whose implementation is here tailored for inverse problems. The so-constructed interpolants are used as inputs for a standard iterative inversion scheme. After providing theoretical results concerning the spectrum of the VSK collocation matrix, we test the method on astrophysical imaging benchmarks.
Interplay between Cryptocurrency Transactions and Online Financial Forums

Cryptocurrencies are a type of digital money meant to provide security and anonymity while using cryptography techniques. Although cryptocurrencies represent a breakthrough and provide some important benefits, their usage poses some risks that are a result of the lack of supervising institutions and transparency. Because disinformation and volatility is discouraging for personal investors, cryptocurrencies emerged hand-in-hand with the proliferation of online users' communities and forums as places to share information that can alleviate users' mistrust. This research focuses on the study of the interplay between these cryptocurrency forums and fluctuations in cryptocurrency values. In particular, the most popular cryptocurrency Bitcoin (BTC) and a related active discussion community, Bitcointalk, are analyzed. This study shows that the activity of Bitcointalk forum keeps a direct relationship with the trend in the values of BTC, therefore analysis of this interaction would be a perfect base to support personal investments in a non-regulated market and, to confirm whether cryptocurrency forums show evidences to detect abnormal behaviors in BTC values as well as to predict or estimate these values. The experiment highlights that forum data can explain specific events in the financial field. It also underlines the relevance of quotes (regular mechanism to response a post) at periods: (1) when there is a high concentration of posts around certain topics; (2) when peaks in the BTC price are observed; and, (3) when the BTC price gradually shifts downwards and users intend to sell.
High Accuracy Interpolation of DEM Using Generative Adversarial Network

Digital elevation model (DEM) interpolation is aimed at predicting the elevation values of unobserved locations, given a series of collected points. Over the years, the traditional interpolation methods have been widely used but can easily lead to accuracy degradation. In recent years, generative adversarial networks (GANs) have been proven to be more efficient than the traditional methods. However, the interpolation accuracy is not guaranteed. In this paper, we propose a GAN-based network named gated and symmetric-dilated U-net GAN (GSUGAN) for improved DEM interpolation, which performs visibly and quantitatively better than the traditional methods and the conditional encoder-decoder GAN (CEDGAN). We also discuss combinations of new techniques in the generator. This shows that the gated convolution and symmetric dilated convolution structure perform slightly better. Furthermore, based on the performance of the different methods, it was concluded that the Convolutional Neural Network (CNN)-based method has an advantage in the quantitative accuracy but the GAN-based method can obtain a better visual quality, especially in complex terrains. In summary, in this paper, we propose a GAN-based network for improved DEM interpolation and we further illustrate the GAN-based method’s performance compared to that of the CNN-based method.
Faking photon number on a transition-edge sensor

We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor. In one experiment, we show that an adversary could fake a photon number result at a certain wavelength by sending a larger number of photons at a longer wavelength, which is an expected and known behaviour. In another experiment, we unexpectedly find that the detector can be blinded by bright continuous-wave light and then, a controlled response simulating single-photon detection can be produced by applying a bright light pulse. We model an intercept-and-resend attack on a quantum key distribution system that exploits the latter vulnerability and, under certain assumptions, able to steal the key.
Digital vs Electronic

The authors attempt to determine which of two adjectives are preferable to be used to describe processes and/or objects of cognition within scientific disciplines related to modern libraries, and whether these two terms are the synonyms to be reasonably equally used. The history of the adjectives digital and electronic as well as of the related terms is described; the definitions are given as they appear in Oxford and Cambridge dictionaries of the English language. The modern-day use of these adjectives, in particular in library and information activities and studies is assessed. The findings of the analysis of the websites of major world library associations are presented, e.g. those of International Federation of Library Associations and Institutions (IFLA) and American Library Association (ALA). The history of use and the current status of the adjectives digital and electronic in library and information practice and scientific studies are analyzed. Within the context of the problems set forward in the article, the state terminological standard GOST 7.0.103-2018 “Library and information services. Terms and definitions” is reviewed. The authors conclude that the two adjectives are synonymous and may be used equally when solving theoretical and/or practical problems in library and information activities and studies; and that the combinations digital/electronic libraries and digital/electronic library science are equally acceptable.
Quantum control with a multi-dimensional Gaussian quantum invariant

The framework of quantum invariants is an elegant generalization of adiabatic quantum control to control fields that do not need to change slowly. Due to the unavailability of invariants for systems with more than one spatial dimension, the benefits of this framework have not yet been exploited in multi-dimensional systems. We construct a multi-dimensional Gaussian quantum invariant that permits the design of time-dependent potentials that let the ground state of an initial potential evolve towards the ground state of a final potential. The scope of this framework is demonstrated with the task of shuttling an ion around a corner which is a paradigmatic control problem in achieving scalability of trapped ion quantum information technology.
Three factor authentication system with modified ECC based secured data transfer: untrusted cloud environment

Abstract Cloud computing (CC) is a technology that delivers its service by means of the internet. In the modern scenario, cloud storage services have gained attention. The cloud environment confronts data breaches expansively in cloud storage, which might bring about the disclosure of personal in addition to corporate data. Thus, the requirement arises for the creation of a more foremost authentication system. Customary authentication schemes depend on techniques, like Password Authentications Protocol (PAP), Challenge Handshakes Authentication Protocols (CHAP), as well as One-Time Pads (OTP), which are often susceptible to malevolent attacks as well as security threats. To shun such issues, this paper proposed a Modified ECC centred secure data transfer and a ‘3’-factor authentication scheme in the untrusted cloud environment. The proposed work comprises ‘3’ steps: authentication, data compression, and safe data transfer. In the authentication phase, the SHA-512 algorithm along with CCP is utilized. After that, the user-uploaded data is compressed utilizing CHA on the server-side. Next, MECC encrypts the compressed data, and then, safely uploaded it to the cloud server (CS). In the investigational appraisal, the proposed work is contrasted with the prevailing methods. The outcomes proved that the proposed work renders better security than the prevailing methods.
Biodiversity Image Quality Metadata Augments Convolutional Neural Network Classification of Fish Species

Biodiversity image repositories are crucial sources for training machine learning approaches to support biological research. Metadata about object (e.g. image) quality is a putatively important prerequisite to selecting samples for these experiments. This paper reports on a study demonstrating the importance of image quality metadata for a species classification experiment involving a corpus of 1935 fish specimen images which were annotated with 22 metadata quality properties. A small subset of high quality images produced an F1 accuracy of 0.41 compared to 0.35 for a taxonomically matched subset low quality images when used by a convolutional neural network approach to species identification. Using the full corpus of images revealed that image quality differed between correctly classified and misclassified images. We found anatomical feature visibility was the most important quality feature for classification accuracy. We suggest biodiversity image repositories consider adopting a minimal set of image quality metadata to support machine learning.
CoVEGI: Cooperative Verification via Externally Generated Invariants

Abstract Software verification has recently made enormous progress due to the development of novel verification methods and the speed-up of supporting technologies like SMT solving. To keep software verification tools up to date with these advances, tool developers keep on integrating newly designed methods into their tools, almost exclusively by re-implementing the method within their own framework. While this allows for a conceptual re-use of methods, it nevertheless requires novel implementations for every new technique. In this paper, we employ cooperative verification in order to avoid re-implementation and enable usage of novel tools as black-box components in verification. Specifically, cooperation is employed for the core ingredient of software verification which is invariant generation . Finding an adequate loop invariant is key to the success of a verification run. Our framework named CoVEGI allows a master verification tool to delegate the task of invariant generation to one or several specialized helper invariant generators. Their results are then utilized within the verification run of the master verifier, allowing in particular for crosschecking the validity of the invariant. We experimentally evaluate our framework on an instance with two masters and three different invariant generators using a number of benchmarks from SV-COMP 2020. The experiments show that the use of CoVEGI can increase the number of correctly verified tasks without increasing the used resources.
Privacy-Preserving &amp; Incrementally-Deployable Support for Certificate Transparency in Tor

Abstract The security of the web improved greatly throughout the last couple of years. A large majority of the web is now served encrypted as part of HTTPS, and web browsers accordingly moved from positive to negative security indicators that warn the user if a connection is insecure. A secure connection requires that the server presents a valid certificate that binds the domain name in question to a public key. A certificate used to be valid if signed by a trusted Certificate Authority (CA), but web browsers like Google Chrome and Apple’s Safari have additionally started to mandate Certificate Transparency (CT) logging to overcome the weakest-link security of the CA ecosystem. Tor and the Firefox-based Tor Browser have yet to enforce CT. In this paper, we present privacy-preserving and incrementally-deployable designs that add support for CT in Tor. Our designs go beyond the currently deployed CT enforcements that are based on blind trust: if a user that uses Tor Browser is man-in-the-middled over HTTPS, we probabilistically detect and disclose cryptographic evidence of CA and/or CT log misbehavior. The first design increment allows Tor to play a vital role in the overall goal of CT: detect mis-issued certificates and hold CAs accountable. We achieve this by randomly cross-logging a subset of certificates into other CT logs. The final increments hold misbehaving CT logs accountable, initially assuming that some logs are benign and then without any such assumption. Given that the current CT deployment lacks strong mechanisms to verify if log operators play by the rules, exposing misbehavior is important for the web in general and not just Tor. The full design turns Tor into a system for maintaining a probabilistically-verified view of the CT log ecosystem available from Tor’s consensus. Each increment leading up to it preserves privacy due to and how we use Tor.
Improve Vision Transformers Training by Suppressing Over-smoothing

Introducing the transformer structure into computer vision tasks holds the promise of yielding a better speed-accuracy trade-off than traditional convolution networks. However, directly training vanilla transformers on vision tasks has been shown to yield unstable and sub-optimal results. As a result, recent works propose to modify transformer structures by incorporating convolutional layers to improve the performance on vision tasks. This work investigates how to stabilize the training of vision transformers \emph{without} special structure modification. We observe that the instability of transformer training on vision tasks can be attributed to the over-smoothing problem, that the self-attention layers tend to map the different patches from the input image into a similar latent representation, hence yielding the loss of information and degeneration of performance, especially when the number of layers is large. We then propose a number of techniques to alleviate this problem, including introducing additional loss functions to encourage diversity, prevent loss of information, and discriminate different patches by additional patch classification loss for Cutmix. We show that our proposed techniques stabilize the training and allow us to train wider and deeper vision transformers, achieving 85.0\% top-1 accuracy on ImageNet validation set without introducing extra teachers or additional convolution layers. Our code will be made publicly available at this https URL .
Personalized Tourism Route Recommendation System Based on Dynamic Clustering of User Groups

Tourism path dynamic planning is an asynchronous group model planning problem. It is required to find group patterns with similar trajectory behavior under the constraint of unequal time intervals. Traditional trajectory group pattern mining algorithms often deal with GPS data with fixed time interval sampling constraints, so they can not be directly used in coterie pattern mining. At the same time, traditional group pattern mining has the problem of lack of semantic information, which reduces the integrity and accuracy of personalized travel route recommendation. Therefore, this paper proposes a semantic based distance sensitive recommendation strategy. In order to efficiently process large-scale social network trajectory data, this paper uses MapReduce programming model with optimized clustering to mine coterie group patterns. The experimental results show that: under MapReduce programming model, coterie group pattern mining with optimized clustering and semantic information is superior to traditional group mode in personalized travel route recommendation quality, and can effectively process large-scale social network trajectory data.
Research and Application of Node.js Core Technology

Web development companies and developers can choose a variety of technology stacks to build Web applications. In the early days of network development, different technologies were used for front-end and back-end development. With the release of node.js, the construction of the website has undergone tremendous changes. Unlike single-threaded PHP and multi-threaded JAVA, a server programming platform based on the Chrome V8 engine JavaScript runtime environment-node.js came into being. Node.js uses its own built and defined attributes to make up for the shortcomings of the background development language in the traditional sense. It is a server-side JavaScript interpreter, which is used to conveniently build web applications with fast response speed and easy expansion. Node.js, with its event-driven, time loop mechanism, and non-blocking I/O model, can realize functions that Core JavaScript does not have or are not perfect, such as file systems, modules, packages, operating system APIs, and network communications. Historically, there has been more than one plan to port JavaScript outside the browser, but Node.js is the best one.
An efficient fully dynamic group signature with message dependent opening from lattice

Abstract Message-dependent opening is one of the solutions to solve the problem of the tracing manager owns excessive power. In this paper, we present a new lattice-based fully dynamic group signature scheme with message-dependent opening by combining an improved version of the fully dynamic group signature scheme proposed by Ling et al and the double encryption paradigm. In addition, we propose an improved underlying zero knowledge protocol, it has a soundness error $\frac {1}{\max (n,p)+1}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>max</mml:mo> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>p</mml:mi> <mml:mo>)</mml:mo> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:mfrac> </mml:math> that is better than the Stern-like protocol, which helps to bring down the communication complexity of the protocol and hence the signature scheme. Our scheme constrains the power of group managers by adding an admitter, and the signature size has a logarithmic relationship with the group size.
Identifying inherent disagreement in natural language inference

Natural language inference (NLI) is the task of determining whether a piece of text is entailed, contradicted by or unrelated to another piece of text.In this paper, we investigate how to tease systematic inferences (i.e., items for which people agree on the NLI label) apart from disagreement items (i.e., items which lead to different annotations), which most prior work has overlooked.To distinguish systematic inferences from disagreement items, we propose Artificial Annotators (AAs) to simulate the uncertainty in the annotation process by capturing the modes in annotations.Results on the CommitmentBank, a corpus of naturally occurring discourses in English, confirm that our approach performs statistically significantly better than all baselines.We further show that AAs learn linguistic patterns and context-dependent reasoning.
Understanding and Mitigating Accuracy Disparity in Regression

With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity on prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it. In this paper, we study the accuracy disparity problem in regression. To begin with, we first propose an error decomposition theorem, which decomposes the accuracy disparity into the distance between label populations and the distance between conditional representations, to help explain why such accuracy disparity appears in practice. Motivated by this error decomposition and the general idea of distribution alignment with statistical distances, we then propose an algorithm to reduce this disparity, and analyze its game-theoretic optima of the proposed objective function. We conduct experiments on four real-world datasets. The experimental results suggest that our proposed algorithms can effectively mitigate accuracy disparity while maintaining the predictive power of the regression models.
What is in the KGQA Benchmark Datasets? Survey on Challenges in Datasets for Question Answering on Knowledge Graphs

Abstract Question Answering based on Knowledge Graphs (KGQA) still faces difficult challenges when transforming natural language (NL) to SPARQL queries. Simple questions only referring to one triple are answerable by most QA systems, but more complex questions requiring complex queries containing subqueries or several functions are still a tough challenge within this field of research. Evaluation results of QA systems therefore also might depend on the benchmark dataset the system has been tested on. For the purpose to give an overview and reveal specific characteristics, we examined currently available KGQA datasets regarding several challenging aspects. This paper presents a detailed look into the datasets and compares them in terms of challenges a KGQA system is facing.
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering

Disfluencies is an under-studied topic in NLP, even though it is ubiquitous in human conversation.This is largely due to the lack of datasets containing disfluencies.In this paper, we present a new challenge question answering dataset, DISFL-QA, a derivative of SQUAD, where humans introduce contextual disfluencies in previously fluent questions.DISFL-QA contains a variety of challenging disfluencies that require a more comprehensive understanding of the text than what was necessary in prior datasets.Experiments show that the performance of existing state-of-the-art question answering models degrades significantly when tested on DISFL-QA in a zero-shot setting.We show data augmentation methods partially recover the loss in performance and also demonstrate the efficacy of using gold data for fine-tuning.We argue that we need large-scale disfluency datasets in order for NLP models to be robust to them.The dataset is publicly available at: https://github.com/google-research-datasets/disfl-qa.
Coordination Between Individual Agents in Multi-Agent Reinforcement Learning

The existing multi-agent reinforcement learning methods (MARL) for determining the coordination between agents focus on either global-level or neighborhood-level coordination between agents. However the problem of coordination between individual agents is remain to be solved. It is crucial for learning an optimal coordinated policy in unknown multi-agent environments to analyze the agent's roles and the correlation between individual agents. To this end, in this paper we propose an agent-level coordination based MARL method. Specifically, it includes two parts in our method. The first is correlation analysis between individual agents based on the Pearson, Spearman, and Kendall correlation coefficients; And the second is an agent-level coordinated training framework where the communication message between weakly correlated agents is dropped out, and a correlation based reward function is built. The proposed method is verified in four mixed cooperative-competitive environments. The experimental results show that the proposed method outperforms the state-of-the-art MARL methods and can measure the correlation between individual agents accurately.