Reviewerly/exHarmony huggingface dataset:
User Experience Design Study of Museum Website Based on TRIZ Theory: A Case of Heyuan Dinosaur Museum

In the Internet age, museums are gradually adopting a combination of online websites and physical museums to serve people. User experience, one of the important criteria to verify the success of a website design, tends to promote the development of the museum itself if applies properly. At present, most museum websites in our country pay insufficient attention to user experience. This paper takes the Heyuan Dinosaur Museum website as an example to redesign the museum website page to improve the user satisfaction of the museum website. This study uses the methods of performance measurement, system usability scale and behavior observation and evaluation to measure user experience, and combines multiple factors to evaluate website user satisfaction. It was found that users of the Heyuan Dinosaur Museum website had low usability score while gaining high satisfaction with the visual and interactive design of the website in the user experience evaluation. Finally, by applying TRIZ theory, this study resulted in a redesigned prototype of the Heyuan Dinosaur Museum website, which received higher user satisfaction and user ratings.
Development of a Hybrid Control Algorithm Based on Neural Networks for Mobile Autonomous Robot Navigation

A control algorithm based on adaptive neural networks is developed to control the navigation of a mobile autonomous ground-based explorer robot. A simulation was performed in the Matlab program to verify the operation of the algorithm, where the set of proximity sensors used by the robot for the detection and evasion of obstacles was simulated.
Implementation of Virtual Learning Objects in the Development of Mathematical Skills: A Qualitative Analysis from the Student Experience

This article reports a research of a qualitative methodology that analyzed narratives of students aged between 10 and 14 years old, who received a learning technological program based on Virtual Learning Objects. As results, it was found that the program applied propitiate significant willingness, happiness, motivation, and innovation towards learning mathematics. There is discussed about the need of the implementation of this methodology in benefit of learning mathematics.
Variational AutoEncoder to Identify Anomalous Data in Robots

For robotic systems involved in challenging environments, it is crucial to be able to identify faults as early as possible. In challenging environments, it is not always possible to explore all of the fault space, thus anomalous data can act as a broader surrogate, where an anomaly may represent a fault or a predecessor to a fault. This paper proposes a method for identifying anomalous data from a robot, whilst using minimal nominal data for training. A Monte Carlo ensemble sampled Variational AutoEncoder was utilised to determine nominal and anomalous data through reconstructing live data. This was tested on simulated anomalies of real data, demonstrating that the technique is capable of reliably identifying an anomaly without any previous knowledge of the system. With the proposed system, we obtained an F1-score of 0.85 through testing.
Network Simulation Software Architecture of Blockchain: A Model-Led Viewpoint

Abstract It was proposed that Blockchain networks would serve some of Contemporary societies’ most important functions. If used in such capacity, blockchain network failures include catastrophes stretching beyond citizens, organizations and nations. As such, the highest levels of analytic and scientific validation for blockchain network protocols must be performed prior to widespread implementation subject to key protection, trust and efficiency. Though, the size of blockchain open-access systems in their imagined scale preclude the likelihood of exact reprocessing and replication in a laboratory setting when conducting analytical evaluations. Instead, it is important to consider abstract operating models – simulators – of proposed technologies. Such simulators must be verified by the scientific community, be highly transparent and reusable to ensure that concept concepts are applied and comparable easily and reliably as instruments for the research industry. We say it will help resolve this need by developing paradigms in information technology, including model-driven creation and product lineages. We define our efforts to build an efficiency and efficient derived domain meta-model and object-focused architecture for the advanced blockchain network modules.
Terrain-aware traverse planning for a Mars sample return rover

NASA and ESA are proposing to retrieve samples from Mars with a rover that will need to drive long distances, beyond what is seen on images from previous downlinks, to complete its mission in less than 200 Martian days. This paper proposes a planning method to support Mars Sample Return by integrating terrain information into the planning phase. The first step consists of predicting rover slip and drive speed using orbital data. When tested with Mars data, slip ranges are predicted and an expected velocity map is successfully drawn. The second step involves planning the traverse for the driving objective of a sol, based on the map of expected slip and drive speed. While driving, the rover monitors the terrain by converting its wheel slip and speed into terrain information, projected forward to confirm or refute predicted behavior, update the map and replan if necessary. Simulation showed successful map updates given new information and replanning when needed. If monitoring is insufficient to confirm traversability, a third step describes an instrument would be deployed to compute intrinsic terrain parameters, information then propagated ahead to update and replan if necessary. Experiments showed results close to the expected parameters of the tested soils.
Approximate Filippov-based Switching for Behavior-based Trajectory Tracking

This paper presents a switching strategy based upon first-order Filippov switching conditions for safely following a trajectory that passes through obstacles. The Filippov switching conditions motivate a sliding mode behavior to bridge the gap between the two control modalities. Thus, a novel wall-following controller is developed that keeps pace with the trajectory while the trajectory temporarily passes through obstacles. The products of this work are a three-mode automaton and vector field behaviors that can be used to simultaneously track the desired trajectory while avoiding obstacles. This paper's findings were used to successfully control a TurtleBot3 Burger robot in a Gazebo simulation.
Blind Estimation and Suppression of Additive Spatially Correlated Gaussian Noise in Images

The paper is devoted to the task of estimation of the parameters of spatially correlated noise and noise suppression in images. Several schemes of noise removal, including multiscale ones, are considered. A convolutional neural network (CNN) for blind estimation of the spectrum of spatially correlated noise images is proposed. It is shown that the proposed network in combination with the BM3D filter provides more efficient noise suppression than existing solutions. A CNN for prediction of the denoising parameters for DRUNet denoiser is also proposed and analyzed. It is shown that the usage of this network and DRUNet for multiscale denoising in comparison with other methods provides better quality of image denoising and processing speed for a wide range of sizes of “noise grain”.
Affective Semantics and Regulatory Modes of the Word “可” in Text-Based Sentiment Analysis

A sentimental sentence usually contains three important components: first, the core expression component; second, the modifier component; and third, the regulatory component. The three components do not exist independently; rather, they interactively form a sentimental sentence. Therefore, in text-based sentiment analysis, while the core expression components are considered the essence of a sentence, all three components need to be considered in a layered structure. The semantics of “可[KE]” in text exhibits a high degree of variation, increasing the difficulty in text-based sentimental analysis. This paper focuses on the screening, classification, and analysis of sentimental sentences involving “可” within the framework of text-based affective analysis. We extracted models and patterns in affective expressions in sentences involving “可”. The purpose of this work is to extract the common usage and patterns of “可” in affective expressions, which, in turn, will be helpful for sentimental analysis.
SaGe-Path: Pay-as-you-go SPARQL Property Path Queries Processing Using Web Preemption

SPARQL property path queries allow to write sophisticated navigational queries on knowledge graphs (KGs). However, the evaluation of these queries on online KGs are often interrupted by fair use policies, returning only partial results. SaGe-Path addresses this issue by relying on the Web preemption and the concept of Partial Transitive Closure (PTC). Under PTC, the graph exploration for SPARQL property path queries is limited to a predefined depth. When the depth limit is reached, frontier nodes are returned to the client. A PTC-client is then able to reuse frontier nodes to continue the exploration of the graph. In this way, SaGe-Path follows a pay-as-you-go approach to evaluate SPARQL property path queries. This demonstration shows how queries that do not complete on the public Wikidata SPARQL endpoint can complete using SaGe-Path. An extended user-interface provides real-time visualization of all SaGe-Path internals, allowing to understand the approach overheads and the effects of different parameters on performance. SaGe-Path demonstrates how complex SPARQL property path queries can be efficiently evaluated online with guaranteed complete results.
Supporting Embodied and Remote Collaboration in Shared Virtual Environments

The COVID-19 pandemic has had a tremendous impact on businesses, educational institutions, and other organizations that require in-person gatherings. Physical gatherings such as conferences, classes, and other social activities have been greatly reduced in favor of virtual meetings on Zoom, Webex or similar video-conferencing platforms. However, video-conferencing is quite limited in its ability to create meeting spaces that capture the authentic feel of a real-world meeting. Without the aid of body language cues, meeting participants have a harder time paying attention and keeping themselves engaged in virtual meetings. Video-conferencing, as it currently stands, falls short of providing a familiar environment that fosters personal connection between meeting participants. This paper explores an alternative approach to virtual meetings through the use of extended reality (XR) and embodied interactions. We present an application that leverages the full-body tracking capabilities of the Azure Kinect and the immersive affordances of XR to create more vibrant and engaging remote meeting environments.
Rational matrix digit systems

Let A be a d×d matrix with rational entries which has no eigenvalue λ∈C of absolute value |λ|<1 and let Zd[A] be the smallest nontrivial A-invariant Z-module. We lay down a theoretical framework for the construction of digit systems (A,D), where D⊂Zd[A] finite, that admit finite expansions of the form x=d0+Ad1+⋯+Aℓ−1dℓ−1(ℓ∈N,d0,…,dℓ−1∈D)for every element x∈Zd[A]. We put special emphasis on the explicit computation of small digit sets D that admit this property for a given matrix A, using techniques from matrix theory, convex geometry, and the Smith Normal Form. Moreover, we provide a new proof of general results on this finiteness property and recover analogous finiteness results for digit systems in number fields a unified way.
<i>q</i>‐frame hash comparison based exact string matching algorithms for DNA sequences

Abstract The importance of string matching is due to its applications in many fields, such as medicine and bioinformatics. Various string matching algorithms are developed to speed up the search. Especially, hash‐based exact string matching algorithms are among the most time‐efficient ones. The efficiency of hash‐based approaches depends on the hash function. Hence, perfect hashing plays an essential role in hash‐based string matching. In this study, two q ‐frame hash comparison‐based exact string matching algorithms, Hq‐QF and HqBM‐QF, are proposed. We have used a collision‐free perfect hash function for DNA sequences in the proposed algorithms. In the first approach, after hash values match for the last q characters, the character comparisons in the Hash‐q algorithm are replaced with q ‐frame hash comparison. In the second approach, we improved the first approach by utilizing the shift size indicated at the th entry in the good suffix shift table. Since the number of character comparisons is minimized, the worst‐case time complexity of the proposed algorithms is . In both approaches, q ‐frame hash comparisons replace most character comparisons as a trade‐off. The results show that the proposed approaches are more efficient than the Hash‐q algorithm in terms of runtime efficiency and the number of character comparisons.
Beyond the Pandemic

This chapter argues that libraries should sustain and increase allocations to cloud computing technologies following the COVID-19 pandemic, rather than falling back into old management styles. The power of the collaborative environment makes it possible to administratively restructure the library to improve efficiency in the virtual domain, where libraries will find themselves operating more and more. Remote work arrangements have altered the relationship between employee and employer, setting new expectations and demanding new ideas about operations. The author discusses how this “new normal” will necessitate changes in how directors govern their staff, especially in an era of reduced budgets.
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Let m be a positive integer, and γ be a non-zero element of F2m. The Lee distance of all cyclic codes and (1+uγ)-constacyclic codes of length 2s over F2m+uF2m is completely determined. In particular, it is shown that the Lee distance of such codes is independent of the choice of a trace orthogonal basis of F2m.
Digital currencies: a problem of trust

The rising cryptocurrencies have revived discussion about the prospects of monetary order and the central bank’s role in it. Functionality is in the core of the competition between the forms of money and the payment landscape could be fractionalized affecting further decline in the efficiency of monetary policy. Central bank digital currency (CBDC) is looked by monetary authorities as a way to respond to technological challenge and fulfill the gap of the market failure related to some imperfections of privately issued digital money. The success of each money form is dependent on the trust as a collective experience. The paper raises the question if the central banks are more credible than private digital money when probable change in the age structure matters for the spread of fintech. Based on empirical analysis, it is found that economic agents differentiate digital money of central banks from those of private issuers. Private cryptocurrencies are considered more reliable when inflationary experience is stronger, while central banks’ independence level and financial stability are not factors of higher trust to CBDC. Also, a country’s institutional features do not indicate that successful central banks can use the “the umbrella” of trust to their own cryptocurrencies while the factors of technological advance fail to show a clear significance. Social capital better contributes to the trust to private digital money. At the same time, the age structure is the strong factor due to which digital currencies are more reliable in younger societies. It is concluded that in the case then trust in cryptocurrencies is not grounded on institutional factors that historically contributed to stability of the monetary order, preconditions for the latter’s higher vulnerability are likely to rise. With the growing role of age structure as a factor of higher trust to digital money, the quality of social interactions will become a very important institutional precondition for the stability of monetary order.
Virtual Reality: A Study of Recent Research Trends in Worldwide Aspects and Application Solutions in the High Schools

The development of science and technology has set many problems for us. In particular, in the field of education, how to use and manage technology in the Schools is very reasonable and scientific to contribute to improving the quality of teaching and learning. In this paper, after highlighting some concepts of Virtual Reality (VR), VR Technology, VR in Vietnam High Schools, we offer solutions to application VR in the Vietnamese High Schools.
Machine Learning Based Anxiety Prediction of General Public from Tweets During COVID-19

In this crisis of COVID19, everyone is staying in touch with the world through social media. This has led to social media becoming a significant source of new information for many people and unfortunately this phenomenon has given birth to a lot of misinformation, chaos and fear in people's minds. This fear is often due to the inadequate and wrong information. Therefore, there is a important need to understand this crisis. Patterns need to be established between popular tweets and its effect on the public's sentiments, especially their fear. So, tweets of three different countries namely United States of America, Federative Republic of Brazil and Republic of India. Sentiment analysis reveals that fear of this unknown and mysterious nature of the coronavirus is dominant among the public. Predominant analysis of tweets within past two months will be done and then a model will be built to predict future reaction of the general public based on the crisis level in the country. Machine Learning algorithms such as 'Logistic Regression (LR)', 'Multinomial Naïve Bayes' and 'Support Vector Machine (SVM)'are used for classification purpose preceded by the pre-processing steps of raw data from each country. 90% of accuracy has been achieved from sentiment classification result. Insights to the fear, sentiments have also been provided. Tweets with negative sentiment and emotion indicates the cases for the pandemic outbreak.
A novel texture descriptor: Homogeneous Rotated Local Binary Pattern (HRLBP)

Invariant rotation in the application of texture classification is generally beneficial due to the material: camera or auto rotation, which can affect objects captured by arbitrary angles. This letter, introduce a new, efficient rotation invariant descriptor for texture analysis appointed Homogeneous Rotated Local Binary Pattern (HRLBP). The goal of this novel method is to take more account of the intrinsic characteristics of the images in rotation changing by using the incidence of homogeneity tolerance h provide from General Adaptive Neighborhood (GAN). A significant features are generated from HRLBP by thresholding the center and each neighbor pixels with an homogeneity tolerance value which help to get more efficient and discriminating features for rotation variation further multi-scale changing owing to use a variation of the parameter of homogeneity tolerance h and radius R. The experiments are evaluated using two publicly available texture database OTC10, furthermore, HRLBP shown a high performance in classification accuracy for problem of rotation variation.
Towards a Practical Information Security Maturity Evaluation Method focused on People, Process and Technology

Organizations are faced with an increased number of security-related challenges and need to safeguard their information and assets. The challenge is even greater for Industry 4.0, as it adopts advanced technologies for automation and data exchange. In this context, the information security controls assessment emerges as a fast, comprehensive way to deal with cybersecurity analysis, helping organizations measure the effectiveness and adequacy of their security controls. This paper presents an approach to help organizations, including Industry 4.0, improve their security maturity in a fast-paced way based on well-recognized frameworks.
Research on the Blended Teaching of College Business English Based on Unipus Platform

Unipus platform is a commonly used online teaching platform in english teaching. It has rich english teaching functions and can be applied to college business english teaching. However, it is not advisable to rely solely on online teaching platform for teaching, because there are some defects in online teaching platform itself, so it will cause some negative effects on teaching when it is applied alone. At this time, classroom teaching should be used to make up for the defects of online teaching platform, the combination of the two can form a complementary relationship and effectively guarantee the quality of teaching. In this case, the hybrid teaching mode of college business english should be developed. Based on the unipus platform, this paper discusses the basic concept of the teaching mode, the advantages and disadvantages of online/offline teaching, and the application methods of hybrid teaching.
Row, Row, Row Your Boat: How to Not Find Weak Keys in Pilsung

Abstract The Pilsung cipher is part of the North Korean Red Star operating system, which was leaked to the West in 2014. Initial analysis by Kryptos Logic reported a possibility of a class of weak keys due to the use of pseudo-random diffusion. Following this lead, we analyzed the cipher and identified a small class of such weak keys. We developed techniques for searching for a key that belongs to the class. After spending thousands of CPU hours, we found a supposedly weak key for a slightly weaker version of Pilsung, but the key did not behave as we expected. On further investigation we found out a crucial misunderstanding in a critical part of the cipher and that no such class of weak keys exists in Pilsung. Thus, this paper makes two main contributions to the art of cryptanalysis. First, it identifies and shows how to investigate a potential weakness in randomizing diffusion, which although does not exist in Pilsung, may affect future designs. Second, it highlights the need for early verification of results in order to identify errors before expending significant resources.
Decision trees based on 1-consequences

In this paper, we study arbitrary infinite binary information systems each of which consists of an infinite set of elements and an infinite set of two-valued non-constant functions (attributes) defined on the set of elements. We consider the notion of a problem over information system, which is described by a finite number of attributes: for a given element, we should determine values of these attributes. As algorithms for problem solving, we study decision trees that use arbitrary attributes from the considered infinite set of attributes and solve the problem based on 1-consequences. In such a tree, we take into account consequences each of which follows from one equation of the kind “attribute = value” obtained during the decision tree work and ignore consequences that can be derived only from at least two equations. As time complexity, we study the depth of decision trees. We prove that in the worst case, with the growth of the number of attributes in the problem description, the minimum depth of decision trees based on 1-consequences grows either as a logarithm or linearly.