Reviewerly/exHarmony huggingface dataset:
Digital tools for optimizing the educational process of a modern university under quarantine restrictions

Aim:The study is topical because of the provision of distance learning based on the experience of Kyiv National University of Culture and Arts.Method: Systemic, socio-communicative, competence approaches, sociological methods (questionnaires and interviews) were chosen as methodological tools of the research.Participants: The sample consisted of 120 students and 30 teachers of the Department of Information, Library and Archival Affairs who were selected during the draw.Data collection: The survey was conducted using Google Forms and mathematical package of statistical data processing Statistica 6.1.Data analysis and results: The results of a survey of teachers and entrants to higher education institutions on the topic "Using social networks and digital platforms for online classes under the conditions of quarantine restrictions" allowed to scientifically substantiate the need for deeper knowledge of such tools as Google Meet (79%), Zoom (13.78%) and Google Classroom (11.62%), which are preferred by entrants.Almost a third of entrants (34.26%) noted the lack of scientific and methodological support for learning the subjects.The study showed high efficiency of messengers in distance education.The study found that in the process of organizing communication in the student-teacher system, it is necessary to consider the priority of Telegram on the basis of which it is necessary to implement a chatbot for convenient and effective exchange of information about the educational process.Further research should focus on the effectiveness of the use of Telegram.The effectiveness of using chatbots should also be considered.Chatbots can be used to automate routine components of the learning process.
Automated Model Hardening with Reinforcement Learning for On-Orbit Object Detectors with Convolutional Neural Networks

On-orbit object detection has received extensive attention in the field of artificial intelligence (AI) in space research. Deep-learning-based object-detection algorithms are often computationally intensive and rely on high-performance devices to run. However, those devices usually lack space-qualified versions, and they can hardly meet the reliability requirement if directly deployed on a satellite platform, due to software errors induced by the space environment. In this paper, we evaluated the impact of space-environment-induced software errors on object-detection algorithms through large-scale fault injection tests. Aside from silent data corruption (SDC), we propose an extended criterial SDC-0.1 to better quantify the effect of the transient faults on the object-detection algorithms. Considering that a bit-flip error could cause severe detection result corruption in many cases, we propose a novel automated model hardening with reinforcement learning (AMHR) framework to solve this problem. AMHR searches for error-sensitive kernels in a convolutional neural network (CNN) through trial and error with a deep deterministic policy gradient (DDPG) agent and has fine-grained modular-level redundancy to increase the fault tolerance of the CNN-based object detectors. Compared to other selective hardening methods, AMHR achieved the lowest SDC-0.1 rates for various detectors and could tremendously improve the mean average precision (mAP) of the SSD detector by 28.8 in the presence of multiple errors.
Improved Bounds for Sampling Solutions of Random CNF Formulas

Let Φ be a random k-CNF formula on n variables and m clauses, where each clause is a disjunction of k literals chosen independently and uniformly. Our goal is, for most Φ, to (approximately) uniformly sample from its solution space.Let α = m/n be the density. The previous best algorithm runs in time npoly(k,α) for any α ≲ 2k/300 [Galanis, Goldberg, Guo, and Yang, SIAM J. Comput.'21]. In contrast, our algorithm runs in almost-linear time for any α ≲ 2k/3.
Testing and Learning Quantum Juntas Nearly Optimally

We consider the problem of testing and learning quantum k-juntas: n-qubit unitary matrices which act non-trivially on just k of the n qubits and as the identity on the rest. As our main algorithmic results, we give1. A -query quantum algorithm that can distinguish quantum k-juntas from unitary matrices that are “far” from every quantum k-junta; and2. A O(4k)-query algorithm to learn quantum k-juntas.We complement our upper bounds for testing and learning quantum k-juntas with near-matching lower bounds of and Ω(4k/k), respectively. Our techniques are Fourier-analytic and make use of a notion of influence of qubits on unitaries.* The full version of the paper can be accessed at https://arxiv.org/abs/2207.05898
Comparative Analysis of LR Parsers in Designing of Compilers

Compiler development has been one of the leading computer research subjects. This field of study aims to understand the link between computer systems and computer languages. Compilers are used to translate code from a high-level programming language into machine code. This process consists of essentially six phases. Analysis of syntax is the second step of compilation. As input to a syntax analyzer, lexical analyzer-generated tokens are used. Syntax analyzers do parsing. Parsing is used to derive a string from a given grammar via the process of derivation. There are essentially two parsing algorithms, called top-down and bottom-up parsing. The four forms of bottom-up parsing LR (Left-To-Right) parsing strategies include Simple LR (SLR), Canonical LR (CLR), Look Ahead LR (LALR), and LR (1). In addition, LR Parsers are mostly used to parse a variety of context-free grammar. This approach is referred to as LR (k) parsing, where L stands for left-to-right input scanning, R stands for creating a reverse right-most derivation, and k is the number of look-ahead input symbols utilized to make parsing decisions. A bottom-up parser helps to reduce the number of top-level grammatical products by working from the leaves up. LR parsers are the most effective deterministic parsers in reality. This research aims to describe the compiler, its purpose, and LR parser algorithms, with the comparison to determine which LR parsing algorithms are efficient among all other LR parsers.
Being (A)part of NIME: Embracing Latin American Perspectives

Latin American (LATAM) contributions to Music Technology date back to the early 1940âs. However, as evidenced in historical analyses of NIME, the input from LATAM institutions to its proceedings is considerably low, even when the conference was recently held in Porto Alegre, Brazil. Reflecting on this visible disparity and joining efforts as a group of LATAM researchers, we conducted a workshop and distributed a survey with members of the LATAM community with the aim of sounding out their perspectives on NIME-related practices and the prospect of establishing a LATAM NIME Network. Based on our findings we provide a contemporary contextual overview of the activities happening in LATAM and the particular challenges that practitioners face emerging from their socio-political reality. We also offer LATAM perspectives on critical epistemological issues that affect the NIME community as a whole, contributing to a pluriversal view on these matters, and to the embracement of multiple realities and ways of doing things.
An Image Feature Extraction to Generate a Key for Encryption in Cyber Security Medical Environments

Cyber security is a term utilized for describing a collection of technologies, procedures, and practices that try protecting an online environment of a user or an organization. For medical images among most important and delicate data kinds in computer systems, the medical reasons require that all patient data, including images, be encrypted before being transferred over computer networks by healthcare companies. This paper presents a new direction of the encryption method research by encrypting the image based on the domain of the feature extracted to generate a key for the encryption process. The encryption process is started by applying edges detection. After dividing the bits of the edge image into (3×3) windows, the diffusions on bits are applied to create a key used for encrypting the edge image. Four randomness tests are passed through NIST randomness tests to ensure whether the generated key is accepted as true. This process is reversible in the state of decryption to retrieve the original image. The encryption image that will be gained can be used in any cyber security field such as healthcare organization. The comparative experiments prove that the proposed algorithm improves the encryption efficiency has a good security performance, and the encryption algorithm has a higher information entropy 7.42 as well as a lower correlation coefficient 0.653.
UX Evaluation Using Firstclick, Performance Measurement, RTA, And Questionnaire On E-Commerce Website

E-commerce has made transactions easier for several persons. SKI (Semadi Kwazay Indoland), a company in the real estate market, is one of the numerous organizations that develop e-commerce websites. The company wants the e-commerce website to attract a wider audience. Therefore, an evaluation is necessary to measure the usefulness of the website. Twenty responses were divided into two groups of ten: the advanced group and the novice group. The selected assessment approaches are performance measurement to evaluate the system's efficiency and error rate, first-click usability testing to evaluate the system's effectiveness, and RTA and the SUS questionnaire to evaluate user happiness. The following are the study's findings: This e-commerce website is quite efficient, as there is no significant difference in the time necessary to complete the 10 activities between the two groups of respondents. The mean participant questionnaire score for the e-commerce website was 49.75, which was much lower than the average SUS score of 68. The e-commerce website page is highly effective, as the performance measurement test results suggest a comparatively low error rate across both novice and advanced respondent groups.
Implementation of Bot Telegram as Broadcasting Media Classification Results of Convolutional Neural Network (CNN) Images of Rice Plant Leaves

Rice plants play an important role in the life of the Indonesian people because rice is the raw material for rice as a staple food. The rice production process does not rule out the possibility of interference by pests and diseases resulting in losses that cause crop failure. Meanwhile, pests on rice plants can be caused by various types, namely types of fungi (leafblast, hispa, brownspot) and types of nuisance animals. In this research, it will be carried out how to classify the image of rice plant leaves using the deep learning Convolutional Neural Network (CNN) algorithm, then the results of the classification are sent to users by utilizing the telegram chat application. The rice plant leaf image dataset is grouped into 4 groups (leafblast, brownspot, hispa and healthy). From several experiments it can be seen the results of system performance, namely the classification speed takes 30-60 seconds.
Digital handwriting characteristics for dysgraphia detection using artificial neural network

Despite all of the technical advancements in writing and text editing with keyboards on numerous devices, writing with a pen remains a fundamental ability in modern human existence. Handwriting disabilities are referred to as dysgraphia. Nonetheless, how well they are taught to write in school, 10-30% of children never attain a respectable level of handwriting. Early identification is critical because it can help children avoid difficulties in their behavioral and academic development. On blank papers attached to digital tablets, 280 individuals were asked to complete the concise evaluation scale for children’s handwriting (BHK), with 218 having typical handwriting and 62 having dysgraphia. In addition to their age and BHK quality and speed scores, 12 variables identifying digital handwriting across several domains (static, kinematic, pressure, and tilt) were collected. In this paper, we provided a rapid and automated dysgraphia classification approach using an artificial neural network (ANN) model. Using digital handwriting traits as an input to the ANN approach, the prediction findings were encouraging and very accurate, reaching 96% accuracy, and they could lead to the development of a new self-administered dysgraphia screening tool.
A Comprehensive Survey of IoT- Based Cloud Computing Cyber Security

The flexible architecture offered by cloud computing allows for the dispersion of resources and data over numerous places, making it possible to access them from a variety of industrial settings. The use, storage, and sharing of resources such as data, services, and industrial applications have all changed as a result of cloud computing. In the past ten years, companies have quickly shifted to cloud computing in order to benefit from increased performance, lower costs, and more extensive access. Additionally, the internet of things (IoT) has significantly improved when cloud computing was incorporated. However, this quick shift to the cloud brought up a number of security concerns and challenges. Traditional security measures don't immediately apply to cloud-based systems and are occasionally inadequate. Despite the widespread use and proliferation of various cyber weapons, cloud platform issues and security concerns have been addressed over the last three years. Deep learning's (DL) quick development in the field of artificial intelligence (AI) has produced a number of advantages that can be used to cloud-based industrial security concerns. The following are some of the research's findings: We provide a detailed evaluation of the structure, services, configurations, and security fashions that enable cloud-primarily based IoT. We additionally classify cloud protection dangers in IoT into four foremost areas (records, network and carrier, programs, and gadgets). We discuss the technological issues raised in the literature before identifying key research gaps. In each class, describe the boundaries using a popular, artificial intelligence, and in-depth studying attitude. and security concerns relating to individuals), which are fully covered; we find and analyze the most recent cloud-primarily based IoT attack innovations; we identify, talk, and verify key safety challenges show the regulations from a standard, synthetic intelligence, and deep learning perspective in every class angle; we first present the technological difficulties identified in the literature before identifying IoT-based cloud infrastructure has significant research gaps which should be highlighted for future research orientations. Cloud computing and cyber security.
Effect of spatial correlations on Hopfield Neural Network and Dense Associative Memories

Hopfield model is one of the few neural networks for which analytical results can be obtained. However, most of them are derived under the assumption of random uncorrelated patterns, while in real life applications data to be stored show non-trivial correlations. In the present paper we study how the retrieval capability of the Hopfield network at null temperature is affected by spatial correlations in the data we feed to it. In particular, we use as patterns to be stored the configurations of a linear Ising model at inverse temperature $\beta$. Exploiting the signal to noise technique we obtain a phase diagram in the load of the Hopfield network and the Ising temperature where a fuzzy phase and a retrieval region can be observed. Remarkably, as the spatial correlation inside patterns is increased, the critical load of the Hopfield network diminishes, a result also confirmed by numerical simulations. The analysis is then generalized to Dense Associative Memories with arbitrary odd-body interactions, for which we obtain analogous results.
Synthetic hyperspectral array video database with applications to cross-spectral reconstruction and hyperspectral video coding

In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in PSNR of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene. The novel hyperspectral video database and source code is available at https:// github.com/ FAU-LMS/ HyViD for use by the research community.
A New Subjective Well-Being Index Using Anchored Best-Worst Scaling

Subjective well-being (SWB) describes an individual’s life evaluation. Direct elicitation methods for SWB via rating scales do not force individuals to trade-off among life domains. This research offers a new method to measure SWB using Anchored Best- Worst Scaling (ABWS). Respondents nominate their most and least satisfactory areas with respect to 11 SWB domains and areas of most and least importance. We extend BWS to elicit anchoring points to provide an absolute measure of domain satisfaction. When ABWS measures of domain satisfaction and importance are combined, a robust measure of individual SWB is produced. Results from surveying 2500 Australians reveals ABWS improves discrimination amongst domains in terms of importance and satisfaction, and identifies differences in SWB, such as by location and age. The method presents a diagnostic tool to focus services, policy, and initiatives in areas to most impact wellbeing (i.e., low satisfaction; high importance).
NomNaOCR: The First Dataset for Optical Character Recognition on Han-Nom Script

In this article, we introduce the NomNaOCR dataset for the old Hán-Nôm script based on 3 tremendous and valuable historical works of Vietnam, including , and With 2953 handwritten Pages collected from the Vietnamese Nôm Preservation Foundation for analyzing and semi-annotating the bounding boxes to generate additional 38,318 Patches containing text along with strings in digital form. This makes NomNaOCR currently become the biggest dataset for script in Vietnam, serving 2 main problems in Optical Character Recognition: Text Detection and Text Recognition. A difference here is that our implementations were all done at the sequence level, which not only saves the annotation cost but also helps us retain the context in the sequence instead of just performing on each individual character as in most previous works. For basic results, we experimented on the validation set of NomNaOCR. By using DBNet model for Text Detection, we reached a F1-score up to 99.65%. With Text Recognition, we used CRNN model and achieved an accuracy of 29.41% at sequence level and 84.73% at character level.
Optimization Trading Strategy Model for Gold and Bitcoin Based on Market Fluctuation

As a new type of digital currency, Bitcoin is considered as “future gold” by various scholars. Therefore, this study considers Bitcoin and gold as a group of hedging assets to conduct investment research and it also discusses the investment rules between Bitcoin and gold: prediction of the rise and fall of Bitcoin, comparison of the characteristics of Bitcoin and gold, and the impact of the transaction procedures of Bitcoin and gold on the final trading results, and formulates trading strategies through optimization algorithms. Then, four machine learning algorithms, i.e., LSTM, BP neural network, Adaboost, and Bagging, are introduced to predict the rise and fall of gold and Bitcoin the next day, and then, the entropy weight method is used to synthesize four predicted results to ensure the robustness of the predicted results. To establish the optimal trading strategy, this study considers the maximum expected return as the goal to develop a single-objective optimization model and historical five-day price volatility as a risk factor. In this study, ant colony, simulated annealing, and genetic algorithms are used to solve the single-objective optimization model. Finally, we conclude that Bitcoin, similar to other financial assets, e.g., gold, is sensitive to shocks and volatile and possesses a relatively quiet cycle. When Bitcoin has an asymmetric impact, Bitcoin and gold can equally treat transactions.
Digital data sets management in university libraries: challenges and opportunities

Purpose The study aims to identify the prospects and challenges associated with current practices regarding digital data sets management in university libraries in Pakistan. Design/methodology/approach A cross-sectional survey approach was used to collect the data from library and information science (LIS) professionals working in public sector university libraries in Pakistan. A four-part questionnaire was used to collect the data from the respondents. The collected data from 371 participants were analyzed using a statistical package for social sciences (SPSS-24 version) and analysis of moment structure (AMOS-24). Findings LIS professionals are better placed to support digital data management practices, such as finding, collecting, assessing and analyzing digital data sets and making digital data publicly discoverable and accessible via open access. In spite of this, a lack of leadership support, interest and cooperation among university departments and the absence of a data management plan, policies and procedures were reported as significant challenges. Practical implications To meet the needs of data users, LIS professionals must become knowledgeable about managing and reusing digital data sets. Due to the demands of the information society, university librarians need to learn about data-centric practices that can enhance research outputs and provide new insights. Originality/value This research paper is extracted from a PhD dissertation to present a contemporary picture of library data management services and the challenges LIS professionals face to provide possible solutions.
Experimental detection of quantum steerability based on the critical radius in an all-optical system

Quantum steering is the ability that one system affects another one without delay by performing local measurements. During the past few years, the research on quantum steering has been verified in a number of experiments by the choices of measurements. But this research cannot detect all steerable states. Recently, the definition of critical radius was proposed by Nguyen et al. [Phys. Rev. Lett. 122, 240401 (2019)]. The critical radius can detect all steerable states for two-qubit states. Therefore the critical radius can be regarded as the best method for detecting the steerability of two-qubit states. However, relevant experimental research is still lacking. In this work, for comparison, we experimentally investigate three steering criteria based on the critical radius, the geometric Bell-like inequality, and the three-setting measurements steering inequality and find that the critical radius can detect the most steerable states. Furthermore, we demonstrate that the critical radius satisfies the scaling property. The results show that we only need to calculate the critical radii of some quantum states to obtain the critical radii of more scaling quantum states. Thus this property can provide the convenience for detecting the steerability of many quantum states.