Reviewerly/exHarmony huggingface dataset:
Web Audio Modules 2.0: An Open Web Audio Plugin Standard

A group of academic researchers and developers from the computer music industry have joined forces for over a year to propose a new version of Web Audio Modules, an open source framework facilitating the development of high-performance Web Audio plugins (instruments, realtime audio effects and MIDI processors). While JavaScript and Web standards are becoming increasingly flexible and powerful, C, C++, and domain-specific languages such as FAUST or Csound remain the prevailing languages used by professional developers of native plugins. Fortunately, it is now possible to compile them in WebAssembly, which means they can be integrated with the Web platform. Our work aims to create a continuum between native and browser based audio app development and to appeal to programmers from both worlds. This paper presents our proposal including guidelines and implementations for an open Web Audio plugin standard - essentially the infrastructure to support high level audio plugins for the browser.
Multiattribute Group Decision-Making Method Based on Quaternary Connection Number of Cloud Models

Based on the advantages of cloud theory and the connection number approach in handling uncertain information, this paper established a new quaternary connection number using the domain of interval corresponding to the centroid of a cloud model’s expectation curve. Consequently, based on the cloud model theory of transformation between qualitative and quantitative uncertainties, this study suggests a model describing uncertain information more precisely by combining the advantages of quaternary connection numbers with three digital aspects of the cloud model. We defined the weighted nearness degree of the new connection number and gave a solution for finding the weight of the weighted nearness degree given that a three-parameter interval number can more precisely represent the expert’s true intention than a classical interval number. The method of multiattribute group decision-making based on a cloud model’s quaternary connection number was developed using a novel methodology to find the evaluation-index weight. According to a comparative analysis, the existing membership cloud gravity center (MCGC) method is nothing more than an exception to our proposed decision-making technique. It was also demonstrated that the proposal may present a more complete picture of experts’ overall evaluations and communicate their preferences by adjusting the quaternion’s uncertain parameters, making the group decision-making approach more widely applicable to a certain extent. The method was used to test a vessel’s counterflooding capabilities to ensure its practicality and supremacy.
Implementing a Hybrid Deep Learning Approach to Achieve Classic Handwritten Alphanumeric MODI Recognition

MODI, synonymous with the Devanagari script, is an ancient script from the 17th century used by the Maratha empire as a symbol of culture and power to propagate Marathi. Due to a decline in its usage, absence of quality script database and an unavailability of good literature, identification and translation of MODI script is demanding. The present work deals with a novel study on the recognition of MODI characters and numerals by using Convolutional Neural Network (CNN) architecture. By using a traditional machine learning classifier, classification is performed, and then through a comparative analysis of Random Forest and XGBoost, the study achieves recognition accuracy of 92% for characters and 93.3% for numerals.
Visualizing Large Collections of URLs Using the Hilbert Curve

Search engines like Google provide an aggregation mechanism for the web and constitute the main access point to the Internet for a large part of the population. For this reason, biases and personalization schemes of search results may have huge societal implications that require scientific inquiry and monitoring. This work is dedicated to visualizing data such inquiry produces as well as understanding changes and development over time in such data. We argue that the aforementioned data structure is very akin to text corpora, but possesses some distinct characteristics that requires novel visualization methods. The key differences between URLs and other textual data are their lack of internal cohesion, their relatively short lengths, and—most importantly—their semi-structured nature that is attributable to their standardized constituents (protocol, top-level domain, country domain, etc.). We present a technique to spatially represent such data while retaining comparability over time: A corpus of URLs in alphabetical order is evenly distributed onto the so-called Hilbert curve, a space-filling curve which can be used to map one-dimensional spaces into higher dimensions. Rank and other associated meta-data can then be mapped to other visualization primitives. We demonstrate the viability of this technique by applying it to a data set of Google search result lists. The data retains much of its spatial structure (i.e., the closeness between similar URLs) and the spatial stability of the Hilbert curve enables comparisons over time. To make our technique accessible, we provide an R-package compatible with the ggplot2-package.
Internet of Things Based Automatic Visitor Counter

In modern times, there are many recreational areas including, Gunung Lampu Tapaktuan Tourism. This destination is very crowded with local and foreign tourists. This study aims to determine the number of visitors, optimize the place, evaluate the area's attractiveness, and know how the tool works using the experimental method. Furthermore, the Internet of Things (IoT) connects physical objects with electronics, software, sensors, and networks that make these objects collect and transmit data. From the results of the trials that have been carried out, every passing visitor is read by an ultrasonic sensor and then sent to the Thingspeak server using the internet network and the registered apikey. The ultrasonic sensor sends data that has been read to the thing peak channel that is connected to the device so that the results of the monitoring are the number of visitors in the form of graphs and tables. The results of testing the tool for seven days contained a total of 1,596 manual data, 1,582 people were detected, and 14 people were not detected. While visitors came out there were 1,559 people caught, and 37 people were not seen. From the seven days of testing, 99.12% of incoming visitors were detected and 97.68% of outgoing visitors were detected. So that it can be concluded, that this tool has been functioning correctly.
Undergraduate Students’ Trust in Using Social Networking Sites

– Recently, social networking sites (SNSs) experienced increased popularity, becoming one of the most significant tools in the present high-tech world. Users can instantly interact with each other by using social media platforms, forming professional connections, friendships, and business opportunities. Nonetheless, users impact trust when using SNSs, which is the most essential component in human connection. The study aims to investigate the impact of users’ trust in using SNSs. Accordingly, 110 valid survey questionnaires were distributed through Google Form. The descriptive statistics were applied for data analysis. The results revealed that perceived privacy, sharing knowledge, and perceived ease of use significantly and positively affected users' trust in SNSs. The study develops the knowledge in improving trust in SNSs by extending the factors based on users’ trust in SNSs
UML Class Model Generation of Images Using Neural Networks

This paper presents the way machine learning and deep learning techniques are used to classify boats and extract their attributes in a UML model. This is based on the development and the implementation of a classification and feature extraction application for boat types. The case study aims to explore the bridge between artificial intelligence algorithms for computer vision and the domain of object-oriented modeling. The paper describes methods applied to process the dataset and their labels to train a neural network model. The resulting model can classify boat types and provide their attributes, based on what can be identified in the image and output the textual result in a UML class diagram format.
Automated Generation of Summary in Six Different Ways from Any Paragraph and Automated Generation of Fill in the Blanks from Any Paragraph

Abstract: Many students finds difficult to read the entire contents during the examination and hence fail to write in the exams too. However, it is required to build the system that can overcome their problems. This paper talks about how to do summary of text based on the Text Summarization algorithm and various different models. For example, the alphabet is the simplest structure, in that it is a collection of letters that can form strings called words. A formal language is one that has regular, context-free, and formal grammar. In addition to the development of computer sciences as a whole, artificial intelligence’s advancements also played a role in our continuing understanding of NLP.
Multilevel Pyramid Network for Monocular Depth Estimation Based on Feature Refinement and Adaptive Fusion

As a traditional computer vision task, monocular depth estimation plays an essential role in novel view 3D reconstruction and augmented reality. Convolutional neural network (CNN)-based models have achieved good performance for this task. However, in the depth map recovered by some existing deep learning-based methods, local details are still lost. To generate convincing depth maps with rich local details, this study proposes an efficient multilevel pyramid network for monocular depth estimation based on feature refinement and adaptive fusion. Specifically, a multilevel spatial feature generation scheme is developed to extract rich features from the spatial branch. Then, a feature refinement module that combines and enhances these multilevel contextual and spatial information is designed to derive detailed information. In addition, we design an adaptive fusion block for improving the capability of fully connected features. The performance evaluation results on public RGBD datasets indicate that the proposed approach can recover reasonable depth outputs with better details and outperform several depth recovery algorithms from a qualitative and quantitative perspective.
Nanoparticle Tracking Analysis of Gold Nanoparticles in Aqueous Media through an Inter-Laboratory Comparison

In the field of nanotechnology, analytical characterization plays a vital role in understanding the behavior and toxicity of nanomaterials (NMs). Characterization needs to be thorough and the technique chosen should be well-suited to the property to be determined, the material being analyzed and the medium in which it is present. Furthermore, the instrument operation and methodology need to be well-developed and clearly understood by the user to avoid data collection errors. Any discrepancies in the applied method or procedure can lead to differences and poor reproducibility of obtained data. This paper aims to clarify the method to measure the hydrodynamic diameter of gold nanoparticles by means of Nanoparticle Tracking Analysis (NTA). This study was carried out as an inter-laboratory comparison (ILC) amongst seven different laboratories to validate the standard operating procedure’s performance and reproducibility. The results obtained from this ILC study reveal the importance and benefits of detailed standard operating procedures (SOPs), best practice updates, user knowledge, and measurement automation.
Challenges for the education system in the era of the Fourth Industrial Revolution

The socio-economic changes taking place, which are the result of technological progress, but also the aging process of the population, cause the continuous evolution of education system functions and determine the need for it adapt to changing realities. The research presented within the chapter realizes a methodological goal of developing a methodology for and assessment of the level of digital, social and cognitive competences in a group of people aged 18–30. The practical goal is a determination, on the basis of research results, of the gaps in competences of young adults. The research results indicate that the socio-economic changes taking place in the era of REV4.0 require a redefinition of education policy, and the challenges and potential benefits of developing key competences are visible in the economic and social areas. From the perspective of the labor market, there are gaps in the skills of employees regarding the use of modern technologies. From the social point of view, the challenge is the inclusivity of the education system, which is possible only by basing education on a certain form of triangle of harmonious development of all key competences that are complementary to each other and condition comprehensive human development.
Measuring n-photon Indistinguishability

Abstract: We propose a scalable measurement of the indistinguishability of a n-photon state harnessing a multi-port interferometer. We demonstrate this method using four photons from a quantum dot single-photon source with an integrated cyclic interferometer.
Temporal Convolutional Network with Wavelet Transform for Fall Detection

Fall detection is a challenging task for human activity recognition but is meaningful in health monitoring. However, for sensor-based fall prediction problems, using recurrent architectures such as recurrent neural network models to extract temporal features sometimes could not accurately capture global information. Therefore, an improved WTCN model is proposed in this research, in which the temporal convolutional network is combined with the wavelet transform. Firstly, we use the wavelet transform to process the one-dimensional time-domain signal into a two-dimensional time-frequency domain signal. This method helps us to process the raw signal data efficiently. Secondly, we design a temporal convolutional network model with ultralong memory referring to relevant convolutional architectures. It avoids the gradient disappearance and explosion problem usefully. In addition, this paper also conducts experiments comparing our WTCN model with typical recurrent architectures such as the long short-term memory network in conjunction with three datasets, UniMiB SHAR, SisFall, and UMAFall. The results show that WTCN outperforms other traditional methods, the accuracy of the proposed algorithm is up to 99.53%, and human fall behavior can be effectively recognized in real time.
METHODOLOGICAL FOUNDATIONS ON THE FORMATION OF RESEARCH COMPETENCE OF FUTURE TEACHERS OF PROFESSIONAL TRAINING

The educational process in higher education is a very complex multicomponent structure. In this study, we will consider only one aspect - the research function of future teachers of historians. It should be noted that the educational process is based on methodological foundations and principles, then the methods and rules of teaching are subject to a certain system. If we consider the formation of the research competence of future teachers as a scientific and pedagogical problem, then first of all, it is necessary to determine the meaning of the word "science". "Science is the search for a system of continuous development of nature and society, as well as objective patterns of thinking as a result of special human activity. Therefore, the basis of science is search, that is, accumulated knowledge," D. Poshaev said. Laws, Basic concepts, and principle (theory), says that there should be an ideal (Poshaev, 2011). The basic, initial state of any theory or doctrine is interpreted as a principle. Researchers in the field of education are actively involved in the educational process. For example, such a study can be called a scientific and pedagogical study. ... Pring's research in education, which is also based on the social sciences, is closely related to education, which distinguishes research focused on knowledge gained through the sciences. That is, there are studies that can be called educational or scientific-pedagogical, pursuing different directions and goals [Kvitkina L.G. The impact of students' research work on improving the quality of specialist training: (Sociological consideration of problems)]. Knowledge of the principles, forms and methods of formation of scientific and cognitive activity is called "methodology". The methodology of pedagogical research is a process of formation of new pedagogical knowledge and cognitive activity aimed at identifying objective patterns of learning, education and development of students.
Supervised Discrete Online Hashing For Large-scale Cross-modal Retrieval

In this paper, we propose a supervised discrete online hashing (SDOH) method for online cross-modal retrieval. Unlike most existing batch-based cross-modal hashing methods that usually accumulate newly arriving data with the previous samples to recompute the hash functions and hash codes, our proposed method can efficiently generate the hash codes of newly arriving training data and retrains the hash functions for query samples only based on the new data. Specifically, we first in parallel calculate the common representation of the multi-modal data with different time stamp by embedding the semantic labels into the common representation, such that the common representation of the old and new training data can be separately calculated and meanwhile heterogeneous gap of multiple modalities can be well reduced. Then, we convert the continuous common representation into a suitable discrete binary space via an orthogonal rotation operation to obtain the hash codes of the new multi-modal training data. Finally, we learn the modality-specific hash functions which can simply convert the query instances into hash codes for cross-modal retrieval. Experimental results on several benchmark databases substantiate the effectiveness and efficiency of the proposed method.
Educational Data Virtual Lab: Connecting the Dots Between Data Visualization and Analysis

Educational Data Virtual Lab (EDVL) is an open-source platform for data exploration and analysis that combines the power of a coding environment, the convenience of an interactive visualization engine, and the infrastructure needed to handle the complete data lifecycle. Based on the building blocks of the FIWARE European platform and Apache Zeppelin, this tool allows domain experts to become acquainted with data science methods using the data available within their own organization, ensuring that the skills they acquire are relevant to their field and driven by their own professional goals. We used EDVL in a pilot study in which we carried out a focus group within a multinational company to gain insight into potential users’ perceptions of EDVL, both from the educational and operational points of view. The results of our evaluation suggest that EDVL holds a great potential to train the workforce in data science skills and to enable collaboration among professionals with different levels of expertise.
Application of Nature in the Motif of Malay Traditional Jewellery in Malaysia

The application of nature in traditional Malay jewellery motifs has been practiced since time immemorial as the main element of decoration and design variations. In this study, the traditional jewellery chosen is dokoh, one of the favourites of the Malays worn on the neck with kebaya. This study aims to identify the application of natural elements in design motifs in Malaysia. Qualitative research involving observation, description, and composition analysis are applied using a formal analytical approach. This research helps to preserve Malaysia's heritage and become a reference for future generations regarding extrinsic and intrinsic values. Keywords: Dokoh; Motif; Malay, Traditional Jewellery eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3796
Optimal bright multimode quantum squeezing via multi-seeding energy-level cascaded four-wave mixing

Quantum Squeezing is one of the most important quantum resources in quantum optics and quantum information. In particular, multimode quantum squeezing, with ultra-low quantum fluctuations and quantum correlations amongst many optical modes, is essential for realizing multipartite entanglement and quantum precision measurements. In this paper, we propose an all-optically controlled scheme to generate three-mode bright quantum correlated beams from energy-level cascaded four-wave mixing (ELC-FWM). By using a linear modes transform approach, the input-output relation and the covariance matrix of the produced states are obtained. Moreover, single-, double- and triple-seeding conditions are investigated to measure the quantum squeezing properties. We find that various permutations of two- and three-mode quadrature squeezing can be generated and optimized to reach the corresponding limit, via only modulating the ratio of the multiple seeds, without need of any post-operating linear optics, e.g., beam splitters. Such weak seeding light controlled scheme suggests the modulation and the optimization of multimode quantum states might be operated at photons-level, providing a reconfigurable and integrated strategy for complex quantum information processing and quantum metrology.
Taming numerical errors in simulations of continuous variable non-Gaussian state preparation

Numerical simulation of continuous variable quantum state preparation is a necessary tool for optimization of existing quantum information processing protocols. A powerful instrument for such simulation is the numerical computation in the Fock state representation. It unavoidably uses an approximation of the infinite-dimensional Fock space by finite complex vector spaces implementable with classical digital computers. In this approximation we analyze the accuracy of several currently available methods for computation of the truncated coherent displacement operator. To overcome their limitations we propose an alternative with improved accuracy based on the standard matrix exponential. We then employ the method in analysis of non-Gaussian state preparation scheme based on coherent displacement of a two mode squeezed vacuum with subsequent photon counting measurement. We compare different detection mechanisms, including avalanche photodiodes, their cascades, and photon number resolving detectors in the context of engineering non-linearly squeezed cubic states and construction of qubit-like superpositions between vacuum and single photon states.
A STUDY ON PERFORMANCE APPRAISAL AND JOB SATISFACTION

Performance Appraisal system plays an important role in overall success of an organization An appropriate appraisal system could increase employee’s satisfaction as well as job satisfaction. If the employees are satisfied with their appraisal, then they are also satisfied with their job. The present study has been conducted to find out the employee’s satisfaction toward performance appraisal. The methodology is based on questionnaire survey to collect the data. The Results will also reveal that the strengths of PA are one of significant factors contributing to job satisfaction among employee on the practice of performance appraisal system.