Reviewerly/exHarmony huggingface dataset:
Security and confidentiality of information under the application of cloud accounting compared to traditional accounting

Due to the continuous development and transition from manual to electronic accounting methods, users are now finding that accounting is simpler and more convenient. The recent shift in accounting to a faster technology has greatly improved its ability to serve users.&#x0D; The research aims at a number of objectives, including getting to know cloud accounting and software-based accounting, comparing the services of the top ten cloud accounting service providers around the world in terms of security and confidentiality to understand how they differ from traditional accounting, and understanding the current impact of cloud accounting using insights from accounting experts. Primary data were collected from accounting experts from various academics and professionals. A questionnaire form has been prepared for this purpose.&#x0D; Secondary data were collected from annual reports, books, international journals, the Internet, and other sources. The information collected was helpful in understanding the concepts and their effectiveness.&#x0D; According to the results, cloud accounting is a much-preferred option over traditional accounting for any organization looking to compete due to its many benefits, including accessibility, affordability, high security, and ease of use.&#x0D; Further study is needed, recent advances in accounting not only help small and medium-sized businesses (SMEs) improve their efficiency but also provide significant cost savings. In such circumstances, it is necessary to ascertain whether companies and financial institutions such as banks that promote these trends in their operations bring clear benefits, as well as how this may affect the business future of newly established companies and institutions. This paper provides insight into cloud accounting in small and medium-sized businesses from a managerial accounting perspective.&#x0D; &#x0D; Paper type: Research paper &#x0D;
Personality Detection on Twitter Social Media Using IndoBERT Method

Personality is the fundamental characteristic of human beings that makes humans unique. Because of these differences in human characteristics, personality becomes a benchmark for consideration in various recruitment processes. One way to predict personality is to apply an interview system or fill out questionnaires which often experience problems due to ineffectiveness in terms of time and cost. Results become inaccurate if prospective employees do not know themselves well. The big five personality method, divided into openness, conscientiousness, extraversion, agreeableness, and neuroticism, is widely used to predict personality. This study uses a deep learning method, IndoBERT, to detect personality based on five dimensions according to the big five personalities whose data is taken from Twitter tweets with crawling data. From the results of these studies, it is known that personality research using the IndoBERT method without a stemming process has a higher accuracy rate of 0.46.
HMDE‐FS: A homogeneous distributed ensemble feature selection framework based on resampling with/without replacement

Summary Applying ensemble feature selection (EFS) models in various problems has not been actively discussed, and there has been a lack of effort to make it applicable in the situations such as distributed environments. Due to restrictions of centralized algorithms such as their poor scalability in the high dimension data and also distributed nature of some data, using the traditional centralized computing for dealing with such problems may be inevitable. This paper aims to develop a homogenous distributed ensemble feature selection (HMDE‐FS) framework through a distributed resampling approach rather than a centralized one. The homogenous ensembles mainly operate along with a resampling process, so applying various methods to resampling can affect the performance of the model. Among various strategies, those with and without replacement are two of the main technique families, hence we investigated the efficiency of two well‐known with/without replacement techniques: bootstrapping (BS) and cross‐validation (CV) inspired method that we named crisscross (CC). The proposed HMDE‐FS approaches are tested on eight datasets, and the heavy experimental results illustrate that these methods considerably reduce runtime, while classification accuracy maintains its competitiveness.
Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative Multi-Agent Systems

For efficient and robust task accomplishment in multi-agent systems, an agent must be able to distinguish cooperative agents from non-cooperative (i.e., uncooperative and adversarial) agents. In this paper, we first develop a trust metric based on which each agent forms its own belief of how cooperative the other agents are, i.e., of how much the other agents contribute to maintaining safety. With safety encoded as Control Barrier Functions (CBFs), the trust metric is in turn used to adjust the rate at which the CBFs allow the system trajectories to approach the boundary of the safe set. This is achieved via a novel Rate-Tunable CBF, which yields less conservative performance compared to an identity-agnostic implementation, where cooperative and non-cooperative agents are treated similarly. The proposed adaptation and control method is evaluated via simulations on heterogeneous multi-agent systems including non-cooperative agents.
A Study of Online Learning Context Optimization Strategies under the Metaverse Perspective

The development of online learning depends on the development and progress of the Internet technology. Meanwhile, metaverse is considered to be a new generation of the Internet that will drive a new iteration of the Internet era. Integrating the metaverse concept into online learning will surely bring a profound revolution to the latter. In 2020, with the outbreak of the epidemic, online learning has become an important learning method for the majority of students studying in universities. However, the effectiveness of online learning is highly concerned and questioned. This study elaborates the connotation and spatial characteristics of metaverse technology, relies on its features and advantages, makes a specific analysis of the inner logic of metaverse integration into online learning, and creates an embodied learning context, immersive learning context, and interactive learning context for online learning according to the current situation of students' online learning, and proposes metaverse online learning context optimization strategies in terms of both classroom teaching optimization and policy optimization, with a view to improving students' online learning effectiveness.
English-Catalan Neural Machine Translation: state-of-the-art technology, quality, and productivity

Recent major changes and technological advances have consolidated machine translation (MT) as a key player to be considered in the language services world. In numerous instances, it is even an essential player due to budget and time constraints. Much attention has been paid to MT research recently, and MT use by professional or amateur users has increased. Yet, research has focused mainly on language combinations with huge amounts of online available corpora (e.g. English-Spanish). The situation for minoritized or stateless languages like Catalan is different. This study analyses Softcatalà’s new open-source, neural machine translation engine and compares it with Google Translate and Apertium in the English-Catalan language pair. Although MT engine developers use automatic metrics for MT engine evaluation, human evaluation remains the gold standard, despite its cost. Using TAUS DQF tools, translation quality (in terms of relative ranking, adequacy and fluency) and productivity (comparing editing times and distances) have been evaluated with the participation of 11 evaluators. Results show that Softcatalà's Translator offers higher quality and productivity than the other engines analysed.
Homography-based Riccati observer design for camera pose estimation

This paper introduces a novel approach for estimating the relative pose of a mobile robot equipped with an onboard IMU, a velocity sensor complemented with a monocular camera observing a planar scene. The proposed solution relies on the design of a deterministic Riccati observer that exploits the first-order approximations of a class of nonlinear systems. It uses the point-feature correspondences of a sequence of images and exploits the homography constraint to derive the system’s measurement equation. The observability analysis, which highlights the uniform observability condition under which local exponential stability is guaranteed, is performed. Moreover, an extension of the observer to depth estimation is provided. Finally, the proposed observer solution is validated through simulation and experimental results.
Reframed Design Thinking and Feasibility Analysis of UX Journey: Integrating User Experience and User Requirement for Solo Software Development

User experience and user requirements are two independent approaches. User requirements address the customer's requirements and expectations for the solution, whereas user experience encompasses all aspects of how the user interacts with and uses the software. The software product should be easy to use and has usable features. Moreover, the additional value for the software is if the product has an attractive design or working environment that is in line with user behaviors, it can occur if integrate software requirements and user experience. Integration escalates developer productivity by focusing on features that meet the user's needs and expectations. That integration improves efficiency in software development by identifying and addressing problems that may arise during the development process, saving developer time and effort in developing software. The usage context of integration of user experience and user requirements in UX Journey contributes increase developer productivity and self-efficacy in developing software by focusing development on features that match the user needs, as well as increasing efficiency in overcoming problems that arise during the development process. UX Journey makes developers feel more confident in their ability to develop quality software.
FORMING PROFESSIONAL DIGITAL COMPETENCE OF STUDENTS OF ENGINEERING AND PEDAGOGICAL SPECIALITIES OF THE AGRO-INDUSTRIAL PROFILE IN TERMS OF HIGHER EDUCATION INSTITUTION’S INNOVATIVE DEVELOPMENT

The article singles out the urgent problem of updating the content of the professional training of the vocational education pedagogues, which, from the modern point of view, should cover the issues of organizing the educational process in the digital environment, the features of the “digital generation” and the methods of their education and upbringing. The readiness to introduce ideas about the digital transformation of the agricultural sector in the content of such specialists' digital competence has been expediency taken into account. The essence and content of the professional digital competence of future vocational education pedagogues for the agro-industrial area and its essential components have been revealed. It has been found such competence can be interpreted as a certain level of specialist's readiness to communicate, associate and collaborate safely, develop digital educational resources and solve professional problems with ICT and tools. The complex systemic nature of the professional digital competence of the future vocational education pedagogues in the agro-industrial profile has been emphasised. In its structure, general, didactic, and professionally oriented components have been distinguished, as well as production-technological and special profile aspects, which reflect the specifics of professional training of specialists for the agro-industrial sector, have been shown. The structure of students' digital competence in engineering and pedagogical specialities for the agro-industrial profile presented as structural and criteria-based components including motivational, informational, instrumental and reflective ones has been substantiated and described. It is the digital educational environment of the higher education institution that is defined as the leading resource supplying this process. Such an environment provides communication, feedback with students, management of their educational activities and vocational training, the construction of a professional educational space, the implementation of network interdisciplinary research and pedagogical support for the students' professional self-determination due to the understanding of the possibilities of professional growth in the information space. The main approaches to using the educational, developmental and educative potential of the digital environment of the higher education institution and open educational space have been revealed. It has been concluded regarding the expediency of application technologies of STEM education, blockchain, telepresence, artificial intelligence, virtual and augmented reality, provisions of innovative engineering education CDIO in the formation of future specialists' professional digital competence.
Short-Term Trajectory Prediction for Full-Immersive Multiuser Virtual Reality with Redirected Walking

Full-immersive multiuser Virtual Reality (VR) envisions supporting unconstrained mobility of the users in the virtual worlds, while at the same time constraining their physical movements inside VR setups through redirected walking. For enabling delivery of high data rate video content in real-time, the supporting wireless networks will leverage highly directional communication links that will “track” the users for maintaining the Line-of-Sight (LoS) connectivity. Recurrent Neural Networks (RNNs) and in particular Long Short-Term Memory (LSTM) networks have historically presented themselves as a suitable candidate for near-term movement trajectory prediction for natural human mobility, and have also recently been shown as applicable in predicting VR users' mobility under the constraints of redirected walking. In this work, we extend these initial findings by showing that Gated Recurrent Unit (GRU) networks, another candidate from the RNN family, generally outperform the traditionally utilized LSTMs. Second, we show that context from a virtual world can enhance the accuracy of the prediction if used as an additional input feature in comparison to the more traditional utilization of solely the historical physical movements of the VR users. Finally, we show that the prediction system trained on a static number of coexisting VR users be scaled to a multi-user system without significant accuracy degradation.
Need Analysis of Dyslexia Students in English Reading Comprehension Instructions

The challenges of learning English as a foreign language presented issue for dyslexia students who had deficiencies in reading comprehension involving information processing, concentration, and sensory motor skills. The aims of this research were to describe the dyslexia students’ needs in English reading comprehension. The sample consisted of 117 dyslexia students aged 13-16 years old from Karya Murni Special Needs School and Ananda Karsa Mandiri School, North Sumatra, Indonesia. The data were in the form of descriptive qualitative research which was taken from interview, questionnaire, and test. The results of this research proved the analysis of: (1) the physical, emotional, and social characteristics of dyslexia students, (2) the characteristics of dyslexia students’ learning style and testing reading comprehension test, (3) the need analysis for dyslexia students in teaching method in the classroom, (4) the need analysis of dyslexia students in reading comprehension, (5) the factors why dyslexia students had difficulty in English as a foreign language, and (6) the factors why dyslexia students had difficulty in English reading comprehension. With needs analysis, it was expected the identification of necessities, lacks, and wants could be formulated properly as a basis to prepare English reading teaching materials.
Improving the performance of quantum approximate optimization for preparing non-trivial quantum states without translational symmetry

Abstract The variational preparation of complex quantum states using the quantum approximate optimization algorithm (QAOA) is of fundamental interest, and becomes a promising application of quantum computers. Here, we systematically study the performance of QAOA for preparing ground states of target Hamiltonians near the critical points of their quantum phase transitions, and generating Greenberger–Horne–Zeilinger (GHZ) states. We reveal that the performance of QAOA is related to the translational invariance of the target Hamiltonian: without the translational symmetry, for instance due to the open boundary condition (OBC) or randomness in the system, the QAOA becomes less efficient. We then propose a generalized QAOA assisted by the parameterized resource Hamiltonian (PRH-QAOA), to achieve a better performance. In addition, based on the PRH-QAOA, we design a low-depth quantum circuit beyond one-dimensional geometry, to generate GHZ states with perfect fidelity. The experimental realization of the proposed scheme for generating GHZ states on Rydberg-dressed atoms is discussed. Our work paves the way for performing QAOA on programmable quantum processors without translational symmetry, especially for recently developed two-dimensional quantum processors with OBC.
Exploring the hidden dimensions of an optical extreme learning machine

Extreme Learning Machines (ELMs) are a versatile Machine Learning (ML) algorithm that features as the main advantage the possibility of a seamless implementation with physical systems. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding in regard to their optical implementations. In this context, this work makes use of an optical complex media and wavefront shaping techniques to implement a versatile optical ELM playground to gain a deeper insight into these machines. In particular, we present experimental evidences on the correlation between the effective dimensionality of the hidden space and its generalization capability, thus bringing the inner workings of optical ELMs under a new light and opening paths toward future technological implementations of similar principles.
Combination of Genetic Algorithm and Neural Network to Select Facial Features in Face Recognition Technique

Face recognition methods are computational algorithms that follow aim to identify a person's image according to the bank of images they have of different people. So far, various methods have been proposed for face recognition, which can generally be divided into two categories based on face structure and based on facial features. Based on this, many algorithms have been introduced and used for face recognition. Genetic algorithm has been one of the successful algorithms for face recognition. In this article, we first briefly explained the genetic algorithm and then used the combination of neural network and genetic algorithm to select and classify facial features The presented method has been evaluated using individual features and combined features of the face region. Composite features perform better than face region features in experimental tests. Also, a comprehensive comparison with other facial recognition techniques available in the FERET database is included in this paper. The proposed method has produced a classification accuracy of 94%, which is a significant improvement and the best classification accuracy among the results established in other studies.
Implementation of Blockchain with Machine Learning Intrusion Detection System for Defending IoT Botnet and Cloud Networks

Significant research has been done on combining intrusion detection and blockchain to increase data privacy and find both current and future threats.This research suggests a machine blockchain framework (MBF) in order to provide distributed intrusion detection with security and blockchain with privacy with the help of smart contracts in IoT networks.An XGBoost algorithm was implemented to work with sequential network data and the intrusion detection approach is explored using the N-BaIoT dataset.In order to protect the network against known malware threats (Mirai, Gafgyt, or Bashlite), the IoT malware attack prediction model created in this study offers a deterrent strategy based on the network traffic statistics.On the other hand, the models need to be taught to recognize new varieties of malware.In this work, we observe how different machine learning models, like Random Forest algorithm and proposed XGBoost algorithm, can accurately predict the infected malware in certain traffic instance.However, we provide a honeypot-based strategy that employs machine learning techniques for the detection of malware in this study.Using data from an IoT Botnet as a dataset helps train a machine learning model in a way that is effective and changes over time.
AI-Driven Pharma Tech Firm Expands Its Discovery Platform into Biologics

Genetic Engineering & Biotechnology NewsVol. 43, No. 1 GENEDGEAI-Driven Pharma Tech Firm Expands Its Discovery Platform into BiologicsExscientia intends to double the addressable target universe of its platform by combining generative AI design and virtual screeningAlex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:11 Jan 2023https://doi.org/10.1089/gen.43.01.02AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 43Issue 1Jan 2023 Information© 2023 by GEN PublishingTo cite this article:Alex Philippidis.AI-Driven Pharma Tech Firm Expands Its Discovery Platform into Biologics.Genetic Engineering & Biotechnology News.Jan 2023.10-11.http://doi.org/10.1089/gen.43.01.02Published in Volume: 43 Issue 1: January 11, 2023PDF download
Quantum Electrodynamics of Dicke States: Resonant One-Photon Exchange Energy and Entangled Decay Rate

We calculate the fully retarded one-photon exchange interaction potential between electrically neutral, identical atoms, one of which is assumed to be in an excited state, by matching the scattering matrix (S matrix) element with the effective Hamiltonian. Based on the Feynman prescription, we obtain the imaginary part of the interaction energy. Our results lead to precise formulas for the distance-dependent enhancement and suppression of the decay rates of entangled superradiant and subradiant Dicke states (Bell states), as a function of the interatomic distance. The formulas include a long-range tail due to entanglement. We apply the result to an example calculation involving two hydrogen atoms, one of which is in an excited P state.