Reviewerly/exHarmony huggingface dataset:
Efficient estimation of the number of clusters for high-dimension data

The exponential growth of digital image data has given rise to the need of efficient content management and retrieval tools. Currently, there is a lack of tools for processing the collected unlabeled data in a schematic manner. K-means is one of the most widely used clustering methods and has been applied in a variety of fields, one of them being image sorting. Although a useful tool for image management, the K-means method is heavily influenced by initializations, the most important one being the need to know the number of clusters a priori. A number of different methods have been proposed for identifying the correct number of clusters for K-means, one of them being the variance ratio criterion (VRC). Despite its popularity, the VRC method comes with two very important shortcomings: it only yields good results when the data dimensionality is low and it does not scale well for a high number of clusters, making it very difficult to use in computer vision applications. We propose an extension to the VRC method that works for increased cluster number and high-dimensionality data sets and therefore is fit for image data sets.
Cybersickness as the virtual reality sickness questionnaire (VRSQ) measures it!? –an environment-specific revision of the VRSQ

Background: Virtual Reality (VR) does not only include the use of stereoscopic images, but also possibilities for an interaction with and participation in a computer-generated environment. However, laboratory studies primarily focus on the first part of the definition only. In this context, comparing results from different VR applications with diverging goals becomes difficult. This is especially true in the field of cybersickness research (visually induced motion sickness in VR), as self-report symptom questionnaires are used. The prominent Simulator Sickness Questionnaire (SSQ) is criticized for the lack of specificity, the double factorial loadings, the outdatedness, and the unrepresentative sample. VR-specific revisions like the Virtual Reality Sickness Questionnaire (VRSQ) address these criticisms but lack generalizability. Methods: The current paper uses a Confirmatory Factor Analysis of the VRSQ with data from three different VR environments and a sample size of N = 244. The environments had different setups, visual complexities, and interaction possibilities. These characteristics influenced the factorial structure of the VRSQ as a moderator. Furthermore, to control for VR-unrelated effects Baseline ratings were taken into account. Results: The Confirmatory Factor Analysis indicated a moderate fit for the global model, but a misspecification for two of the three environments. Only the environment similar to the original VRSQ paper converged with the model. Conclusions: In conclusion, a detailed description of the VR environment is required in scientific method reports. Focusing on VR accessibility for physically impaired in addition to healthy subjects, an added Baseline measurement can address the discriminant validity. Until generalizable VR-specific revisions of the SSQ are validated, the paper suggests using the Δ-SSQ in aggregated raw format.
PENGENALAN KOMODITAS BUAH-BUAHAN DENGAN METODE PEMBELAJARAN INTERAKSI SOSIAL DI POS PAUD PERMATA HATI SURAKARTA

Fruits are one of the horticultural commodities that play an important role for the health of the body. Fulfillment of balanced nutrition needs to be done because good nutrition will go hand in hand with good health. Nutrition Guide for Balanced Diet is the principle of consuming daily foods that contain nutrients in the type and amount according to the needs of each person according to the age group. Early childhood is a child in the age range of 0 to 6 years old who has a golden age where children develop rapidly. Early childhood requires quality and creative social interactions so that children are motivated. The purpose of this Community Service activity is to provide education on the introduction of local Indonesian fruit commodities with social interaction learning methods to students at Permata Hati Preschool Surakarta City. The method used is social interaction learning so that students have the will to consume fruits as an effort to fulfill balanced nutrition that supports growth. The activities that have been carried out have resulted in students from the Permata Hati Preschool Surakarta City knowing local Indonesian fruits and having a willingness to eat fruit to support growth.
An Assessment of Cybersecurity Awareness Level in Namibia: Case Study of Windhoek

A lot of people and devices are connected in cyberspace and life is getting easier day by day due to the internet innovations. However, the more the world is connected the higher risk it gets. Cybersecurity is being challenged by cybercrimes. Therefore, the level of people's awareness is beneficial in combating cybercrimes. Awareness empowers people with the ability and the skills to identify, recognise, avoid and prevent cybercrimes and attacks. This study aimed to evaluate the level of cybersecurity awareness among Namibian who are residents of Windhoek. The study was based on quantitative data collected from 267 responses through a Google questionnaire. The finding indicated that there is a highly positive perception by Namibians of cyber awareness. They also indicated that Namibians are aware of cyber threats and crimes as well as countermeasures however, they are challenged by the absence of cyber security legislatures in Namibia. The study recommends a future study on the factors affecting the lack of cybersecurity policies in Namibia may be carried out.
Optimization of Leaky-ESN parameters based on dragonfly and whale algorithm

Abstract In this research, we present a spiral flying mode-based dragonfly algorithm (DA-WOA). Since the traditional Dragonfly algorithm (DA) has a <?CDATA $L\dot{e}{vy}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>L</mml:mi> <mml:mover accent="true"> <mml:mrow> <mml:mi>e</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>̇</mml:mo> </mml:mrow> </mml:mover> <mml:mi mathvariant="italic">vy</mml:mi> </mml:math> flight, the final results obtained are random in nature and have poor global search capability. Changing to a spiral flight pattern not only enlarges the search area but also makes it easy to find the optimal solution. The Swish function is also used to optimize the various flight coefficients of the DA to avoid the gradient descent approaching 0. Adding a fitness coefficient to the final position updating equation can better control the distance between dragonflies and natural enemies and thus find the optimal solution. The convergence speed of the DA-WOA algorithm and the superiority of the global search are verified by 13 test functions. In this paper, DA-WOA is applied to Leaky-ESN to optimize three parameters of the Leaky-ESN system, namely, the leakage rate ( α ), the spectral radius of the connectivity weight matrix ( ρ ), and the input scale factor ( s in ). The experimental results demonstrate that DA-WOA has very strong prediction ability as well as very quick convergence speed for the predicted Sin and MG time series.
Jaynes–Cummings model in a weak gravitational wave background

Abstract Working in the framework of the linearized theory, we analyze the influence of a weak gravitational wave on the dynamics of the Jaynes–Cummings model, describing the interaction between a two-level quantum system and the electromagnetic field in a cavity. We show that the gravitational wave induces a small, time-dependent modulation in the generalized Rabi frequency Ω of the system. Such a modulation has the same frequency as the incoming gravitational wave. We also find that the atomic inversion acquires a similar time-dependent modulation. After a <?CDATA $\pi/2-$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>π</mml:mi> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> <mml:mo>−</mml:mo> </mml:math> pulse of the electromagnetic driving field in the cavity, an atomic population N 0 , previously prepared in its excited state, ends showing a small unbalance <?CDATA $\Delta N = N_e-N_g$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="normal">Δ</mml:mi> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>N</mml:mi> <mml:mi>e</mml:mi> </mml:msub> <mml:mo>−</mml:mo> <mml:msub> <mml:mi>N</mml:mi> <mml:mi>g</mml:mi> </mml:msub> </mml:math> between the excited and the de-excited atoms. The unbalance amplitude is proportional to HN 0 , with H being the gravitational strain. We briefly comment about possible astrophysical implications.
Quantum-classical hybrid neural networks in the neural tangent kernel regime

Abstract Recently, quantum neural networks or quantum–classical neural networks (qcNN) have been actively studied, as a possible alternative to the conventional classical neural network (cNN), but their practical and theoretically-guaranteed performance is still to be investigated. In contrast, cNNs and especially deep cNNs, have acquired several solid theoretical basis; one of those basis is the neural tangent kernel (NTK) theory, which can successfully explain the mechanism of various desirable properties of cNNs, particularly the global convergence in the training process. In this paper, we study a class of qcNN composed of a quantum data-encoder followed by a cNN. The quantum part is randomly initialized according to unitary 2-designs, which is an effective feature extraction process for quantum states, and the classical part is also randomly initialized according to Gaussian distributions; then, in the NTK regime where the number of nodes of the cNN becomes infinitely large, the output of the entire qcNN becomes a nonlinear function of the so-called projected quantum kernel. That is, the NTK theory is used to construct an effective quantum kernel, which is in general nontrivial to design. Moreover, NTK defined for the qcNN is identical to the covariance matrix of a Gaussian process, which allows us to analytically study the learning process. These properties are investigated in thorough numerical experiments; particularly, we demonstrate that the qcNN shows a clear advantage over fully classical NNs and qNNs for the problem of learning the quantum data-generating process.
The Open Advantage: Winning the Adversarial Battle with Open-Source Models

In the era of proprietary, black-box Generative AI (GAI) models, this paper shifts the focus towards a more transparent and accessible alternative: open-source models. Despite the current dominance of closed-source large language models (LLMs) such as ChatGPT in the market, their inherent privacy risks and the potential dangers posed by Big Tech's AI models outweigh their advantages. This paper argues that adopting open-source large language models offers legal organizations a more advantageous pathway to incorporating GAI into their operations. Open-source models provide firms with complete control over their GAI infrastructure, empowering firms to adopt innovative, responsive, and responsible strategies that align with societal expectations of transparency. Furthermore, this paper illustrates the advantages of open-sources model such as control, transparency, and reliability. In addition, this paper discusses the regulatory ambiguities surrounding GAI and the incompatibility of non-transparent GAI systems with the adversarial nature of law. Finally, the paper concludes that transitioning towards open-source large language models will equip the legal industry to navigate through the evolving ethical complexities of GAI. In conclusion, this paper urges legal practitioners to adopt open-source paradigms, not merely for competitive advantage, but to forge an ethical, sustainable path for GAI in law. Firms that adopt open-source frameworks will be at the forefront of innovation and industry pioneers.Author comments: Many thanks are owed when writing this paper. To begin with, thank you to Sean Harrington for pushing me to write this article despite my stubbornness. Second, thank you to Christopher Pratt and Omar Kamali for the technical insight into machine learning. I have learned a lot from the two of you, and still have more to learn. More importantly, thanks to my good friend for their support and reality checks when I become overly focused. I know I can be a pain when I get tunnel vision. Finally, thank you to Hugging Face and the open-source community for making the information accessible.
Robotic Arm Trajectory Planning Method using Deep Deterministic Policy Gradient with Hierarchical Memory Structure

Traditional robotic arm path planning methods are mainly carried out in the tool center point operation space, and frequently solve inverse kinematics problems, thus consuming a large number of computational resources. In contrast, using positive kinematics for planning in the joint space not only improves the efficiency, but its analytic solution also has higher accuracy. In order to better cover the environmental state space, this paper adopts the full-preserving experience preservation approach. In order to realize fast and efficient sampling of high reward value experience, this study constructs an innovative hierarchical memory structure and eliminates the overfitting phenomenon that may be caused by biased sampling through the Bias-Free strategy. Experimentally validated in the continuous path planning task of a textile robot arm, the proposed hierarchical memory deep deterministic gradient strategy method (HM-DDPG) demonstrates excellent performance and practicality in the textile robot arm path planning problem.
Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy through Selective Feature Emphasis

Kidney tumor segmentation from MR images became a pivotal research area in kidney cancer diagnosis and treatment planning. Accurate and efficient segmentation enables precise tumor localization, treatment planning, and monitoring of disease progression. Former studies have demonstrated the remarkable capability of the U-Net architecture in semantic segmentation of kidney tumors. A recent variant of the U-Net architecture, known as 3D-CU-Net, has been specifically designed with fully-connected dense skip connections to tackle the kidney tumor segmentation challenges related to network depth invariance, segmentation errors, and enforced feature fusion. While the 3D-CU-Net model demonstrated improved effectiveness in kidney tumor segmentation, it still exhibits significant limitations, including challenges in precise localization, fixed feature selection, image diversity, limited contextual information, and computational complexity. To address the limitations of the 3D-CU-Net model, this paper introduces the Attention 3D-CU-Net as a novel variant. The attention-based mechanism is seamlessly integrated with the 3D-CU-Net, prioritizing informative features to enhance segmentation accuracy by concentrating on selective regions. This innovative approach serves to significantly improve the model’s performance, particularly in challenging cases. The proposed model is evaluated on the TCGA-KIRC dataset, a widely used benchmark for kidney tumor segmentation. In comparative experiments, we evaluated the results using metrics like IoU, DSC and accuracy. Our Attention 3D-CU-Net model outperforms the baseline 3D-CU-Net and U-Net with notably higher scores: IoU (0.92), DSC (0.94), and accuracy (0.96).
Framework for Ethically Designed Microtransactions in the Metaverse

Microtransactions, small in-app purchases within virtual environments, have become increasingly prevalent in modern gaming, often leading to addictive behaviors and financial burdens, particularly among young male players. The potential consequences of such microtransactions within the metaverse extend beyond gaming, as they provide companies with unprecedented control over user experiences and the ability to capitalize personal data for targeted advertising and manipulative practices. This article presents a comprehensive framework for the ethical analysis and design of microtransactions in the metaverse to address these concerns. This three-dimensional framework explores Motivation, Engagement, and Fairness to evaluate the impact of microtransactions on user autonomy, social interactions, and overall fairness. Additionally, the paper conducts a running example on World of Warcraft to assess the alignment of its microtransaction design choices with the proposed ethical principles. By shedding light on these issues, this article aims to raise awareness among consumers, developers, and policymakers about the need for responsible practices, consumer protection, and privacy preservation in the metaverse. It emphasizes fostering collaboration and transparency to safeguard users from misuse while promoting a user-centric metaverse ecosystem.
Tasaheel: An Arabic Automative Textual Analysis Tool—All in One

This paper demonstrates Tasaheel, an automative Arabic textual analysis tool. It offers two types of textual analysis utilities: traditional natural language processing tasks; such as stemming, segmentation, normalization, name entity recognition, and part of speech tagging, by integrating open-sourced Arabic natural language processing packages. The second type of utility offers novel corpus analysis methods, including a detailed word use summary of part of speech, emotion, polarity, linguistics, and domain-specific word labeling. Tasaheel is the first Arabic tool that analyses affixes as a type of comprehensive textual analysis, along with options to search and handle data. We anticipate that Tasaheel will be a conducive tool for Arabic textual analysis.
Instagram Clone Using Python

Social Media gives the common man a platform to voice with complete freedom of expression, be it for supporting a cause or for addressing a national or international issue of any sensitivity. Business prospects and job opportunities gear up as social media is a stage with global audience to showcase our talents. Social media started off as a trend among youth but today it has emerged as a medium that influences even Presidential elections. Instagram a is a social media app that has grown immensely over the years, and it continues to broaden its horizons. The photo-sharing app has gone a long way from once being a favorite for vacationers sharing their holiday pictures to now appealing to all kinds of influencers, advertisers, and brands. The early version of Instagram was a html prototype for photo-sharing. But as time progressed, a bundle of languages were used to create this awesome clutter free app. Through this project, we’ll discover a way to build a basic functional clone of Instagram, relying on Kotlin and Firebase.A growing number of people are interacting on the web to express and share their views
Prolog Education in Selected High Schools in Bulgaria

2022 marks the 50th anniversary of the logic programming language Prolog. In this regard, the authors will join the international initiative known as “Prolog Education and Thinking”. The initiative aims to acquaint students mainly with logical programming and Artificial Intelligence through the Prolog language. The article presents shortly the project called “Digital Bulgaria in Prolog”, with which we want to join the above-mentioned initiative. The main topic and the architecture of the supporting software are also presented. Furthermore, the beginning and the current state of the introduction of artificial intelligence education in two Bulgarian schools are briefly discussed.
The Use of Mobile Technologies in Education with an Emphasis on a Student-Centered Approach

The use of mobile technologies for implementing a student-centered approach has become increasingly relevant in modern education. This article explores the possibilities and benefits of integrating mobile technologies into the educational process, with a focus on addressing students' needs and interests. The advantages of individualization of education, improvement of access to knowledge, stimulation of interaction and expansion of educational experience are considered. Additionally, the challenges related to the necessity of training teachers and creating appropriate resources for successful integration of mobile technologies into the learning process are addressed. This approach offers new opportunities for active and effective student engagement, stimulates their creative thinking and independence, and contributes to the development of modern skills required in today's information society. In conclusion, the importance of further research and practical implementation of the student-centered approach using mobile technologies in education is emphasized, aiming to enhance the quality of learning and students'development.
Development of a Java Syntax Analyzer for C/C++ Code Recognition

This article describes the development of a parser in the Java programming language. The analyzer recognizes a program written in C code. The implemented parser recognizes and separates words from C program code and recognizes the type of each word. The types of words that the implemented parser recognizes are keyword, identifier, constant, operator, and delimiter. The result of the execution of the designed parser is in the form of a pair of values: <word - type>. Performed testing of the analyzer with C code submission in stages and at once.
Analysis of Source Code Based on Changes in its State Over Time - Using User Behavior Models

One of the popular trends for increasing productivity in the development of software products is related to integrating tools based on artificial intelligence, which actively supports software engineers' actions in dealing with syntactic and semantic problems. A parallel can quickly be drawn between a similar class of tools and the platforms to train future developers. However, the main drawback of most media of this nature is the focus on the result of the problem and the complete ignoring of the intermediate steps aimed at analyzing the behavior of the developer. In this article, we will pay attention to analyzing syntactic, semantic, platform, and analytical problems generated as a result of working on programming problems in a virtual educational environment.
Modelling of Grid-Connected PV-Systems Using Real Meteorological Data

This paper focuses on developing a PV -grid modelling approach accounting for the vast majority of activities and factors related to processing of raw meteorological data - estimation of GTI and internal cell temperature, creating, tuning grid connected PV power system and its MPPT control and last but not least automatic analysis of the simulated results. For verification purposes, 4 case study Matlab models were developed and simulated for 64 full days with real meteorological data- two Simulink power models accounting for size differences small(<5kWp) and medium (5-30kWp) and 2 configurations for systems with solar tracking and without. Then a third script for system energy performance analysis was created and presented.
Development of an Ontology for Bulgarian Soil Types

The article presents the development of an ontology for the Taxonomic List of Soils in Bulgaria (according to the FAO World System). In addition to soil types, the ontology includes knowledge about the different agro-climatic regions in Bulgaria that are suitable for cultivating various agricultural crops. The ontology is part of the knowledge base of the ZEMELA platform for smart agriculture.