Reviewerly/exHarmony huggingface dataset:
Genetic Structural NAS: A Neural Network Architecture Search with Flexible Slot Connections

Selecting an appropriate neural network architecture and hyper-parameters to optimize performance for a given application is a difficult task. To overcome this challenge, Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) have been introduced. However, these techniques are often computationally-expensive and require significant amounts of execution time. To address this issue, we propose a semi-automated NAS approach that optimizes pre-existing architectural structures using genetic algorithms and eliminates unsuccessful combinations through shallow network training. The effectiveness of our technique was verified through an experiment that produces a family of AlexNet-like neural networks comprising 1296 models for image classification tasks. The computational study was conducted with five runs of a genetic algorithm, resulting in the deep models with mean loss of 0.7044 and accuracy of 0.7334, both with low standard deviations, outperforming the original AlexNet model with a significant margin.
The Effect of Learning Motivation on Writing Skills of Observation Results in The Covid 19 Pandemic

The study was conducted to determine the effect of learning motivation on the results of writing observational report text (laporan hasil observasi, or LHO) skills, especially during the pandemic. The researcher makes a hypothesis that there is a significant influence on learning motivation in writing LHO texts. This research method is descriptive quantitative. The research data were processed by using a statistical package of the social sciences (SPSS) and Microsoft Excel applications. The number of samples were 32 students of class X SMAK BPK PENABUR Singgasana. The instruments used are test and non-test. The test data is the result of learning to write LHO texts, while the non-test data is the response of students to the questionnaire on the motivation to learn Indonesian. The results of the Spearman Rank test, have a significance of 0.002 and have a correlation of 0.521, meaning that between learning motivation and learning outcomes of LHO text materials, there is a moderate level relationship. The direction of the relationship between variables is positive. It can be explained that high learning motivation will produce high learning scores as well. Teachers need to give enthusiasm and present interesting learning consistently so that the students can be more motivated and obtain good learning outcomes as well.Â Â ABSTRAKÂ Penelitian dilakukan untuk mengetahui pengaruh motivasi belajar terhadap hasil keterampilan menulis teks laporan hasil observasi (LHO), khususnya di masa pandemi. Peneliti membuat hipotesis yaitu terdapat pengaruh yang signifikan antara motivasi belajar terhadap menulis teks LHO. Metode penelitian ini yaitu deskriptif kuantitatif. Pengolahan data penelitian menggunakan aplikasi statistical package of the social sciences (SPSS) dan microsoft excell. Jumlah sampel adalah 32 peserta didik kelas X SMAK BPK PENABUR Singgasana. Instrumen yang digunakan yaitu tes dan nontes. Data tes adalah nilai hasil belajar menulis teks LHO sedangkan data nontes merupakan respon dari peserta didik terhadap angket motivasi belajar Bahasa Indonesia. Hasil uji Rank Spearman, memiliki signifikansi 0,002 dan memiliki korelasi sebesar 0,521, artinya antara motivasi belajar dengan hasil belajar materi teks LHO terdapat hubungan dengan tingkat sedang. Arah hubungan antarvariabel, positif. Hal tersebut dapat dijelaskan bahwa motivasi belajar yang tinggi akan menghasilkan nilai belajar tinggi pula. Guru perlu selalu memberikan semangat dan menyajikan pembelajaran yang menarik supaya siswa termotivasi dan memperoleh hasil belajar yang baik.
Integrating E-Learning and Digital Literacy Into Module Content and Delivery in Higher Education

This chapter discusses how the top-down approach to embedding digital literacy within universities and higher education institutes (HEIs) contributes to the lack of digital literacy, digital competency, and digital proficiency embedded within modules in university programmes. This it is suggested is related to the lack of integration between the key stakeholders (students, academics, and educational technologists). The nature of digital gaps are suggested, and an approach which builds from the operational level up towards the strategic is suggested. An example using a marketing module at first year of a degree programme is given. This approach should assist in designing-in digital literacy into modules.
The Role of Self-Determination Theory in Adopting Metaverse for Healthcare and Diagnostics among Healthcare Professionals

Metaverse technology is not a new phenomenon. Yet, its evolving nature has facilitated many fields, including healthcare and diagnostics. Notably, the relevant technology has widely been adopted among healthcare practitioners due to the attributed benefits and factors that further increase its value for medical purposes. The relevant technology is widely adopted worldwide, and United Arab Emirates is of no exception. This research also focused on the factors under the self-determination theory for the metaverse in healthcare and diagnostics in the United Arab Emirates. The researchers applied the cross-sectional design and obtained data from doctors working in different healthcare organizations in the three cities of the UAE. Employing the Structural Equation Modelling for data analysis revealed that Autonomy and Relatedness are two important factors that affect the Virtual Simulation and Perceived Value regarding metaverse technology for healthcare purposes. Besides, Virtual Simulation and Perceived Value further affect Emirati doctors' intention to adopt metaverse technology for healthcare purposes. Overall, the findings supported the study assumptions, indicating metaverse as an integral part of medical practices and diagnostics, as also affirmed under the self-determination theory. This research concluded that the metaverse had revolutionized the healthcare and diagnostics industry in the United Arab Emirates, leading to more efficient and accurate medical treatments. Further, the study limitations are discussed accordinaly,
Multi-objective Search for Gender-fair and Semantically Correct Word Embeddings (HOP GECCO'23)

Mitigating algorithmic bias during the development life cycle of AI-enabled software is crucial given that any bias in these algorithms is inherited by the software systems using them. At the Hot-off-the-Press GECCO track, we aim at disseminating our article Multi-objective search for gender-fair and semantically correct word embeddings. Applied Soft Computing, 2023 [5]. In this work, we exploit multi-objective search to strike an optimal balance between reducing gender bias and improving semantic correctness of word embedding models, which are at the core of many AI-enabled systems. Our results show that, while single-objective search approaches are able to reduce the gender bias of word embeddings, they also reduce their semantic correctness. On the other hand, multi-objective approaches are successful in improving both goals, in contrast to existing work which solely focuses on reducing gender bias. Our results show that multi-objective evolutionary approaches can be successfully exploited to address bias in AI-enable software systems, and we encourage the research community to further explore opportunities in this direction.
What You Always Wanted to Know About Evolution Strategies, But Never Dared to Ask

tutorial Share on What You Always Wanted to Know About Evolution Strategies, But Never Dared to Ask Author: Hans-Georg Beyer Vorarlberg University of Applied Sciences, Dornbirn, Austria Vorarlberg University of Applied Sciences, Dornbirn, Austria https://orcid.org/0000-0002-7455-8686View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 878–894https://doi.org/10.1145/3583133.3595041Published:24 July 2023Publication History 0citation37DownloadsMetricsTotal Citations0Total Downloads37Last 12 Months37Last 6 weeks31 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Study on Overcoming Communication Barriers in a College Campus Environment

Indian Institute of Technology Guwahati is a national institute that sees an influx of students of linguistically diverse backgrounds from all over India and abroad every year. The most commonly spoken languages are Hindi, Bengali and Assamese. For students who do not know these common languages, every activity turns into a hassle in their day-to-day lives owing to the communication barriers. They face trouble in settling down during the initial days and in developing meaningful relationships with their peers in the long run. The study in this paper focuses on the need to facilitate better and more effective communication thereby easing the students’ problems. We conducted a detailed literature review which helped us identify the barriers and enablers of communication. Next, we interviewed campus students who didn’t speak the commonly spoken languages. Our primary research revealed that immersion was crucial to facilitating effective communication and better language learning. Further, we learnt that students were interested in learning the commonly spoken language, but not like a course subject. During our analysis, we observed five stages of new language acquisition: Denial to learn a new language > Non-verbal/gestural communication > Learning numbers via transactions > Picking up phrases > Saturation. Whether by choice or otherwise, learning eventually takes place, as it is a necessity more than anything else. Our final design direction was to gamify the experience so that language acquisition is easy, and there is an element of delight. Our solution is a multiplayer card game that equips students with the basics of a language that in turn helps them go about their daily lives and forge bonds with their peers in the long run.
Evolving Better Initializations For Neural Networks With Pruning

Recent work in deep learning has shown that neural networks can be pruned before training to achieve similar or even better results than training the full network. However, existing pruning methods are limited and do not necessarily yield optimal solutions. In this work, we show that perturbing the network by re-initializing the pruned weights and re-pruning can improve performance. We propose to iteratively re-initialize and re-prune using a hill climbing (1 + 1) evolution strategy. We examine the cause of these improvements and show that this method can consistently improve the subnetwork without increasing its size, pointing to a potential new application of evolutionary computing in deep learning.
How to Tell a Fish from a Bee: Constructing Meta-Heuristic Search Behaviour Characteristics

New meta-heuristics are often neglected in terms of the methods used to describe them. Uninspired descriptions are given of the speed to the global minima and the number of local minima found, but little to describe the search characteristics that make it possible to do so. The lack of search behaviour descriptors results in a loss. Without discussing the tools used to make meta-heuristics efficient, we are unable to apply those same tools elsewhere. By using such descriptors, older meta-heuristics can be improved using new ideas, and new meta-heuristics can be continually iterated on. Without such a cycle, all meta-heuristics are destined to be shelved.
Artificial Intelligence’s Function In Chatbots That Use NLP And SVM Algorithms

A healthy lifestyle depends heavily on proper healthcare. Nevertheless, it might be exceedingly challenging to schedule a doctor’s appointment for any health issue. Before contacting a doctor, the goal is to develop a medical chatbot utilizing machine learning that can identify the illness and offer basic information about it. By using a medical robot, this will save healthcare expenses and increase access to medical information. The bots are computational models that communicate with people by using speech recognition. In order to recognize the language terms, make a query choice, and provide a response, the chatbot saves the data in a database. N-gram, TF-IDF, and mutual information are used to calculate ranking and text similarity. Each verb in the supplied input word will be assigned a score and additional comparable phrases will be found for the specified query. When a bot formulates a query that is unclear or not found in the system, a supplier, an expert software, responds.
Utilization of Blockchain Technology in Online Education for Sharing Notes and Report – An Empirical Analysis

The new educational strategy known as online education has grown significantly with the advancement of internet technology. There are still many issues with authenticity, credit certification and credentials, student privacy, and a broad selection of courses with this sort of education. Whether colleges would approach blockchain technology differently than standard research and instruction was the main question addressed. By participating in supported examinations, using an exploration strategy that incorporates a literature review, contextual analysis investigation, and the Business Model Canvas, which offers three hypothetical commitments and heading on what Blockchain is, and by utilizing an examination technique that incorporates supported examinations. The discoveries of this study show the headway in internet discovering that is projected because of joining blockchain innovation with the Smart Program Education stage, which depends on MOOCs. The end might give an assessment of the specialized particulars and essential uses of Blockchain innovation as well as answers for issues with Blockchain-based web-based training. So that more can be anticipated in models and will be developed to give a superior substitution, help, correspondence, conversation on blockchain, and conversation on how instructive organizations capability should be invited by Blockchain innovation as digital technology.
Framework for Ethical Implementation of Remote Proctoring in Education

The COVID-19 pandemic and subsequent rapid transition of educational assessment from in-person to online formats saw a rise in remotely proctored assessments increase. While this modality had grown in years prior to the pandemic, it grew globally in early 2020. Adoption of technology-assisted solutions for remote proctoring was sudden and widespread. Now, as institutions more strategically plan long-term, sustainable solutions, it is important to analyze both the needs of the institution and the technologies available to make the most informed decisions. The technologies available in remotely proctored tests are examined for benefits and potential concerns. In addition, a framework for the ethical implementation of remote proctoring is presented as a guide for institutions that offers the opportunity for input from multiple user groups.
AVX-Based Acceleration of ARIA Block Cipher Algorithm

Block cipher algorithms encrypt sensitive personal, financial, and confidential information to prevent unauthorized access. The ARIA is a general block cipher algorithm with an involutional SPN structure optimized for lightweight environments and hardware implementation. This study focuses on implementing ARIA in the crypto-subsystem of the Linux kernel because it has yet to be implemented despite being recognized as a global standard. This study improves the practicality of ARIA by implementing it in the Linux kernel with reasonable performance and attempts to reduce CPU cycles for substitution and diffusion operations while alleviating the lack of ARIA-specific instructions in existing CPUs. To achieve this, the study implemented the AVX, AVX2, and AVX512 versions of ARIA that can operate in parallel in addition to two types of ARIA-specific substitution functions using AES-NI and GFNI. We implemented an accelerated version of ARIA that performs up to 10.6 times better than the generic version. The optimization of the affine transformation in AES-NI based ARIA has been shown to reduce the required cycle count by 32.2%. Moreover, ARIA demonstrated competitive speeds when compared to other algorithms, such as Camellia, that are implemented in the Linux kernel.
eDA3-X: Distributed Attentional Actor Architecture for Interpretability of Coordinated Behaviors in Multi-Agent Systems

In this paper, we propose an enhanced version of the distributed attentional actor architecture (eDA3-X) for model-free reinforcement learning. This architecture is designed to facilitate the interpretability of learned coordinated behaviors in multi-agent systems through the use of a saliency vector that captures partial observations of the environment. Our proposed method, in principle, can be integrated with any deep reinforcement learning method, as indicated by X, and can help us identify the information in input data that individual agents attend to during and after training. We then validated eDA3-X through experiments in the object collection game. We also analyzed the relationship between cooperative behaviors and three types of attention heatmaps (standard, positional, and class attentions), which provided insight into the information that the agents consider crucial when making decisions. In addition, we investigated how attention is developed by an agent through training experiences. Our experiments indicate that our approach offers a promising solution for understanding coordinated behaviors in multi-agent reinforcement learning.
Hybrid Model Predictive Control with Penalty Factor Based on Image-Based Visual Servoing for Constrained Mobile Robots

For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive target bias gain when using traditional IBVS control methods. The traditional IBVS control is transformed into an optimization problem with constraints in the finite time domain, by defining the optimization function based on the mobile robot’s positional deviation and image feature point deviation, while using actuator saturation and speed limit as constraints. Based on this, a convex optimization function with penalty factors is defined and combined with incremental model predictive control. This control strategy could ensure the emergency braking performance of the mobile robot when the image feature points are massively obscured by obstacles in dynamic scenes, while improving the accuracy and real-time of its trajectory tracking control. Finally, simulation comparisons are conducted to verify the effectiveness of the proposed control method.
Exploring the Potential of Blockchain Technology in Enhancing Security of smart systems

This research paper explores the potential of blockchain technology to enhance the security of smart systems. Blockchain technology is a distributed ledger technology that provides a secure, immutable and transparent platform for data storage and transactions. This paper first provides an introduction to the fundamentals of blockchain technology and its security benefits. We then look into the definition of smart systems and their unique security requirements. We then investigate the potential of blockchain technology to meet the security requirements of smart systems. We review the advantages and disadvantages of using blockchain technology for smart security. We also look at existing blockchain technologies and their security features. We analyze the potential of these technologies to improve the security of smart systems and discuss their potential future role. Finally, we present our conclusions and discuss the implications of our findings.
Route Planning for Emergency Evacuation Using Graph Traversal Algorithms

The automatic identification of various design elements in a floor-plan image has gained increasing attention in recent research. Emergency-evacuation applications can benefit greatly from automated floor-plan solutions, as they allow for the development of horizontal solutions instead of vertical solutions targeting a specific audience. In addition to that, current evacuation plans rely on static signs without taking into account the dynamic characteristics of each emergency case. This work aims to extract information from a floor-plan image and transform it into a graph that is used for pathfinding in an emergency evacuation. First, the basic elements of the floor-plan image, i.e., walls, rooms and doors, are identified. This is achieved using Panoptic-DeepLab, which is a state-of-the-art deep neural network for the panoptic segmentation of images, and it is available from DeepLab2, an image segmentation library. The neural network was trained using CubiCasa5K, a large-scale floor-plan image dataset containing 5000 samples, annotated into over 80 floor-plan object categories. Then, using the prediction of each pixel, a graph that shows how rooms and doors are connected is created. An application that presents this information in a user-friendly manner and provides graph editing capabilities was developed. Finally, the exits are set, and the optimal path for evacuation is calculated from each node using Dijkstra’s algorithm.
Recent Developments in the Listing of Patents in FDA's Orange Book

Biotechnology Law ReportVol. 42, No. 4 The Holman ReportRecent Developments in the Listing of Patents in FDA's Orange BookBy Christopher M. HolmanBy Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri-Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.Search for more papers by this authorPublished Online:7 Aug 2023https://doi.org/10.1089/blr.2023.29318.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 4Aug 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:By Christopher M. Holman.Recent Developments in the Listing of Patents in FDA's Orange Book.Biotechnology Law Report.Aug 2023.168-183.http://doi.org/10.1089/blr.2023.29318.cmhPublished in Volume: 42 Issue 4: August 7, 2023Online Ahead of Print:July 24, 2023PDF download
Empirical Analysis of Crossover-Based Evolutionary Algorithms on Rugged Landscape

The crossover is considered as an essential component of the evolutionary algorithms. There exist different ways of how to use it, and in this paper we compare two approaches commonly used in pseudo-Boolean optimization, the classic approach of (μ + 1) GA using a uniform crossover which combines the best features of the parents, and the approach of the (1 + (λ, λ)) GA genetic algorithm, where crossover is used as a repair mechanism after a strong mutation. We study empirically how these two approaches perform on the rugged landscapes and conclude that the classic approach works better, but only when we use mechanisms which support the diversity in the population.