Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Facial Landmarks for Denture Models

The neoclassical canon proportions for face evaluation were defined by artists and anatomists in the 17th and 18th centuries. These proportions are used as a reference for planning facial or dental reconstruction treatments. However, the vertical canon assumption that the face is divided vertically into three equal thirds, which was adopted a long time ago, has not been verified yet. We used 2D photos freely available online and annotated them with anthropometric landmarks using machine learning to verify this hypothesis. Our results indicate that the vertical dimensions of the face are not always divided into equal thirds. Thus, this vertical canon should be used with caution in cosmetic, plastic, or dental surgeries, and reconstruction procedures. In addition, when working with 2D images, we observed that landmarking 2D images can be inaccurate due to pose sensitivity. To address this problem we proposed the use of 3D face landmarking. Our results indicate that regardless of the 3D face scan pose, we were able to annotate the face scans with close to accurate landmarks.
Analysis of Cost-Sensitive Algorithms for Degree of Imbalancing

Class imbalance, along with other characteristics of the data, such as feature set and class separability, could affect the performance of most machine learning algorithms. This can be attributed to the algorithm’s primary assumption about the data being class-balanced and indifferent weightage among different misclassification errors. Class imbalance can be handled using both data and algorithmic-level methods. In this paper, we work on cost-sensitive learning, applied at the algorithmic level. We use the Cost-Sensitive Logistic Regression (CSLR) algorithm as a reference. We propose a methodology to empirically evaluate the performance of cost-sensitive algorithms over varying degrees of imbalanced data. Cost-sensitive learning induces a cost matrix consisting of the weighting scheme of different misclassification errors into the algorithm’s training process. This, in turn, forces the model to penalize the misclassification errors according to the skewness of the data to reduce the learning bias of the model towards the majority class. we present empirical evaluations of the reference model over four popular datasets and analyse its behaviour using MAE and Kappa values.
Age Analysis with Convolutional Neural Networks

The use of facial recognition technologies is increasingly present in everyday life and more widespread in many information systems. This type of technology can be found in authentication methods, access systems, photo editing software and different security mechanisms or surveillance cameras, among others. The large amount of image-related data currently being handled has made it possible to train artificial neural structures based on Machine Learning capable of obtaining all kinds of characteristics from a person's image, such as age, skin color or gender. The aim of this work is to use neural techniques that consistently trained allow us to obtain a model to determine the age of a person from the image of his or her photograph. The results obtained show a very high accuracy rate, so that the applicability of the model is possible in different scenarios.
AJNA - Voice Assisted Captioning Tool for the Blind

In India, there are currently over 18 million blind or visually impaired persons which makes up about 20% of the world’s blind population. The inability to read text or identify objects with ease has impaired their quality of living greatly. Automatic caption generation that describes visual content of images captured in real time has garnered significant attention over the years. Several approaches have been proposed to achieve this task. However, they were not well-suited for mobile devices due to the fact that they were resource-intensive. This paper proposes to employ an attention based LSTM model that performs the task of captioning images that are part of the VizWiz dataset. The generated caption describes the scene captured by the image. The model generates text captions in English and these are converted to voice modality to facilitate the usage by blind people.
On Applying Boolean Masking to Exponents

Implementations of block ciphers typically use Boolean shares of intermediate states to prevent side-channel analysis. Implementations of public-key cryptographic algorithms are typically protected by using the algebraic properties of the algorithms. In this work, we present an exponent splitting technique with minimal impact on performance based on Boolean shares. This method can be combined with other countermeasures to make a side-channel resistant implementation with minimal increase in execution time.
PENYULUHAN TINDAK TUTUR YANG SANTUN DALAM MEMPENGARUHI ETIKA BERBAHASA ANAK DI RULI BALOI KOLAM, BATAM

Language is a means of communication between humans in people's lives in the sound of speech produced by human speech instruments. Language in its function as a means of communication its existence is very important in society. Without language in communicating, between speakers and interlocutors will have difficulty in capturing the communication message. Communication through language allows everyone to adjust to their physical and social environment and to learn the habits, culture and background of the interlocutor. Our culture judges that speaking using polite language will show our true ethics, education, and culture as human beings who are well respected as human beings. Whether or not a speech is polite depends on the size of the politeness of the people speaking a language. Speech in Indonesian is generally considered polite if speakers or speech partners use words that do not contain direct ridicule, do not command directly, and respect others. Good and correct use of language will be necessary in conveying messages to others. Both direct and indirect messages use message characteristics that need to be understood by others. The use of language to communicate is inseparable from the determinants of communication acts and the principles of politeness and realized in communication acts.
Fast Incremental Updating Frequent Pattern Growth algorithm for Mining

Abstract: When a new incremental database is added to an existing database, certain existing frequent item sets may become infrequent item sets, and vice versa. This is one of the most difficult tasks in association rule mining. As a result, certain old association rules may become invalid, while others may become legitimate. It's possible that rules will arise. For incremental association rule mining, we devised a new, more efficient method. A new Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth) was developed using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFPGrowth), as well as a compact sub-tree appropriate for incremental mining of frequent patterns Item sets. This approach extracts previously mined frequent item sets and their support counts from the original database, then uses them to efficiently mine frequent item sets from the updated database minimizing the amount of original database rescans. Our algorithm was able to minimize when compared to individual FP-Growth, needless sub-tree creation consumes more resources and time. According to the results, our algorithm's average execution time for pattern growth mining is 46 percent faster than apriori and eclat algorithm. This method for mining incremental association rules and our findings could directly aid computer business intelligence designers and developers methods
Model Pembelajaran Online di Kelas Tingkat Sekolah Dasar

This study aims to describe the portrait of online learning at SD Negeri 15 Lalembuu, explain the obstacles in its implementation, and describe students' learning motivation in following this learning model. Data collection methods consist of interviews, observation, and documentation, which are then analyzed through data reduction, data presentation, and conclusion. The study shows that online learning at SD Negeri 15 Lalembuu, Konawe Selatan District, uses the WhatsApp application; the teacher creates class groups consisting of teachers, students, and student's parents. Assignments are sent via the WhatsApp group. However, some students did not have cell phones and did not understand the teacher's presentation while studying. Student learning motivation in online learning is divided into motivation that arises from within the student himself and motivation that arises from outside the student's self, namely the family environment, school environment, and community environment.Keywords: Student motivation for learning, online learning, and online learning limitations
ANALISIS SISTEM PENGGAJIAN TENAGA KONTRAK UIN SUNAN KALIJAGA YOGYAKARTA

UIN (Universitas Islam Negeri) Sunan Kalijaga is an Islamic State University that previously named IAIN SunanKalijaga. It was established in 26 September 1951 and it was one of the oldest university that established by the Ministry of Religious Affairs. As for the human resources whose working in the university, there are public servants who appointed to work there by the National Administrative system and also contract workers. The contract workers are the employee of UIN SunanKalijaga that legally being appointed by the Rector of the University through a decree signed by the Rector itself. The contract system will be renewed annually, started in the beginning of the year. Within the contract, there are terms such as rights and obligations including the amount of the salary. And, the amount of the salary will be changed every 3 years. There basic calculations for the salary system by referring the current national’s public servant salary system and minimum local wage system. Therefore, based on the issue above, this thesis would like to highlight some questions in order to answer some questions; what factors affecting the change of the salary system for the contract workers in UIN SunanKalijaga, Why to factors becoming push factors in the changing of salary system of contract workers and how the current reality of contract workers salary system at UIN Sunan Kalijaga. The aim of the research will be to identify what factors causing the changing of salary system at UIN SunanKalijaga, and to analyse the push factors as well to evaluate the salary system of contract workers at UIN SunanKalijaga. Meanwhile the theoretical framework being employed on this research will be the Salary Theory and Compensation theory. And, as for the method of research, there will be descriptive qualitative and the data will be gathered through primary and secondary data. Documents will be used as source of information and respondents will interviewed to gain some insights of the policy.
Project-based Learning: Formation of Students’ Digital Competencies

The article deals with the issue of digital competencies formation among higher education students. The study focuses on the project-based learning technology as one of the leading educational technologies used in the digital skills formation of a modern specialist. We draw on the fact that practice-oriented education and the formation of interdisciplinary skills are current trends in the preparation of future specialists. Several implemented projects are presented as the result of interdisciplinary project-research activities of students.
A Novel Approach for Improving Accuracy for Distributed Storage Networks

Abstract With the development of storage technology and Internet technology, cloud storage continues to make its impact. Scalability, reliability, and lowered costs have made cloud storage widely used with success in businesses and individuals. The advent of the blockchain has brought some changes. As the incentive layer for IPFS, Filecoin allows storage resources to become tradable, greatly extending storage capacity. However, the process of testing the integrity of data still needs constant improvement. In this chapter, we propose a new data audit proof, in which nodes continuously upload hashed data that has been added to random numbers, and the smart contract will compare the result to verify the integrity of the data. Meanwhile, data owner could calculate and then challenge to verify the data integrity. There are audit miners responsible for regulating the behavior of miners and the protection of users’ data, and audit miners in a state of semi-participation. It is demonstrated later in the chapter that this proof is accurate enough and resistant to attacks.
DistilSR: A Distilled Version of Gene Expression Programming Symbolic Regression

Symbolic Regression (SR) is the task of finding closed-form expressions that describe the relationship between variables in a dataset. Current SR methods tend to neglect a large portion of the search space of 'short' expressions in favor of longer expressions which are less explainable. In contrast to current SR methods, we propose to prioritize expression length over prediction performance. We do so by systematically searching through the search space of 'short' expressions, utilizing K-expressions from Gene Expression Programming. However, the search space of 'short' expressions is large, scaling approximately exponentially with the number of variables in a dataset. To reduce the size of the search space, we propose a method, termed DistilSR, which replaces terminal symbols with weighted linear combinations of variables. We show that DistilSR exactly recovers the ground-truth equation of 16 synthetic datasets 100% of the time, outperforming 14 benchmark SR methods in SRBench. DistilSR also shows outperformance on 14 real-world datasets when compared against 14 benchmark SR algorithms and 4 benchmark non-SR algorithms from SRBench. These equations were also consistently shorter. Finally, to further enforce sparsity of weights, we propose a method of actively setting uninfluential weights to 0, achieving even shorter expressions with competitive prediction performance.
Co-evolving Recurrent Neural Networks and their Hyperparameters with Simplex Hyperparameter Optimization

Designing machine learning models involves determining not only the network architecture, but also non-architectural elements such as training hyperparameters. Further confounding this problem, different architectures and datasets will perform more optimally with different hyperparameters. This problem is exacerbated for neuroevolution (NE) and neural architecture search (NAS) algorithms, which can generate and train architectures with a wide variety of architectures in order to find optimal architectures. In such algorithms, if hyperparameters are fixed, then suboptimal architectures can be found as they will be biased towards the fixed parameters. This paper evaluates the use of the simplex hyperparameter optimization (SHO) method, which allows co-evolution of hyperparameters over the course of a NE algorithm, allowing the NE algorithm to simultaneously optimize both network architectures and hyperparameters. SHO has been previously shown to be able to optimize hyperparameters for convolutional neural networks using traditional stochastic gradient descent with Nesterov momentum, and this work extends on this to evaluate SHO for evolving recurrent neural networks with additional modern weight optimizers such as RMSProp and Adam. Results show that incorporating SHO into the neuroevolution process not only enables finding better performing architectures but also faster convergence to optimal architectures across all datasets and optimization methods tested.
An analysis of choice functions for Fuzzy ART using grammatical evolution

The Fuzzy Adaptive Resonance Theory (ART) algorithm is effective for unsupervised clustering. The Fuzzy ART choice function is an integral part of the Fuzzy ART algorithm. One of the challenges is that different choice functions are effective for different datasets. This work evolves the choice function using GE. The study compares the evolved choice functions to manually created choice functions. This study compares two different grammars for the GE, a basic grammar that includes only functions from the Fuzzy ART algorithm and an extended grammar that includes additional functions. This work also compares different fitness functions for GE. Analysis is done using ten UCI benchmark datasets and three real-world sentiment analysis datasets, it is found that the evolved functions using the extended grammar perform better than the manually created functions. The best fitness function to use for the GE is dataset dependent.
Generative Hyper-heuristics

tutorial Share on Generative Hyper-heuristics Authors: Daniel R. Tauritz Auburn University, Auburn, USA Auburn University, Auburn, USA https://orcid.org/0000-0002-8233-7701View Profile , John R. Woodward Loughborough University, Loughborough, UK Loughborough University, Loughborough, UK https://orcid.org/0000-0002-2093-8990View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1069–1098https://doi.org/10.1145/3583133.3595033Published:24 July 2023Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks9 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
Synthesizing Effective Diagnostic Models from Small Samples using Structural Machine Learning: a Case Study in Automating COVID-19 Diagnosis

The global COVID-19 pandemic has demonstrated the urgent need for diagnostic tools that can be both readily applied and dynamically calibrated by non-specialists, in terms of a sensitivity/specificity tradeoff that complies with relevant healthcare policies and procedures. This article describes the design and deployment of a novel machine learning algorithm, Structural Machine Learning (SML), that combines memetic grammar-guided program synthesis with self-supervised learning in order to learn effectively from small data sets while remaining relatively resistant to overfitting. SML is used to construct a signal processing pipeline for audio time-series, which then serves as the diagnostic mechanism for a wide-spectrum, infrasound-to-ultrasound e-stethoscope. In blind trials supervised by a third party, SML is shown to be superior to Deep Learning approaches in terms of the area under the ROC curve, while allowing for transparent interpretation of the decision-making process.
Comparison Of Three Versions Of Whale Optimization Algorithm (WOA) On The Bbob Test Suite

This paper benchmarks the canonical version of Whale Optimization Algorithm (WOA) with two recent adaptations: "Enhanced WOA (E-WOA)" and "modified Symbiotic organism search - Differential Evolution - WOA (m-SDWOA)" with the desire to research the improvement potential of the WOA. We also used Black-Box Differential Evolution with normalization of the scaling factor (BBDE-N), included in the data set of the bbob test suite, as a baseline to compare the performance. The results show that E-WOA and m-SDWOA improve significantly efficiency and the percentage of problems hit by WOA.
Single and Multi-Objective Bilevel Optimization

tutorial Open Access Share on Single and Multi-Objective Bilevel Optimization Authors: Carlos Henggeler Antunes Department of Electrical and Computer Engineering, INESC Coimbra, University of Coimbra, Coimbra, Portugal Department of Electrical and Computer Engineering, INESC Coimbra, University of Coimbra, Coimbra, Portugal https://orcid.org/0000-0003-4754-2168Search about this author , Maria Joao Alves University Coimbra, CeBER, Faculty of Economics, Coimbra, Portugal University Coimbra, CeBER, Faculty of Economics, Coimbra, Portugal https://orcid.org/0000-0002-2268-0110Search about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 835–853https://doi.org/10.1145/3583133.3595065Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 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 SiteeReaderPDF
Uberization of Telecom Networks for Cost-Efficient Communication and Computing

This article discusses the uberization of telecommunication and computing network services. The Uber-like platform business model is discussed for application in future networks together with interesting analogies of communication and computing (2C) resource-sharing models. The economy of this sharing is discussed, and some recommendations for network uberization are provided.
6G-Enabled Anomaly Detection for Metaverse Healthcare Analytics in Internet of Things

As an emerging concept, the metaverse incorporates a range of advanced technologies and offers a great opportunity to enhance the experiences of healthcare in clinical practice and human health. However, many cyber security issues often occur in the metaverse healthcare analytics such as DDoS attack, probe attack, and port scanning attack. Fortunately, 6G-enabled intrusion detection can detect anomalous activities with the help of an anomaly detection algorithm for metaverse healthcare analytics. Nevertheless, different from static data, data streams in metaverse healthcare have the intrinsic characteristics of infiniteness, correlation, and distribution change. Traditional static data anomaly detection algorithms do not consider these characteristics, which may result in low accuracy and efficiency. In this paper, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> tream <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> nomaly <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> etection (DS_AD) approach driven by 6G network is proposed for metaverse healthcare analytics, which incorporates a sliding window and model update into LSHiForest. DS_AD uses a change detection mechanism to optimize the model update. The core design utilizes hash functions to partition data spaces to find anomalies. To validate the feasibility of DS_AD, multiple groups of experiments are designed and executed on SMTP and HTTP datasets. Experimental results show that compared with baselines, our proposal performs favorably for data streams in terms of accuracy and efficiency.