Reviewerly/exHarmony huggingface dataset:
Single-Block Recursive Poisson–Dirichlet Fragmentations of Normalized Generalized Gamma Processes

Dong, Goldschmidt and Martin (2006) (DGM) showed that, for 0&lt;α&lt;1, and θ&gt;−α, the repeated application of independent single-block fragmentation operators based on mass partitions following a two-parameter Poisson–Dirichlet distribution with parameters (α,1−α) to a mass partition having a Poisson–Dirichlet distribution with parameters (α,θ) leads to a remarkable nested family of Poisson—Dirichlet distributed mass partitions with parameters (α,θ+r) for r=0,1,2,⋯. Furthermore, these generate a Markovian sequence of α-diversities following Mittag-Leffler distributions, whose ratios lead to independent Beta-distributed variables. These Markov chains are referred to as Mittag-Leffler Markov chains and arise in the broader literature involving Pólya urn and random tree/graph growth models. Here we obtain explicit descriptions of properties of these processes when conditioned on a mixed Poisson process when it equates to an integer n, which has interpretations in a species sampling context. This is equivalent to obtaining properties of the fragmentation operations of (DGM) when applied to mass partitions formed by the normalized jumps of a generalized gamma subordinator and its generalizations. We focus primarily on the case where n=0,1.
A cross-cultural study of the influence of environmental factors on VR gamers experience of spatial presence, enjoyment, and subjective workload: A preliminary study.

This study is a first step toward identifying some factors influencing spatial presence, enjoyment, and cognitive workload in virtual reality (VR) game play.The study was conducted in the Northeastern U.S. (n = 40) and Southwest Nigeria (n = 40) using a factorial experimental design with three environmental factors (i.e., lighting, flooring, and in-game sound) at two levels per factor.Participants were randomly assigned to one of eight experimental conditions, playing a VR American football game using Oculus Rift S on 3 separate days, under a different condition each time.Following VR game play, measures of spatial presence (α = .86),enjoyment (α = .89),and subjective workload (α = .78)were collected through self-report surveys.A factorial analysis of variance (ANOVA) was used to examine the influence of environmental factors on spatial presence, enjoyment, and subjective workload.The study found main effects of (a) lighting on subjective workload for the Nigerian sample (Day 2 and Day 3), (b) flooring on subjective workload for both cohorts (Day 2), and (c) in-game sound on spatial presence and subjective workload for the Nigerian sample (Day 3) and on spatial presence for the U.S. sample (Day 2 and Day 3).An interaction effect of lighting and flooring on subjective workload was found for the Nigerian sample on Day 1 and Day 2. An interaction effect of lighting, flooring, and in-game sound on enjoyment was found for the Nigerian sample on Day 3. We present implications for theory and practice.
Fuzzy based sensorless tracking controller on the dual-axis PV panel for optimizing the power production

In general active sun trackers move because they respond to light sensors that measure the intensity of sunlight. However, sensor-based trackers are usually more expensive than sensor-less trackers. In addition, based on several studies, a comparison between the sensor and sensorless based tracker only reports lower tracking error and higher power generation for sensor-based than sensorless tracker. However, it does not include an analysis of energy use on the sensor. Therefore, this study aims to design a sensorless closed-loop tracking system for solar panels with two degrees of freedom. The tracking controller in this study is based on the Fuzzy Logic Controller (FLC) method. In this study, a dual-axis PV can increase power output by 20.2% compared to a fixed PV (0 ° axis position). In comparison to a fixed PV, dual-axis PV adjusts the solar panel perpendicular to the sun's position to optimize electrical conversion.
A Backpropagation Extreme Learning Machine Approach to Fast Training Neural Network-Based Side-Channel Attack

This work presented new Deep learning Side-channel Attack (DL-SCA) models that are based on Extreme Learning Machine (ELM). Unlike the conventional iterative backpropagation method, ELM is a fast learning algorithm that computes the trainable weights within a single iteration. Two models (Ensemble bpELM and CAE-ebpELM) are designed to perform SCA on AES with Boolean masking and desynchronization/jittering. The best models for both attack tasks can be trained 27x faster than MLP and 5x faster than CNN respectively. Verified and validated using ASCAD dataset, our models successfully recover all 16 subkeys using approximately 3K traces in the worst case scenario.
Sentiment Analysis Of Practice Service's Questionnaire Using Naïve Bayes Classifier Method

Questionnaire is one instrument to measure the quality of service given by a college, particularly practicum service, including laboratory assistants. The large number of students is time and energy-consuming to analize the sentiment from the questionnaire. Therefore, a sentiment classifier is required to classify the students&#x0027; comments from the questionnaire. The system was built using Naive Bayes Classifier to determine the statements in questionnaire expresss a positive, negative, or neutral sentiment. The method will calculate the Vmap value (probability theory) and the statement will be classified into the class with the highest Vmap value. The calculation was started by processing the questionnaire through case folding, tokenization, normalization, stemming, and filtering, then, finally, the classification with Naive Bayes Classifier. The research shows that Naive Bayes Classifier can analyze the sentiment in a questionnaire with the highest accuracy is 83,33&#x0025; (240 training data and 60 testing data), compared to 81&#x0025; accuracy with 200 training data and 100 testing data.
Android Based Personal Finance Management Application: Design and Development

The aim of this work is to design an application with the main function to ease the user in the process of managing their personal finance. The process of evaluating their financial activities record should becomes easier because the application enables their own financial goal to be monitored, controlled, and evaluated using the data. There are two phases in this research: (1) concept evaluation phase, and (2) content realization phase. The first phase produced a list of approved features that had undergone a series of concept testing. All features support all three time period: past, present, and future. Users can see their past activities, record their present transaction, and plan their future goals. The second phase produced a design for a mobile application, specifically in a form of use case diagram and class diagram. Feature comparison between similar applications had also been done twice, in the beginning and at the end. All process are done to ensure that the concept are designed as objective as possible.
Measuring Group Separability in Geometrical Space for Evaluation of Pattern Recognition and Dimension Reduction Algorithms

Evaluating group separability is fundamental to pattern recognition. A plethora of dimension reduction (DR) algorithms has been developed to reveal the emergence of geometrical patterns in a low-dimensional space, where high-dimensional sample similarities are approximated by geometrical distances. However, statistical measures to evaluate the group separability attained by DR representations are missing. Traditional cluster validity indices (CVIs) might be applied in this context, but they present multiple limitations because they are not specifically tailored for DR. Here, we introduce a new rationale called projection separability (PS), which provides a methodology expressly designed to assess the group separability of data samples in a DR geometrical space. Using this rationale, we implemented a new class of indices named projection separability indices (PSIs) based on four statistical measures: Mann-Whitney U-test p-value, Area Under the ROC-Curve, Area Under the Precision-Recall Curve, and Matthews Correlation Coefficient. The PSIs were compared to six representative cluster validity indices and one geometrical separability index using seven nonlinear datasets and six different DR algorithms. The results provide evidence that the implemented statistical-based measures designed on the basis of the PS rationale are more accurate than the other indices and can be adopted not only for evaluating and comparing group separability of DR results but also for fine-tuning DR algorithms’ hyperparameters. Finally, we introduce a second methodological innovation termed trustworthiness, a statistical evaluation that accounts for separability uncertainty and associates to the measure of each index a p-value that expresses the significance level in comparison to a null model.
A Fairness of Data Combination in Wireless Packet Scheduling

With the proliferation of artificial intelligence (AI) technology, the function of AI in a sixth generation (6G) environment is likely to come into play on a large scale. Moreover, in recent years, with the rapid advancement in AI technology, the ethical issues of AI have become a hot topic. In this paper, the ethical concern of AI in wireless networks is studied from the perspective of fairness in data. To make the dataset fairer, novel dataset categorization and dataset combination schemes are proposed. For the dataset categorization scheme, a deep-learning-based dataset categorization (DLDC) model is proposed. Based on the results of the DLDC model, the input dataset is categorized based on the group index. The datasets based on the group index are combined using various combination schemes. Through simulations, the results of each dataset combination method and their performance are compared, and the advantages and disadvantages of fairness and performance according to the dataset configuration are analyzed.
Edukasi Pembuatan Desain Grafis Menarik Menggunakan Aplikasi Canva

One of the capabilities that need to be updated in the era of the COVID-19 pandemic is the ability to utilize technology to disseminate information and communication. In disseminating information, it is necessary to have a media that supports the delivery of information, one of which is design media. Canva is a graphic design application that can help design, create, and edit designs for beginners. These designs can be in the form of greeting cards, posters, brochures, infographics, and even presentations. The service is carried out in partnership with the Mahardika Vocational School of Karangploso. The training material on designing using Canva is focused on poster design. This activity takes place with the transfer of knowledge and technology in the form of Canva training in designing more attractive posters. There are three sessions in the training, an introduction and understanding session, an implementation session, and a training simulation session. The training is carried out through online meetings using the Zoom Meeting application. The training was attended by 27 participants who were students of the Mahardika Vocational School of Karangploso. The evaluation results obtained from the google form score showed an average score of 8.4. These results indicate a positive response from the participants
DGAN-KPN: Deep Generative Adversarial Network and Kernel Prediction Network for Denoising MC Renderings

In this paper, we present a denoising network composed of a kernel prediction network and a deep generative adversarial network to construct an end-to-end overall network structure. The network structure consists of three parts: the Kernel Prediction Network (KPN), the Deep Generation Adversarial Network (DGAN), and the image reconstruction model. The kernel prediction network model takes the auxiliary feature information image as the input, passes through the source information encoder, the feature information encoder, and the kernel predictor, and finally generates a prediction kernel for each pixel. The generated adversarial network model is divided into two parts: the generator model and the multiscale discriminator model. The generator model takes the noisy Monte Carlo-rendered image as the input, passes through the symmetric encoder–decoder structure and the residual block structure, and finally outputs the rendered image with preliminary denoising. Then, the prediction kernel and the preliminarily denoised rendered image is sent to the image reconstruction model for reconstruction, and the prediction kernel is applied to the preliminarily denoised rendered image to obtain a preliminarily reconstructed result image. To further improve the quality of the result and to be more robust, the initially reconstructed rendered image undergoes four iterations of filtering for further denoising. Finally, after four iterations of the image reconstruction model, the final denoised image is presented as the output. This denoised image is applied to the loss function. We compared the results from our approach with state-of-the-art results by using the structural similarity index (SSIM) values and peak signal-to-noise ratio (PSNR) values, and we reported a better performance.
The Aesthetics of Prosthetics

The history of limb prostheses and soft-tissue replacements has centered largely on discussions of mimesis: the attempt to make the body whole again and mitigate the “uncanny gaze” that might call attention to visible deviations from the normative human body. This mimetic impulse has dominated prosthetic design theory for the last two hundred years. Recent developments in robotics and medical device technology—aided by the history of art and design—have questioned the necessity of mimesis in artificial body parts by embracing the uncanny and exploring the social and philosophical precepts of what it means to be human. However, these fundamental questions of mimetic prostheses and body augmentation are not novel to modernity. Ancient and medieval societies grappled with the same issues when confronted with disability or nonnormative bodies, and they frequently rejected mimesis as a guiding principle for making the body whole again. Instead, they often pushed the boundaries of bodily agency through a keen awareness of materiality, designing prostheses that subverted the power of the uncanny gaze while embracing the uniqueness of the trans-, meta- or even posthuman body.
Adoption Computerized Certificate Transparency And Confidentiality

Due to the unavailability of issuing entities, the increasing number of valuable documents that are still being issued in printed form has an impact on forgery and cannot be verified. Academic certification is an achievement that everyone desires because it has a positive effect and has a continuity in their social life. These activities are listed as being able to identify, analyze, and try out any of the ledger options that are available. arise to provide greater efficiency, reliability, and a level of independence. By implementing a prototype that could issue, verify, and distribute certificates, the fact of the concept was proposed. The experimental test results are given, as well as the use of blockchain technology for the purpose of analyzing. Finally, this work outlines the current growth and maturity of equipment encountered, reports progress and limitations, and reveals issues that still need to be addressed.
Conformance evaluation of the top‐100 Ethereum token smart contracts with Ethereum Request for Comment‐20 functional specifications

This study is an attempt to evaluate the compliance problem regarding the current status of the establishment of agreed-upon interfaces for tokens, which are the most important type of applications of the Ethereum blockchain. The authors analysed the existing application programming interface standard interface called Ethereum Request for Comment 20 (ERC-20), proposed functional ERC-20 specifications, and evaluated the real-world token smart contracts that have been deployed and used for transactions on a blockchain. For the evaluation, the authors developed an automatic tool with a test suite based on the proposed functional specifications. The authors' experiment revealed that no top-100 market capitalization token smart contracts turned out to agree perfectly based on the functional specifications. In addition, the authors found the de facto standard behaviour deduced through testing with the functional specifications and suggested a possible countermeasure for the compliance problem in dealing with multiple tokens together.
Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study

The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. Among these approaches, some works have studied dynamism in scientific literature by employing text analysis techniques that rely on topic models to study the dynamics of research topics. Unlike topic models that do not delve deeper into the content of scientific publications, for the first time, this paper uses temporal word embeddings to automatically track the dynamics of scientific keywords over time. To this end, we propose Vec2Dynamics, a neural-based computational history approach that reports stability of k-nearest neighbors of scientific keywords over time; the stability indicates whether the keywords are taking new neighborhood due to evolution of scientific literature. To evaluate how Vec2Dynamics models such relationships in the domain of Machine Learning (ML), we constructed scientific corpora from the papers published in the Neural Information Processing Systems (NIPS; actually abbreviated NeurIPS) conference between 1987 and 2016. The descriptive analysis that we performed in this paper verify the efficacy of our proposed approach. In fact, we found a generally strong consistency between the obtained results and the Machine Learning timeline.
Near Real Time AI Personalization for Notifications at LinkedIn

Notifications at LinkedIn are very crucial for our members to stay informed about their network, discover professionally relevant content, conversations and courses, as well as identify potential career opportunities. For the Notifications AI team, our mission is to use AI to notify the right members, about the right content, at the right time and frequency through the right channel (push, in app or email) to maximize member value. In this talk we will give an overview of the AI systems and models behind these decisions.
Abstract ideas and patent-eligible subject matter in the US patent system: who can provide guidance in this uncertain legal landscape?

The present article is an adaptation of a paper written by Ekaterina Stolbova in the context of her Master of Laws at the University of Antwerp (2019–2020). In March 2021, the Enlarged Board of Appeal of the European Patent Office (EPO) issued its keenly awaited decision G 1/19 on computer-implemented simulations.1 This was the second opportunity for the Enlarged Board of Appeal to clarify the assessment of the patentability of computer-implemented inventions.2 Computer-implemented inventions (ie computer programs) are accepted by the EPO as being, by reason of their technical character, inventions within the meaning of Article 52(1) of the European Patent Convention (EPC),3 while computer programs ‘as such’ are excluded from the category of ‘inventions’ and thus from patentability.4 However, the ‘technicality’ requirement has been an object of different interpretations by national courts and by the Boards of Appeal. While technicality in traditional inventions involves concrete and tangible components that are often easy to identify even by persons who are not skilled in the art, technicality in computer-related inventions is more challenging to grasp. In fact, computer-related inventions have no physical implementation, and their result is intangible.5
Joint Hamming Coding for High Capacity Lossless Image Encryption and Embedding Scheme

Encryption is a widely used solution to prevent privacy leakage and illegal spread when sensitive images are uploaded to cloud storage. Hiding technology also allows confidential data to be embedded into encrypted images for secret communication. As image accuracy without distortion is essential within certain fields (such as medicine and the military), sensitive images must be completely decrypted back into the original images. However, an encrypted image is a noise-like pattern that is meaningless to a user; thus, it is difficult for a user to find the accurate image they desire. Take keywords as search indexes and embed them in encrypted images for encrypted image retrieval as an example. This idea has been extended by Chen and Line’s scheme to achieve higher capacity with reversibility. The proposed scheme adjusts the coding results according to smooth and complex images to increase its hiding capacity. In addition, two thresholds are designed to adjust the predicted pixel value to be close to the original one. Experiments show that compared with the other schemes, the proposed method achieves superior results. In addition, a hidden encrypted image can be extracted from the cover image. Afterward, the hidden secrets can be completely extracted, and sensitive images can also be perfectly restored.
Thoughts on Construction of Practical Training Base in Vocational Colleges

This paper expounds the importance of the construction of practical training bases in vocational colleges and the relevant policies promulgated by the state in recent years."High level and specialization" is an important feature of the future development of the training base of vocational colleges.In this context, it is a very important research work to systematically sort out the problems existing in the construction and operation of the existing training bases and put forward targeted suggestions.The problems of the construction and operation of the existing training bases are sorted out, and a questionnaire survey is carried out.On this basis, seven suggestions are put forward.The research content of this paper not only provides reference for the future construction of vocational training bases, but also points out the direction for the upgrading and reconstruction of existing training bases.