Reviewerly/exHarmony huggingface dataset:
Things of Interest Recommendation with Multidimensional Context Embedding in the Internet of Things

The emerging Internet of Things (IoT) makes users and things closely related together, and the interactions between users and things generate massive context data, where the preference information in time, space, and textual content is embedded.Traditional recommendation methods (e.g., movie, music, and location recommendations) are based on static intrinsic context information, which lacks consideration regarding real-time content and spatiotemporal features, failing to adapt to the personalized recommendation in IoT.Therefore, to meet users' interests and needs in IoT, a novel effective and efficient recommendation method is urgently needed.The paper focuses on mining users' things of interest in IoT via leveraging multidimensional context embedding.Specifically, to address the challenge from massive context data embedding different user preference information, the paper employs Convolutional Neural Networks (CNN) to mine the intrinsic content information of things and learn their represent.To solve the real-time recommendation problem, the paper proposes a real-time multimodal model embedded into location, time, and some instant content information to track the features of users and things.Furthermore, the paper proposes a matrix factorizationbased framework using the regularization method to fuse realtime context embedding and intrinsic information embedding.The experimental results demonstrate the proposed method tailored to IoT is adaptable and flexible, and able to capture user personalized preference effectively.
Media Art—XXI Century: Genesis, Art Programs, Education Problems International Scientific Conference

The article reviews the reports delivered on November 1–3, 2022, at the International Research-to-Practice Conference Media Art—XXI Century: Genesis, Art Programs, Education Problems. It was organized by the Russian Academy of Arts, the Research Institute of Theory and History of Fine Arts, the State Institute for Art Studies, Stroganov Moscow State Academy of Design and Applied Arts, the Moscow Architectural Institute (State Academy), and the National Academy of Design. The conference aimed at comprehending the current state of media art in the context of digitalization of classical works and methods of their exposure; at identifying key program and aesthetic dominants in media art; and at systematizing the most relevant educational projects. The organizers suggested the following aspects as the major research focuses: classical arts and media in digital discourse; new media in the socio-cultural environment; current media forms and their genesis; new media in the urban environment; interdisciplinary and authorial projects; educational processes and new competencies. Cultural institutions today are faced with the search for a definition of materiality, with fixation of transitory art; and in response, contemporary art studies are forced to develop new theoretical concepts to be used in scholarly discourse. It is how contemporary media, digital and post-digital technologies influence the state and development of contemporary art and art history practice, architecture, design, and decorative arts, that became the key research task of Media Art—XXI Century Conference. The conference demonstrated the wide range of aesthetic, art history, culturological and pedagogical aspects, which are incorporated in the creation, exposure, and further circulation of media art in the socio-cultural space.
Artificial Intelligence Techniques for Electronics

Artificial intelligence technology has become an indispensable element in the [...]
Kelayakan media flash flipbook pada submateri darah

This study aims to determine the feasibility of flash flipbook as a medium for sub blood material learning. The research method is descriptive with survey and the research instrument is a validation sheet. The feasibility of flash flipbook was measured through material validity test and media validity test. The validation aspect of flash flipbook material consists of format, content, and language. The aspect of media validation from flash flipbook consists of general aspects, soft engineering, visual engineering, and audio communication. The results of flash flipbook validation obtained an average value of validation by material experts 3.35 valid criteria and the average value of validation by media experts 3.42 valid criteria. It can be concluded that flipbook flash is suitable to be used as a learning medium on sub blood material.
Smooth Monotonic Networks

Monotonicity constraints are powerful regularizers in statistical modelling. They can support fairness in computer supported decision making and increase plausibility in data-driven scientific models. The seminal min-max (MM) neural network architecture ensures monotonicity, but often gets stuck in undesired local optima during training because of vanishing gradients. We propose a simple modification of the MM network using strictly-increasing smooth non-linearities that alleviates this problem. The resulting smooth min-max (SMM) network module inherits the asymptotic approximation properties from the MM architecture. It can be used within larger deep learning systems trained end-to-end. The SMM module is considerably simpler and less computationally demanding than state-of-the-art neural networks for monotonic modelling. Still, in our experiments, it compared favorably to alternative neural and non-neural approaches in terms of generalization performance.
Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models

Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given. This, however, is not reasonable in many real-world scenarios. In latency-sensitive applications, rather than waiting for the full utterance, translations should be spoken as soon as the information in the input is present. In this work, we introduce a system for simultaneous S2ST targeting real-world use cases. Our system supports translation from 57 languages to English with tunable parameters for dynamically adjusting the latency of the output -- including four policies for determining when to speak an output sequence. We show that these policies achieve offline-level accuracy with minimal increases in latency over a Greedy (wait-$k$) baseline. We open-source our evaluation code and interactive test script to aid future SimulS2ST research and application development.
Blockchain-Enabled Vaccination Registration and Verification System in Healthcare Management

Client-server-based healthcare systems are unable to manipulate a high data volume, prone to a single failure point, limited scalability, and data integrity. Particularly, several measures introduced to help curb the spread of Covid-19 were not effective and patient records were not adequately managed and maintained. Most vaccination-proof certificates were forged by unauthorized parties and no standard verification medium exists. Therefore, this paper proposes a blockchain-enabled vaccination management system (VMS). VMS utilizes smart contracts to store encrypted patients record, generate vaccination certificates, and verify the legitimacy of the certificate using a QR code. VMS prototype is implemented using Ethereum, a public blockchain and simulations performed based on Apache JMeter and Hyperledger Caliper to assess its performance in terms of throughput, latency and response time, and the average time per transaction. Results show VMS achieved an average: response time of 132.24 ms, the throughput of 379.89 tps, latency of 204.60 ms, and time of transactions is 10s-12s for 1000 transactions. Also, its comparison with the centralized database shows the traditional database’s effectiveness in transaction processing but lacks data privacy and security strengths. We, therefore, recommend the use of blockchain in the healthcare system and other related sectors such as elections, and student records management to ensure data privacy and security and rid the system of a single point of failure.
Experimental Verification of a One-Dimensional Diffraction-Limit Coronagraph

We performed an experimental verification of a coronagraph. As a result, we confirmed that, at the focal region where the planetary point spread function exists, the coronagraph system mitigates the raw contrast of a star-planet system by at least $1\times10^{-5}$ even for the 1-$λ/D$ star-planet separation. In addition, the verified coronagraph keeps the shapes of the off-axis point spread functions when the setup has the source angular separation of 1$λ/D$. The low-order wavefront error and the non-zero extinction ratio of the linear polarizer may affect the currently confirmed contrast. The sharpness of the off-axis point spread function generated by the sub-$λ/D$ separated sources is promising for the fiber-based observation of exoplanets. The coupling efficiency with a single mode fiber exceeds 50% when the angular separation is greater than 3--4$\times 10^{-1}λ/D$. For sub-$λ/D$ separated sources, the peak positions (obtained with Gaussian fitting) of the output point spread functions are different from the angular positions of sources; the peak position moved from about $0.8λ/D$ to $1.0λ/D$ as the angular separation of the light source varies from $0.1λ/D$ to $1.0λ/D$. The off-axis throughput including the fiber-coupling efficiency (with respect to no focal plane mask) is about 40% for 1-$λ/D$ separated sources and 10% for 0.5-$λ/D$ separated ones (excluding the factor of the ratio of pupil aperture width and Lyot stop width), where we assumed a linear-polarized-light injection. In addition, because this coronagraph can remove point sources on a line in the sky, it has another promising application for high-contrast imaging of exoplanets in binary systems.
A Comparative Study of Machine Learning Techniques to Predict Types of Breast Cancer Recurrence

The prediction of breast cancer recurrence is a crucial problem in cancer research that requires accurate and efficient prediction models.This study aims to compare the performance of different machine learning techniques in predicting types of breast cancer recurrence.In this study, the performance of logistic regression, decision tree, K-Nearest Neighbors, and artificial neural network algorithms was compared on a breast cancer recurrence dataset.The results show that the artificial neural network algorithm outperformed the other algorithms with 91% accuracy, followed by the decision tree (DT) algorithm and K-Nearest Neighbors (kNN) also performed well with accuracies of 90.10% and 88.20%, respectively, while the logistic regression algorithm had the lowest accuracy of 84.60%.The results of this study provide insight into the effectiveness of different machine learning techniques in predicting types of breast cancer recurrence and could guide the development of more accurate prediction models.
Linked Deep Gaussian Process Emulation for Model Networks

Modern scientific problems are often multi-disciplinary and require integration of computer models from different disciplines, each with distinct functional complexities, programming environments, and computation times. Linked Gaussian process (LGP) emulation tackles this challenge through a divide-and-conquer strategy that integrates Gaussian process emulators of the individual computer models in a network. However, the required stationarity of the component Gaussian process emulators within the LGP framework limits its applicability in many real-world applications. In this work, we conceptualize a network of computer models as a deep Gaussian process with partial exposure of its hidden layers. We develop a method for inference for these partially exposed deep networks that retains a key strength of the LGP framework, whereby each model can be emulated separately using a DGP and then linked together. We show in both synthetic and empirical examples that our linked deep Gaussian process emulators exhibit significantly better predictive performance than standard LGP emulators in terms of accuracy and uncertainty quantification. They also outperform single DGPs fitted to the network as a whole because they are able to integrate information from the partially exposed hidden layers. Our methods are implemented in an R package $\texttt{dgpsi}$ that is freely available on CRAN.
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data

We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained unlabelled data in order to scale up performance. Most existing semi-supervised methods rely on the assumption that labelled and unlabelled samples are drawn from the same distribution, which limits the potential for improvement through the use of free-living unlabeled data. Consequently, the generalizability and scalability of semi-supervised learning are often hindered by this assumption. Our method aims to overcome these constraints and effectively utilize unconstrained unlabelled data in semi-supervised learning. UnMixMatch consists of three main components: a supervised learner with hard augmentations that provides strong regularization, a contrastive consistency regularizer to learn underlying representations from the unlabelled data, and a self-supervised loss to enhance the representations that are learnt from the unlabelled data. We perform extensive experiments on 4 commonly used datasets and demonstrate superior performance over existing semi-supervised methods with a performance boost of 4.79%. Extensive ablation and sensitivity studies show the effectiveness and impact of each of the proposed components of our method.
Analysis of Self-Organizing Maps and Fuzzy C-Means methods in Clustering Teacher Data for Nominations of Candidates for Education Unit Supervisors

The quality of education is very dependent on the management of education management one of the important factors in the management of education is monitoring and evaluation. Therefore, continuous Education Supervision supported by the appointment of competent supervisors will have implications for the quality of education. Supervision of education management is carried out by the principal or teacher appointed to carry out managerial and academic supervision in certain educational units. The problem that often occurs is that grouping teacher data to be selected as supervisor candidates are still conventional. Therefore, a teacher data grouping model is needed to obtain useful information in planning strategic steps and policy regulations for determining prospective supervisors for academic units. By utilizing Information and Communication Technology, especially in the field of Data Mining, this Teacher Data Grouping research uses the Fuzzy C-Means and Self Organizing Maps method, and the grouping results are analyzed by measuring the spread of data on each grouping formed by using cluster variance. The output of the Teacher Data grouping process using the Fuzzy C-Means and Self Organizing Maps methods can bring up a group of Teacher Data nominations that are competent to be selected as candidates for supervisors for certain educational units. The research results were obtained by forming several groupings in Fuzzy C-Means by providing an error accuracy value of 0.1 and Self Organizing Maps, which is set by the learning rate and learning rate; the results obtained are grouping with 3 clusters by providing a learning rate of 0.8 and a learning rate of 0.7 in the Self Organizing Maps method has a Variant value that is ideal compared to grouping on Fuzzy C-Means and rather than the same method by forming different groups.
ANALYSIS OF THE EFFECT OF VSM ON THE MEMORY ACQUISITION PROCESS USING THE DYNAMIC ANALYSIS METHOD

At first, forensics was restricted to studying data that was stored on a system's hard disk. However, as storage capacity and data encryption increased, applying conventional digital forensic procedures became more challenging. As a result, memory forensics techniques are developed, or are frequently referred to as live forensics, because the process is quicker and more sophisticated. Volatile memory forensics, often known as live forensics, are necessary for this condition. Live forensics has flaws, specifically that some programs can fail when the computer is in active VSM (virtual secure mode). This results in the retrievable evidence being lost. Therefore, determining the cause is essential. The software-based memory acquisition tools Autopsy, Isobuster, DumpIt, and Magnet RAM Capturer are just a few examples. According to the findings of the experiments, the tools that have crashed include DumpIt v1.3.2.20110401. A dynamic code analysis using WindBg as a tool was utilized to study the impact of VSM on the memory acquisition tool. This study's goal is to identify the instances of crashes in various forensic instruments, which will be highly useful for forensic experts performing investigations.
Visualisasi Data Sebagai Layanan Perpustakaan dalam Membantu Pertumbuhan Ekonomi

Data visualization is a series of data processing processes that produce information in a dynamic visual form.Many libraries around the world have embraced data visualization as a decision-making tool to assist them in making key decisions, but few have discussed the possibilities for libraries to employ this technology to help their users' economic growth.The closeness to life in the library, the opportunities for libraries as services based on social inclusion, as well as the challenges that arise for librarians are discussed in this article.The method used is a literature study of literature sources related to data visualization, business, libraries, and librarian competencies.The result is that the concept of data visualization resembles daily activities in the library, namely repackaging information and making statistics.Data visualization aids librarians in transforming data into actionable information for decision-making and serving it to users.Data visualization opens up opportunities for libraries to be used as services based on social inclusion to help the economic growth of their users.The challenge for librarians is the limited skillsets for this technology.To develop their skills, librarians are urged to enroll in education and training programs.
Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah

The need for crude palm oil has increased due to the large demand for vegetable oils in various parts of the world. Beginning in March 2022, the price of crude palm oil set a record high which caused international cooking oil prices to soar, especially for Indonesia. This study aims to predict the price of crude palm oil with test parameters, namely hidden neurons and activation functions. The method used is Extreme Learning Machine (ELM). This method is a development of the artificial neural network (ANN) method which can overcome weaknesses in the learning speed process. There are several stages in this study: (1) pre-processing the data by normalizing the data and dividing the data using the time series split method, (2) analyzing the data using the ELM method by testing parameters, namely hidden neurons and activation functions, (3) analyzing the results of the best parameter trials, (4) calculating forecasting data using the best parameters that have been obtained, and (5) analyzing the forecasting results that have been obtained. This study uses daily data on the price of crude palm oil from April 1 2021 to April 14 2022 obtained from the Investing website. The results of the research that has been carried out obtained MAPE and RMSE values of 0.0173 and 0.0308 with the best parameters namely the number of hidden neurons of 5 and the binary sigmoid activation function. Based on the results obtained, it is hoped that it will make it easier for the government to determine the price of crude palm oil in the future.
HOSPITAL LENGTH OF STAY PREDICTION WITH ENSEMBLE LEARNING METHODE

The hospital length of stay (LoS) is the number of days an inpatient will stay in the hospital. LoS is used as a measure of hospital performance so they can improve the quality of service to patients better. However, making an accurate estimate of LoS can be difficult due to the many factors that influence it. The research conducted aims to predict LoS for treated patients (ICU and non-ICU) with cases of brain vessel injuries by using the ensemble learning method. The Random Forest algorithm is one of the ensembles learning methods used to predict LoS in this study. The dataset used in this study is primary data at PHC Surabaya Hospital. From the results of the simulations performed, the random forest algorithm is able to predict LoS in a dataset of treated patients (ICU and non-ICU) with cases of brain vessel injuries. And the simulation results show a type II error value of 0.10 while the value of type I error is 0.16.
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning

Multi-robot path finding in dynamic environments is a highly challenging classic problem. In the movement process, robots need to avoid collisions with other moving robots while minimizing their travel distance. Previous methods for this problem either continuously replan paths using heuristic search methods to avoid conflicts or choose appropriate collision avoidance strategies based on learning approaches. The former may result in long travel distances due to frequent replanning, while the latter may have low learning efficiency due to low sample exploration and utilization, and causing high training costs for the model. To address these issues, we propose a path planning method, MAPPOHR, which combines heuristic search, empirical rules, and multi-agent reinforcement learning. The method consists of two layers: a real-time planner based on the multi-agent reinforcement learning algorithm, MAPPO, which embeds empirical rules in the action output layer and reward functions, and a heuristic search planner used to create a global guiding path. During movement, the heuristic search planner replans new paths based on the instructions of the real-time planner. We tested our method in 10 different conflict scenarios. The experiments show that the planning performance of MAPPOHR is better than that of existing learning and heuristic methods. Due to the utilization of empirical knowledge and heuristic search, the learning efficiency of MAPPOHR is higher than that of existing learning methods.
Efficient volumetric mapping of multi-scale environments using wavelet-based compression

Volumetric maps are widely used in robotics due to their desirable properties in applications such as path planning, exploration, and manipulation. Constant advances in mapping technologies are needed to keep up with the improvements in sensor technology, generating increasingly vast amounts of precise measurements. Handling this data in a computationally and memory-efficient manner is paramount to representing the environment at the desired scales and resolutions. In this work, we express the desirable properties of a volumetric mapping framework through the lens of multi-resolution analysis. This shows that wavelets are a natural foundation for hierarchical and multi-resolution volumetric mapping. Based on this insight we design an efficient mapping system that uses wavelet decomposition. The efficiency of the system enables the use of uncertainty-aware sensor models, improving the quality of the maps. Experiments on both synthetic and real-world data provide mapping accuracy and runtime performance comparisons with state-of-the-art methods on both RGB-D and 3D LiDAR data. The framework is open-sourced to allow the robotics community at large to explore this approach.