Reviewerly/exHarmony huggingface dataset:
Artificial intelligence, patents and health innovation

The use of eHealth applications has been advocated by researchers, policymakers and governments to better control the COVID-19 contagion and improve delivery of healthcare services. Development of these technologies inevitably relies on adequate intellectual property (and in particular patent) protection. The international community and academic debate have thus far focused, and rightly so, on the need to balance the public interest in access to health technologies with the private interest of IP holders in recouping investments. This chapter instead expands on certain uses of AI-empowered eHealth apps which have manifested during the pandemic. More specifically, it focuses on whether inventions related to these apps can fall within patentable subject matter by satisfying the inventive step and disclosure requirements.
A COMPREHENSIVE EXPERIMENTAL STUDY FOR EVALUATING THE PERFORMANCE OF WELL-KNOWN CNN PRE-TRAINED MODELS IN NOISY ENVIRONMENTS

This work aims at testing the efficiency of the pre-trained models in term of classifying images in noisy environments. To this end, three different well-known pre-trained models i.e. MobileNet, ResNet, and GoogleNet have been tested using a high-quality image dataset, then the model has been saved to be tested after injecting some noise to the images. Particularly, we proposed injecting Gaussian noise into the images in the original dataset gradually to see how the performance of that models can affect with respect to the proportion of the noise in the image. We noted that the performance of the model dropped down dramatically in accordance with the noise proportions, where the classification accuracy of the tested models dropped down from 98.66, 99.00, 98.00% for MobileNet, ResNet, and GoogleNet respectively when the original dataset has been used to 35.33, 73.33, 68.99% when 75% noised dataset has been used for testing the models. To the best of our knowledge this is the first time that the effects of noise on deep learning models have been experimentally studied. The obtained results reflect the need for some auxiliary models that should be used as a pre-processing phase to improve the performance of these models in order to be applied in real-life applications.
Mathematical Game Theory at St. Petersburg State University

The paper is dedicated to the 300th anniversary of St. Petersburg State University. Creation of the school of mathematical game theory at St. Petersburg State University is associated with Nikolai Nikolaevich Vorobyov. The school has an interesting history described in the paper. More than 50 scientists working in the leading Russian and international scientific centers all over the world in the field of differential, dynamic, and cooperative games were the students of this school.
Pengembangan e-LKPD Berbasis Live Worksheet Pada Muatan PPKn Siswa Kelas 4 SD Negeri 2 Karanganyar Kebumen

The aim of this research is to describe: 1) development of live worksheet-based e-LKPD on PPKn content for grade 4 students at SD Negeri 2 Karanganyar Kebumen; 2) the feasibility of developing Live Worksheet-based e-LKPD for PPKn content for grade 4 students at SD Negeri 2 Karanganyar, Kebumen. This type of research is Development (R&amp;D) with the ADDIE model. Data collection techniques use observation, questionnaires and interviews. Data is processed through quantitative and qualitative analysis. The results show that the development of Live Worksheet-Based e-LKPD Teaching Materials for Class 4 PPKn Content at SD Negeri 2 Karanganyar, Kebumen produced teaching material media in the form of Live Worksheet-Based E-LKPD. Media experts assess the feasibility of 96.55% with the criteria "Very Good". The material expert assessed the feasibility as 85.92% with the criteria "Very Good". The survey results were then tested in the field to determine the responses of teachers and students with the results from class teachers obtaining a "Very Good" percentage of 89.72% and a student response rate of 94.75% "Very Good".
Resonantly driven piezoelectric MEMS for programmable photonic circuits

We report the design and characterization of piezoelectric MEMS optical modulators in large-scale programmable integrated photonics. The mechanical resonances are exploited to achieve modulation enhancement for a high-speed, periodic optical switch.
Unlocking hidden knowledge: Harnessing AI, technology, and design for accessible research

This paper is based upon a presentation at the APE conference 2023, during the ‘STM’s New Dotcoms to Watch session’ by CPO and Co-Founder Alejandro Garcia Garcia. It discussed the limitations of the existing format of published journal articles and the barriers it creates. It discusses how this can be tackled using a combination of AI, design innovation and software to create viable alternatives, and extend the reach of the content to readers outside of Academia.
Significance of handcrafted features in human activity recognition with attention-based RNN models

Sensors incorporated in devices are a source of temporal data that can be interpreted to learn the context of a user. The smartphone accelerometer sensor generates data streams that form distinct patterns in response to user activities. The human context can be predicted using deep learning models built from raw sensor data or features retrieved from raw data. This study analyzes data streams from the UCI-HAR public dataset for activity recognition to determine 31 handcrafted features in the temporal and frequency domain. Various stacked and combination RNN models, trained with attention mechanisms, are designed to work with computed features. Attention gave the models a good fit. When trained with all features, the two-stacked GRU model performed best with 99% accuracy. Selecting the most promising features helps reduce training time without compromising accuracy. The ranking supplied by the permutation feature importance measure and Shapley values are utilized to identify the best features from the highly correlated features. Models trained using optimal features, as determined by the importance measures, had a 96% accuracy rate. Misclassification in attention-based classifiers occurs in the prediction of dynamic activities, such as walking upstairs and walking downstairs, and in sedentary activities, such as sitting and standing, due to the similar range of each activity’s axis values. Our research emphasizes the design of streamlined neural network architectures, characterized by fewer layers and a reduced number of neurons when compared to existing models in the field, to design lightweight models to be implemented in resource-constraint gadgets.
Improving Scientific Literature Classification: A Parameter-Efficient Transformer-Based Approach

Transformer-based models have been utilized in natural language processing (NLP) for a wide variety of tasks like summarization, translation, and conversational agents. These models can capture long-term dependencies within the input, so they have significantly more representational capabilities than Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Nevertheless, these models require significant computational resources in terms of high memory usage, and extensive training time. In this paper, we propose a novel document categorization model, with improved parameter efficiency that encodes text using a single, lightweight, multiheaded attention encoder block. The model also uses a hybrid word and position embedding to represent input tokens. The proposed model is evaluated for the Scientific Literature Classification task (SLC) and is compared with state-of-the-art models that have previously been applied to the task. Ten datasets of varying sizes and class distributions have been employed in the experiments. The proposed model shows significant performance improvements, with a high level of efficiency in terms of parameter and computation resource requirements as compared to other transformer-based models, and outperforms previously used methods.
Intrusion detection in the IoT data streams using concept drift localization

&lt;abstract&gt;&lt;p&gt;With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.&lt;/p&gt;&lt;/abstract&gt;
Development Of Service Deployment Models In Private Cloud

One model for gaining access to computer resources that is gaining popularity is cloud computing. There are three main types of services offered by cloud providers. These include software as a service, platform as a service, and infrastructure as a service. Private cloud computing is defined and its advantages are discussed in this project. After that, we'll go into the features, architecture, and implementation of private clouds. One of the main goals of this project is to provide the groundwork for future research on private cloud computing.Both the private cloud provider's and the consumer's duties are detailed in this project. The server's processing capacity may be used by any user with authorization to run applications, save data, or execute any other kind of computational operation. Since the application no longer requires a specific personal computer to run, users are free to do so from any location within the college grounds; this is because the processing power for the application is provided by a server, which is also accessible via a network that is linked to the internet or other connection platforms. Because of Moore's law, which states that the price of computing power continues to fall as the amount of accessible processing power increases, all of this is now within reach.
Bioscientists in the 2008 Survey of New Zealand Scientists and Technologists

Our paper explores differences and similarities of New Zealand bioscientists relative to the larger science community both in terms of their attributes and their opinions on some important issues. The most recent Survey of New Zealand Scientists and Technologists, from which data for this paper were extracted, will serve as a rough guide to a greater understanding of issues that engage the scientific community generally and bioscientists in particular (Sommer 2010). Such understanding will contribute to the advancement of a bio-based economy. The analysis demonstrates specialised capabilities of the New Zealand survey not previously developed. We comment first on the origins and purpose of the 2008 Survey and then reveal some of the distinguishing attributes and opinions of bioscientists compared to all other scientists. There are some important messages in this analysis for both public policy and private action. There are also some conundrums to contemplate.
FAIR digital objects for academic publishers

For 200 years, collective scholarly knowledge was advanced by reporting new findings in the form of narrative text that is rich in logic, pinned to data, hedged with cautious nuance, and yielding novel claims. Authors' narratives evolved over the years into the now familiar academic research article, whose form has radiated into thousands of specialized intellectual niches (i.e., journal titles). In the last decades the corpus of collective scholarly knowledge (both narrative text and published data) has come to exceed human comprehension and challenges the ability of researchers, even those working in narrowly defined disciplines, to keep up. As a response, a wide range of abstracting and indexing services emerged and were among the first to push toward “electronic” publishing. By now, articles are routinely made available in digital repositories, but still the content and the form remain bound to human readers while the powerful information processing capabilities of machines, which ought to assist the researcher, are marginalized to the mundane calculation of impact scores. Today, the long-form narrative and the lack of standards in the academic publishing industry make the bulk of this information notoriously difficult for the machine process and reuse in scientific applications. As such, the classical research article has become increasingly untenable as a meaningful unit of intellectual progress. Since 2016, the FAIR Principles have provided guidance on how to compose data, including information contained in narrative text, to make them machine actionable. Recent developments in FAIR Digital Objects are now being exploited in academic publishing to expose FAIR information at the source, avoiding cumbersome text mining, and making exact meaning available to machine assistants supporting human scholars. Here I describe some of these long-term developments and how they relate to a new Sage/IOS Press journal called FAIR Connect. These trends signal an inevitable movement towards the FAIRifcation of scholarship, and hint at key issues that will impact the practice and business of academic publishing.
The Development of Android-based Computer and Basic Network Learning Media

Abstracts: This study aims to produce valid and practical android-based learning media. Learning methods that are not yet optimal and there is no Android-based learning media can be used as a supporting medium for the learning process. This study used the Research and Development (R&amp;D) method with the ADDIE development model. The subjects of this study were 20 students as a sample. Data collection techniques using a questionnaire with a Likert scale. The results of this study indicate that the average value of the media expert's validity test is 0.68 with the valid category and the material expert is 0.67 with the valid category. For the practicality test on the teacher's response it reached 83.33% in the very practical category and for student responses it reached 90.92 % in the very practical category . Based on the assessment along with input from media experts and material experts as well as the results of field trials of Android-based learning media, it is feasible to use.&#x0D; &#x0D; Keyword: learning media, android based, valid and practical, R&amp;D, ADDIE
Personal Information Technology Infrastructure Quality (PITIQ) Influence on Information and System Quality in LMS Success Models

The COVID-19 Pandemic has forced educational institutions, including in higher education level, to change teaching and learning process. They must carry out all teaching and learning processes using the Learning Management System (LMS). Information Technology Infrastructure (ITI) plays an essential role in LMS. Most studies have been focused on ITI based on organizational perspectives, and only few of them is based on personal resource. This study examined the impact of Personal Information Technology Infrastructure Quality (PITIQ) on the success of LMS. It used the DeLone and McLean model. The participants were 105 students using LMS in Indonesian public universities. This study also employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the survey data. The results indicated that PITIQ provide a significant impact on both the LMS quality of the system and the LMS quality of information. Based on R-square results, the implications of variables in the proposed model were LMS System Quality (52.9%), LMS Information Quality (47.6%), Student Satisfaction (59.6%), the Use (17.0%), and Net Benefit (72.1%). The quality of the system and information from the LMS provider would not be well received if the PITIQ was of low quality. The system’s quality and information quality affect student satisfaction with the LMS. Furthermore, students can experience the Net Benefits of LMS if they continuously use and are satisfied by LMS services.
Factors Affecting the Not Implementation of Medical Record Tracer

Tracer is an important tool in controlling the use of medical record files. Usually used to replace medical records that are out of storage. These exit instructions remain in storage until the borrowed medical records are returned and stored again. This exit sign is equipped with a pocket for storage of request slips. The purpose of this study was to determine the factors that influence the non-application of a medical record tracer at the Pratama Klambir clinic in 2021. This research method used a descriptive qualitative method which was carried out from November to completion. The informants of this research were 6 informants, namely 1 key informant, 4 main informants and 1 triangular informant obtained by purposive sampling technique.The study obtained the man factor (human resources), namely the education of officers who did not graduate from medical records and had never attended training, machines (machines) that had not been applied to the use of tracers and lacked storage racks, materials (raw materials), namely the folders used were still made of plastic and had not used color codes, methods (methods/procedures), namely SPO had not been carried out properly, and money, namely there was a shortage of money. The conclusion in this study is that there are many factors (human resources), machines (machines), materials (raw materials), method (methods/procedures) and money (money) which causes the medical record tracer not to be applied at the Pratama Klambir clinic.
ASSOCIATED SULFANTIMONIDE MINERALIZATION AT A UNIQUE SULFIDE-INDTIUM-MANGANESE DEPOSIT IN RUSSIA

In the peripheral vein-carbonate zone of the Vysokogornoye sulfide-indiummanganese deposit, a wide association of polycomponent sulfoantimonides, genetically related to the main mineralization, was found. The identified sulfoantimonides are divided into six groups (in order of decreasing frequency of occurrence) – lead, silverlead, silver-copper, silver, lead-copper and copper-lead, copper, which in turn can be combined into three series of solid solutions – copper - lead, copper-silver and silverlead, differing in the degree of miscibility in the cationic sublattices and the atomic proportion Sb/S. The distribution of sulfoantimonides by groups and series in the studied deposit agrees quite closely with the general distribution of sulfoantimonides in nature. The identified association of polycomponent sulfoantimonides increases the industrial prospects of the deposit.
STATE AND PROSPECTS OF DEVELOPMENT OF HIGHER EDUCATION

The article deals with the conditions and prospects for the development of world higher education in the context of globalization and digitalization of society. It is shown that every year the socio-cultural role of education increases, the Internet turns it into a mass form of education. Such trends in world education are analyzed as a gradual decrease in the level of commercialization of the education sector and the transformation of education into a value in itself; the desire to strengthen interdisciplinarity in the educational process and the inclusion of the discipline “impact on the planet”; development of distance education with elements of interactivity and a change in the form of education, etc. It is concluded that digitalization is qualitatively changing the education system, dictating all new innovative projects in the education system.
Pelatihan Quiziz Sebagai Media Interaktif Penunjang Pembelajaran Era 5.0

The main problem for teachers is the mastery of information technology, added to the administrative burden, making them reluctant to add insights to support learning. Especially at SD N 1 Petir. The implementation method used is preparation, implementation and evaluation. The implementation of this activity runs smoothly starting from opening to closing. The material presented includes the history of quizizz, the usefulness of quizizz, the benefits of quizizz and the features of quizizz. The results obtained after this training were that the teachers conveyed that there was an increase in insight into the use of the quizizz application as an interactive media to support learning.
Electronic Resource Usage Data: An Introduction and Data Dashboard Template

This article provides an overview of COUNTER5 metrics and reports, describes the Usage Data Dashboard template, and provides several takeaways for designing data collection workflows. Drawing from research data management (RDM) principles surrounding documentation, the Usage Data Dashboard template can support library workers and improve processes of collecting and interpreting electronic usage data.
SCP: SCENE COMPLETION PRE-TRAINING FOR 3D OBJECT DETECTION

Abstract. 3D object detection using LiDAR point clouds is a fundamental task in the fields of computer vision, robotics, and autonomous driving. However, existing 3D detectors heavily rely on annotated datasets, which are both time-consuming and prone to errors during the process of labeling 3D bounding boxes. In this paper, we propose a Scene Completion Pre-training (SCP) method to enhance the performance of 3D object detectors with less labeled data. SCP offers three key advantages: (1) Improved initialization of the point cloud model. By completing the scene point clouds, SCP effectively captures the spatial and semantic relationships among objects within urban environments. (2) Elimination of the need for additional datasets. SCP serves as a valuable auxiliary network that does not impose any additional efforts or data requirements on the 3D detectors. (3) Reduction of the amount of labeled data for detection. With the help of SCP, the existing state-of-the-art 3D detectors can achieve comparable performance while only relying on 20% labeled data.