Reviewerly/exHarmony huggingface dataset:
Automating Animation using Python Scripting in Autodesk Maya

Coding empowers automation. Scripts can handle mundane and repetitive tasks in an efficient and precise manner. This course will offer a glimpse into the power of using scripting in Python to automate animation-related tasks in Autodesk Maya using a hands-on interactive format. Attendees will learn how to streamline simple tasks using the magic of scripting. The course will cover Setting up the Python environment in Maya, executing Python commands in the Script Editor, manipulating objects using Python scripting, querying animation data, keyframe manipulation, and creating and modifying. By the end of the course, attendees should walk away with a strong understanding of how the Python language, Maya commands and the ability to write scripts for animating in Maya. Hopefully, they will have the tools, confidence, and initiative to explore more advanced scripts independently. Attendees should have Autodesk Maya, Python, and Visual Studio Code pre-loaded on their devices if they intend to follow along.
OpenSE: Efficient Verifiable Searchable Encryption With Access and Search Pattern Hidden for Cloud-IoT

The Internet of Things (IoT) has greatly changed our lives and generated a large amount of data. Cloud storage helps IoT limited-resource IOT devices process the massive data. However, cloud servers are untrusted in most scenarios as they may illegally obtain sensitive data. Although existing symmetric searchable encryption (SSE) schemes can protect the privacy of outsourced data while preserve data availability, Most of them leak access and search patterns to the cloud server to gain better performance. Such leakages will be used to recover private information. Meanwhile, semi-honestly secure searchable encryption cannot prevent attacks done by the malicious server such as returning the false search result. Therefore, it is still a challenge to prevent malicious cloud server misbehavior, and preserve patterns, simultaneously. This paper proposes OpenSE to solve the aforementioned problems. First, this paper constructs FastOPE as a major building block. With the OPE protocol, the verifiable searchable encryption OpenSE can be trivially realized. After that, security proofs show that OpenSE is secure against malicious cloud servers with access and search pattern hidden. Finally, we implement experiments on real datasets to compare OpenSE with some state-of-the-art works in terms of running time of setup phase and search phase as well as storage overhead. The experimental results show that OpenSE outperforms the state-of-the-art works in terms of setup phase and storage overhead. In addition, the theoretic comparison shows that OpenSE outperforms most existing works in terms of security, in which OpenSE enjoys both verifiability and pattern hidden.
Study of the possibility of using M-sequences with frequency shift keying to simulate the seismic-electric effect in a layered geological environment

The article analyzes and mathematically models the use of a frequency-shift keyed signal coded by M-vibration using a multilayer model of the underlying soil. This type of signal has been shown to exhibit a signal-to-noise ratio of about 1.17 at the earth's surface and in the presence of hydrocarbon layers at a depth of 1000 m.
Design of a GIS Database for Surface Mining

Abstract The mining industry is the branch that provides necessary mineral raw materials for a whole series of economic activities, with the main beneficiaries being the energy industry (through coal consumers), companies specialized in the processing of uranium concentrates, chemical industry, food industry, construction materials industry, non-ferrous metallurgical industry, etc. Thus, in accordance with the Romanian mining industry strategy 2017-2035, one of the objectives of the industrial policy in the mining field in Romania is the development and implementation of a database system for the mining sector. Specifically, the goal consists in a development of a tool for tracking and implementing the Mining Law, which allows the collection, storage, administration, modification and analysis of data regarding licenses and permits. At the same time, the development of a visualization and geographic information system (GIS) of mining exploration and exploitation perimeters, as support for the promotion of concessions for mineral resources, is being pursued. In this way, there is a tendency towards the automated and computerized management of the production processes. The development of such systems with practical applicability requires the design, development, and implementation of specific models and databases. The structure and quality of the designed data models directly influence the exploitability and analytical capability of the systems. This paper analyzes the possibilities of designing and implementing a database for the management of the surface mining activities. In the first part of the paper, the basic principles regarding the design and population of databases are presented, as well as their correlation with user requirements and exploitation needs. In the second part, a data model was proposed for the management of mining activities, in accordance with the main stages in the life cycle of a mining operation. In the end, several scenarios for the exploitation of GIS databases were presented, as well as their interconnection with data sources external to the system.
Use of an Electronic Medical Record Alarm to Prevent Iatrogenic Interventions in Patients with Psychogenic Non-Epileptic Seizures (S7.003)

Sunday, April 3May 3, 2022Free AccessUse of an Electronic Medical Record Alarm to Prevent Iatrogenic Interventions in Patients with Psychogenic Non-Epileptic Seizures (S7.003)Serena Yin, Lydia Wolkiewicz, Bichun Ouyang, Elana Wagner, Sarah Abdel-Hadi, and Adriana Bermeo OvalleAuthors Info & AffiliationsMay 3, 2022 issue98 (18_supplement)https://doi.org/10.1212/WNL.98.18_supplement.405 Letters to the Editor
Comprehensive Analysis of Blockchain Algorithms

INTRODUCTION: Blockchain technology has gained significant attention across various sectors as a distributed ledger solution. To comprehend its applicability and potential, a comprehensive understanding of blockchain's essential elements, functional traits, and architectural design is imperative. Consensus algorithms play a critical role in ensuring the proper operation and security of blockchain networks. Consensus algorithms play a vital role in maintaining the proper operation of a blockchain network, and their selection is crucial for optimal performance and security.&#x0D; OBJECTIVES: The objective of this research is to analyse and compare various consensus algorithms based on their performance and efficiency in mining blocks.&#x0D; METHODS: To achieve this, an experimental model was developed to measure the number of mined blocks over time for different consensus algorithms.&#x0D; RESULTS: The results provide valuable insights into the effectiveness and scalability of these algorithms. The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems.&#x0D; CONCLUSION: The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems. By enhancing our knowledge of consensus algorithms, this research aims to facilitate the development of more secure and efficient blockchain applications.
Educational Opportunities

Journal AWWAVolume 115, Issue 10 p. 79-79 Educational Opportunities Educational Opportunities First published: 06 December 2023 https://doi.org/10.1002/awwa.2206Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume115, Issue10December 2023Pages 79-79 RelatedInformation
FRAUD DETECTION AND ANTI-MONEY LAUNDERING APPLYING MACHINE LEARNING TECHNIQUES IN CRYPTOCURRENCY TRANSACTIONAL GRAPHS

Cryptocurrencies have advantages such as lower costs, efficiency, and security, but are vulnerable to fraud due to a lack of controls and anonymity. Criminals use virtual currencies for quick, anonymous transactions. Robust measures are needed to
Exploiting Spatial Attention Mechanism for Improved Depth Completion and Feature Fusion in Novel View Synthesis

Many image-based rendering (IBR) methods rely on depth estimates obtained from structured light or time-of-flight depth sensors to synthesize novel views from sparse camera networks. However, these estimates often contain missing or noisy regions, resulting in an incorrect mapping between source and target views. This situation makes the fusion process more challenging, as the visual information is misaligned, inconsistent, or missing. In this work, we first implement a lightweight network based on the transformer, which is well-known for its capability to model long-range relationships within input data, to extract spatial features from color images. These features are then used to enhance the quality of completed depth maps. Furthermore, we combine a sequential deep neural network with a spatial attention mechanism to effectively fuse the projected features from multiple source viewpoints. This approach enables us to integrate information from an arbitrary number of source viewpoints as well as improve accuracy in synthesized views. Experimental results on challenging datasets demonstrate that our method achieves superior synthesized image quality compared to state-of-the-art (SOTA) methods. Github page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tmanh/nvs23</uri> .
Design And Implementation Of Asset Management System For University Laboratories

This paper mainly aims to improve the asset management efficiency of electrical laboratories in universities, and proposes a web-based intelligent asset management system for university laboratories. The RFID technology is applied to the lower computer, the upper computer adopts the B/S architecture, the front end uses Vue.js and Element-UI component library to dynamically implement the page, and the back end is built with the node.js and express framework, and then paired with the MySQL database, so as to realize the informatization of the entire asset management.
Medical Image Analysis of Multiple Myeloma Diagnosis Using CNN and KNN Based Approach

Leukocytes, which are produced in the bone marrow, account for about 1% of all blood cells. When the production of these neutrophils becomes unchecked, cancer of the blood occurs. In order to better classify Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM), two of the three types of cancer studied, the proposed study makes use of the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a cancer characterized by an excess generation of lymphocytes in the bone marrow. However, malignant cells from multiple myeloma (MM) do not enter the bloodstream and instead remain in the bone marrow. Healthy stem cells are crowd out, which prevents the body from producing enough new blood cells. In the past, this would necessitate the dedicated time and energy of an expert. The proposed approach makes use of preprocessing, segmentation, feature extraction, and evaluating model performance. Median filters are utilized in the preparation of data because the capturing and transmitting of medical images is often hindered by noise. Cell Segmentation, as well as Nucleus and Cytoplasm Segmentation, are two common techniques used in segmentation. Gaussian pyramids is used for feature extraction. The CNN-KNN Model is used to evaluate the performance of the model. The KNN and CNN models are used as benchmarks against which the suggested method is assessed. It far surpasses simpler methods in comparison.
OffensEval 2023: Offensive language identification in the age of Large Language Models

Abstract The OffensEval shared tasks organized as part of SemEval-2019–2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the community with benchmark datasets in Arabic, Danish, English, Greek, and Turkish. The datasets were annotated using the OLID hierarchical taxonomy, which since then has become the de facto standard in general offensive language identification research and was widely used beyond OffensEval. We present a survey of OffensEval and related competitions, and we discuss the main lessons learned. We further evaluate the performance of Large Language Models (LLMs), which have recently revolutionalized the field of Natural Language Processing. We use zero-shot prompting with six popular LLMs and zero-shot learning with two task-specific fine-tuned BERT models, and we compare the results against those of the top-performing teams at the OffensEval competitions. Our results show that while some LMMs such as Flan-T5 achieve competitive performance, in general LLMs lag behind the best OffensEval systems.
Preface: Special issue on NLP approaches to offensive content online

We are delighted to present the Special Issue on NLP Approaches to Offensive Content Online published in the Journal of Natural Language Engineering issue 29.6. We are happy to have received a total of 26 submissions to the special issue evidencing the interest of the NLP community in this topic. Our guest editorial board comprised of international experts in the field has worked hard to review all submissions over multiple rounds of peer review. Ultimately, we accepted nine articles to appear in this special issue.
Embracing the Forbidden Pages: The Importance of Children Reading Banned Books

This article delves into the significance of children engaging with banned books, illuminating how such literature fosters a multifaceted learning experience. By navigating through various thematic lenses, this discourse reveals the manifold advantages of allowing young readers to explore these controversial texts. From cultivating independent thought and fostering diversity appreciation to nurturing resilience and championing freedom of expression, the analysis showcases the profound impact of banned books on children's cognitive and emotional development. Through their portrayal of real-life challenges and provision of diverse perspectives, these books serve as windows into the complexities of the human experience. Additionally, the article underscores their role in addressing mental health struggles, fostering empathy, and humanizing marginalized individuals. By exploring these subheadings, this study offers a comprehensive exploration of how embracing banned books equips children with the tools to better understand themselves, their peers, and the world around them.
Increase digital health literacy with the practical implementation of an e-learning system according to WCAG compliance standards

Individuals with cognitive difficulties (CD) have very limited access to appropriate learning resources. Their unique individual needs and requirements prevent them from accessing traditional online formal and informal learning methods, and resources are usually inadequate. We develop an e-learning platform with health content as an opportunity for all to increase digital health literacy, including people with cognitive disabilities. The paper aims to present some feature's settings important for people with a wide range of cognitive disabilities.
VEHICLE POSE AND SHAPE ESTIMATION IN UAV IMAGERY USING A CNN

Abstract. Vehicle reconstruction from single aerial images is an important but challenging task. In this work, we introduce a new framework based on convolutional neural networks (CNN) that performs monocular detection, pose, shape and type estimation for vehicles in UAV imagery, taking advantage of a strong 3D object model. In the final training phase, all components of the model are trained end-to-end. We present a UAV-based dataset for the evaluation of our model and additionally evaluate it on an augmented version of the Hessingheim benchmark dataset. Our method presents encouraging pose and shape estimation results: Based on images of 3 cm GSD, it achieves median errors of up to 5 cm in position and 3° in orientation, and RMS errors of ±7 cm and ±24 cm in planimetry and height, respectively, for keypoints describing the car shape.
People’s political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data

Abstract Background As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people’s residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people’s concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people’s private locations. Methods We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people’s political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables. Results We observed a tradeoff relationship between people’s privacy concerns and the acceptance (and perceived social benefits) of the control measures. People’s perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns. Conclusions Our results first suggest that people’s privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people’s acceptance and perceived social benefits of the control measures but may not reduce people’s privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people’s privacy concerns tend to increase with their conservatism and individualism.
A DESCRIPTIVE STUDY OF THE VOCABULARY MASTERY IN IDENTIFYING PREFIX AND SUFFIX OF THE SECOND GRADE STUDENTS OF SMK TELEKOMUNIKASI GENTENG

This research is a descriptive research about study of the vocabulary mastery in identifying prefix and suffix of the second grade students of SMK Telekomunikasi Genteng in the 2021/2022 academic year. The purpose of this research was to find the student’ ability in identifying vocabulary mastery in prefix and suffix. Students at SMK Telekomunikasi Genteng are the population. The researcher chose the second grade students of Teknik Transmisi Jaringan Department as the sample in this research. This research uses a quantitative descriptive method. The instrument used in this research was a multiple choice test. The results showed that the students’ ability in identifying students’ vocabulary mastery in prefix is “good“, categorized with a total of 65,8. The results of students’ ability in identifying students’ vocabulary mastery in suffix is “average” categorized with a total 58,3. Based on the results of the research above, it can be concluded that students at SMK Telekomunikasi, especially the second grade of Teknik Transmisi Jaringan Department, have a “good” category in prefix, it means the students have already understood about some prefix vocabularies. The majority of correct prefix vocabularies place at multiple choice test number 1,9,17 such as: Exchange, input, interconnect. Furthemore, the result of suffix is “average” category, it means the students less of understanding about suffix. The majority of correct suffix vocabularies at multiple choice test number 2 such as: communication.
An Incremental Domain Adaptation Method for Object Detection of Unmanned Surface Vehicle

Visual object detection is expected to provide adequate environmental information for the unmanned surface vehicle (USV), but its performance usually drops due to the changeable marine scenarios, such as weather and light conditions. To address this problem, the most common way is domain adaptation, which can adapt the model trained for the source domain to the target domain. However, the existing domain adaptation approaches aim to improve the detection performance on the target domain while the performance on the source domain suffers from a significant decrease. To solve this catastrophic forgetting while adapting to a new domain, we proposed an incremental domain adaptation approach for object detection. Our method can be divided into two steps: firstly, we translate images from the source domain into images that match the data distribution of target domain images; secondly, an incremental learning method based on knowledge distillation is adopted to learn the generated images. Experiments on Singapore Maritime Dateset and our simulation dataset suggest that the proposed method can successfully adapt to new domains and prevent performance degradation in the previous domain. Consequently, our method can efficiently and timely adapt to various marine scenarios that the USV encounters to improve its robustness of environmental perception.
What Makes Systemic Discrimination 'Systemic?' A Taxonomy of Testable Amplification Mechanisms

Systemic discrimination has become one of the most salient, mis- understood, and definitionally amorphous concepts, not only in the social sciences, but also in political and legal circles as well. Drawing on work spanning economics, public health, education, sociology, and law, I argue that what makes systemic discrimination “systemic” is in part, the amplification of inequities. I develop a taxonomy of amplification mechanisms using a formal model that embeds a wide range of well-established scientific principles, making the framework readily applicable for social scientists. Finally, I show that the amplification mechanisms can either be targeted directly and disrupted, or exploited, amplifying the effects of equity-focused policy interventions like a “social hyper-drive.” Real-world examples discussed include but are not limited to reparations for slavery and Jim Crow, housing vouchers, police shootings, affirmative action, covid-19, and educational interventions.