Reviewerly/exHarmony huggingface dataset:
Lightweight model of remote sensing ship classification based on YOLOv7-tiny improvement

Abstract A lightweight ship classification and detection approach based on modified YOLOv7-tiny is presented to address the issue that the classification and detection of ships in optical remote sensing images is prone to mistakes and missed detection due to the complicated background in the offshore. Due to the uneven distribution of data set samples, this method applies a lightweight feature extraction module E_DSC to the backbone network to reduce the number of parameters and calculations of the feature extraction network and improve network reliability. On the other hand, the MPDIoU loss function is introduced to improve the network prediction box coverage and improve the network prediction accuracy. On a dataset of remote sensing images of ships, this strategy has been tested. The findings reveal that it improves the network’s average accuracy of ship recognition and classification by 3.3%. The amount of parameters and calculation is also better than YOLOv7-tiny with a 3% reduction in parameters and a 22% reduction in the calculation.
IMPROVING LABORATORY CLASSES IN CHEMISTRY IN HIGHER EDUCATIONAL INSTITUTIONS BASED ON INFORMATION TECHNOLOGY

The article discusses new forms of teaching chemistry in universities. The most natural way is to use a computer based on the characteristics of chemistry as a science. This, in turn, is a simulation of chemical processes and phenomena, laboratory use of a computer in interface mode, computer support for the process of presenting educational material and monitoring its assimilation. KEYWORDS: informatization, modeling of chemical processes, personal scientific laboratory, computer technology.
Communication Management in Preventing Negative Impact of Online Games on Students in Elementary School

The research focuses on how Communication Management in Preventing the Negative Impact of Online Games on Students at SD Negeri 09 South Jakarta. The phenomenon of using online games among elementary school children is very widespread, which is feared is the negative impact experienced by elementary school students. These impacts include students who are addicted to being lazy such as children not interacting with people around them, lazy to study or do assignments from school, negative performance, lazy school, lazy to eat, difficult to concentrate and only interested in playing games. These elementary school children have not been able to filter out what is right or wrong to apply in their lives. For this reason, it is necessary to manage school communication in order to develop good communication to prevent the negative impact of online games at SD Negeri 09 Jakarta. Because through good communication management carried out by school residents, students in this elementary school can take advantage of technology and become media literate so as to avoid negative behavior of online online games. By using the concept of communication management, and the concept of online games, and the concept of elementary school. The methodology used in this research is a qualitative method with in-depth interviews with principals, teachers, students and parents and conducting data validity tests with data credibility tests.
A Pairing-free Provable Secure and Efficient Identity-based Identification Scheme with Anonymity

In this paper, we propose a Blind Identity-Based Identification (Blind IBI) scheme based on the Guillou-Quisquater (GQ) scheme. Our proposed scheme combines the benefits of traditional Identity-Based Identification (IBI) schemes that can authenticate a user's identity without relying on a trusted third party with the Blind Signature (BS) scheme that provides anonymity. As a result, the proposed scheme assures absolute user privacy during the authentication process. It does not rely on a third party, yet the verifier can still be assured of the user's identity without the user actually revealing it. In our work, we show that the proposed scheme is provably secure under the random oracle model, with the assumption that the one-more-RSA-inversion problem is difficult. Furthermore, we demonstrate that the proposed scheme is secure against passive, active, and concurrent impersonation attacks. In conclusion, the proposed scheme is able to achieve the desired blindness property without compromising the security of the GQ-IBI scheme it is based upon.
The scholarly journal and its dependence on many interconnections

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51 Chumashan

Chumashan languages have been the topic of linguistic studies on a number of grammatical phenomena: reduplication (Applegate 1976; Saba Kirchner 2010), sibilant harmony (Beeler 1970; Harrington 1974; Mithun 1997; Poser 2004), nominalization (Mithun 2002), and more (Mithun 1999, Wash 2001). However, the list of completed dictionaries and grammars of Chumashan languages is short, which can be particularly problematic when developing revitalization language materials. This chapter gives a sketch of Chumashan grammar, as well as current revitalization efforts for Chumashan languages.
Studi Eksperimental: Pengaruh Media Pembelajaran Interaktif Terhadap Hasil Belajar Dasar-Dasar Elektronika Elemen Alat Ukur Listrik di SMK Negeri 2 Payakumbuh

This study employs a quantitative approach with an experimental design. The main objective of this research is to determine the extent of the influence of interactive learning media on the subject of Fundamentals of Electronics for students studying electrical measurement, electronics, and instrumentation at SMK Negeri 2 Payakumbuh. The study population consists of students from class X TAV 1 and X TAV 2 at SMK N 2 Payakumbuh for the academic year 2023/2024, totaling 61 students. The research instrument used is in the form of objective questions administered at the end of the learning process after each has undergone a specific treatment. The results of the study indicate that the calculated t-value is greater than the tabulated t-value (4.60 &gt; 1.67). This implies the rejection of the null hypothesis (H0) and the acceptance of the alternative hypothesis (H1). Therefore, it can be concluded that the use of interactive learning media significantly influences the learning outcomes of students in the study of electrical measurement, electronics, and instrumentation at SMK Negeri 2 Payakumbuh.
Experiencing virtual reality and visual memory

The number of studies investigating different aspects of the use of virtual reality and VR goggles is constantly growing. However, there is currently a lack of analyses related to the visual memory of resources learned in immersive VR. Therefore, a study was conducted to determine the level of memorisation of visual elements presented using VR headsets or a traditional computer. The study involved 120 students divided into three equal groups. In the first group, the participants used VR goggles and the presentation was supervised by a lecturer who drew attention to key elements and monitored the pace of the action. In the second group, users worked entirely on their own with the VR headsets, and in the third group they watched a narrated presentation on a computer screen. The main research objective was to determine the differences in the amount of elements memorised between the study groups. The study used both quantitative and qualitative data. The ANOVA analysis of variance showed that there was a statistically significant difference between all groups. The highest level of memorisation of visual elements was recorded in the group using VR goggles and supervised by a guide. The lowest was in the group that used VR headsets alone. Participants in the study noted that immersive VR is so fascinating that it is difficult for them to supervise their own time and activity in a way that ensures success. As a result, 70% of people working alone with VR goggles did not manage to familiarise themselves with all the material.
Game-Theoretic Randomness for Proof-of-Stake

Many protocols in distributed computing rely on a source of randomness, usually called a random beacon, both for their applicability and security. This is especially true for proof-of-stake blockchain protocols in which the next miner or set of miners have to be chosen randomly and each party’s likelihood to be selected is in proportion to their stake in the cryptocurrency. The chosen miner is then allowed to add a block to the chain. Current random beacons used in proof-of-stake protocols, such as Ouroboros and Algorand, have two fundamental limitations: Either (i) they rely on pseudorandomness, e.g. assuming that the output of a hash function is uniform, which is a widely used but unproven assumption, or (ii) they generate their randomness using a distributed protocol in which several participants are required to submit random numbers which are then used in the generation of a final random result. However, in this case, there is no guarantee that the numbers provided by the parties are uniformly random and there is no incentive for the parties to honestly generate uniform randomness. Most random beacons have both limitations. In this work, we provide a protocol for distributed generation of randomness. Our protocol does not rely on pseudorandomness at all. Similar to some of the previous approaches, it uses random inputs by different participants to generate a final random result. However, the crucial difference is that we provide a game-theoretic guarantee showing that it is in everyone’s best interest to submit uniform random numbers. Hence, our approach is the first to incentivize honest behavior instead of just assuming it. Moreover, the approach is trustless and generates unbiased random numbers. It is also tamper-proof and no party can change the output or affect its distribution. Finally, it is designed with modularity in mind and can be easily plugged into existing distributed protocols such as proof-of-stake blockchains.
Heuristic Search Optimisation Using Planning and Curriculum Learning Techniques

Learning a well-informed heuristic function for hard planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic knowledge, it is not obvious what concrete information is learned and whether techniques aimed at understanding the structure help in improving the quality of the heuristics. This paper presents a network model that learns a heuristic function capable of relating distant parts of the state space via optimal plan imitation using the attention mechanism which drastically improves the learning of a good heuristic function. The learning of this heuristic function is further improved by the use of curriculum learning, where newly solved problem instances are added to the training set, which, in turn, helps to solve problems of higher complexities and train from harder problem instances. The methodologies used in this paper far exceed the performances of all existing baselines including known deep learning approaches and classical planning heuristics. We demonstrate its effectiveness and success on grid-type PDDL domains, namely Sokoban, maze-with-teleports and sliding tile puzzles.
Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

Abstract Background Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Consequently, the addition of automated detection and classification of malaria using laboratory methods can provide patients with more accurate and faster diagnosis. Therefore, this study used a machine-learning model to predict Plasmodium falciparum ( Pf ) antigen positivity (presence of malaria) based on sociodemographic behaviour, environment, and clinical features. Method Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Outcomes were classified as Pf-positive or Pf-negative, corresponding to the presence or absence of malaria, respectively. Results Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic curve for the training set (AUC = 84%; 95% confidence interval [CI]: 75–93%) and test set (AUC = 83%; 95% CI: 63–100%). Increased odds of malaria were associated with higher body weight (adjusted odds ratio (AOR) = 4.50, 95% CI: 2.27 to 8.01, p &lt; 0.0001). Even though the association between the odds of having malaria and body temperature was not significant, patients with high body temperature had higher odds of testing positive for the Pf antigen than those who did not have high body temperature (AOR = 1.40, 95% CI: 0.99 to 1.91, p = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI: 1.30 to 4.66, p = 0.006) or experienced fever (AOR = 2.10, 95% CI: 0.88 to 4.24, p = 0.099), headache (AOR = 2.07; 95% CI: 0.95 to 3.95, p = 0.068), muscle pain (AOR = 1.49; 95% CI: 0.66 to 3.39, p = 0.333), and vomiting (AOR = 2.32; 95% CI: 0.85 to 6.82, p = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI: 0.41 to 0.90, p = 0.012) and BMI (AOR = 0.47, 95% CI: 0.26 to 0.80, p = 0.006). Conclusion Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict Pf antigen positivity may be a valuable tool for clinical decision-making.
A Study of the Diversification of Materials of Korean New Operas: Focusing on selected works by the Creative Factory of Performing Arts

Starting in 1950, Korean Opera, which has a history of 73 years to date in 2023, served as an motive to pave the way for the creation of Korean opera through the “MOM Creation Contest” organized by Korea National Opera in 2009. Since then, it has been changed to the Creative Factory of Performing Arts organized by the Arts Council Korea since 2014, and new operas have been discovered every year through continuous support until 2023, and 17 new operas have been finally selected. This study examined the diversified aspects of material utilization for new operas discovered through the Creative Factory of Performing Arts, focusing on the material of the libretto, which is the first work and basic element of the creation of an opera.&#x0D; In order to study the material diversification of new opera selected through the Creative Factory of Performing Arts, six classification criteria were set, each of which is a genre, method of libretto, original foundation, the spatial background of work, the temporal background of work, and basic material. In addition, in order to help understand, this study also examined aspects of the diversification of materials of new operas in overseas opera theaters such as the United States, Germany, the United Kingdom, Austria, Italy, and France.&#x0D; As a part of the development of cultural contents, I hope that this study will help understand the trend of diversification through the recently discovered Korean new operas, help revitalize creation, and further promote convergent development in all fields of performing arts.
On the Wavelet Convolution Neural Networks for Ultra-Sound Based Breast Cancer Detection

Breast cancer is one of the deadliest types of cancer, and it comes in a wide variety of forms, resulting in a wide variety of detection methods. Several deep learning techniques have been applied to decrease unnecessary biopsies and lessen the burden on radiologists. One of the most popular architectures for this task is the Convolutional Neural Networks (CNNs). This paper aims to explore the integration of convolutional neural networks (CNN) and wavelet transform (WT) to identify the optimal combination and architecture of these methods for efficient detection of breast cancer in ultrasound images. To accomplish this task, the wavelet convolutional neural network (WCNN) structures are proposed and trained for the mission of screening breast cancer abnormalities embedded in ultrasound type of images. Compared with other two popular networks, ResNet50 and MobileNetV2, it has been found that the proposed WCNN has produced a satisfactory solution, with an accuracy of 98.24%, precision of 97.29%, recall of 100%, and F measure of 98.24%.
Journalistic Convergence in Improving Content to Local Newspaper Readers

The presence of the Internet has a huge impact on current technological developments, including the print media industry in Indonesia, especially in Southeast Sulawesi. This encourages print media to innovate so that it can become a medium needed by readers. This study aims to determine journalistic
Distributed Algorithms and Their Application in Big Data Analytics

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Explanation AI and Interpret-ability in Machine Learning Models

In rеcеnt timеs, thе usе of machinе lеarning modеls has incrеasеd rapidly in various fiеlds. As a rеsult, thеrе is a growing nееd for AI systеms that can bе еasily undеrstood and еxplainеd. This rеviеw papеr takеs a closе look at thе latеst dеvеlopmеnts in making AI modеls morе undеrstandablе and how thеy arе usеd with diffеrеnt typеs of machinе lеarning modеls. Wе carеfully еxaminе important rеsеarch papеrs, mеthods, and еxamplеs to еxplain how thе fiеld of modеl intеrprеtability is changing. This hеlps us bеttеr undеrstand thе challеngеs, important discovеriеs, and how thеsе can impact various industriеs. Wе еxplorе various tеchniquеs that makе modеls еasiеr to undеrstand, such as visualizing fеaturеs, mеthods to attributе modеl dеcisions, and crеating simplеr modеls that mimic complеx onеs. Furthеrmorе, wе еmphasizе how intеrprеtability is not only about building trust and undеrstanding for usеrs but also about mееting rеgulatory rеquirеmеnts and еnsuring еthical AI usе. Wе bring togеthеr thе bеst practicеs currеntly in usе and look at what futurе rеsеarch might focus on. This papеr aims to providе a clеar undеrstanding of how еxplanation AI plays a crucial rolе in crеating strong and rеsponsiblе machinе lеarning modеls for rеal-world applications.
Review Paper on IoT Thread Detection using Deep CNN Classifier

Abnormal activity will lead to uncommon changes in the crowd behavior. In other words, the crowd motion changes conform to certain rules for valid behaviors, while for abnormal events the motion changes are uncontrolled. The motion-changed rules to detect and localize abnormal behavior in crowd videos. Specifically, we first generate the motion patterns based on the descriptor of collectiveness. Then each frame pair is represented as a transfer matrix whose elements are the difference of a set of motion patterns. Thereafter, the motion-changed rules are constructed in the transformation space using a bag-of-words approach. Finally, the proposed approach measures the similarity between motion-changed rules and the incoming video data to examine whether the actions are anomalous. The approach is tested on the UMN dataset and a challenging dataset of crowd videos taken from the railway station. The experimental results demonstrate the effectiveness of the proposed method for detection abnormal behavior
Adding uncertainty to the estimation of log data using a gradient-boosted tree approach

PreviousNext You have accessThird International Meeting for Applied Geoscience & Energy Expanded AbstractsAdding uncertainty to the estimation of log data using a gradient-boosted tree approachAuthors: Marianne RauchKeyla GonzalezMarianne RauchTGS and Keyla GonzalezTGShttps://doi.org/10.1190/image2023-w03-01.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit AbstractMachine learning (ML) applications have infiltrated geosciences, especially in wireline log estimations, interpretation of salt bodies, and fault definitions. Other applications such as seismic processing, automated first break picking, velocity analysis, and pre-stack inversions for rock property have been tackled with this technology. Our industry has large datasets that are especially suitable for data-driven methods. However, ML algorithms have been labeled as a "black box" as the actual process is not evident to the end user. Furthermore, the uncertainty of the results is always questioned. We applied ML to estimate missing log data at a basin scale on millions of wells, and our findings indicate that measuring the uncertainty of the results in a meaningful way is extremely difficult. Instead of spending efforts quantifying uncertainty, we spent more time and work cleaning the input data to ML, which proved to be the most effective in improving the quality of the ML inference results. When estimations are compared to blind data, the data fit is very good if the input data have been cleaned and the uncertainties are small. If the input data are noisy, the comparisons are poor, and the uncertainty is larger.Keywords: machine learning, uncertainty, data qualityPermalink: https://doi.org/10.1190/image2023-w03-01.1FiguresReferencesRelatedDetails Third International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2023 Pages: 1812 publication data© 2023 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 14 Dec 2023 CITATION INFORMATION Marianne Rauch and Keyla Gonzalez, (2023), "Adding uncertainty to the estimation of log data using a gradient-boosted tree approach," SEG Technical Program Expanded Abstracts : 1780-1782. https://doi.org/10.1190/image2023-w03-01.1 Plain-Language Summary Keywordsmachine learninguncertaintydata qualityPDF DownloadLoading ...
A conceptual of System Model of Youth Digital Hub in Using Web 2.0 Application

The Youth Digital Hub (YDH) is a prominent center of digital information designed to effectively support the Youth Community of Practice (YCoP) by providing them with timely and relevant information. However, there is a lack of discussion on the best practices regarding a conceptual system model tha
Optimization of Library Material Stock Opname Activities at the Department of Libraries and Archives of Kolaka Regency

This research is entitled optimization of stock opname activities of library materials at the Regional Library and Archives Office of Kolaka Regency. The purpose of this research is to find out how the optimization of stock opname activities of library materials takes place at the Library and Archiv