Reviewerly/exHarmony huggingface dataset:
Audio-Enhanced Text-to-Video Retrieval using Text-Conditioned Feature Alignment

Text-to-video retrieval systems have recently made significant progress by utilizing pre-trained models trained on large-scale image-text pairs. However, most of the latest methods primarily focus on the video modality while disregarding the audio signal for this task. Nevertheless, a recent advancement by ECLIPSE has improved long-range text-to-video retrieval by developing an audiovisual video representation. Nonetheless, the objective of the text-to-video retrieval task is to capture the complementary audio and video information that is pertinent to the text query rather than simply achieving better audio and video alignment. To address this issue, we introduce TEFAL, a TExt-conditioned Feature ALignment method that produces both audio and video representations conditioned on the text query. Instead of using only an audiovisual attention block, which could suppress the audio information relevant to the text query, our approach employs two independent cross-modal attention blocks that enable the text to attend to the audio and video representations separately. Our proposed method's efficacy is demonstrated on four benchmark datasets that include audio: MSR-VTT, LSMDC, VATEX, and Charades, and achieves better than state-of-the-art performance consistently across the four datasets. This is attributed to the additional text-query-conditioned audio representation and the complementary information it adds to the text-query-conditioned video representation.
Leveraging Label Variation in Large Language Models for Zero-Shot Text Classification

The zero-shot learning capabilities of large language models (LLMs) make them ideal for text classification without annotation or supervised training. Many studies have shown impressive results across multiple tasks. While tasks, data, and results differ widely, their similarities to human annotation can aid us in tackling new tasks with minimal expenses. We evaluate using 5 state-of-the-art LLMs as "annotators" on 5 different tasks (age, gender, topic, sentiment prediction, and hate speech detection), across 4 languages: English, French, German, and Spanish. No single model excels at all tasks, across languages, or across all labels within a task. However, aggregation techniques designed for human annotators perform substantially better than any one individual model. Overall, though, LLMs do not rival even simple supervised models, so they do not (yet) replace the need for human annotation. We also discuss the tradeoffs between speed, accuracy, cost, and bias when it comes to aggregated model labeling versus human annotation.
An Isometric Stochastic Optimizer

The Adam optimizer is the standard choice in deep learning applications. I propose a simple explanation of Adam's success: it makes each parameter's step size independent of the norms of the other parameters. Based on this principle I derive Iso, a new optimizer which makes the norm of a parameter's update invariant to the application of any linear transformation to its inputs and outputs. I develop a variant of Iso called IsoAdam that allows optimal hyperparameters to be transferred from Adam, and demonstrate that IsoAdam obtains a speedup over Adam when training a small Transformer.
Implementation of the K-Nearst Neighbor (k-NN) Algorithm in Classification of Angora and Country Cats

There are so many types of mixed cats from various cat breeds, so many people find it difficult to identify and classify them. Therefore, we need a method that can classify the type of cat breeds. In this study the authors used the K-Nearest Neighbor (k-NN) algorithm to make it easier to recognize and classify cat breeds based on certain characteristics. The author took samples of 2 types of cat races, namely the Anggora race and the Kampung race. The implementation stage is to determine the euclidean distance and sort it and then determine the value of K to find the nearest neighbor. In testing, the authors used 50 training data and 50 test data with 6 attributes used, namely body shape, nose width, nose height, food type, hair type and hair length. The results of the classification of cat breeds using the k-NN method obtained an accuracy rate of 94% and an error rate of 6%.
OnePose: One-Shot Object Pose Estimation without CAD Models

We propose a new method named OnePose for object pose estimation. Unlike existing instance-level or category-level methods, OnePose does not rely on CAD models and can handle objects in arbitrary categories without instance- or category-specific network training. OnePose draws the idea from visual localization and only requires a simple RGB video scan of the object to build a sparse SfM model of the object. Then, this model is registered to new query images with a generic feature matching network. To mitigate the slow runtime of existing visual localization methods, we propose a new graph attention network that directly matches 2D interest points in the query image with the 3D points in the SfM model, resulting in efficient and robust pose estimation. Combined with a feature-based pose tracker, OnePose is able to stably detect and track 6D poses of everyday household objects in real-time. We also collected a large-scale dataset that consists of 450 sequences of 150 objects.
ANALISIS SENTIMEN PELAKSANAAN KULIAH ONLINE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

This writing aims to assess the satisfaction of students regarding the implementation of online lectures, categorized into three classes: positive, neutral, and negative. Data collection was conducted using Twint from the social media platform Twitter, with a total of 25,000 tweets. The data processing process to determine sentiment analysis utilized the support vector machine algorithm. With this algorithm, the obtained results show an accuracy rate of 76.86% for positive sentiment. The precision is 0.49, recall is 0.53, and the F1 score is 0.51
Retracted: Application of Wireless Sensor Network Technology in Multipoint Control in Music Performance Management System

[This retracts the article DOI: 10.1155/2022/2783944.].
Pengaruh Metode Pembelajaran Index Card Match terhadap Motivasi dan Hasil Belajar Kognitif Siswa SMP

This study aims to find out how the Index Card Match learning method influences the motivation and cognitive learning outcomes of students at SMP Negeri 4 Jonggat, Central Lombok Regency. Learning by using the Index Card Match learning method is expected to have students highly motivated in learning to obtain high learning outcomes as well. Through this application it can also make it easier for teachers to improve ways of thinking and increase student involvement in the teaching and learning process. The type of research used in this research is a quasi-experimental (quasi-experimental), this design has a control group, but it does not fully function to control the variables that affect the implementation of the experiment. The experimental class received treatment using the Index Card Match learning method, while the control class used the conventional method. To obtain the necessary data, this study used the t-test analysis technique. From the results of the study, based on the results of the hypothesis testing of the experimental class and the control class using the t-test, the tcount was 0.13 and the ttable was 2.12. This means that the tcount value is smaller than the ttable value or 0.13 &lt;2.12. This shows that Ha is rejected and H0 is accepted, so there is an influence of the Index Card Match learning method on students' motivation and cognitive learning outcomes.
Retracted: A Secure Private Cloud Storage Platform for English Education Resources Based on IoT Technology

[This retracts the article DOI: 10.1155/2022/8453470.].
Retracted: English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining

[This retracts the article DOI: 10.1155/2021/1890120.].
Retracted: Optimization of College English Classroom Teaching Efficiency by Deep Learning SDD Algorithm

[This retracts the article DOI: 10.1155/2022/1014501.].
Retracted: Design and Implementation of Dance Online Teaching System Based on Optimized Load Balancing Algorithm

[This retracts the article DOI: 10.1155/2022/3829118.].
Retracted: The Management of Educational Talents in Vocational Colleges Based on Wireless Network in the Artificial Intelligence Era

[This retracts the article DOI: 10.1155/2022/2298139.].
Retracted: A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

[This retracts the article DOI: 10.1155/2022/5112375.].
Retracted: Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching

[This retracts the article DOI: 10.1155/2022/5755327.].
Retracted: Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis

[This retracts the article DOI: 10.1155/2022/4293102.].
Retracted: Application of Accelerometer to Monitor Students’ Exercise Load in 50 m Round Trip

[This retracts the article DOI: 10.1155/2022/3142677.].
Retracted: Ant Colony Algorithm-Based Audit Supervision to Promote the Optimization of New Infrastructure Investment Environment

[This retracts the article DOI: 10.1155/2022/7454982.].
Validitas Modul Berbasis Website pada Materi Ekosistem untuk SMA Kelas X

Limited teaching materials used by teachers and students such as printed books make it difficult for teachers to provide explanations and students find it difficult to understand learning outside of school. The textbooks used in general are good but need to be updated because the pictures presented in the printed books are not clear and are in black and white. Teachers rarely use modules and teachers never use web-based teaching materials. With internet facilities that are already available, teachers can further improve the quality of learning activities, such as using web-based teaching materials as learning resources. Therefore, by utilizing the internet as a learning resource, simple teaching materials are needed, namely website-based modules. This study validates a web-based module on ecosystem material for class X SMA. website based. The method used in this research is development (Research &amp; Development) with the ADDIE model. The stages of development research are carried out with several stages that have been modified according to the needs of researchers, namely: 1) the analysis stage; 2) the design stage (design); and 3) development stage. At the analysis stage, needs analysis is carried out, at the design stage, modules are compiled, and at the development stage, expert validation is carried out. The instrument used in this study is a validation sheet which includes validation of the feasibility of content, language, presentation, graphics, and media use. The results showed that the validity of the website-based module was very valid with an average of 91%. Based on these results it can be concluded that the resulting website-based module is very valid and suitable for use as teaching material.
Real-Time 3D Ultrasound Reconstruction Using Octrees

Ultrasound is a major medical imaging modality that is widely used in healthcare because of advantages such as the use of nonionizing radiation, ease of operation, real-time imaging from different perspectives, and low operation costs. The most common ultrasound modality produces two-dimensional images (2D) with a 1D-array transducer. However, in recent years, three-dimensional ultrasound (3D US) imaging has become increasingly relevant. There are many reasons behind this shift. For example, 3D US images are easier to register with 3D images from another modality while patients undergo procedures or during presurgical planning. In particular, 3D freehand ultrasound (FUS) imaging yields 3D US images of large anatomical regions at low cost. An area of interest is scanned with a conventional 1D-array transducer, which is tracked with an attached device; the resulting 2D US images are input into a reconstruction algorithm; and the brightness values are assigned to a 3D image. Several 3D reconstruction algorithms in FUS imaging have been proposed and clinically used, and in the present work, we report a new neighbor search-based approach for reconstructing 3D FUS images based on hierarchical octrees with Morton key coding that can be implemented on GPUs using CUDA® kernels to exploit multithreading. Our approach achieves considerably faster throughput for high-resolution 3D images and can reconstruct 3D US images with dimensions of 128 × 128 × 128 voxels in approximately 0.5 s. The proposed approach is a viable option for obtaining 3D US images in real time based on sets of freehand 2D ultrasound images acquired with 1D-array transducers.
Exploring Somali Sentiment Analysis: A Resource-Light Approach for Small-scale Text Classification

Sentiment analysis, a fundamental task in natural language processing (NLP), plays a crucial role in understanding people's opinions and emotions expressed in textual data. While sentiment analysis has been extensively studied for major languages, under-resourced languages like Somali have received limited attention in this domain. This paper aims to address this research gap by proposing a resource light approach for sentiment analysis in Somali, which is tailored to the language's unique characteristics and limited linguistic resources. We present a methodology that combines lexicon-based methods and feature engineering techniques to effectively extract sentiment information from Somali text. A sentiment-annotated dataset was created through crowdsourcing, enabling the training and evaluation of a sentiment classification model specifically designed for Somali. Experimental results demonstrate the competitive performance of our approach compared to existing sentiment analysis techniques for under resourced languages. The findings highlight the feasibility of sentiment analysis in Somali, even with a small-scale dataset, and shed light on the implications for sentiment analysis in other under-resourced languages. This research contributes to the advancement of sentiment analysis capabilities for under resourced languages, empowering researchers and practitioners to gain insights from sentiment information in diverse linguistic contexts.
Classification of Mammography Images using Bag of Features Model

The BoF model is a tool widely used in the fields of classification and image search, itspopularity is largely based on its simplicity. As part of our work we will be interested in an imageclassification mammography based on the BOF model with SVM algorithm application and the SIFTdescriptor. These algorithms show performance, the results have been compared with other algorithms. Inthe first phase, the SVM was used to compare the result with the MLP. In the same work, two SIFT andORB descriptors were used. To assess the effectiveness of the proposed work, the classification rate(Acouracy) and the precision rate were calculated. The best classification rates are obtained by applyingthe MLP algorithm and using the SIFT descriptor on the base images (Ultra Sounde Brest Images Dataset)with a rate of 86% a small increase compared to SVM with a rate equal to 84%. Accuracy rate with 80%for MLP and 74% for SVM respectively.
An analytical study of direct normal irradiaance for concentrated solar energy applications for the mechria region of Algeria

SUN FLUX calculates solar irradiance on Earth's surface quickly and accurately. Direct solar radiation is critical for concentrated solar power plants, which determine their opening surface size based on reference radiation values. To reduce investment costs, a statistical analysis was performed using direct radiation, meteorological data from NSDRB, and cloud cover statistics to identify optimum ranges for direct solar radiation. CSP plant performance is evaluated and power tower systems are simulated using the SAM software tool.
Retracted: Innovative Practice of Music Education in the Universities in the Context of 5G Network

[This retracts the article DOI: 10.1155/2022/3451422.].
Retracted: Sports Training System Based on Convolutional Neural Networks and Data Mining

[This retracts the article DOI: 10.1155/2021/1331759.].
Retracted: Application of Multidirectional Mutation Genetic Algorithm and Its Optimization Neural Network in Intelligent Optimization of English Teaching Courses

[This retracts the article DOI: 10.1155/2021/4297600.].
Retracted: A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

[This retracts the article DOI: 10.1155/2022/7632841.].
Retracted: DC Motor Control Technology Based on Multisensor Information Fusion

[This retracts the article DOI: 10.1155/2022/1447333.].
Retracted: Application Effect Analysis of Clinical Nursing Pathway in the Care of Neonatal Hypoxic-Ischemic Encephalopathy

[This retracts the article DOI: 10.1155/2022/9379361.].
Retracted: Advanced Deep Learning Human Herpes Virus 6 (HHV-6) Molecular Detection in Understanding Human Infertility

[This retracts the article DOI: 10.1155/2022/1422963.].
ЗАСОБИ ТЕЛЕКОМУНІКАЦІЙНОГО ЗВ’ЯЗКУ У ВИЩІЙ ШКОЛІ

he 21st century is a century of rapid changes and constant improvement of various types of telecommunication networks. The sphere of education has not been left behind by innovation: almost all educational institutions of Ukraine are covered by the Internet, and there is an extensive network of satellite, cable, ether analogue or digital communication in it. The introduction of innovative telecommunication tools into the educational process provides an opportunity to create the most favourable conditions for those who study in educational institutions. The purpose of the article is to determine the peculiarities of the use of telecommunication technologies in higher education. Telecommunications refers to transfer of arbitrary messages over a distance using technical means (telephone, telegraph, radio, television, etc.) and reception, processing, and storage of messages. The most important place among telecommunications undoubtedly belongs to Internet technologies. The electronic form of information transfer is faster compared to the traditional one, and provides the exchange of materials with a wider range of specialists. Telecommunications in higher education are used for: – distance learning, which involves interactivity, flexibility, individualization, economy, information security; – search and research tasks; – participation in distance events; – joint activity on the project; – international and domestic conferences. The example of modern telecommunication tools in higher education is the Modular Object-Oriented Dynamic Learning Environment System (MOODLE), which makes it possible to master tools for computerized independent learning, including distance learning. Telecommunications facilitate communication, the ability to independently search for the necessary information among a huge number of electronic sources, use of various forms in monitoring educational achievements. They make it possible to increase the efficiency and quality of education, the depth of interdisciplinary connections, provide an individual approach to education, and increase the amount of independent work.
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems

Long-term user engagement (LTE) optimization in sequential recommender systems (SRS) is shown to be suited by reinforcement learning (RL) which finds a policy to maximize long-term rewards. Meanwhile, RL has its shortcomings, particularly requiring a large number of online samples for exploration, which is risky in real-world applications. One of the appealing ways to avoid the risk is to build a simulator and learn the optimal recommendation policy in the simulator. In LTE optimization, the simulator is to simulate multiple users’ daily feedback for given recommendations. However, building a user simulator with no reality-gap, i.e., can predict user’s feedback exactly, is unrealistic because the users’ reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy. In this paper, we present a practical simulator-based recommender policy training approach, Simulation-to-Recommendation (Sim2Rec) to handle the reality-gap problem for LTE optimization. Specifically, Sim2Rec introduces a simulator set to generate various possibilities of user behavior patterns, then trains an environment-parameter extractor to recognize users’ behavior patterns in the simulators. Finally, a context-aware policy is trained to make the optimal decisions on all of the variants of the users based on the inferred environment-parameters. The policy is transferable to unseen environments (e.g., the real world) directly as it has learned to recognize all various user behavior patterns and to make the correct decisions based on the inferred environment-parameters. Experiments are conducted in synthetic environments and a real-world large-scale ride-hailing platform, DidiChuxing. The results show that Sim2Rec achieves significant performance improvement, and produces robust recommendations in unseen environments.
An Evaluation Framework For Digital Image Forensics Tools

The boom of digital cameras, photography, and social media has drastically changed how humans live their day-to-day, but this normalization is accompanied by malicious agents finding new ways to forge and tamper with images for unlawful monetary (or other) gains.Disinformation in the photographic media realm is an urgent threat.The availability of a myriad of image editing tools renders it almost impossible to differentiate between photorealistic and original images.The tools available for image forensics require a standard framework against which they can be evaluated.Such a standard framework can aid in evaluating the suitability of an image forensics tool for use in a criminal investigation, commercial operation, or academic research.This paper proposes an evaluation framework designed for image forensics tools based on the conformance methodology of testing that employs test assertions and test cases.It is then used to evaluate four image forensics tools namely FotoForensics, Ghiro, Imago Forensics, and Exif Reader.The comparative insight of test results produced by the framework provides a ground for ranking the tools from best to least comprehensive.The results also provide information necessary for users to make intelligent choices about tools and help vendors shortlist areas of improvement in their tools.
Attribute Graph Neural Networks for Strict Cold Start Recommendation : Extended Abstract

Recently, deep learning based methods, especially graph neural network (GNN), have made impressive progress on rating prediction problem in recommender systems. However, the performance of existing methods drops quickly in the cold start scenario. More importantly, such methods are unable to learn the preference embedding of a strict cold start user/item since there is no interaction for this user/item. In this work, we develop a novel framework Attribute Graph Neural Networks (AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. AGNN can produce the preference embedding for a strict cold user/item by learning on the distribution of attributes with an extended variational auto-encoder (eVAE) structure. It also contains a new graph neural network variant (gated-GNN) to effectively aggregate various attributes of different dimensions in a neighborhood. Empirical results demonstrate that AGNN achieves the new state-of-the-art performance.