Reviewerly/exHarmony huggingface dataset:
Correlation Filters for UAV Online Tracking Based on Complementary Appearance Model and Reversibility Reasoning

Correlation filter (CF)-based approaches have been widely applied in online object tracking tasks for unmanned aerial vehicles (UAVs) due to their high computational efficiency and low memory consumption. One of the key steps is to perform correlation operations between the appearance model (AM) and the filter. However, as the difficulty in controlling the learning rate of the AM, most existing trackers are prone to causing degradation. In this paper, we propose a novel complementary AM (CAM) consisting of a primary model (PM) and a secondary model (SM). Specifically, the learning rates of the PM and SM are approximately complementary, allowing the CAM to consider both past and current information. Moreover, in order to take full advantage of historical information, a CAM-based reversibility reasoning approach is proposed for CF training. It can robustly handle the variations in object appearance. Then we further create a deep tracker by fusing convolutional features which demonstrates more outstanding performance. We also embed the CAM into two advanced trackers to validate the scalability of the CAM. Comprehensive experiments on six challenging UAV tracking benchmarks have indicated the superiority of our method compared to other 36 state-of-the-art CPU- and GPU-based trackers, with a speed of 45 FPS running on a cheap CPU.
Deep Photo-Geometric Loss for Relative Camera Pose Estimation

CNN-based absolute camera pose estimation methods lack scene generalizability as the network is trained with scene-specific parameters. In this paper, we aim to solve the scene generalizability problem in 6-DoF camera pose estimation using a novel deep photo-geometric loss. We train a CNN-based relative pose estimation network end-to-end, by jointly optimizing the proposed deep photo-geometric loss along with the pose regression loss. Most traditional pose estimation methods use local keypoints to find 2D-2D correspondences, which fails under occlusion, textureless surfaces, motion blur, or repetitive structures. Given camera intrinsics, poses and depth, our method generates uniform 2D-2D photometric correspondence pairs via epipolar geometry during the training process with constraints to avoid textureless surfaces and occlusion, without the need of manually annotated keypoints information. The network is then trained with the correspondences information in such a way that not only the network learns from auxiliary photometric consistency information but also efficiently leverages scene geometry, consequently, we call it photo-geometric loss. The input to the photo-geometric loss layer is taken from the activation maps of the deep network, which contains much more information than a simple 2D-2D correspondence, and thus alleviating the need to choose a robust pose regression loss and its hyperparameters. With extensive experiments on three public datasets, we show that the proposed method significantly outperforms state-of-the-art relative pose estimation methods. The presented method also depicts state-of-the-art results on these datasets under cross-database evaluation settings, which proves its significance in terms of scene generalization.
Correlation between Entropy and Prediction Error in VR Head Motion Trajectories

The general understanding of user behaviour has been often overlooked in the field of Virtual Reality (VR) and Extended Reality (XR) at large. In this work, we want to fill this gap by exploring the relationship between the way in which users navigate in immersive content and the predictability of their trajectories. Inspired by works from social science, our key assumption is that there are navigation trajectories that can be accurately predicted, while others exhibit eclectic patterns that are more challenging to anticipate. However, it is not yet clear how to effectively distinguish between these behaviours. In this context, we conduct an extensive data analysis across multiple datasets investigating users' movements in VR. The ultimate goal is to understand if a specific metric from information theory, such as the entropy of trajectory, can be adopted as a discriminating metric between predictable navigation trajectories and unpredictable ones. Our findings reveal that users with highly regular navigation styles tend to exhibit lower entropy, indicating higher predictability of their movements. Conversely, users with more diverse navigation patterns show higher entropy and lower predictability in their trajectories. Answering the question "how can we distinguish users more predictable than others?'' would be crucial for different purposes in future immersive applications such as enabling new modalities for live streaming services but also for the design of more personalised and engaging VR experiences.
E-COMMERCE SYSTEM FOR “TOKO MIRING” HOMEWARE STORE

&lt;p&gt;“Toko Miring” is a growing company selling&lt;br /&gt;household appliances, where the promotion and sales process&lt;br /&gt;is still conventional. This means that the customer has to go to&lt;br /&gt;the Italic Shop to be able to purchase the product. The&lt;br /&gt;development of an e-commerce sales system at the Toko&lt;br /&gt;Miring store is a step to increase sales and product promotion&lt;br /&gt;so that it can provide benefits for the company. The purpose&lt;br /&gt;of building an e-commerce system for selling this product is&lt;br /&gt;that customers can place orders for products without having&lt;br /&gt;to come to the Miring Shop, the company can also solve the&lt;br /&gt;problem of product processing, order processing so that it can&lt;br /&gt;make it easier for consumers to get information about&lt;br /&gt;products from the Miring Store. This system was created&lt;br /&gt;using PHP and MySQL as the program. The result of making&lt;br /&gt;this project is an e-commerce system for Toko Miring which&lt;br /&gt;can be used as a means of sales promotion that can be&lt;br /&gt;accessed anywhere and anytime, the buying process can also&lt;br /&gt;take place without having to come to the store.&lt;/p&gt;
Multi-sensor fusion estimation algorithm based on IMM and SCC

Aiming at the problems of vague information acquisition, insufficient accuracy, and poor robustness of a single sensor, this paper investigates the information fusion algorithm of multiple sensors. Firstly, the motion of the moving body is modeled using staged CV, CA, and CT models, and the complete motion of a single maneuvering target is simulated based on 2D planes or 3D space. Secondly adaptive implementation of Kalman's nonlinear filter estimation in maneuvering targets under a single sensor based on the Interactive Multi-Model (IMM) algorithm. The multi-sensor IMM results are then estimated and fused based on the Simple Convex Combination (SCC) fusion algorithm. Finally, the single-sensing and multi-sensing fusion performances are simulated and evaluated by experiments to prove the effectiveness of the designed track fusion algorithm under multi-sensing.
Oversampling algorithm based on generative adversarial network

In supervised learning, standard algorithms are mostly designed to deal with balanced data classes, but it is inevitable to encounter imbalanced data classes in some situations. How to learn from imbalanced data is still a challenging problem. A common approach is to generate artificial data or duplicate existing classes to balance the class distribution. Many traditional algorithms can handle imbalanced classes, but they often fail to enhance enough features and tend to produce unnecessary noise points. In this paper, we propose an improved network based on generative adversarial networks (GANs) and improved K-means Smote oversampling method to oversample the data. This method replaces the noise input of GANs with the oversampled results, and then generates new oversampled data through adversarial networks. By conducting experiments on six datasets, we show that this method can effectively improve the classification results of classifiers on oversampled data.
Gigue: A JIT Code Binary Generator for Hardware Testing

Just-in-time compilers are the main virtual machine components responsible for performance. They recompile frequently used source code to machine code directly, avoiding the slower interpretation path. Hardware acceleration and performant security primitives would benefit the generated JIT code directly and increase the adoption of hardware-enforced primitives in a high-level execution component.
Evaluation of Iron Profiles and Coagulation Profiles in Ischemic Heart Diseases Patients in Elmer Nimr Hospital, Sudan

Back ground chemic heart disease (IHD) isa constellation of diseases that includes stableangina,unstableangina,myocardialinfarction,andsuddencardiacdeath.It belongs to the group of cardiovascular diseases and is the most common type among them. Iron is an essential trace element .it plays an important role in maintaining various cellular function and enzymatic reactions. On the other hand, excessive intake of iron is known to be a risk factor for the progression of atherosclerosis. Abnormal iron deposition in the heart causes hem chromatists and dilated cardiomyopathy, which leads to is chemic heart disease .Objectives :This study aims to investigate the role of serum iron ,serum ferritin ,total iron-binding capacity(TIBC), PT, and PTT in is chemic heart disease(IHD)and their relationship with other risk factors for IHD This is a prospective, case-control, cross-sectional analytical study conducted at Elmer Narmer University Hospital, Shendi City, to assess their on profile and coagulation profile of patients with is chemic heart disease. The study included (30) patients diagnosed with is chemic hear the diseases and the study group was compared with the mean of the control group (20). Serum iron ,ferritin ,and TIBC were estimated using a Spectro photo meter ,and PT and PTT were estimated using manual methods. Data were collected using a structured personal questionnaire and the program (SPSS) version(11.5)was used for data analysis
PELATIHAN PEMBUATAN IKLAN PEMASARAN PRODUK PROMOSI MENGGUNAKAN APLIKASI VOICE MAKER PENGISI AUDIOTEXT TO SPEECH (TTS) DI SMK BM BUDI AGUNG MEDAN

Product promotion is an activity of introducing products, persuading, reminding and influencing the public so that potential buyers want to buy the goods and services offered. Advertising in the form of communication that has the intention of conveying information about the products offered with global marketing communications and e-marketing using a combination of graphics, animation and sound. This training is aimed at students at private schools in the city of Medan, SMK Budi Agung Class XII, majoring in marketing, in the form of multimedia practicum training using Macromedia Flash, making product advertisement animations and advertising voiceovers using the Voice maker Free Text to Speech (TTS) application. This training is a higher education tridharma each semester carrying out community service as a routine each semester.
Manajemen Mutu Pembelajaran Kitab Kuning

Al-Iman Islamic Boarding School has long been known as one of the Islamic boarding schools in Purworejo which has formal educational institutions and excels in the field of yellow books. The graduating students have the ability to master the yellow book. Over time, Islamic boarding schools have experienced rapid development. Currently, Islamic boarding schools have formal educational institutions from early childhood education to tertiary level. The number of students continues to increase until it reaches three thousand. Under these conditions, quality management is needed in learning the yellow book so that the superiority of Islamic boarding schools as producers of students who are literate in the book can be maintained. This research aims to determine the quality management process in yellow book learning for Madrasah Tsanawiyah and Aliyah students. This research uses descriptive qualitative research methods by collecting data through interviews, observation and documentation. The research results show that the quality management process of yellow book learning is carried out in three stages, namely quality planning, quality control and quality improvement. Quality planning is carried out by determining graduate competency standards that must be mastered by students, identifying new students, identifying the needs of new students, designing the curriculum, system and daily schedule for learning the yellow book. Quality control is carried out by assessing the competency of students in mastering the yellow book, comparing these results with the competency standards of graduating students and adopting policies based on the results of the assessment. Quality improvement is carried out by evaluating the performance of yellow book learning, determining the necessary infrastructure, improving existing learning systems and creating new systems, forming education administrators, providing provision in the form of knowledge, training and motivation for new educational administrators, and monitoring performance. education administrator.Keywords: quality management; learning; yellow book.
A comparative analysis of email phishing detection methods: a deep learning perspective

The study comprehensively evaluates deep learning models for detecting email phishing attacks. The study applies deep learning models, including convolutional neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM), and bidirectional long short-term memory, to a dataset of phishing and legitimate emails. It evaluates their performance based on several metrics, including accuracy and precision, recall, and F1-score. The LSTM model achieved the highest accuracy of 99.41%, outperforming all other models, while the RNN model had the worst accuracy among all the models evaluated. This chapter provides valuable insights into the potential of deep learning models for detecting email phishing attacks. It highlights the need for further research to develop more effective and reliable approaches. The study also underscores the importance of developing robust and accurate phishing detection systems, given the significant impact that emails phishing attacks can have on individuals and organizations. The findings of this study have practical implications for the development of email phishing detection systems and provide a foundation for future research in this area.
Artificial Flowers

Abstract This chapter explores the role that life plays in the way intellectual property law interacts with nature and how this changes with artificial or synthetic biology. The chapter shows that despite their copyright registration as sculptures, artificial flowers are not simply artistic objects. Instead, artificial flowers are hybrid objects that are artificial and at the same time extrinsically intertwined with nature. In this sense, the lineage of artificial flowers within intellectual property is closer to synthetic biology than it is to biology; to a subject matter inspired by nature but which takes up engineering standards and techniques to build nature from scratch.
Implementation of EDAS Method in the Selection of the Best Students with ROC Weighting

This study aims to provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The EDAS method requires a lot of input, and preference must be precise in the determination of the weight of the criteria. To fix the problem of weighting criteria in the EDAS method, the Centroid Rank Order (ROC) method is used. ROC is a simple method used to assign weight values to each criterion used. The results of this study provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The application of the EDAS method in the selection of exemplary student candidates resulted in exemplary prospective students obtained on behalf of Hadi Santoso with a final score of 0.70885 and obtained 1st rank. The results of these recommendations can help the school determine the selection of the best students by applying the EDAS method and ROC weighting.
Virtual Sensor to Impute Missing Data Using Data Correlation and GAN-Based Model

Continuous automation of conventional manufacturing and industrial processes is an inevitable trend of the Fourth Industrial Revolution. Every aspect of life has been affected by Internet of Things (IoT), which refers to the growing number of connected and functional devices that rely on data gathered by IoT sensors. In fact, a system’s activity may be terminated or become faulty as a result of a sensor failure because the data flow was disrupted. Therefore, this work leverages machine learning methods which have lately been used in a number of applications and produced cutting-edge outcomes to resolve missing data problem with a virtual sensor solution. The approach uses the correlation of collected data to support a Generative Adversarial Networks (GAN) based model in imputing missing values. Compared to other recent virtual sensor techniques using other machine learning models, our approach achieved better performance around 20% in the considered datasets with different metrics.
Indicators Affecting National Governance in the Developing Economies: Evidence from Pakistan

Abstract The governance system of any country is required to be stable in any country. This study is the initial attempt to find the indicators of governance in Pakistan. The data has been collected from World Bank Data Bank from 2002-2016. The correlation test and Structural Equation Modelling is applied to the data. The governance is the construct variable in Structural Equation Modelling. The results shows that Voice and Accountability, Political Stability and Violence Terrorism, Government Effectiveness, Gross Domestic Product per Capital Growth, Gross National Index growth. Gender diversity and Human Development are the factors of governance in Pakistan. Keywords: Governance, Structural Equation Modelling, Correlation, Gender diversity and Human Development index, Indicators.
PMM: A Smart Shopping Guider Based on Mobile AR

Augmented reality (AR) is a burgeoning interaction technology with the ability to provide users with immersive everyday experiences. Shopping is among the most common experiences. When consumers purchase products, they often encounter difficulties in obtaining detailed information relevant to their interests, such as ingredients, origin, and product comparisons, leading to frustration. This situation can be undeniably frustrating. We can use visualization technology [18] to organize it more orderly and friendly. This paper proposes a mobile augmented reality-based application framework, Product Magic Mirror (PMM), which helps to integrate basic visualization design into the application. The augmented information can be rich, e.g. they can be some visualizations and vivid data videos. In the evaluation, we simulated a goods purchase scene, and applied information to the real world by using AR. The real environment and virtual objects were superimposed on the same screen in real time, so as to achieve an experience beyond reality [24]. In our user survey, users rated our tools as a way to make better shopping choices, giving them a positive score for an immersive shopping experience. We predict that displaying information such as instructions, ingredients, and/or user review information next to products could allow consumers to make better consumption choices while reducing decision time and making shopping more immersive.
DFuzzer: Diversity-Driven Seed Queue Construction of Fuzzing for Deep Learning Models

In light of high-performance computer processing, massive datasets, and mighty algorithms, we are rapidly entering an age where the advanced deep learning (DL) capabilities are integrated into the contemporary software systems to fulfill critical tasks. Like “traditional” software, DL systems are not immune to faults, some of which may even cause catastrophic disasters. As a mainstream testing technique for DL systems, fuzzing attempts to generate a large amount of semirandom yet syntactically valid test cases, from which the so-called adversarial inputs can be found, indicating the detection of faults. Test cases in fuzzing are generated based on a seed queue, which is constructed by randomly selecting seeds from the existing test suite (that is, the set of test cases). In this article, we propose a diversity-driven approach, namely DFuzzer, for constructing seed queues in fuzzing. We particularly develop two algorithms, namely DFuzzer-IB and DFuzzer-FB, based on the information theory and deep features, respectively, to improve the diversity of seed queues. Experimental studies have been conducted to evaluate the proposed techniques based on five fuzzers, three datasets, and seven DL models. The experimental results show that both strategies can significantly improve the performance of the state-of-the-art fuzzers for DL, including DeepXplore, DLFuzz, Tensorfuzz, DeepHunter, and DeepSmartFuzzer, not only in terms of finding more adversarial inputs for triggering faults but also achieving higher coverage. Our article demonstrates that the improved diversity of seed queues and the resultant test cases can help achieve a high testing effectiveness of fuzzing.
Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm

This study leverages advanced data mining and machine learning techniques to delve deeper into the impact of sports activities on physical health and provide a scientific foundation for informed sports selection and health promotion. Guided by the Elastic Net algorithm, a sports performance assessment model is meticulously constructed. In contrast to the conventional Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, this model seeks to elucidate the factors influencing physical health indicators due to sports activities. Additionally, the incorporation of the Random Forest algorithm facilitates a comprehensive evaluation of sports performance across distinct dimensions: wrestling-type sports, soccer-type sports, skill-based sports, and school physical education. Employing the Top-K criterion for evaluation and juxtaposing it with the high-performance Support Vector Machine (SVM) algorithm, the accuracy is scrutinized under three distinct criteria: Top-3, Top-5, and Top-10. The pivotal innovation of this study resides in the amalgamation of the Elastic Net and Random Forest algorithms, permitting a holistic contemplation of the influencing factors of diverse sports activities on physical health indicators. Through this integrated methodology, the research achieves a more precise assessment of the effects of sports activities, unveiling a range of impacts various sports have on physical health. Consequently, a more refined assessment tool for sports performance detection and health development is established. Capitalizing on the Elastic Net algorithm, this research optimizes model construction during the pivotal feature selection phase, effectively capturing the crucial influencing factors associated with different sports activities. Concurrently, the integration of the Random Forest algorithm augments the predictive prowess of the model, enabling the sports performance assessment model to comprehensively unveil the extent of impact stemming from various sports activities. This study stands as a noteworthy contribution to the arena of sports performance assessment, offering substantial insights and advancements to both sports health and research methodologies.