Update Features Research Articles

Content Strategies to Improve the Performance of Audio Streaming Services: Focusing on Content Genre and Update Features

The AuSS market witnessed substantial growth in 2022, with a strong estimated annual expansion from 2023 to 2030. Prominent companies like Amazon, Spotify, and YouTube are intensifying their competition by acquiring smaller firms in the audio content industry. To navigate this intense AuSS market competition, this study aims to categorize AuSS content genres and investigates the impact of content genre and update features on content performance (measured by content usage and subscriber size). Based on econometric analysis, we explore how the relationship between content genre and content performance is influenced by content update features. Empirical data was collected from Naver’s Audioclip, South Korea’s leading internet portal. For the research results, the content performance was different depending on the content genre. The more the regularity and frequency of content updates, the better the content performance. The effect of content update frequency on content performance differed by each content genre. Based on the findings of this study, we suggest the academic and managerial implications for content strategies to enhance the AuSS content performance of related companies.

Implementation of the GBI Bethany Tower of Christ Congregation Management Information System Design

Along with the development of the digital era and pandemic conditions have increased the need for systems capable of working remotely. This motivated the researchers to conduct observations at the Bethany Tower of Christ Church. Based on observations and interviews that have been conducted with church pastors and deacons, it is found that the system already owned by the church has a lack of features, one of which is the feature of submitting requests to the church. In addition, there is also a need to update features related to adding devotionals, news, and posts to make it more practical and efficient. The development of this system uses the Waterfall method in which in the process the researcher first conducts direct observation and research at the Bethany Tower of Christ Church followed by analyzing the needs of the church and congregation which results in the design of a congregation management system. As for after the design is done, the system implementation is carried out. After that, researchers conducted testing of system maintenance. Based on the overall results obtained from the survey of respondents' assessment of the website, an average assessment of 87.3% was obtained, indicating that the website is feasible and has impressive respondent satisfaction. Researchers still find shortcomings in the system created, namely the lack of some features and the presence of bugs that need to be fixed. However, researchers still hope that the designed system can be useful for congregations and churches and can continue to be developed and maintained so that the system can be better.

Energy-informed graph transformer model for solid mechanical analyses

Physics-informed neural network (PINN) exists some challenges, such as independent and uncorrelated drawbacks leading to convergence impediments, limited interpretability and lack of generalization. In this paper, a novel energy-informed graph transformer model is proposed to overcome the drawbacks of PINN. In the proposed model, the graph neural network-based-attention mechanism is proposed to dynamically calculate weight coefficients between objects of graph-structured data, and then to aggregate weighted combinations of the neighbor objects features to update features of the target objects. The loss function is constructed with homoscedastic uncertainty by introducing trainable scalar parameters, which can be optimized to achieve the best performance of the network as it changes dynamically the topology of the loss function involved in the optimization process. Numerical results show that the proposed method can effectively increase the efficiency, robustness and accuracy of the network approximation of forward and inverse problems of solid mechanics. Furthermore, the proposed model demonstrates excellent generalization capabilities when applied to new problem using transfer learning strategy.

Over-the-Air Firmware Update in LoRaWAN Networks: A New Module-based Approach

Over the last years, a huge number of things has been connected to the Internet to support applications for smart cities, smart industries, smart agriculture and smart environment, etc. In order to fix bugs, update features or perform dynamic adaptations on these things, Efficient Firmware Update Over the Air (FUOTA) methods are required. In this paper, we propose a modular-based approach for the development of firmware of MCU-based IoT devices, and a new FUOTA method for devices communicating using LoRaWAN. This method enables partial and dynamic updates of modules forming the device firmware, and does not require a system reboot to apply these updates. This approach and this method have been implemented on STM32 MCUs. The experimental results show good performance in terms of update size and network traffic compared to the traditional FUOTA method and monolithic-based firmware development approach, which both impose full firmware updates.

Electron Emission Channeling for lattice location of radioactive isotopes in single crystals: Improvements from a Timepix3 quad detector and new PyFDD data analysis software

Electron Emission Channeling (EC) is a powerful technique for the investigation of the lattice location of radioactive isotopes implanted into single crystals. After implantation the isotopes occupy certain lattice locations in the crystal, which can in some cases be altered by annealing. Upon decay, the emission of a charged particle, typically a beta, may result in a channeling trajectory when its starting lattice location is aligned with major symmetry axes or planes of the crystal. By measuring the emission anisotropy in the direction of these axes for distinct annealing temperatures, the lattice location of the isotope can be determined with great precision and insightful information can be obtained on how annealing affects the occupied sites. This work reports on the installation of a Timepix3 quad detector and Katherine Gen2 readout in an experimental setup located at ISOLDE at CERN. The large increase in the number of pixels of the Timepix3, in comparison to previously used pad detectors, required more sophisticated tools for data treatment and fitting of channeling patterns. From this need, the PyFDD software was born . Its latest update features an intuitive graphical interface, with tools for noise masking, pattern visualization, simulations browsing, chi-square or maximum likelihood based fits and gamma background correction.

Improving surface defect detection with context-guided asymmetric modulation networks and confidence-boosting loss

Segmentation networks based on deep learning are widely used in the field of surface defect detection to ensure product quality. However, due to the complexity of defects and limited datasets, it is difficult for segmentation networks to achieve good performance in single-shot predictions. Therefore, we propose a novel context-guided asymmetric modulation network (called CAMNet) to improve the segmentation performance of existing methods. Inspired by the idea of cascading, CAMNet employs a coarse-to-fine segmentation framework that treats coarse predictions as priors to refine the extracted deep features. For this purpose, two asymmetric feature modulation modules, APM and ACM, are constructed in the spatial and channel dimensions, respectively. They are specially designed to leverage the rich context to aggregate and update features for fine prediction, and they are also lightweight and efficient, allowing for a significant reduction in computational complexity without sacrificing performance. In addition, a confidence-boosting loss is proposed to further widen the performance gap between the two prediction stages of CAMNet. Extensive experiments on three industrial datasets (PCB, Magnetic-tile, and DAGM2007) confirm the effectiveness of our method, showing that CAMNet can consistently achieve performance gains across different baselines and input scales. For example, CAMNet improves the IoU scores of FCN and GCNet on DAGM2007 by 5.92% and 4.98%, respectively.

Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds.

Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i.e., the point cloud videos. We empirically categorize the temporal information into short-term and long-term patterns. To encode the short-term data, we present a Grid Message Passing Network (GMPNet), which considers each grid (i.e., the grouped points) as a node and constructs a k-NN graph with the neighbor grids. To update features for a grid, GMPNet iteratively collects information from its neighbors, thus mining the motion cues in grids from nearby frames. To further aggregate the long-term frames, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU), which contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module. STA and TTA enhance the vanilla GRU to focus on small objects and better align the moving objects. Our overall framework supports both online and offline video object detection in point clouds. The evaluation results on the challenging nuScenes benchmark show the superior performance of our method, achieving 1st on the leaderboard without any bells and whistles, by the time the paper is submitted.

CFD Study on Water Impact of Spent Stage on Floats Using Overset Grid and Volume of Fluid Approach

A transient multiphase CFD analysis using the VOF method coupled with a 6DOF solver is used to model the water impact of a launch vehicle spent stage with floats. Overset Mesh motion technique was used to model the 6DOF based motion of the body. The stability of the spent stage water impact solution was studied by varying implicit mesh update features. The water impact of spent stage with different diameter floats was also studied. The CFD model is validated for the water impact of a wedge which was available in the literature. The CFD predicted results for the wedge case are within the 5% of the measurement in the prediction of maximum penetration depth.

Analisis Penerimaan Learning Management System di Perguruan Tinggi Menggunakan Technology Acceptance Model (TAM)

This study aims to evaluate the utilization of the Learning Management System (LMS) by students of Universitas Terbuka (UT). The primary focus of this research is to comprehend the usage of LMS in higher education, with the research locations divided into three geographical zones: West (UT-Jakarta, UT-Surabaya, UT-Serang, UT-Pekanbaru, UT-Palembang, UT-Aceh), Central (UT-Pontianak, UT-Samarinda, UT-Makassar, UT-Kendari), and East (UT-Majene, UT-Mataram, UT-Palu, UT-Ternate, UT-Jayapura). The focus of the research is to identify factors influencing students not to participate in online classes, particularly related to the suboptimal utilization of the Online Tutorial service. In this study, 400 respondents were surveyed, all of whom are UT students. Data analysis was conducted using the SmartPLS application, utilizing the Technology Acceptance Model (TAM). The model involves both external variables and those within the TAM. The research findings indicate that the system quality significantly influences the use of LMS by students. Based on the gathered information, universities should enhance and regularly update features within the LMS to better support users efficiently.

Task-Specific Loss for Robust Instance Segmentation With Noisy Class Labels

Deep learning methods have achieved significant progress in the presence of correctly annotated datasets in instance segmentation. However, object classes in large-scale datasets are sometimes ambiguous, which easily causes confusion. Besides, limited experience and knowledge of annotators can lead to mislabeled object semantic classes. To solve this issue, a novel method is proposed in this paper, which considers different roles of noisy class labels in different sub-tasks. Our method is based on two basic observations: firstly, the foreground-background annotation of a sample is correct even though its class label is noisy. Secondly, symmetric loss benefits the model robustness to noisy labels but harms the learning of hard samples, while cross entropy loss is the opposite. Based on the two basic observations, in the foreground-background sub-task, cross entropy loss is used to fully exploit correct gradient guidance. In the foreground-instance sub-task, symmetric loss is used to prevent incorrect gradient guidance provided by noisy class labels. Furthermore, we apply contrastive self-supervised loss to update features of all foreground, to compensate for insufficient guidance provided by partially correct labels especially in the highly noisy setting. Extensive experiments conducted with three popular datasets (i.e., Pascal VOC, Cityscapes and COCO) have demonstrated the effectiveness of our method in a wide range of noisy class label scenarios.

Token-Based Authorization and Authentication for Secure Internet of Vehicles Communication

The Internet of Vehicles (IoV) communication platform provides seamless information exchange facilities in a dynamic mobile city environment. Heterogeneous communication is a common medium for information exchange through autonomous resources distributed and accessed using infrastructure units. Cyber-security is a primary concern in accessing autonomous information from the distributed resources due to anonymity and different types of targeted adversaries. This article proposes token-based authorization and authentication (TAA) for securing IoV communications. The proposed method relies on blockchain technology and random forest learning for authorization and key management for authentication, respectively. In this process, frequent change in tokens and key update features are restricted in a view to maximize the seamlessness in information exchange. Authentication is preceded by knowledge of the data classification without errors to prevent additional overhead. Blockchain-based authorization helps to update specific fields of the tokens to retain the communication ratio by reducing vehicle-to-vehicle losses. The performance of the proposed method is assessed using appropriate simulations for these metrics by varying vehicle density, error rate, and classification sets.

Polymorphic uncertainty quantification for engineering structures via a hyperplane modelling technique

This paper proposes a hyperplane modelling technique aided polymorphic uncertainty quantification strategy for various engineering applications. Accumulative experiences from industrial applications have continuously revealed that probability distribution characteristics for system properties cannot always be precisely determined, due to the scarcity of available information. Thus, the polymorphic uncertainty is introduced to consider fuzzy and random uncertainty simultaneously. To tackle such uncertainty quantification challenges, Multiple Target Strategy (MTS) and Single Target Strategy (STS) are proposed. MTS is a generalized polymorphic uncertainty quantification strategy, capable of providing sufficient amounts of the statistical information of the concerned structural response. On the contrary, STS considers the polymorphic uncertainty quantification in a more precise and efficient manner. Both strategies are implemented based on their respective hyperplane models. For hyperplane model construction, a newly developed ensemble meta-regression technique, namely AdaBoost Extended Support Vector Regression (Ada-X-SVR), is embedded with a novel kernel function. The proposed hyperplane modelling aided polymorphic uncertainty quantification framework provides the feasibility of both generalized and targeted estimations, auto-learning and information update features. Furthermore, to demonstrate the applicability and computational efficiency of the proposed strategies, a benchmark analysis with an available analytical solution and three engineering applications with linear and nonlinear performance are fully investigated.

AN APPROACH TO UPDATING VECTOR FEATURES BY CLUSTERING ALGORITHM

Abstract. The availability of Geographic Information System (GIS) data has increased in recent years, as well as the need to update its data. One way of updating GIS vector data is by deleting and inserting data to the databases by manually defined unique value. Other one is by deleting and adding data manually. These methods all require manual predefined unique values or manual operations which is not suitable for updating data in a timely and fast way, especially large numbers of data are updated. Therefore, an automated vector data updating method has the potential to significantly increase productivity, particularly as existing GIS vector data application increase in size, the data become outdated more quickly. In this paper, we consider the difference between GIS vector data and other data that GIS vector data have spatial coordinates and topology relationships, propose an approach to updating of vector features based on clustering algorithm. First, the minimum bounding box and its center point of vector features are calculated, and then clusters the center points with DBSCAN to get some clusters. In the end, extract the smallest bounding box of every cluster and update vector features with the boxes. This approach uses smallest bounding box to update features is to reduce database query cost and improves the data update efficiency. Experiments show that the method is effective and feasible.

Fast and Accurate Motion Correction for Two-Photon Ca2+ Imaging in Behaving Mice.

Two-photon Ca2+ imaging is a widely used technique for investigating brain functions across multiple spatial scales. However, the recording of neuronal activities is affected by movement of the brain during tasks in which the animal is behaving normally. Although post-hoc image registration is the commonly used approach, the recent developments of online neuroscience experiments require real-time image processing with efficient motion correction performance, posing new challenges in neuroinformatics. We propose a fast and accurate image density feature-based motion correction method to address the problem of imaging animal during behaviors. This method is implemented by first robustly estimating and clustering the density features from two-photon images. Then, it takes advantage of the temporal correlation in imaging data to update features of consecutive imaging frames with efficient calculations. Thus, motion artifacts can be quickly and accurately corrected by matching the features and obtaining the transformation parameters for the raw images. Based on this efficient motion correction strategy, our algorithm yields promising computational efficiency on imaging datasets with scales ranging from dendritic spines to neuronal populations. Furthermore, we show that the proposed motion correction method outperforms other methods by evaluating not only computational speed but also the quality of the correction performance. Specifically, we provide a powerful tool to perform motion correction for two-photon Ca2+ imaging data, which may facilitate online imaging experiments in the future.

Delta and the Variants

This update features the chronology and physiology of Delta, currently the most prominent variant of coronavirus disease 2019 (COVID-19). We offer a template for further appreciation of the ongoing evolution of the COVID-19 variants.

Adaptive Classification Using Incremental Linearized Kernel Embedding

This paper considers the problem of adaptive classification for performing pattern discrimination in varying conditions when new data arrives. A new efficient method is presented to incrementally update features in a Nyström-approximated linearized kernel embedding (LKE). Our method leverages a fast eigendecomposition algorithm for symmetric Arrowhead matrices. The proposed method can also be applied to kernel principal component analysis (KPCA) or similar problems. A mechanism is proposed which allows the incremental linearized kernel embedding to be used for updating of dictionaries in a sparse representation-based classification algorithm. The method is based on transporting the dictionaries into the embedding expanded with new data points and avoids the need to learn new dictionary matrices every time new data becomes available. The effectiveness of the developed methods is illustrated on two handwritten digit image data sets namely MNIST and USPS. Classification performance before and after sequential embedding updates is evaluated and compared. Comparisons are also made between our incremental LKE algorithm and the conventional approach to updating the empirical kernel map in terms of their computational requirements and numerical stability.

Impact of Societal Events on Frequency Stability Considering LED TVs in Low Inertia Trending Power Systems

Due to environmental concerns, renewable energy sources (RESs) gradually replace conventional power generations. Over the past decade, wind and solar electricity generation have increased from 55 to 275 million MW·h and from 2 to 96 million MW·h, respectively, in the U.S. Recently, FERC order 2222 allows distributed energy resources, including distributed renewable generation and energy storage, to participate in the electricity wholesale market through aggregations. Limited rotating inertia of renewable generation resources results in reduced system inertia, which will increase the rate of change of frequency due to sudden mismatches of generation and demand. During the high visibility national social event, the brightness of LED TV display put simultaneous demand change in power systems. With the development of slim, tightly packaged, and yearly update features of smart TV, the penetration of different LED displays climbed rapidly from 9% to 60% in the U.S. during 2012–2019. In this article, the impact of societal events on frequency stability caused by large viewership on LED TVs are discussed. Increasing RESs penetration and FERC order 2222 are considered in this study. The power demand data of a 55-inch LED TV are collected and analyzed to address the problem. This article discusses the frequency stability issue caused by LED TVs in a low inertia power system. It can help system operators to develop frequency control practices accordingly.

Implementation of Operational Strategy Planning in Doku Application

Background - Health services are one of the most widely used public services, administered by BPJS Health reaching 222.5 million people as of December 31, 2020. This figure is equivalent to 81.3% of the total population in Indonesia. The Doku application is an application that will help level I Health Facilities and Hospitals to manage BPJS patient services. The Doku application will replace the conventional BPJS patient services at Level I Health Facilities and Hospitals to online. This business opportunity is quite large, even though there are threats from existing applications, with more brands and users, but based on the results of QSPM is product development strategy, Doku will always update features and add according to market needs. Method - Product design operational management approach. Result - This product design helps companies to implement user requirements in the form of user interface. Keywords: Application, Doku, Feature Update, Business opportunity.

Supporting features updating of apps by analyzing similar products in App stores

Facing the increasingly fierce competition, app developers have to update features of their products continually. In this process, developers need to not only consider users’ demands but also pay attention to what other similar apps do so that they can stay one step ahead in the competition. App stores provide large-scale useable data for achieving this goal while how to use them efficiently becomes a new challenge for developers. In this paper, we aim at helping developers make feature updating strategies of their apps by analyzing data of similar products in App stores. Firstly, we identify similar apps by using texts in app descriptions and UI. Then, we gain and integrate the information of updated features in these apps from their release texts. Furthermore, we match reviews with the related updated features, which helps developers predict the payback if they adopt a similar updating strategy. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that our approach can analyze the data reasonably and provide useful information for developers making feature updating strategy in the evolution of their own products.

Secure over-the-air software updates in connected vehicles: A survey

Current trends forecast that Over-the-Air (OTA) software updates will be highly significant for future connected vehicles. The OTA update will enable upgrading the vehicle functionalities or bug fixations in the embedded software installed on its Electronic Control Units (ECUs) remotely. The introduction of OTA updates in the automotive industry has brought many advantages for both the Original Equipment Manufacturer (OEM) and the driver/owner. However, in terms of security, OTA updates are highly critical as they need complete access to the in-vehicle communication network.This survey highlights and discusses OTA software updates in the automotive sector, mainly from the security perspective. The major objective of this survey is to deliver a comprehensive outline of various research directions and approaches in OTA update technologies in vehicles. At first, we discuss the connected vehicle technology and then integrate the relationship of OTA update features with the connected vehicle. We further discuss both promising and secure OTA update approaches, that have gained a lot of attention recently. Furthermore, we present a comprehensive comparative study of the existing OTA update approaches on the basis of strengths, weaknesses and evaluation setup. The survey also focuses on the existing vehicle features that support OTA updates, and customer satisfaction and usability. Finally, we identify possible future research directions of OTA updates for automobiles, particularly in the area of security.