Search

Modern web services widely provide RESTful APIs for clients to access their functionality programmatically. Fuzzing is an emerging technique for ensuring the reliability of RESTful APIs. However, the existing RESTful API fuzzers repeatedly generate invalid requests due to unawareness of errors in the invalid tested requests and lack of effective strategy to generate legal value for the incorrect parameters. Such limitations severely hinder the fuzzing performance. In this paper, we propose DynER, a new test case generation method guided by dynamic error responses during fuzzing. DynER designs two strategies of parameter value generation for purposefully revising the incorrect parameters of invalid tested requests to generate new test requests. The strategies are, respectively, based on prompting Large Language Model (LLM) to understand the semantics information in error responses and actively accessing API-related resources. We apply DynER to the state-of-the-art fuzzer RESTler and implement DynER-RESTler. DynER-RESTler outperforms foREST on two real-world RESTful services, WordPress and GitLab with a 41.21% and 26.33% higher average pass rate for test requests and a 12.50% and 22.80% higher average number of unique request types successfully tested, respectively. The experimental results demonstrate that DynER significantly improves the effectiveness of test cases and fuzzing performance. Additionally, DynER-RESTler finds three new bugs. [ABSTRACT FROM AUTHOR]

Objective. The goal is to develop a web service for detecting aerial objects that detects a flying object, highlights it in an image and classifies it as a threat, since modern aerial object detection systems do not always cope with the task of detecting unmanned aerial vehicles due to their small size, low flight altitude and the use of materials that are barely noticeable to radar stations. Unmanned aerial vehicles that operate without operator control make it difficult to detect by radio signals. To detect UAVs, it is proposed to use a system based on optical scanning of the sky around protected objects. The system should be capable of autonomous operation and include aerial object detectors created on the basis of computer vision and artificial intelligence technologies. Method. The research and development of the airspace monitoring web service are based on the methods of system analysis, synthesis, and deduction. Result. The visual part of the web interface has been designed and developed; a dataset has been formed from open sources for the correct detection of flying objects; a neural network detector has been developed for classifying flying objects that pose a danger; a software module has been developed that allows for the automatic detection of identification flags of dangerous air objects with subsequent provision of reports in txt files in yolo format (coordinates are normalized). Conclusion. Separation of the visual part of the service will allow for distributed deployment of the server part, increasing flexibility and scalability. Development of the administrator control panel will allow for effective control of the service, management of settings and users. As a result, the web service will be able to: monitor the sky around protected objects, automatically detecting and classifying air objects and identifying air objects by threat level, providing information for taking necessary measures.

Web services (WS) are the preferred approach in realizing the service-oriented computing paradigm. However, this comes with challenges such as complexity and uncertainty that hinder their practical application. Bayesian networks (BNs) are one of the techniques used to address these challenges. The objective of this mapping study was to determine what is known about the use of Bayesian networks in web services research. To do this, we identified and selected rigorously 69 articles (out of the 532 identified) published on the subject in 2001–2021. We then classified and analyzed these articles by Web service themes (Service Composition, Service Management, Service Engineering), Objectives (Description, Prediction, Prescription), Types of BN (Basic, Combined, Extended), and Evaluation methods (Proof of concept, Experiment, No evaluation). In doing so, we hope to provide a clear understanding of the subject. We also identify and suggest avenues for future research. Thus, the review results can help researchers and practitioners interested by the application of BNs in WS research. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The proliferation of available Web services presents a big challenge in selecting suitable services. Various methods have been devised to predict Quality of Service (QoS) values, aiming to address the service selection problem. However, these methods encounter numerous limitations that hinder their prediction accuracy. A key issue stems from the dynamic nature of the service environment, leading to fluctuations in QoS values due to factors like network load and hardware issues. To mitigate these challenges, QoS selection methods have leveraged contextual information from the surrounding environments, such as service invocation time, user, and service locations. Among these methods, Collaborative Filtering (CF) has gained notable importance. In recent years, several CF methods have incorporated service invocation time into their prediction processes, giving rise to what is commonly known as time-aware CF methods. Despite the increasing adoption of time-aware CF methods, there remains a notable absence of a dedicated and comprehensive literature review on this topic. Addressing this gap, this paper conducts an analysis of the literature, reviewing the forty (40) most prominent studies in this domain. It offers a thematic categorization of these studies along with an insightful analysis outlining their objectives, advantages, and limitations. The review also identifies key research gaps and proposes potential directions for future investigations. Overall, this literature review serves as an up-to-date resource for researchers engaged in service-oriented computing research. [ABSTRACT FROM AUTHOR]

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables the delegating of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support the CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows encountered limitations to be overcome and the flexibility known from high-level solutions and high performance achieved with low-level programming to be maintained without the need for a high financial background. [ABSTRACT FROM AUTHOR]

High-density point clouds expressing attractive 3D images are attracting attention. These gigantic media require large bandwidth allocations, making them problematic to stream to resource-constrained hand-held devices. This paper proposes a method for point cloud compression and streaming of large point clouds using a web server. Storing large point cloud videos on a web server allows users to publish data sets without using additional applications or sending large amounts of data ahead of time. HTTP/2 improves transfer efficiency by compressing headers into binary format and reduces latency by sending many multiplexed requests using a single TCP session. The large size of the point cloud data and the current limitations of wireless channels demand point cloud compression methods. MATLAB is used for the purpose of analysis, at results show an average compression ratio of 30:1. Our proposed system achieves lower average latency between successive frames resulting in an increased frame rate to 72.46 fps when streaming 4M points, and 82.51 fps in the case of streaming 800K points compared to much lower rates in conventional work.

One of the important sectors in the economy is the metallurgical industry, which supplies raw materials and semi-finished products to other strategically important industries. The issue of quality control in this area is very relevant. Since the quality of the metal depends on the quality of subsequent products, products that must withstand all prescribed loads and regulations, as well as ensure safe operation for many years. Accordingly, all products of the metallurgical industry are subject to mandatory certification. The quality certificate is an official document and confirms the declared characteristics of the metal. All information from the quality certificate should be recorded in the information system in such a way that it is possible to track each specific batch of metal with the characteristics declared according to the certificate from purchase to release of finished products. The article discusses new possibilities of using web services for the exchange of information on quality certificates between contractors with various information systems. The essence of this innovative method is that the metal supplier transmits the data according to the quality certificate together with the electronic printed form, and the buyer accepts this data and writes it to the integration bus using web services for subsequent transmission to the necessary information systems. Thus, the risk of errors that are present during manual input will be eliminated, the time for entering and processing information will be reduced to ensure the full operation of technical control in the organization. Purpose. Optimization of the process of material and technical support by metal rolling for enterprises of the machine-building industry. Methodology: the article used survey, analysis, comparison, modeling. Results: a new method of transmitting information on quality certificates using web services, which allows you to avoid errors during manual entry, as well as optimize the registration process of admission. Practical implications it is advisable to apply the obtained results for enterprises of the machine-building industry engaged in the purchase of rolled metal.

Abstract Background Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome’s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. Results Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine. Conclusions NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org .

The Chihuahuan Desert is a distinctive and ecologically diverse region that is particularly susceptible to climate change. To effectively characterize the ecosystem services in this area, access to extensive geospatial data is crucial. Web services have emerged as a powerful solution for querying and downloading large volumes of climate geospatial data. Specifically, the Chihuahuan Desert Web Service (ChDWS) has been developed to streamline access to climate data. This service features an intuitive interface that enables researchers and decision-makers to efficiently query and download substantial datasets related to the region’s climate. With an internet connection, users can access the ChDWS more efficiently and conveniently from anywhere. This enhances the ability of researchers and decision-makers to obtain the data necessary for their studies and assessments. Web services, such as the ChDWS, offer significant advantages in querying and bulk downloading large climate geospatial datasets. They facilitate access to essential data for climate research and assessments, ultimately aiding in the better understanding and management of climate change impacts in the Chihuahuan Desert. By supporting bulk downloads in CSV and GeoTiff formats, web services like the ChDWS streamline data management and facilitate a better understanding and management of climate change impacts in the Chihuahuan Desert.

Summary: Web service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing QoS‐based service recommendation methods are inability to effectively capture valuable content and structural information from services. These methods often rely solely on low‐order explicit feature intersections in QoS information, do not fully utilize the high‐order implicit feature intersections, and ignore the rich semantic information existing in service descriptions and user preferences. To address this problem, this paper proposes a Web service recommendation method via combining knowledge distillation representation and DCNMIX quality prediction. This method combines content‐based and structure‐based service classification and service prediction based on multi‐dimensional service quality information. First, it builds a service relationship network using semantic features extracted from service descriptions. Second, it designs a graph neural network knowledge distillation framework. The teacher model extracts the knowledge of the graph neural network model, and the student model learns the structure‐based and feature‐based prior knowledge of the service relationship network. Then the student model is used to learn the knowledge of the teacher model, classify Web services, and obtain service representations. Finally, based on service representations and multi‐dimensional QoS information, it exploits the DCNMIX model to learn the explicit and implicit features intersections of Web services and obtain the prediction score and ranking of Web services. The experimental results on the ProgrammableWeb dataset show that the proposed method outperforms the state‐of‐the‐art baselines in terms of Recall, F1, Logloss, and AUC_ROC. [ABSTRACT FROM AUTHOR]

Background: Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome's structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. Results: Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine. Conclusions: NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org. [ABSTRACT FROM AUTHOR]

The advent of IoT devices increased internet usage more than smartphones and personal computers. The manual analysis of the Web Service Description Language (WSDL) document is quite expensive and time-consuming, hence this paper proposes a novel deep learning architecture to overcome the issue associated with web service classification. A structural self-organized deep belief network (SSODBN) is used for real-time web service classification in different fields such as Education, Smart electricity, Intelligent road networks, Health and social care, and Sports, water, and gas distribution. The SSODBN architecture utilizes a dropout strategy to minimize the interrelationship between the feature detectors and a regularized reinforced transfer function to eliminate the irrelevant weights. The main advantage offered by the S-DBN architecture is the improved preprocessing with feature selection. The Starling Murmuration Optimizer (SMO) is utilized in this paper to minimize the reconstruction error of the S-DBN architecture. The security of the smart city architecture is mainly improved via the blockchain defined network (BDN) using user-authenticated blocks. The experimental results revealed that the proposed method managed the scalability, latency, and centralization issues with superior data transmission. [ABSTRACT FROM AUTHOR]

ABSTRACT Both nanopore-based DNA sequencing and CRISPR/Cas-based gene editing represent groundbreaking innovations in molecular biology and genomics, offering unprecedented insights into and tools for working with genetic information. For students, reading, editing, and even writing DNA will be part of their everyday life. We have developed a laboratory procedure that includes (i) the biosynthesis of a guide RNA for, (ii) targeting Cas9 to specifically linearize the pBR322 plasmid, and (iii) the identification of the cutting site through nanopore DNA sequencing. The protocol is intentionally kept simple and requires neither living organisms nor biosafety laboratories. We divided the experimental procedures into separate activities to facilitate customization. Assuming access to a well-equipped molecular biology laboratory, an initial investment of approximately $2,700 is necessary. The material costs for each experiment group amount to around $130. Furthermore, we have developed a freely accessible website (https://dnalesen.hs-mittweida.de) for sequence read analysis and visualization, lowering the required computational skills to a minimum. For those with strong computational skills, we provide instructions for terminal-based data processing. With the presented activities, we aim to provide a hands-on experiment that engages students in modern molecular genetics and motivates them to discuss potential implications. The complete experiment can be accomplished within half a day and has been successfully implemented by us at high schools, in teacher training, and at universities. Our tip is to combine CRISPR/Cas gene targeting with nanopore-based DNA sequencing. As a tool, we provide a website that facilitates sequence data analysis and visualization.

Hydroponics is based on nutrients in water. It must be regularly monitored to prevent plant defects. The Internet of Things has become a solution for remote hydroponic monitoring and is currently being tested on the Yuan Hidroponik Kelompok Wanita Tani (KWT). This system will send data every minute, and each data has a possibility of loss in transmission. There is a chance that this system will be implemented in other hydroponic organizations. As more devices are involved, it will affect server resources. This research will compare Message Queue Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) as popular protocols used in IoT. A test with increasing clients shows that at 50 clients HTTP needs 87% CPU, while MQTT needs 22.63% CPU. A test with increasing payload shows that at 10,000 payload HTTP needs 94% CPU while MQTT needs 28.35% CPU. A test with fixed clients and payloads shows that HTTP has a CPU limit based on the clients involved. A transfer time test shows that HTTP needs 177.344 seconds while MQTT needs 3.24 seconds. An acceptance rate is calculated by incrementing the count for every incoming payload. It shows that HTTP can receive 30,000 payloads, unlike MQTT which can only receive 1680 payloads before losses.

Abstract Background As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved. Results Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support. Conclusions In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.

The adoption of adaptive neuro-fuzzy inference systems (ANFIS) and metaheuristic optimization approaches has been widely observed in recent research. Even so, integrating these methods improves the model’s capability to solve complex problems. A novel enhanced prediction method based on COOT bird optimization was developed for selecting the optimal parameters of ANFIS in the current study. This method combines COOT optimization with ANFIS to model the quality of service (QoS) characteristics of web services by using the adaptive neuro-fuzzy inference system COOT (ANFIS-COOT). In this instance, the quality of the web service (QWS) dataset was obtained from the GitHub database, which consists of 120 web services data, and then evaluated using the presented model on the dataset for estimating response time and throughput of web services. As significant evidence of ANFIS-COOT’s efficiency, the similar QWS data set is analyzed using four different prediction models: ANFIS, ANFIS-Beetle Antennae Search (ANFIS-BAS), ANFIS-Reptile Search Algorithm (ANFIS-RSA), and ANFIS-Snake Optimizer (ANFIS-SO). Moreover, the exploratory study used statistical benchmarks such as root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and determination coefficient ( $R^{2}$ ) to emphasize the accuracy of the proposed model. Based on analysis results, the presented model achieved optimal values of RMSE (59.7473), MAE (15.8531), MAPE (0.0705), and $R^{2}$ of 96.32 %, as well as RMSE (1.335), MAE (1.1255), MAPE (0.1818), and $R^{2}$ of 97.12 % for modelling response time and throughput of web services, compared to other models. Eventually, this report demonstrates the viability of the ANFIS-COOT while tackling a complex problem and improving predictive performance.

Complaints are quite an important thing in a village community services, with the submission of complaints, allows community administrators to improve the quality of the village and solve many common problems. A slow response in handling complaints can give a bad image to the village administrators. Babadan Village is one of the villages in Bantul Regency, Yogyakarta. In this village, complaints are sent by the villagers by contacting the head of the village through the social media WhatsApp. However, over time this method is no longer considered effective, and causes a lack of responsiveness in handling complaints. Therefore, through the development of this application, it can hopefully facilitate the villagers in submitting complaints to the administrator and can also make the administrator faster in handling complaint reports from its villagers. The systems planning begins with analyzing the current system, prototyping, testing, and then ends with implementation. Then the development method of android mobile-based applications will be done by applying the RESTful API Technology. Based on the results given from this study, the application was able to function in line with the objectives that have been described.

Background: As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved. Results: Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support. Conclusions: In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/. All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis. [ABSTRACT FROM AUTHOR]

Connected vehicles (CVs) are an emerging technology in intelligent transportation systems. Currently, many data-driven intelligent transportation systems (D2ITS) use CV data. Unfortunately, these D2ITS still need serious improvement before they meet higher-level visualization needs. Thus, we aim to develop a new, intelligent data-driven transportation system framework. We focus on visualizing real-time CV data using a big data analytic system in urban areas. In response, we first propose an effective real-time data distribution approach within the Vehicular Ad-hoc NETwork. Second, we develop novel strategies for aggregating, extracting and ingesting data. We provide scalable and fault-tolerant delivery methods without interruption or delay. Finally, we proposed a novel visualization REpresentational State Transfer (REST) web service. We used Simulation of Urban MObility, OMNET++ and Veins to simulate a traffic incident dataset. Then, we tested the Basic Safety Messages in an experimental big data cluster. We used NIFI, Kafka and Cassandra for ingestion, distribution, delivery and storage. The results show accurate performance for packet loss, packet delivery and communication delay. Also, it indicates high throughput and low latency for distributed data delivery systems. Additionally, we obtained the smallest response time for the RESTFUL visualization web service. [ABSTRACT FROM AUTHOR]

The cyber-physical system is a security-embedded intelligent system that integrates and organizes physical resources to offer effective and comfortable services for humans. Web service discovery is a significant challenge due to the increased rise of web services on the internet. It serves as an important method for service discovery and management. Quality of Service (QoS)-based web service ranking is a crucial task that needs to be solved for optimal web service selection. The recommendation models fulfill the user requirements by gathering, processing, and evaluating information on user characteristics. To achieve enhanced service quality, optimal physical space, and improved security, many scholars utilized collaborative filtering methods as an efficient recommendation system. Meanwhile, the integrated methods require an efficient learning model to accurately predict the service ratings. Therefore, this paper proposes a novel light GBM-optimized long short-term memory (LGBM-OLSTM) model to predict service ratings more accurately. The proposed prediction model combines the operations of both LGBM and LSTM to predict information on service ratings. The LSTM and LGBM classifier is integrated to enhance the generalization and memorization of the web service prediction application. Using the web service function description, the LGBM models the sparse features using nonlinear feature transformation. The LSTM extracts the generalized and abstract web service description to classify the QoS-based web service prediction using word order and context information. The Binary Chimp Optimization (BCO) algorithm is used for automatic hyperparameter tuning of the LSTM and LGBM architectures to minimize the complexity, and overfitting issues, and improve the training accuracy. In this, the light GBM model trains the web service database and ranks them based on their absolute values. The effectiveness of the proposed LGBM-OLSTM model is evaluated using metrics such as normalized discounted cumulative gain, mean average precision, recall, precision, and accuracy. The simulation result shows that the LGBM-OLSTM model obtains a greater accuracy rate of about 98.7% and a root mean square error (RMSE) of 0.958. [ABSTRACT FROM AUTHOR]

Based on cloud providers' reports on service outages, it has become clear that how a web service is deployed is of great importance. Clearly, using one service supplier is insufficient because it introduces single points of failure. In this paper, a novel high-availability multi-cloud model intended for a web service is proposed, which is free from such shortcomings yet preserves convenient assets of computing clouds. The methodology used to improve web service availability should involve several cloud suppliers and devise management techniques that control access to them. This is achieved by means of the server availability tracking algorithm, which controls client apps' access to the service. Moreover, typical benefits and problems involved in choosing IT infrastructure for a web service are elaborated. State-of-the-art cloud computing models, such as IaaS, PaaS, SaaS, BPaaS, and INaaS, are outlined. Operating systems statistics used for web services are included. Open-source monitoring software solutions are gathered, which help administrators to monitor and govern web servers. [ABSTRACT FROM AUTHOR]

In this paper, further information regarding two of the most popular web service architectures, which are Representational State Transfer (REST) and Simple Object Access Protocol (SOAP), is presented. For this, we adopted the PRISMA strategy which helped in identifying studies relevant to the chosen questions. In order to conduct the analysis of REST and SOAP, we formulated three such questions. After applying the other inclusion criteria and completion of scoring with the use of PRISMA flow chart, twelve studies were identified which met the inclusion criteria. In the process of analysis and interpretation of the selected literature, we examined many aspects of REST and SOAP relative to their performance, scalability and interoperability. Therefore, it should be understood that the goal of this particular work will be to help further this particular discussion on how likely such web service technologies will prove to be in practice. We also performed a local experiment comparing the SOAP and REST APIs in terms of performance, and thus providing additional experimental data about these technologies concerning response time and system behavior under controlled conditions. [ABSTRACT FROM AUTHOR]

In industry, RESTful APIs are widely used to build modern Cloud Applications. Testing them is challenging, because not only do they rely on network communications, but also they deal with external services like databases. Therefore, there has been a large amount of research sprout in recent years on how to automatically verify this kind of web services. In this article, we present a comprehensive review of the current state-of-the-art in testing RESTful APIs based on the analysis of 92 scientific articles. These articles were gathered by utilizing search queries formulated around the concept of RESTful API testing on seven popular databases. We eliminated irrelevant articles based on our predefined criteria and conducted a snowballing phase to minimize the possibility of missing any relevant paper. This survey categorizes and summarizes the existing scientific work on testing RESTful APIs and discusses the current challenges in the verification of RESTful APIs. This survey clearly shows an increasing interest among researchers in this field, from 2017 onward. However, there are still a lot of open research challenges to overcome. [ABSTRACT FROM AUTHOR]

Background: Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary. Results: Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet. Conclusions: InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign. A free web service is also provided: http://www.injection.design. [ABSTRACT FROM AUTHOR]

The Baikal Natural Territory (BNT) and Khuvsgul region have similar environmental problems. It is relevant to carry out integrated scientific studies and monitor the state of the components of the natural environment in these areas. This paper presents the results of a number of joint Russian–Mongolian projects aimed at studying and developing new methods and technologies for integrated environmental monitoring and prediction. A digital platform has been created to support scientific research and environmental monitoring. This platform makes it possible to collect, store, process, and analyze large arrays of heterogeneous spatiotemporal data and predict environmental situations using a set of mathematical and information models, services, and machine learning methods. The authors have also developed methods and web services for environmental monitoring based on the processing of Earth remote sensing (RS) data. A technology for classifying multispectral Sentinel-2 satellite images has been created that makes it possible to distinguish 12 classes of the land cover using artificial intelligence methods. A service for monitoring the state of the atmosphere over large areas has been created based on the processing of Sentinel-5P satellite data. This service makes it possible to display the concentrations of SO2, NO2, CO, CH4, H2O, O3, formaldehydes, and aerosols in the air. [ABSTRACT FROM AUTHOR]

Digital technologies significantly simplify housing and communal services management processes. However, when using digital technologies in housing and communal services, obstacles and risks may arise. This article presents the results of a study by the Laboratory of Marketing Research and Development R&D Marketing.Lab, Perm State National Research University on the attitude of the population to the digitalization systems of urban management operating in the city of Perm. The survey results confirm that Perm residents with higher education have a positive attitude towards the idea of automatic transmission of readings using Internet of Things sensors. Most of the 56-65 years old age category of the population of Perm either have a negative or neutral attitude towards the use of Internet of Things sensors in the housing and communal services sector. Governmental information system for housing and communal services has a low rating among residents of Perm. The most convenient for users of housing and communal services among web services is the website of the Permenergosbyt company.