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GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in emerging exascale systems. It provides network-independent, high-performance communication primitives including Remote Memory Access (RMA) and Active Messages (AM). GASNet-EX is an evolution of the popular GASNet communication system, building upon over 20 years of lessons learned, and the primary goals are high performance, interface portability, and expressiveness. The library has been used to implement parallel programming models and libraries such as UPC, UPC++, Fortran coarrays, Legion, Chapel, and many others.This anthology collects together the four separate volumes that currently comprise the GASNet-EX specification, as of the 2024.5.0 release of GASNet-EX.

Nonprofit Organizations (NPOs) are adopting technology to improve their quality of services, scale up, or reduce operation costs. However, due to the heterogeneity of systems they use, NPOs face system-integration challenges when collaborating with other organizations. Middleware is an intermediary software that assists dissimilar systems in working together. This paper explores middleware applications, opportunities, and challenges within the sector. It extensively reviewed the current state of research on middleware usage in the nonprofit sector for all papers published in Scopus and Web of Science (WoS) until 2023. Out of 127 papers returned, only 31 remained after removing duplicates, invalid entries, and out-of-scope publications. Then, we synthesized insights from a thorough survey of these selected papers. In light of the survey results, we observed that NPOs primarily use middleware in a few of the Sustainable Development Goals (SDGs), namely, health (SDG 3), NPO operations (SDG 8 and 9), NPO collaborations (SDG 17), development of sustainable cities (SDG 11), security and disaster management (SDG 16), and education (SDG 4). We also identified several challenges related to using middleware in the nonprofit sector, which include privacy, security, system development and performance, data processing and transfer, and volunteer attrition. [ABSTRACT FROM AUTHOR]

The rise of robotics and the Internet of Things (IoT) could potentially represent a significant shift towards a more integrated and automated future, where the physical and digital domains may merge. However, the integration of these technologies presents certain challenges, including compatibility issues with existing systems and the need for greater interoperability between different devices. It would seem that the rigidity of traditional robotic designs may inadvertently make these difficulties worse, which in turn highlights the potential benefits of modular solutions. Furthermore, the mastery of new technologies may introduce additional complexity due to the varying approaches taken by robot manufacturers. In order to address these issues, this research proposes a Robot Operating System (ROS2)-based middleware, called the "ROS2-based gateway", which aims to simplify the integration of robots in different environments. By focusing on the payload layer and enabling external communication, this middleware has the potential to enhance modularity and interoperability, thus accelerating the integration process. It offers users the option of selecting payloads and communication methods via a shell interface, which the middleware then configures, ensuring adaptability. The solution proposed in this article, based on the gateway concept, offers users and programmers the flexibility to specify which payloads they want to activate depending on the task at hand and the high-level protocols they wish to use to interact with the activated payloads. This approach allows for the optimisation of hardware resources (only the necessary payloads are activated), as well as enabling the programmer/user to utilise high-level communication protocols (such as RESTful, Kafka, etc.) to interact with the activated payloads, rather than low-level programming. [ABSTRACT FROM AUTHOR]

As the adoption of large-scale model-based AI grows, the field of robotics is undergoing significant changes. The emergence of cloud robotics, where advanced tasks are offloaded to fog or cloud servers, is gaining attention. However, the widely used Robot Operating System (ROS) does not support communication between robot software across different networks. This paper introduces ROS Gateway, a middleware designed to improve the usability and extend the communication range of ROS in multi-network environments, which is important for processing sensor data in cloud robotics. We detail its structure, protocols, and algorithms, highlighting improvements over traditional ROS configurations. The ROS Gateway efficiently handles high-volume data from advanced sensors such as depth cameras and LiDAR, ensuring reliable transmission. Based on the rosbridge protocol and implemented in Python 3, ROS Gateway is compatible with rosbridge-based tools and runs on both x86 and ARM-based Linux environments. Our experiments show that the ROS Gateway significantly improves performance metrics such as topic rate and delay compared to standard ROS setups. We also provide predictive formulas for topic receive rates to guide the design and deployment of robotic applications using ROS Gateway, supporting performance estimation and system optimization. These enhancements are essential for developing responsive and intelligent robotic systems in dynamic environments. [ABSTRACT FROM AUTHOR]

In a context-aware Internet of Things (IoT) environment, the functional contexts of devices and users will change over time depending on their service consumption. Each iteration of an IoT middleware algorithm will also encounter changes occurring in the contexts due to the joining/leaving of new/old members; this is the inherent nature of ad hoc IoT scenarios. Individual users will have notable preferences in their service consumption patterns; by leveraging these patterns, the approach presented in this article focuses on how these changes impact performance due to functional-context switching over time. This is based on the idea that consumption patterns will exhibit certain time-variant correlations. The maximum likelihood estimation (MLE) is used in the proposed approach to capture the impact of these correlations and study them in depth. The results of this study reveal how the correlation probabilities and the system performance change over time; this also aids with the construction of the boundaries of certain time-variant correlations in users' consumption patterns. In the proposed approach, the information gleaned from the MLE is used in arranging the service information within a distributed service registry based on users' service usage preferences. Practical simulations were conducted over small (100 nodes), medium (1000 nodes), and relatively larger (10,000 nodes) networks. It was found that the approach described helps to reduce service discovery time and can improve the performance in service-oriented IoT scenarios. [ABSTRACT FROM AUTHOR]

Causal Consistency has been proven to be the strongest type of consistency that can be achieved in a fault-tolerant, distributed system. This paper describes an implementation of D-Thespis, which is an approach that employs the actor mathematical model of concurrent computation to establish a distributed middleware that enforces causal consistency on a widely used relational database management system (RDBMS). D-Thespis prioritises developer experience by encapsulating the intricacies of causal consistency behind an interface that is accessible over standard REST protocol. Here, we discuss several novel results. Firstly, we define a method that builds a causally consistent DBMS supporting elastic horizontal scalability. Secondly, we deliver a cloud-native implementation of the middleware and provide results and insights on 6804 benchmark configurations executed on our implementation while running on a public cloud infrastructure across several data centres. The evaluation concerns transaction processing performance, an evaluation of our implementation's update visibility latency, and a memory profiling exercise. The results of our evaluation show that under a transactional workload, a single-node installation of our implementation of D-Thespis is 1.5 times faster than a relational DBMS running serialisable transaction processing, while the performance of the middleware can improve by more than three times when scaled horizontally within the same data centre. Our study of the memory profile of the D-Thespis implementation shows that the system distributes its memory requirements evenly across all the available machines, as it is scaled horizontally. Finally, we also illustrate how our middleware propagates data changes across geographically-distributed infrastructures in a timely manner: our tests show that most of the effects of data change operations in one data centre are available in a remote data centre within less than 300 ms over and above the network round trip latency between the two data centres. [ABSTRACT FROM AUTHOR]

Supply chain bottlenecks in port operations lead to significant delays and inefficiencies. Blockchain technology emerges as a viable solution, offering tamper-resistant ledgers, secure transactions, and automation capabilities. While considerable research on developing blockchain-based solutions currently exist, there is a lack of studies that specifically focus on optimizing port document management to speed up supply chain operations. In this paper, a supply-blockchain functional prototype for optimizing port operations using Hyperledger Fabric is introduced. In terms of core functionality, the prototype allows initiation of smart contract corresponding to functions such as creating and editing port-related documents, minimizing manual interventions and enhancing efficiency to reduce port congestion. Furthermore, it provides live tracking of completed events and transactions, facilitating transparency and streamlined oversight. The permissioned nature of Hyperledger Fabric ensures security and robust access controls, aligning well with sensitive port operations. Hyperledger Firefly and its connector framework was used as the middleware to facilitate blockchain integration and various functions of the prototype, while chaincode developed using Go language was used to package and deploy smart contracts. The supply-blockchain framework was used as the theoretical framework for prototype development, and agile project management was adopted to ensure timely completion. The results based on functional and performance testing demonstrate the prototype's potential in alleviating port documentation bottlenecks and quickly delivering benefits to key stakeholders. [ABSTRACT FROM AUTHOR]

This study aims to significantly improve air quality monitoring through the innovative application of Artificial Intelligence (AI). Introducing the Artificial Intelligence Kualitas Udara (AIKU) model, this research offers a novel approach by integrating advanced machine learning algorithms with environmental sensors to predict air quality in real-time more accurately than traditional methods. The novelty of the AIKU model lies in its sophisticated data analytics framework, which processes high-frequency environmental data to assess air quality changes dynamically. The technique employs calibrating and deploying the AIKU model across various urban and suburban settings and analyzing its performance against conventional monitoring systems such as the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). The results demonstrate that AIKU significantly outperforms these traditional systems in both accuracy and speed of response, highlighting its effectiveness in real-time environmental monitoring. Furthermore, the AIKU model's scalability and adaptability are tested, showing promising potential for application in densely populated urban areas and less populated rural settings. This research contributes to environmental monitoring by demonstrating how AI can transform traditional methodologies into more effective, scalable, and intelligent ecological management systems. This research provides substantial evidence that the AIKU model can serve as a powerful tool for sustainable and smart development worldwide, enhancing the ability of governments and organizations to respond to environmental challenges promptly and effectively. Doi: 10.28991/HIJ-2024-05-03-017 Full Text: PDF

The United States Food and Drug Administration (FDA) is using Remote Interactive Evaluations (RIE) to assess regulatory compliance, review submission material, or determine the timing of future inspections. Here, we look at some of the impacts of RIE on GxP laboratories. Although RIE is voluntary, is this an offer that you cannot refuse?, Since the pandemic, changes have occurred in the regulatory oversight of GxP laboratories because of travel restrictions or country access. Instead of an inspection (physical entry to a facility) via [...]

Radio frequency identification (RFID) is a contemporary technology that enables the identification of objects and facilitates the transmission of additional information, making it possible to achieve real-time object tracking in a mobile object network and to report information on the object's current state at each step. RFID devices continuously generate large amounts of data, and collecting, filtering, and consolidating these data are therefore crucial tasks, which characterize RFID data management by a set of challenges. However, one of the greatest challenges in this field is managing large volumes of data in complex applications, where real-time operation is vital, given that the volume and speed of RFID data often exceed the capacity of the existing technological infrastructure. The aim of this study is to propose an RFID middleware that manages both the RFID hardware network and the large amounts of data that are captured, in order to process and transmit the collected data under the right conditions for ultimate use by an information system. This new RFID middleware architecture, named BTMiddleware combines complex event processing (CEP) with a MongoDB database to offer large-volume data streaming, processing, and storage in real time, as well as better interoperability thanks to the use of the JavaScript object notation (JSON) format for data presentation. [ABSTRACT FROM AUTHOR]

https://www.csoonline.com/article/524286/what-is-siem-security-information-and-event-management-explained.html solutions have been essential in cybersecurity for many years, but as the digital environment grows more complex older SIEM systems are posing significant challenges for the security professionals who [...]

Purpose. Developing a system of remote communication via mobile internet and SMS messages between a ground control station and a drone integrated with a microcomputer. Methodology. Experiments were conducted on an FPV-drone with a flight controller that is physically integrated with a Raspberry Pi 5 microcomputer via UART protocol. As a mobile network source a mobile USB-dongle with a Ukrainian SIM-card was used. An Ubuntu virtual private server was rented and set up. MAVLink communication between the drone and a ground control station was set up using MAVLink Router application and a middleware server. Helper systemd services and bash scripts were set up on the server and the drone microcomputer to initiate a connection on system start and in case of failure. As a ground control system QGroundControl was used. Mobile USB-dongle was set up to support serial ports which accept AT commands. Python scripts to monitor and send SMS messages were developed. Findings. Implemented communication between a drone and a ground control station via mobile network. Originality. According to the results of the research, developed instruments with combination of which the risk of communication loss between a drone and a ground control station can be reduced. Practical value. The results can be used to implement a remote communication system between a drone and a ground control station and to plan autonomous flight missions. [ABSTRACT FROM AUTHOR]

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy. [ABSTRACT FROM AUTHOR]

Streaming platforms have revolutionized the digital entertainment industry, but challenges and research opportunities remain to be addressed. One current concern is the lack of transparency in the business model of video streaming platforms, which makes it difficult for content creators to access viewing metrics and receive payments without the intermediary of third parties. Additionally, there is no way to trace payment transactions. This article presents a computational architecture based on blockchain technology to enable transparency in audience management and payments in video streaming platforms. Smart contracts will define the business rules of the streaming services, while middleware will integrate the metadata of the streaming platforms with the proposed computational solution. The proposed solution has been validated through data transactions on different blockchain networks and interviews with content creators from video streaming platforms. The results confirm the viability of the proposed solution in enhancing transparency and auditability in the realm of audience control services and payments on video streaming platforms. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAVs) are advancing at a fast speed with the improvement of the automotive industry, which opens up new possibilities for different attacks. A Distributed Denial-of-Service (DDoS) attacker floods the in-vehicle network with fake messages, resulting in the failure of driving assistance systems and impairment of vehicle control functionalities, seriously disrupting the normal operation of the vehicle. In this paper, we propose a novel DDoS attack detection method for in-vehicle Ethernet Scalable service-Oriented Middleware over IP (SOME/IP), which integrates the Hawkes process with Long Short-Term Memory networks (LSTMs) to capture the dynamic behavioral features of the attacker. Specifically, we employ the Hawkes process to capture features of the DDoS attack, with its parameters reflecting the dynamism and self-exciting properties of the attack events. Subsequently, we propose a novel deep learning network structure, an HP-LSTM block, inspired by the Hawkes process, while employing a residual attention block to enhance the model's detection efficiency and accuracy. Additionally, due to the scarcity of publicly available datasets for SOME/IP, we employed a mature SOME/IP generator to create a dataset for evaluating the validity of the proposed detection model. Finally, extensive experiments were conducted to demonstrate the effectiveness of the proposed DDoS attack detection method. [ABSTRACT FROM AUTHOR]

* Context.--Electronic synoptic pathology reporting using xPert from mTuitive is available to all pathologists in British Columbia, Canada. Comparative feedback reports for pathologists and surgeons were created by using the synoptic reporting software. Objective.--To use data stored in a single central data repository to provide nonpunitive confidential comparative feedback reports (dashboards) to individual pathologists and surgeons for reflection on their practice and to use aggregate data for quality improvement initiatives. Design.--Integration of mTuitive middleware in 5 different laboratory information systems to have 1 software solution (xPert) sending discrete data elements to the central data repository was performed. Microsoft Office products were used to build comparative feedback reports and made the infrastructure sustainable. Two different types of reports were developed: individual confidential feedback reports (dashboards) and aggregated data reports. Results.--Pathologists have access to an individual confidential live feedback report for the 5 major cancer sites. Surgeons get an annual confidential emailed PDF report. Several quality improvement initiatives were identified from the aggregate data. Conclusions.--We present 2 novel dashboards: a live pathologist dashboard and a static surgeon dashboard. Individual confidential dashboards incentivize use of nonmandated electronic synoptic pathology reporting tools and have increased adoption rates. Use of dashboards has also led to discussions about how patient care may be improved. (Arch Pathol Lab Med. 2024;148:190-199; doi: 10.5858/arpa.2021-0542-OA), Cancer is the leading cause of death in Canada and is responsible for 30% of all deaths. (1) Lung, breast, colorectal, and prostate cancer are the most commonly diagnosed types [...]