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Prompts to large language models (LLMs) have evolved beyond simple user questions. For LLMs to solve complex problems, today's practices are to include domain-specific instructions, illustration of tool usages, and long context such as textbook chapters in prompts. As such, many parts of prompts are repetitive across requests, and their attention computation results can be reused. However, today's LLM serving systems treat every request in isolation, missing the opportunity of computation reuse.This paper proposes Preble, the first distributed LLM serving platform that targets and optimizes for prompt sharing. We perform a study on five popular LLM workloads. Based on our study results, we designed a distributed scheduling system that co-optimizes computation reuse and load balancing. Our evaluation of Preble on two to 8 GPUs with real workloads and request arrival patterns on two open-source LLM models shows that Preble outperforms the state of the art avg latency by 1.5x to 14.5x and p99 by 2x to 10x.

This research introduces a novel recommender system for adapting singlemachine problems to distributed systems within the MapReduce (MR) framework, integrating knowledge and text-based approaches. Categorizing common problems by five MR categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learning models recommend solutions for distributed systems. Results demonstrate the logistic regression model's effectiveness, with a hybrid approach showing adaptability. The study contributes to advancing the adaptation of single-machine problems to distributed systems MR, presenting a novel framework for tailored recommendations, thereby enhancing scalability and efficiency in data processing workflows. Additionally, it fosters innovation in distributed computing paradigms. [ABSTRACT FROM AUTHOR]

The composite quantile regression estimator is widely acknowledged for its robustness and efficiency, offering a compelling alternative to both ordinary least squares and quantile regression estimators in linear models. However, when data is not randomly distributed across different workers in distributed settings, existing methods for composite quantile regression become statistically inefficient. To address this limitation, we present a novel one-step upgraded pilot composite quantile regression method. Our proposed approach involves two essential steps. In the first step, we obtain a pilot estimator by leveraging a small random sample collected from different workers. Subsequently, in the second step, we perform one-step updating based on the pilot estimator, involving the summarization of sample moment quantities on each worker. The resulting estimator is theoretically proven to be as statistically efficient as the composite quantile regression estimator using the entire sample, without relying on restrictive assumptions about randomness. Furthermore, the resulting estimator inherits the robustness and efficiency advantages of the composite quantile regression estimator, while also being computationally efficient in terms of communication cost and storage usage. To validate the practical performance of our proposed method, we conduct numerical studies using simulated and real data, demonstrating its effectiveness in real-world scenarios. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has enabled the collection of vast amounts of data that can be used to improve various aspects of our lives. However, the astronomical volume of data generated by these IoT devices has raised significant concerns pertaining to privacy preservation. The amalgamation of the Internet of Things (IoT) with blockchain technology has engendered a promising solution for securing and managing IoT data, but it is still susceptible to privacy breaches. Recently, differential privacy (DP) has been proposed as a promising technique to alleviate these issues. In this paper, we design and propound a complete end-to-end blockchain-based architecture by implementing differential privacy at the stream level generated by IoT devices by deploying Laplace noise and Gaussian noise utilizing low complex cryptography mechanism and fast convergence consensus protocol to surmount the privacy preservation issues in IoT based blockchain network. Our novel DP-based framework introduces the concept of privacy levels as low, medium, and high as set by the data owner and also analyzes the impact of different parameters on the effectiveness of the approach and provides recommendations for tuning them. The workflow of our proposed framework consists of three phases: Data generation phase, Data Sharing phase, and Data Analysis phase. During the Data generation phase, the data owner will first determine the desired level of privacy protection (low, medium, high) and set the privacy budget (epsilon) and sensitivity (delta) of the data. Based on the budget value, the privacy module will generate noise from either Laplace or Gaussian distribution as requested by the data owner. The Data Sharing phase is mainly responsible for transmitting and processing the transactions inside the blockchain network. This is followed by the data analysis phase, which will check for the budget value and the amount of noise added to the data before the noisy data is handed over to the end user. We demonstrate the efficacy of our approach through multiple experimental evaluations and simulation results evince that our approach attains high levels of privacy preservation while upholding data utility and blockchain consistency. Overall, our proposed framework provides a promising solution to the privacy challenges in IoT-based blockchain systems, offering adjustable privacy levels to accommodate different privacy requirements. This DP-based approach and the adjustable privacy levels ensure alignment with the growing regulatory requirements for data privacy, such as GDPR, demonstrating compliance with these regulations and building trust with customers. [ABSTRACT FROM AUTHOR]

This paper considers the solvability of several fundamental problems in asynchronous message-passing distributed systems in the presence of Byzantine processes using distributed algorithms. These problems are the following: mutual exclusion, global snapshot recording, termination detection, deadlock detection, predicate detection, causal ordering, spanning tree construction, minimum spanning tree construction, all–all shortest paths computation, and maximal independent set computation. In a distributed algorithm, each process has access only to its local variables and incident edge parameters. We show the impossibility of solving these fundamental problems by proving that they require a solution to the causality determination problem which has been shown to be unsolvable in asynchronous message-passing distributed systems. [ABSTRACT FROM AUTHOR]

UAVs are widely used for multiple tasks, which in many cases require autonomous processing and decision making. This autonomous function often requires significant computational capabilities that cannot be integrated into the UAV due to weight or cost limitations, making the distribution of the workload and the combination of the results produced necessary. In this paper, a dual-stage processing architecture for object detection and tracking in Unmanned Aerial Vehicles (UAVs) is presented, focusing on efficient resource utilization and real-time performance. The proposed system delegates lightweight detection tasks to onboard hardware while offloading computationally intensive processes to a ground server. The UAV is equipped with a Raspberry Pi for onboard data processing, utilizing an Intel Neural Compute Stick 2 (NCS2) for accelerated object detection. Specifically, YOLOv5n is selected as the onboard model. The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. Communication between the UAV and the server is maintained through a high-speed Wi-Fi link, with a fallback to a 4G connection when needed. The ground server, equipped with an NVIDIA A40 GPU, employs YOLOv8x for object detection and DeepSORT for multi-object tracking. The proposed architecture ensures real-time tracking with minimal latency, making it suitable for mission-critical UAV applications such as surveillance and search and rescue. The results demonstrate the system's robustness in various environments, highlighting its potential for effective object tracking under limited onboard computational resources. The system achieves recall and accuracy scores as high as 0.53 and 0.74, respectively, using the remote server, and is capable of re-identifying a significant portion of objects of interest lost by the onboard system, measured at approximately 70%. [ABSTRACT FROM AUTHOR]

Consensus algorithms play a critical role in maintaining the consistency of blockchain data, directly affecting the system's security and stability, and are used to determine the binary consensus of whether proposals are correct. With the development of blockchain-related technologies, social choice issues such as Bitcoin scaling and main chain forks, as well as the proliferation of decentralized autonomous organization (DAO) applications based on blockchain technology, require consensus algorithms to reach consensus on a specific proposal among multiple proposals based on node preferences, thereby addressing the multi-value consensus problem. However, existing consensus algorithms, including Practical Byzantine Fault Tolerance (PBFT), do not support nodes expressing preferences. Instead, the proposal to reach consensus is directly decided by specific nodes, with other nodes merely verifying the proposal's validity, which can easily result in monopolistic or dictatorial outcomes. In response, we proposed the Aggregating Preferences with Practical Byzantine Fault Tolerance (AP-PBFT) consensus algorithm, which allows nodes to express preferences for multiple proposals. AP-PBFT ensures the validity of consensus results through a consensus output protocol, and incentivizes nodes to act honestly during the consensus process by incentive mechanism. First, AP-PBFT leverages Verifiable Random Function to select both consensus nodes and a primary node from the candidates. The primary node gathers proposals, assembles them into a proposal package, and broadcasts it to other consensus nodes. The consensus nodes independently vote to express their preferences for different proposals in the package, execute the consensus output protocol to reach local consensus, and the primary node aggregates these results to form the global consensus. Once the global consensus is finalized, AP-PBFT evaluates node behavior based on the consensus output protocol, penalizes nodes that acted maliciously, and rewards those that adhered to the protocol. Additionally, nodes can interact and adopt different strategies while executing the consensus output protocol, which can influence the consensus outcome. Therefore, we established an evolutionary game model based on hypergraph to analyze these interactions. Theoretical analysis shows that the incentive mechanism in AP-PBFT effectively encourages nodes to honestly follow the consensus output protocol, ensuring that AP-PBFT satisfies the properties of consistency, validity, and termination. Finally, the simulation results demonstrate that the AP-PBFT algorithm possesses good scalability and the capability to handle dynamic changes in nodes, surpassing some mainstream consensus algorithms in terms of transaction throughput and consensus achievement time. Moreover, AP-PBFT can incentivize honest behavior among consensus nodes, thereby enhancing the reliability of consensus and strengthening the security of the network. [ABSTRACT FROM AUTHOR]

The accurate and timely acquisition of high-frequency three-dimensional (3D) displacement responses of large structures is crucial for evaluating their condition during seismic excitation on shaking tables. This paper presents a distributed high-speed videogrammetric method designed to rapidly measure the 3D displacement of large shaking table structures at high sampling frequencies. The method uses non-coded circular targets affixed to key points on the structure and an automatic correspondence approach to efficiently estimate the extrinsic parameters of multiple cameras with large fields of view. This process eliminates the need for large calibration boards or manual visual adjustments. A distributed computation and reconstruction strategy, employing the alternating direction method of multipliers, enables the global reconstruction of time-sequenced 3D coordinates for all points of interest across multiple devices simultaneously. The accuracy and efficiency of this method were validated through comparisons with total stations, contact sensors, and conventional approaches in shaking table tests involving large structures with RCBs. Additionally, the proposed method demonstrated a speed increase of at least six times compared to the advanced commercial photogrammetric software. It could acquire 3D displacement responses of large structures at high sampling frequencies in real time without requiring a high-performance computing cluster. [ABSTRACT FROM AUTHOR]

Traditional voting schemes are often overwhelmed by problems such as deception, influence, and incompetence, which can be resolved by applying blockchain technology with transparency, decentralization, and immutability. This study proposes a safe and indisputable digital voting system with blockchain technology to maintain the integrity of the voting procedure. The reliability and privacy of the voting procedure are upheld with distributed ledger technology and cryptographic techniques. The essence of the proposed method is the immutability of the blockchain ledger, which ensures a tamper-proof record of each cast vote, promoting transparency and offering a way of audit for free verification. The proposed method employs cryptographic protocols to protect individual votes while preserving complete transparency and verifiability of the voting procedure. The InterPlanetary Filesystem (IPFS) is applied to ensure data integrity. Moreover, the practical Byzantine Fault Tolerance (pBFT) consensus algorithm is utilized to remove glitches in distributed settings. The proposed approach provides a decentralized platform where voters can cast their votes from anywhere without difficulty using an internet connection, eliminating the need for physical ballot papers and polling stations. Using immutable ledger and cryptographic security aspects in blockchain, the reliability of the voting procedure can be protected while maintaining voter anonymity and confidentiality. Finally, it is shown that the proposed scheme outweighs other existing approaches. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) has recently attracted a lot of attention due to its ability to train a machine learning model using data from multiple clients without divulging their privacy. However, the training data across clients can be very heterogeneous in terms of quality, amount, occurrences of specific features, etc. In this paper, we demonstrate how the server can observe data heterogeneity by mining gradient trajectories that the clients compute from a two-dimensional mapping of high-dimensional gradients computed by each client from its bottom layer. Based on these ideas, we propose a new clustered federated learning with gradient trajectory method, called CFLGT, which dynamically clusters clients together based on the gradient trajectories. We analyze CFLGT both theoretically and experimentally to show that it overcomes several drawbacks of mainstream Clustered Federated Learning (CFL) methods and outperforms other baselines.

The traditional centralized flight control system architecture is independent of other systems such as electromechanical and mission systems，making it difficult to meet the requirements of modern fighter system comprehensive design. The distributed system architecture with openness and comprehensive control features is an inevitable trend in technological development. In this paper，the fault-tolerant design for flight control systems based on distributed system architecture is studied. Firstly，the typical system architecture of distributed flight control systems and the objectives of their fault-tolerant design are introduced. Secondly，based on the operational timing and working mode characteristics of the distributed system，a time-based fault-tolerant design is conducted. Finally， based on the composition and system architecture characteristics of the distributed system，a multi-level voting/ monitoring surface is designed. By analyzing the running time sequence，composition and architecture characteristics of distributed system，the key entry points of fault-tolerant design of distributed system are sorted out，and the solutions and implementation methods of fault-tolerant design scheme are expounded. The engineering practice shows that the fault-tolerant design scheme studied in this paper can ensure the stable and reliable operation of flight control system and effectively improve the system security.

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office 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.)

Cybercrime is one of the fastest-growing crimes worldwide. It is observed that every seven seconds, cyber attackers penetrate cyber systems. While detecting an anomaly or attack, the log system is one of the crucial components of any system storing and managing all the events. It has always been challenging to detect an anomaly in logs. This is because of continuous and ever-changing log events and their mutability property. In this paper, we develop a machine learning-based artificial intelligence approach to address this issue of log analysis by proposing two modules. The first one is anomaly detection using different machine learning models. The second one is a distributed immutable storage system for securely storing the logs. In addition, we present a descriptive and user-friendly web application by integrating all modules using HTML, CSS, and Flask Framework on the Heroku cloud environment. The results demonstrate that the proposed hybrid machine learning models are capable of achieving 99.7% accuracy in detecting network anomalies. [ABSTRACT FROM AUTHOR]

Data center storage systems have different workloads and characteristics. In a centralized managed storage paradigm (software defined data center), providing a fault-tolerant system requires the best possible placement, and the least possible numbers of software defined storage (SDS) controllers. The separation of storage intelligence from storage resources raises the SDS controller's placement problem, which in turn, raises the reliability's issue (the main controller's failure leads to unavailability). The unavailability—down time—cost too much to an organization. To protect against the single point of failure, and at the same time, facilitate a fault-tolerant storage network, center of gravity technique is presented for optimal location selection. The method analyzes different work-loads, distance, and link bandwidth among controllers and OpenFlow enabled switches, and proposes multiple optimal locations for the controllers' deployment. We show the best optimal controllers' placement for various scenarios, e.g., single optimal placement for a big enterprise, and multiple proposed optimal placements for multiple SDS controllers containing various openFlow switches. These controllers can be the part of a large distributed Internet of Things (IoT) architecture. Experiments show that the work-load, distance, and the bandwidth are three important factors that can affect the whole network's reliability (the controllers' placement). [ABSTRACT FROM AUTHOR]

The exponential growth in internet data usage has created a pressing demand for highly efficient Network Intrusion Detection Systems (NIDS) capable of scaling with ever-increasing bandwidths to safeguard sensitive information. A cornerstone of NIDS, packet inspection, hinges on the ability to rapidly identify and analyze patterns within incoming data streams. The more diverse and extensive the pattern database, the more robust and effective the NIDS becomes. While the parallel failure-less version of the Aho-Corasick (AC) algorithm provides maximum parallelism, it faces significant memory constraints due to the large transition tables generated when dealing with a vast number of patterns. To mitigate this limitation and enhance the scalability of NIDS, we introduce a novel parallel failure-less compressed hashed variation of the Aho-Corasick algorithm. Our proposed approach leverages the power of compression and hashing techniques to significantly reduce memory consumption without compromising performance. Empirical evaluations demonstrate that our algorithm requires only a fraction (approximately the square root) of the memory footprint compared to the original parallel failure-less Aho-Corasick algorithm, making it a more practical and scalable solution for modern NIDS architectures. [ABSTRACT FROM AUTHOR]

The diversity of approaches for retaining participants in a Blockchain-based system complicates benchmarking. The majority of proposals for rewarding and penalizing participants in these systems are limited to their own set of data and scenarios, making it hard to compare their effectiveness. To overcome these challenges, we developed PyIncentiveBC, a free, open-source, and modular simulator designed to evaluate the reliability of any approach, incorporating a dynamic and proportionate incentivization mechanism proposed in our previous work. PyIncentiveBC aims to provide the scientific communities with an extensible software solution facilitating the benchmarking of existing approaches with new ones proposed by them. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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.)

Consider a large-scale distributed system in which each computing device is observing triggers from an external source. Distributed Trigger Counting (DTC) algorithm is used to detect the state of the system when the aggregated number of the observed triggers reaches a predefined value. In this paper, we propose a simple and efficient DTC algorithm: Cascading Thresholds (CT). We mathematically show that CT is an optimal DTC algorithm in terms of the total number of exchanged messages among the devices (message complexity). For the maximum number of received messages per device (MaxRcv), CT is sub-optimal. The average message complexity of CT is O(Nlog(W/N)), and MaxRcv of it is O(klog(W/N)+N), where W is the number of triggers to be detected, N is the number of devices, and k is the degree of a node in the tree-like structure. Compared to the previous optimal algorithm (TreeFill), CT is much simpler: in our implementation the code size is about 2.5 times smaller. Also, unlike TreeFill CT does not require complicated mechanisms including distributed locking. Experimental results show that CT has a lower message complexity and MaxRcv compared to the previous work (CoinRand and RingRand). Furthermore, CT and TreeFill show a similar performance. From its simplicity, CT is more practical than previous work including TreeFill, CoinRand and RingRand.

In this article, we propose a distributed synchronization solution to achieve decentralized coordination in a system of collaborative robots. This is done by leveraging cloud-based computing and 5G technology to exchange causal ordering messages between the robots, eliminating the need for centralized control entities or programmable logic controllers in the system. The proposed solution is described, mathematically formulated, implemented in software, and validated over realistic network conditions. Further, the performance of the decentralized solution via 5G technology is compared to that achieved with traditional coordinated/uncoordinated cabled control systems. The results indicate that the proposed decentralized solution leveraging cloud-based 5G wireless is scalable to systems of up to 10 collaborative robots with comparable efficiency to that from standard cabled systems. The proposed solution has direct application in the control of producer–consumer and automated assembly line robotic applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Cryptologic Research (2097-4116) is the property of Editorial Board of Journal of Cryptologic Research 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.)

Issues of forecasting the financial and economic performance of companies in the aviation industry are examined with account for their subindustry affiliation. It is proposed to use a modeling methodology based on specific indicators that link the dynamics of the targets of the Program for the development of the industry and subindustries with companies' performance. [ABSTRACT FROM AUTHOR]

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions. [ABSTRACT FROM AUTHOR]

The object of this researching is the process of maneuvering a sea-based vehicle under compressed conditions, which requires one hundred percent reserve of thrusters (THRs) of various modifications and locations. The main problem is the provision of energy-efficient control over the ship's motion at low speed in the horizontal plane using a high-level predictive controller. The hierarchy of the motion control system (MCS) is usually divided into several levels with the help of a high-level motion controller and the THR motor control distribution algorithm. This allows for a modular software structure where a high-level controller (HLC) can be designed without using comprehensive information about the THR motors. However, for a certain reference of THR configurations, such a decoupling can lead to reduced control performance due to the limitations of HLC regarding the physical constraints of the vessel and the behavior of MCS. The main results of the researching are methods to improve control performance using a nonlinear model predictive control (MPC) as a basis for the designed motion controllers due to its optimized solution and ability to consider constraints. A decoupled system was implemented for two simple motor tasks showing dissociation problems. The shortcomings were eliminated through the development of a nonlinear MPC controller, which combines the motion controller and the distribution of control over THR motors. To preserve the discrete modularity of the control system and achieve adequate performance, a nonlinear MPC controller with time-varying constraints was designed. This has made it possible to take into account the current limitations of the THR control system, increase the accuracy of control, and reduce the response time of the system by 10 %. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Polytechnic University is the property of Journal of Shanghai Polytechnic University Editorial Office 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.)

Interior design is a complex system that includes many elements, such as user needs, spatial structure, material selection, color matching, lighting layout, etc. The relationships between them are complex and intricate. How to design space according to user needs is one of the most challenging issues in interior design. A distributed 3D interior design system based on artificial intelligence algorithms was adopted to address this issue. The system divided indoor spatial information into several subsets. It assigned subsets to various professional designers based on user needs, and generated multiple design schemes through artificial intelligence algorithms, which were then screened, modified, and optimized by each professional designer. In this way, designers from different professional backgrounds can personalized design the space according to their own understanding, thereby avoiding issues such as information fragmentation and low efficiency in traditional interior design. The results showed that the distributed 3D interior design system based on artificial intelligence algorithms can improve the overall performance from an average of 82.68% to 94.94%, effectively avoiding the problem of information fragmentation. [ABSTRACT FROM AUTHOR]

A Decentralized Social Media Network is platform that operates on a distributed system of nodes rather than a central server. It utilizes technologies such as block-chain and peer to peer network to enable data security, privacy and user control over the data. This system offers users to communicate and share information anonymously, while also promoting free speech and resistance to censorship and surveillance. Compared to traditional centralized social media platforms, a decentralized social media networks offers a more democratic and user-driven alternative that priorities the security, privacy, and freedom of expression for its users. One of the key benefits of decentralized social media network is the increased security and privacy it provides. Since the network is distributed across multiple nodes, there is no single point of failure or central authority that can be hacked or compromised. In addition, users have greater control over their data and can choose which information to share and with whom. [ABSTRACT FROM AUTHOR]

A Distributed Elevator Fault Diagnosis System (DEFDS) is developed to tackle frequent malfunctions stemming from the widespread distribution and aging of elevator systems. Due to the complexity of elevator fault data and the subtlety of fault characteristics, traditional methods such as visual inspections and basic operational tests fall short in detecting early signs of mechanical wear and electrical issues. These conventional techniques often fail to recognize subtle fault characteristics, necessitating more advanced diagnostic tools. In response, this paper introduces a Principal Component Analysis–Long Short-Term Memory (PCA-LSTM) method for fault diagnosis. The distributed system decentralizes the fault diagnosis process to individual elevator units, utilizing PCA's feature selection capabilities in high-dimensional spaces to extract and reduce the dimensionality of fault features. Subsequently, the LSTM model is employed for fault prediction. Elevator models within the system exchange data to refine and optimize a global prediction model. The efficacy of this approach is substantiated through empirical validation with actual data, achieving an accuracy rate of 90% and thereby confirming the method's effectiveness in facilitating distributed elevator fault diagnosis. [ABSTRACT FROM AUTHOR]

Electric vehicles were introduced to the market as a way to reduce dependency on internal combustion engine-driven transportation systems. However, this method increased the burden on the current electrical grid rather than reducing it. In the power grid, distributed generation ideas are presented to reduce this burden. In order to reduce the distribution systems' overall real power loss, this research demonstrates how hybrid optimization algorithms, including genetic algorithms and Monte Carlo simulation methodologies, can be used to determine the best location and size for various types of distributed generation with electric vehicle scheduling. The suggested approach's practicality was tested using a 16-bus distribution test system. At both static and ZIP load models, the more effective kinds of distributed generation with electric vehicle couples are DG2 with fuel cell electric vehicles. [ABSTRACT FROM AUTHOR]

We consider the problem of real-time log anomaly detection for distributed system with deep neural networks by unsupervised learning. There are two challenges in this problem, including detection accuracy and analysis efficacy. To tackle these two challenges, we propose GLAD, a simple yet effective approach mining for anomalies in distributed systems. To ensure detection accuracy, we exploit the gradient features in a well-calibrated deep neural network and analyze anomalous pattern within log files. To improve the analysis efficacy, we further integrate one-class support vector machine (SVM) into anomalous analysis, which significantly reduces the cost of anomaly decision boundary delineation. This effective integration successfully solves both accuracy and efficacy in real-time log anomaly detection. Also, since anomalous analysis is based upon unsupervised learning, it significantly reduces the extra data labeling cost. We conduct a series of experiments to justify that GLAD has the best comprehensive performance balanced between accuracy and efficiency, which implies the advantage in tackling practical problems. The results also reveal that GLAD enables effective anomaly mining and consistently outperforms state-of-the-art methods on both recall and F1 scores. [ABSTRACT FROM AUTHOR]

Introduction: To support research projects that require medical data from multiple sites is one of the goals of the German Medical Informatics Initiative (MII). The data integration centers (DIC) at university medical centers in Germany provide patient data via FHIR® in compliance with the MII core data set (CDS). Requirements for data protection and other legal bases for processing prefer decentralized processing of the relevant data in the DICs and the subsequent exchange of aggregated results for cross-site evaluation. Methods: Requirements from clinical experts were obtained in the context of the MII use case INTERPOLAR. A software architecture was then developed, modeled using 3LGM², finally implemented and published in a github repository. Results: With the CDS tool chain, we have created software components for decentralized processing on the basis of the MII CDS. The CDS tool chain requires access to a local FHIR endpoint and then transfers the data to an SQL database. This is accessed by the DataProcessor component, which performs calculations with the help of rules (input repo) and writes the results back to the database. The CDS tool chain also has a frontend module (REDCap), which is used to display the output data and calculated results, and allows verification, evaluation, comments and other responses. This feedback is also persisted in the database and is available for further use, analysis or data sharing in the future. Discussion: Other solutions are conceivable. Our solution utilizes the advantages of an SQL database. This enables flexible and direct processing of the stored data using established analysis methods. Due to the modularization, adjustments can be made so that it can be used in other projects. We are planning further developments to support pseudonymization and data sharing. Initial experience is being gathered. An evaluation is pending and planned. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)