Client-server Interaction Research Articles

Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support

Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous (“Go”/”No-Go”) zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed.

3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms

In light of the rapid advancements in big data and artificial intelligence technologies, the trend of uploading local files to cloud servers to mitigate local storage limitations is growing. However, the surge of duplicate files, especially images and videos, results in significant network bandwidth wastage and complicates server management. To tackle these issues, we have developed a multi-parameter video quality assessment model utilizing a 3D convolutional neural network within a video deduplication framework. Our method, inspired by the analytic hierarchy process, thoroughly evaluates the effects of packet loss rate, codec, frame rate, bit rate, and resolution on video quality. The model employs a two-stream 3D convolutional neural network to integrate spatial and temporal streams for capturing video distortion details, with a coding layer configured to remove redundant distortion information. We validated our approach using the LIVE and CSIQ datasets, comparing its performance against the V-BLIINDS and VIDEO schemes across different packet loss rates. Furthermore, we simulated the client-server interaction using a subset of the dataset and assessed the scheme's time efficiency. Our results indicate that the proposed scheme offers a highly efficient solution for video quality assessment.

INFORMATION TECHNOLOGY OF SOFTWARE DATA SECURITY MONITORING

The article proposes an overview of an information technology of software data security monitoring with the aim of increasing the security of auxiliary and end-user software in run-time using the designed universal architecture with the capabilities of synchronized data security validation processes in client-server interactions and integration into software development technologies using unified interfaces for specifying extensible sets of validation rules. An analysis of the subject area was conducted, during which it was established that the tasks of software data security validation are included in the requirements of well-known data security standards, and the related vulnerabilities are noted as highly important. It was also established that the traditional monitoring of only software input data is not enough, therefore the problem of security monitoring of various categories of software data is relevant. The capabilities of existing tools to monitor security and correctness of software data are incomplete: focus on certain categories of data, platform dependency, narrow integration capabilities with other software development tools, limited usage, complex or limited extensibility, difficult reuse of well-known verified solutions etc. Based on the analysis of the existing software data security monitoring tools, the cons of the existing implementations were identified, and a universal architecture of the software framework was proposed as a solution. The analysis of the vulnerabilities of various categories of software data was conducted along with the recommended methods of implementation of data security. Discovered methods of data security implementation were used in the proposed solution. The article examines the structure of the proposed information technology, provides the universal architecture of the software framework, demonstrates the verification of the work of the developed tool, and provides the assessment of the effectiveness of usage of the data security monitoring framework in software development. The proposed architecture and directions of further improvements of the framework allow significant extension of its functionality and easy integration into popular software development technologies. It is assumed that the developed information technology of software data security monitoring will be widely used in commercial software development as well as in educational and scientific appliance.

Energy consumption of smartphones and IoT devices when using different versions of the HTTP protocol

HTTP is frequently used by smartphones and IoT devices to access information and Web services. Nowadays, HTTP is used in three major versions, each introducing significant changes with respect to the previous one. We evaluated the energy consumption of the major versions of the HTTP protocol when used in the communication between energy-constrained devices and cloud-based or edge-based services. Experimental results show that in a machine-to-machine communication scenario, for the considered client devices – a smartphone and a Single Board Computer – and for a number of cloud/edge services and facilities, HTTP/3 frequently requires more energy than the previous versions of the protocol. The focus of our analysis is on machine-to-machine communication, but to obtain a broader view we also considered a client–server interaction pattern that is more browsing-like. In this case, HTTP/3 can be more energy efficient than the other versions.

White-Box Fuzzing RPC-Based APIs with EvoMaster: An Industrial Case Study

Remote Procedure Call (RPC) is a communication protocol to support client-server interactions among services over a network. RPC is widely applied in industry for building large-scale distributed systems, such as Microservices. Modern RPC frameworks include, for example, Thrift, gRPC, SOFARPC, and Dubbo. Testing such systems using RPC communications is very challenging, due to the complexity of distributed systems and various RPC frameworks the system could employ. To the best of our knowledge, there does not exist any tool or solution that could enable automated testing of modern RPC-based services. To fill this gap, in this article we propose the first approach in the literature, together with an open source tool, for fuzzing modern RPC-based APIs. The approach is in the context of white-box testing with search-based techniques. To tackle schema extraction of various RPC frameworks, we formulate a RPC schema specification along with a parser that allows the extraction from source code of any JVM RPC-based APIs. Then, with the extracted schema we employ a search to produce tests by maximizing white-box heuristics and newly defined heuristics specific to the RPC domain. We built our approach as an extension to an open source fuzzer (i.e., EvoMaster ), and the approach has been integrated into a real industrial pipeline that could be applied to a real industrial development process for fuzzing RPC-based APIs. To assess our novel approach, we conducted an empirical study with two artificial and four industrial web services selected by our industrial partner. In addition, to further demonstrate its effectiveness and application in industrial settings, we report results of employing our tool for fuzzing another 50 industrial APIs autonomously conducted by our industrial partner in their testing processes. Results show that our novel approach is capable of enabling automated test case generation for industrial RPC-based APIs (i.e., 2 artificial and 54 industrial). We also compared with a simple gray-box technique and existing manually written tests. Our white-box solution achieves significant improvements on code coverage. Regarding fault detection, by conducting a careful review with our industrial partner of the tests generated by our novel approach in the selected four industrial APIs, a total of 41 real faults were identified, which have now been fixed. Another 8,377 detected faults are currently under investigation.

ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection

Driver distraction detection (3D) is essential in improving the efficiency and safety of transportation systems. Considering the requirements for user privacy and the phenomenon of data growth in real-world scenarios, existing methods are insufficient to address four emerging challenges, i.e., data accumulation, communication optimization, data heterogeneity, and device heterogeneity. This paper presents an incremental and cost-efficient mechanism based on federated meta-learning, called ICMFed, to support the tasks of 3D by addressing the four challenges. In particular, it designs a temporal factor associated with local training batches to stabilize the local model training, introduces gradient filters of each model layer to optimize the client–server interaction, implements a normalized weight vector to enhance the global model aggregation process, and supports rapid personalization for each user by adapting the learned global meta-model. According to the evaluation made based on the standard dataset, ICMFed can outperform three baselines in training two common models (i.e., DenseNet and EfficientNet) with average accuracy improved by about 141.42%, training time saved by about 54.80%, communication cost reduced by about 54.94%, and service quality improved by about 96.86%.

A Federated Platform Enabling a Systematic Collaboration Among Devices, Data and Functions for Smart Mobility

Through the vast adoption and application of emerging technologies, the intelligence and autonomy of smart mobility can be substantially elevated to address more diversified demands and supplies. Along with this trend, a systematic collaboration among three essential elements of smart mobility services, namely devices, data and functions, is being studied to comprehensively break down the intrinsic barriers that existed in current solutions, to support the integration of connectable devices, the fusion of heterogeneous data, the composability of reusable functions, and the flexibility in their cooperations. To enable such a collaboration, this paper proposes a federated platform, called Future Mobility Sensing Advisor (FMSA), which can 1) manage the three elements through standardized interfaces separately and uniformly; 2) create a fully connected knowledge graph to orchestrate the three elements efficiently and effectively; 3) support the client-server interaction in centralized and federated modes to handle service requests and edge resources with various availability and accessibilities jointly and adaptively; and 4) accommodate various mobility services to foster harmonious and sustainable mobility tenderly and invisibly. Moreover, the efficiency and effectiveness of the platform are also tested through a performance evaluation, and a pilot supported at the Great Boston Area, respectively. As a result, it shows that FMSA can 1) achieve high performance by using the two interaction modes selectively, and 2) renovate smart mobility towards sustainability through personalized services that can measure user preferences and system objectives mutually.

HEDA

Recent years have witnessed the rapid development of the encrypted database, due to the increasing number of data privacy breaches and the corresponding laws and regulations that caused millions of dollars in loss. These encrypted databases may rely on different techniques, such as cryptographic primitives and trusted execution environments. In this work, we investigate the feasibility of utilizing fully homomorphic encryption (FHE) to support unbounded database aggregation queries, which typically involve comparisons as filtering predicates and a final aggregation. These operators are theoretically supported by FHE, but need careful algorithm design to maximize the efficiency and have not been explored before. We creatively use two types of FHE schemes, i.e. , one for numerical and one for binary value, to enjoy their advantages respectively. To bridge the encrypted values between these two schemes for seamless query processing without client-server interaction, we propose a novel ciphertext transformation mechanism, which is of independent research interest, to close this gap. We further implement our system and test it over three TPC-H queries and a query over a real social media e-commerce database. Evaluation results show that, to process an aggregation query over 8 k encrypted rows takes about 430 seconds. Although it is slower than plaintext processing in magnitudes and still has much room for improvement, as the very first work in this domain, our system demonstrates the feasibility of using FHE to process OLAP queries.

A Triple-Step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement

Federated Learning in asynchronous mode (AFL) is attracting much attention from both industry and academia to build intelligent cores for various Internet of Things (IoT) systems and services by harnessing sensitive data and idle computing resources dispersed at massive IoT devices in a privacy-preserving and interaction-unblocking manner. Since AFL is still in its infancy, it encounters three challenges that need to be resolved jointly, namely: 1) how to rationally utilize AFL clients, whose local data grow gradually, to avoid overlearning issues; 2) how to properly manage the client-server interaction with both communication cost reduced and model performance improved; and finally, 3) how to effectively and efficiently aggregate heterogeneous parameters received at the server to build a global model. To fill the gap, this article proposes a triple-step asynchronous federated learning mechanism (TrisaFed), which can: 1) activate clients with rich information according to an informative client activating strategy (ICA); 2) optimize the client–server interaction by a multiphase layer updating strategy (MLU); and 3) enhance the model aggregation function by a temporal weight fading strategy (TWF), and an informative weight enhancing strategy (IWE). Moreover, based on four standard data sets, TrisaFed is evaluated. As shown by the result, compared with four state-of-the-art baselines, TrisaFed can not only dramatically reduce the communication cost by over 80% but also can significantly improve the learning performance in terms of model accuracy and training speed by over 8% and 70%, respectively.

Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach

Understanding energy consumption patterns is crucial for energy demand-side management. Unlike traditional data mining or machine learning-based methods, this paper presents visual analysis methods for exploring energy consumption data from spatial, temporal, and spatiotemporal dimensions, including variability, segmentation, and energy demand shifts. To support the proposed methods, we develop a visual analysis tool that allows users to explore consumption data and validate their hypotheses based on visual results through human–client–server interactions. In particular, we propose a novel potential flow-based method to model energy demand shift patterns and have integrated it into the proposed analysis tool. We comprehensively evaluate the proposed methods and the tool using real-world electricity consumption data from the Shanghai Pudong district, and compare with traditional data mining methods. The results demonstrated the effectiveness and superiority of the proposed visual analysis methods, including its ability to discover the spatiotemporal variability of energy demand, customer groups, and demand shift patterns across different geographical areas and time horizons. All results can be well explained by knowledge of the energy consumption in the study region.

Find out if your protein is O‐GlcNAc modified: The O‐GlcNAc database

Post‐translational modifications (PTMs) are ubiquitous and essential for protein function and signaling, motivating the need for sustainable benefit and open models of web databases. Highly conserved O‐GlcNAcylation is a case example of one of the most recently discovered PTMs, investigated by a growing community. Historically, details about O‐GlcNAcylated proteins and sites were dispersed across literature and in non‐O‐GlcNAc‐focused, rapidly outdated or now defunct web databases. In a first effort to fill the gap, we recently published a human O‐GlcNAcome catalog with a basic web interface. Based on the enthusiasm generated by this first resource, we extended our O‐GlcNAcome catalog to include data from 42 distinct organisms and released the O‐GlcNAc Database v1.2. In this version, more than 14 500 O‐GlcNAcylated proteins and 11 000 O‐GlcNAcylation sites are referenced from the curation of 2200 publications. Here, we present the extensive features of the O‐GlcNAc Database, including the user‐friendly interface, back‐end and client–server interactions. Finally, this database system can be administrated with little to no programming skills and is meant to be an example of a useful, sustainable and cost‐efficient resource, which exclusively relies on free open‐source software elements (www.oglcnac.mcw.edu).

SIMULATION MODELING AND OPTIMIZATION OF THE OPERATION OF A PARALLEL SERVER WITH FAILURES IN ANYLOGIC

Modern scientific research is increasingly raising issues of the processing of large amounts of data. The widespread use of client-server interaction technology and cloud computing at the moment raises questions about the efficiency of a parallel server, as well as the ability to predict results depending on the degree of load and characteristics of the equipment. This article simulates a parallel server with failures in the AnyLogic environment, and then performs multidimensional optimization by the weighted sum method. As part of the study, a simulation model of a queuing system with failures was built. It contains a server simulator, terminals, a failure simulator and statistics collection segments. The used parallel server model is abstract and rather generalized and makes it possible to concretize it by introducing additional dependencies and refining characteristics. The experiment with optimal parameters allowed to obtain the following gain in system efficiency indicators: processor load parameter (by memory) — a gain of 7%; processor load parameter (by load factor) — a gain of 8%; probability of terminal downtime — a gain of 5.7%; the failure rate of the main computer — 36 times less than the initial configuration; the number of interrupted programs — 7 less. In addition, it should be noted that the total number of completed requests remained at the same level — 462-465, for the reason that the intensity of the terminals did not vary. Since the results of replications (“runs”) are unique and the values of the optimized function vary for different replications, the built-in possibility of a variable number of replications (from 5 to 10) with a confidence probability of 95% and an error level of 0.5 was used. The obtained results suggest the possibility of further research of the model and its development in the AnyLogic environment.

Single Page Application for Weather Tracking App

The purpose of the article is to review the concepts for creating web applications, analyze the advantages and disadvantages of modern technologies for weather forecasting. The article presents modern client-server technologies. The concept of a single-page application and other technologies that allow interaction between the client and the server online are discussed in detail. The development of an application for tracking weather conditions based on the architecture of the SPA is presented. The research methodology includes processing theoretical information and analyzing the architecture of the application, built on the principle of SPA, analyzing the advantages and disadvantages of approaches to client-server interaction, using Angular, Vue, React libraries, using APIs to create applications. The novelty of the research is the analysis of the modern concepts and technologies that allow you to develop web applications that can be used to build a modern web application of the Single Page Application concept, a detailed analysis of Angular, Vue, React libraries and user interaction with such systems, analysis of Service Workers technology as a way of hashing. Conclusions. The article analyzes modern technologies for creating web applications, presents their advantages and disadvantages, discusses the concept of Single Page Application and describes the development of an application for tracking weather conditions, provides analogues for comparative characteristics, discusses problems that arise when the client part interacts with the server application of SPA principles.

Frequency-hiding order-preserving encryption with small client storage

The range query on encrypted databases is usually implemented using the order-preserving encryption (OPE) technique which preserves the order of plaintexts. Since the frequency leakage of plaintexts makes OPE vulnerable to frequency-analyzing attacks, some frequency-hiding order-preserving encryption (FH-OPE) schemes are proposed. However, existing FH-OPE schemes require either the large client storage of size O ( n ) or O (log n ) rounds of interactions for each query, where n is the total number of plaintexts. To this end, we propose a FH-OPE scheme that achieves the small client storage without additional client-server interactions. In detail, our scheme achieves O ( N ) client storage and 1 interaction per query, where N is the number of distinct plaintexts and N ≤ n . Especially, our scheme has a remarkable performance when N ≪ n . Moreover, we design a new coding tree for producing the order-preserving encoding which indicates the order of each ciphertext in the database. The coding strategy of our coding tree ensures that encodings update in the low frequency when inserting new ciphertexts. Experimental results show that the single round interaction and low-frequency encoding updates make our scheme more efficient than previous FH-OPE schemes.

Client-server sessions in linear logic

We introduce coexponentials, a new set of modalities for Classical Linear Logic. As duals to exponentials, the coexponentials codify a distributed form of the structural rules of weakening and contraction. This makes them a suitable logical device for encapsulating the pattern of a server receiving requests from an arbitrary number of clients on a single channel. Guided by this intuition we formulate a system of session types based on Classical Linear Logic with coexponentials, which is suited to modelling client-server interactions. We also present a session-typed functional programming language for client-server programming, which we translate to our system of coexponentials.

Federated Continuous Learning With Broad Network Architecture.

Federated learning (FL) is a machine-learning setting, where multiple clients collaboratively train a model under the coordination of a central server. The clients' raw data are locally stored, and each client only uploads the trained weight to the server, which can mitigate the privacy risks from the centralized machine learning. However, most of the existing FL models focus on one-time learning without consideration for continuous learning. Continuous learning supports learning from streaming data continuously, so it can adapt to environmental changes and provide better real-time performance. In this article, we present a federated continuous learning scheme based on broad learning (FCL-BL) to support efficient and accurate federated continuous learning (FCL). In FCL-BL, we propose a weighted processing strategy to solve the catastrophic forgetting problem, so FCL-BL can handle continuous learning. Then, we develop a local-independent training solution to support fast and accurate training in FCL-BL. The proposed solution enables us to avoid using a time-consuming synchronous approach while addressing the inaccurate-training issue rooted in the previous asynchronous approach. Moreover, we introduce a batch-asynchronous approach and broad learning (BL) technique to guarantee the high efficiency of FCL-BL. Specifically, the batch-asynchronous approach reduces the number of client-server interaction rounds, and the BL technique supports incremental learning without retraining when learning newly produced data. Finally, theoretical analysis and experimental results further illustrate that FCL-BL is superior to the existing FL schemes in terms of efficiency and accuracy in FCL.

Automatization and self-maintenance of the O-GlcNAcome catalog: a smart scientific database.

Post-translational modifications (PTMs) are ubiquitous and essential for protein function and signaling, motivating the need for sustainable benefit and open models of web databases. Highly conserved O-GlcNAcylation is a case example of one of the most recently discovered PTMs, investigated by a growing community. Historically, details about O-GlcNAcylated proteins and sites were dispersed across literature and in non-O-GlcNAc-focused, rapidly outdated or now defunct web databases. In a first effort to fill the gap, we recently published a human O-GlcNAcome catalog with a basic web interface. Based on the enthusiasm generated by this first resource, we extended our O-GlcNAcome catalog to include data from 42 distinct organisms and released the O-GlcNAc Database v1.2. In this version, more than 14 500 O-GlcNAcylated proteins and 11 000 O-GlcNAcylation sites are referenced from the curation of 2200 publications. In this article, we also present the extensive features of the O-GlcNAc Database, including the user-friendly interface, back-end and client–server interactions. We particularly emphasized our workflow, involving a mostly automatized and self-maintained database, including machine learning approaches for text mining. We hope that this software model will be useful beyond the O-GlcNAc community, to set up new smart, scientific online databases, in a short period of time. Indeed, this database system can be administrated with little to no programming skills and is meant to be an example of a useful, sustainable and cost-efficient resource, which exclusively relies on free open-source software elements (www.oglcnac.mcw.edu).

Methods for secure cloud processing of big data

We study new methods of secure cloud processing of big data when solving applied computationally-complex problems with secret parameters. This is one of the topical issues of secure client-server communication. As part of our research work, we model the client-server interactions: we give specific definitions of such concepts as “solvable by the protocol”, “secure protocol”, “correct protocol”, as well as actualize the well-known concepts-“active attacks” and “passive attacks”. First, we will outline the theory and methods of secure outsourcing for various abstract equations with secret parameters, and then present the results of using these methods in solving applied problems with secret parameters, arising from the modeling of economic processes. Many economic tasks involve processing a large set of economic indicators. Therefore, we are considering a typical economic problem that can only be solved on very powerful computers.

Modern web application development technologies

One of the key areas of modern information technology is the area of programming. Programming is the creation of various programs. If earlier it was, for the most part, about writing programs for the interaction of a user and a computer, today the applied field of programming is no longer limited to anything. These are programs for portable gadgets, medical equipment, space nanotechnology, and so on. Within the framework of this research, the work will focus on web development and specifically - on the development of a web application. This direction is quite popular, since the Internet segment has played a key role in human life. Whether it’s a government-owned company, a private enterprise, or a personal blog, having a website or application is a kind of business card. A web application is a client-server interaction model using browser software. In terms of their structure and logic of work, web applications often resemble the most common desktop solutions, but with one condition - such applications are always launched and executed in the browser.

Smart SDN Management of Fog Services to Optimize QoS and Energy.

The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.