Client-server Research Articles

FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records

With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated learning client-server architecture, utilizes an improved multi-layer perceptron and logistic regression network as the model backbone, employs the federated momentum gradient algorithm as the local model training optimizer, and uses the federated Gaussian differential privacy algorithm to protect the security of the computation process. The experiment evaluates the model's classification performance using the confusion matrix, explores the impact of client collaboration on model performance, demonstrates the model's suitability for imbalanced data distribution, and confirms the effectiveness of federated learning for model training. FLM-ICR achieves the accuracy, precision, recall, specificity, and F1 score of 0.795, 0.735, 0.835, 0.75, and 0.782, respectively, outperforming existing research methods and balancing classification performance and privacy security, making it suitable for IoV computation and analysis of private data.

Sterling Integrator File Transfer Protocol Configurations

IBM Sterling Integrator supports a wide range of file transfer and communication protocols, including SFTP, Connect: Direct, and Mailbox (HTTP). While connecting with client servers, it’s imperative that these protocols and all other prerequisites for a successful file transfer, including authentication protocols, encryption, and file naming conventions, match between the IBM Sterling adapter for a connection and the client server.

Smartphone-Enabled Interaction on Large Displays—A Web-Technology-Based Approach

This paper proposes the design and implementation of a system for simultaneous communication of several users over a single display, such as large public displays or video walls. Motivated by the lack of software frameworks that are both free and suited for the desired level of complexity, we present a novel, out-of-the-box solution for collaborative and remotely controlled application use. The solution is based exclusively on web technologies, thus significantly facilitating usability and reach, and eliminating the need for additional software installations or the use of additional devices. The architecture of the developed solution is modular and comprises three main components: (1) the environment, (2) the remote controllable application (RCA), and (3) the controller. The environment serves as a central hub that manages the data flow and ensures synchronization between the user devices. The RCA, which runs on the large display, serves as the interface for interacting with the content, while the controller, which is loaded on the users’ smartphones, enables control and interaction with the RCA in real time. By using web sockets and a server–client architecture, the system supports device- and platform-independent remote control and centralized collaboration through an off-the-shelf communication framework entirely flexible regarding client application. In order to demonstrate the possibilities of the proposed system, several different scenarios were covered, and solutions were implemented and tested on a large display in the faculty lobby.

АРХІТЕКТУРА ПРОГРАМНОЇ СИСТЕМИ ДЛЯ ВИРІШЕННЯ ЗАДАЧІ КЛАСИФІКАЦІЇ НА ОСНОВІ ПРИВАТНИХ ДАНИХ

Data analysis and artificial intelligence systems are becoming widely used in various spheres of human life. This is confirmed by more typical cases of their use, in particular, the selection of recommendations for the user in e-commerce, the detection of spam in e-mail services, and the moderation of user comments; as well as cases of personal use of such tools (for example, chatbots ChatGPT, Google Bard, Microsoft Copilot have appeared and gained significant popularity in the last two years). One of the key elements of such systems is data, which is necessary for training and testing software systems of intelligent data analysis. A significant amount of diverse data contributes to the construction of a software system with high accuracy. Considering this, the task of choosing and preparation of datasets that can be used in the construction of such systems is important. One of the difficulties in this task is the presence of private information in the datasets, which limits their use for systems of intelligent data analysis. The paper is devoted to the development of the software system architecture for solving the classification problem based on private data. The existing methods and architectural approaches for privacy-preserving in machine learning were considered. The architecture of the software system was proposed, the characteristic feature of which is the protection of private datasets by using of functional encryption, which allows to increase the number of datasets for training publicly available data analysis and artificial intelligence systems. The proposed architecture of the software system is based on the client-server architecture and functional encryption. The components are a classifier, a generator of encryption keys, and modules of functional encryption and decryption. Prospects for further research were discussed.

Software Defined Netconomics

This paper is based on the fair analysis on the economic benefits of software defined networking. The paper will explain vividly the concept of software defined networking and its applications together with the economic advantage(s) with respect to both the client perspective and server perspective. In general, SDN provides lower hardware and operational cost in an excessive traffic load of nowadays users. It has tighter security and faster response time with improved management and planning of server controller. SDN when deployed provides better service(s) with respect to lowering cost and of course with better efficiency. Software Defined Network (SDN) is a great revolutionary idea to change IT Industry of which includes cloud computing and many others. The beauty of its architecture is the decoupling of the essential control and data planes from the application layer, thus the underlying infrastructure is abstracted but the network intelligence is logically centralized with state. This architecture gives access to the enterprise to control, program, automate, scale and secure the network according to the business needs, together with securing a safe benefit in competiveness that has risen due to fast advancement in technology. This architecture is even more important to fulfilling the demands of various clients in the current model of networking. The concept of SDN has the potential to change the current networking model. With SDN, for network services, the underlying network infrastructure can be abstracted by the administrators for many applications. In theory, it can be shown that SDN has many benefits over current IT systems but in practical systems, IT companies are reluctant to work with, tend to oppose nor are they ready to deploy SDN. In this paper, the economic benefits are explored and discussed without bias, and it will be shown that new protocol of SDN provides many economic benefits and guidance to IT companies to establish more secure and efficient network.

Design of a robotic system to assist in the treatment of severe COVID‐19 patients

AbstractThe article presents the conceptualization, development, and implementation of a sophisticated mobile robotic system with the purpose of providing aid in the medical care of those afflicted with severe cases of COVID‐19. The robotic system will engage in verbal communication with the patient and provide updates regarding the external environment. Additionally, it will facilitate the delivery of food, beverages, and other consumable items to the isolation room. The robot has the capability to navigate to its intended location using two distinct modes: autonomous mode and online control mode. The hardware is constructed within a mobile robot system that is interconnected to the Internet over a 4G mobile network. The system employs a client–server software architecture wherein the transmission of data between the client and the server occurs through various transport protocols. Experimental simulations were conducted in an active treatment room to evaluate the performance of autonomous operating mechanisms, including obstacle avoidance positioning, safe destination navigation, and feedback display for remote robot operation.

A multi-objective approach for communication reduction in federated learning under devices heterogeneity constraints

Federated learning is a paradigm that proposes protecting data privacy by sharing local models instead of raw data during each iteration of model training. However, these models can be large, with many parameters, provoking a substantial communication cost and having a notable environmental impact. Reducing communication overhead is paramount but conflictual to maintaining the model’s accuracy. Most research has dealt with the different factors influencing communication reduction separately without addressing their correlations. Moreover, most of them do not consider the heterogeneity of clients’ hardware. Finding the optimal configuration to fulfil all these training aspects can become intractable for classical techniques. This work explores the add-in that multi-objective evolutionary algorithms can provide for solving the communication overhead problem while achieving high accuracy. We do this by 1) realistically modelling and formulating this task as a multi-objective problem by considering the devices’ heterogeneity, 2) including all the communication-triggering aspects, and 3) applying a multi-objective evolutionary algorithm with an intensification operator to solve the problem. A simulated client–server architecture of four devices with four different processing speeds is studied. Both fully connected and convolutional neural network models are investigated with 33,400 and 887,530 weights, respectively. The experiments are performed using the MNIST and Fashion-MNIST datasets. A comparison is made between three approaches using an extensive set of metrics. Results prove that our approach obtains solutions with better accuracy than the full-communication setting and other methods while getting reductions in communications by around 1,000 times in most cases and up to 10,000 times in some cases compared to the maximum communication setting.

Application analysis of data mining in shopping APP

Dingdong Buying Vegetable is a cohort of emerging entities that have swiftly gained prominence within the business domain in recent times. The data mining function of its data platform APP is undeniably linked to the underlying factor contributing to its commercial success. This article examines the fundamental principles, practical manifestations, and pertinent research instances of data mining. It specifically centers on the primary interface of the Dingdong Buying Vegetable APP, scrutinizing its design and distinctive attributes tailored to specific customers. Furthermore, it conducts an in-depth analysis of the correlation between the platforms commercial success and its data mining functionality. The primary aspect in which the data mining function of the Dingdong Buying Vegetable APP is expected to be manifested is through extensive data mining. The process of mining client data and conducting a full comparison and analysis of sales data for all products sold is a highly intricate and exhaustive endeavor. The use of these data mining functions serves multiple purposes. Firstly, it efficiently identifies customers genuine requirements on the client side, enabling the recommendation of suitable products and fostering customer reliance on the application. Additionally, it facilitates accurate decision-making in marketing products on the product side by comparing and analyzing diverse data pertaining to the sold products. This approach helps prevent the sale of unsought items and effectively minimizes company expenditure.

Virtual Assistance System for Teaching Physics Experiments in University Students

According to previous studies, it can be seen that education has been facing new challenges to improve the quality of student teaching by achieving a better interpretation of the conceptual part and the experimental part as they are directly related, improving learning in natural science courses that require students to carry out experiments in laboratories to consolidate their knowledge. The problem raised in this research is that the teaching of the theoretical part is more important than the experimental part with respect to the Physics course due to the little time students have in the laboratories, as well as the limited space that does not allow them to freely carry out experiments in laboratories as poor infrastructure, resulting disadvantageous for students by not demonstrating the hypotheses raised from the theory that would allow them to consolidate their knowledge. The aim of this research work is to develop a virtual assistance system for teaching physics experiments to university students with a high level of reality in order to improve the student's experimental practice, taking advantage of a new form of centralised teaching that prioritises the student's time and space through a dynamic interface. The methodology used for this research is based on a client-server model, a TCP/IP data protocol that allows the computer to send and receive data, graphical features of the operating system with the Microsoft Windows platform for interaction with the virtual laboratory without time restriction. According to the tests carried out, it was observed that the virtual assistance system was performed efficiently by detailing the tests of its corresponding function with an efficiency of 95.14%, which is a high value for a virtual laboratory system that allows experimental processes to be carried out. It was concluded that this system presents a dynamic interaction to perform experiments in the virtual physics laboratory with the appropriate materials for better learning.

Web-based Academic Information System Security

Web-based Academic Information System (SIA) has been used by all students of the Faculty of Information Technology KH. A. Wahab Unwaha Jombang University. Because all student academic records are stored through the campus network, it is necessary to conduct research on security so that a secure system is obtained. This research was conducted with steps including analysis and testing of installed systems, analyzing needs, designing problem solutions, making repair modules, installing modules and retesting repair modules. From the results of the research conducted, it can be concluded that there are weaknesses in the login system. The weaknesses include the use of the Student Identification Number (NIM) as the default username and password, username and password data is not encrypted before being sent to the server over the network, traces of usernames and passwords left in the browser as a cache or in the password manager can be seen as unencrypted plaintext. From the security analysis results, the SIA login system can be improved by applying HMAC MD5 encryption technology and Challenge Handshake Authentication Protocol (CHAP). Challenge is generated by the server randomly and used as a key in the HMAC MD5 encryption process. With the use of challenge passwords sent in the form of hash values will always be different in each session. Javascript on the client side is used to perform encryption so that the data before being sent to the server is already in an encrypted state.

MOBILE ATTENDANCE SYSTEM USING GPS AND FACIAL AUTHENTICATION

Student attendance in any educational institutions is a critical content of the administration of school and the major task of the course lecturer who maintain attendance. The conventional method of monitoring students' attendance in the classroom typically involves requiring students to physically record their attendance. Initially, this process was carried out manually using pen and paper. However, as the student population grows, this approach becomes increasingly burdensome, time-consuming, and susceptible to mistakes. To avoid these challenges, this paper proposes a Mobile attendance system using GPS locator and facial authentication algorithm to monitor students’ attendance using android mobile devices. The mobile attendance app is based on Client-Server Architecture where the server is hosted in the cloud and clients are with mobile devices with in-built GPS Locator. To secure the integrity of the attendance, the GPS locator helps in locating the precise geographical position of the students present, a mock detector is also integrated into the app to detect fake location of the students in the classroom meeting and the facial image used by the student at runtime support startup camera.

Application Programming Interface (API) Development for Student Attendance System Using QR Code

Designing an application that requires communication between platforms need an intermediary to do it. The implementation of Application Programming Interface (API) from a designed system was done so the developers are able to access programmatic functions between different platforms. In the era of the information technology development as it is today, there are still many applications that are not computerized, such as the attendance recording systems. This research aims to develop API well on an Android-based student attendance system that uses the Quick Response code (QR code). To do so, first of all the design of system architecture was created to understand the communication flow between the platform. After that, the software development is done along with the test of API web services. In the end, with the integration of the Android client and web server on Laravel and assisted by the RESTful based web service, made that designed student attendance system becomes a safe and reliable.

Automatic Mobile Learning System for the Constant Preparation of the Student Community

Introduction: the events that occurred with the pandemic caused a drastic change in all activities with direct contact due to the high risk of contagion, with educational centers being affected by the closure measures and the imposition of virtual classes to continue with student preparation, leading many students to see the need to have a computer to take their classes, eventually showing boredom due to the lack of desire to be in front of a computer, This to a certain extent weakens their interest in learning and affects their learning because mobile devices have become more important due to the various applications that provide students with information. For this reason, we propose mobile learning that allows students to have more information, as well as interaction with different students so that they have the opportunity to learn on a constant basis.Objective: the objective is to create an automatic mobile learning system for the constant preparation of the student community.Method: a methodology based on a client-server model to take advantage of the various educational resources accompanied by the good support it provides the subjects for students with the interaction of a mobile application.Results: through the operation of the system, it was visualized that the tests carried out with the students were presented with an efficiency of 96,70 %,Conclusions: this system presents a high efficiency that allows to reinforce the subjects that need more prominence in the student's learning and progress of level through the teacher's evaluations

Management Information Systems for Tree Fruit—1: A Review

A farm management information system (MIS) entails record keeping based on a database management system, typically using a client-server architecture, i.e., an information system, IS, coupled with a variety of tools/methods/models for the support of operational management. The current review adopts a multivocal approach to consider academic and commercial developments in MISs for orchard management, based primarily on the refereed literature but extending to grey literature and interviews of Australian mango orchard managers. Drivers for orchard MIS development include increasing the orchard size and management complexity, including regulatory requirements around labour, chemical spray use and fertilisation. The enablers include improvements in within-orchard communications, distributed (web) delivery systems using desktop and mobile devices, and sensor systems and predictive models, e.g., for pest management. Most orchard MIS-related publications target the commodities of apple, grape, mango and olive in the context of management of plant health (pest and disease), plant development, irrigation and labour management. Harvest forecast and MIS modules are only now beginning to emerge, in contrast to a long history of use in grain production. The commercial systems trend towards an incorporation of financial information, an integration of data from multiple sources and a provision of dashboards that are tailored to the user. Requirements for industry adoption of a MIS are discussed in terms of technical and design features, with a focus on usability and scalability.

FedTKD: A Trustworthy Heterogeneous Federated Learning Based on Adaptive Knowledge Distillation.

Federated learning allows multiple parties to train models while jointly protecting user privacy. However, traditional federated learning requires each client to have the same model structure to fuse the global model. In real-world scenarios, each client may need to develop personalized models based on its environment, making it difficult to perform federated learning in a heterogeneous model environment. Some knowledge distillation methods address the problem of heterogeneous model fusion to some extent. However, these methods assume that each client is trustworthy. Some clients may produce malicious or low-quality knowledge, making it difficult to aggregate trustworthy knowledge in a heterogeneous environment. To address these challenges, we propose a trustworthy heterogeneous federated learning framework (FedTKD) to achieve client identification and trustworthy knowledge fusion. Firstly, we propose a malicious client identification method based on client logit features, which can exclude malicious information in fusing global logit. Then, we propose a selectivity knowledge fusion method to achieve high-quality global logit computation. Additionally, we propose an adaptive knowledge distillation method to improve the accuracy of knowledge transfer from the server side to the client side. Finally, we design different attack and data distribution scenarios to validate our method. The experiment shows that our method outperforms the baseline methods, showing stable performance in all attack scenarios and achieving an accuracy improvement of 2% to 3% in different data distributions.

Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks

Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, due to the fading effects of the wireless channel, the outage of wireless transmission degenerates the learning efficiency when FL is applied in wireless IoT networks. In order to address this issue, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided cellular IoT networks. By taking both the amount of training data and wireless resource consumption into consideration, we formulate the problem as a mixed integer non-linear programming to maximize the utility of the network. To solve the problem effectively, an alternative direction-based algorithm is proposed by decomposing the original problem into two sub problems. The simulation results indicate that the proposed algorithm substantially improves the FL learning performance and reduces the consumption of wireless resources compared with existing methods.

Parallel Processing Distributed-Memory Approach Influences on Performance of Multicomputer-Multicore Systems Using Single-Process Single-Thread

Based on client/server architecture concepts, this research suggests a method for creating a multicomputer-multicore distributed memory system that can be implemented on distributed-shared memory systems. Both of number of the participated computers and number of existed processors for each of these computers, this research was depended with the specific design and its implementation. The suggested system has two primary phases: monitoring and managing the programmes that may be executed on multiple distributed-multi-core architectures with (2, 4, and 8) CPUs to perform a certain job. There might be a single client and unlimited servers in the network. The implementation phase relies on three separate scenarios covering most of the design space. The suggested system can determine the start time, duration, CPU use, kernel time, user time, waiting time, and end time for each server in the system. Single-Process Single-Thread (SPST) is considered a possible situation while developing User Programmes (UPs). The findings confirmed that more processing power (more servers and more processors on each server) increases the speed at which tasks can be solved. There was a 2.877-fold gain in task processing speed after considering three different possible SPST UPs situations. The C# programming language is used to create this system.

Oil Filter Machine for Deep Frying with Smartphone Control and Monitoring using Client-Server Communication Protocol

The prevalence of fried snack consumption in our surroundings has become a widespread phenomenon. These fried snacks, commonly relished by many, are frequently served as side dishes with our main meals and are also a popular choice for satisfying quick hunger cravings. By utilizing the client-server communication method, this device can be assisted in its operation. The device employs ESP8266 as both the transmitter and receiver, while a smartphone serves as the control and monitoring tool. The primary purpose of this setup is to enhance the efficiency of oil-based food consumption, promoting healthier habits. The data collected from the device is split into two categories. Firstly, the communication data consists of the average RSSI (-52dBm) measured at a distance of 1 meter, with RSSI values increasing and becoming more susceptible as the distance grows. Secondly, the machine was tested for oil drainage in food, resulting in a weight decrease of 2.6% after 60 seconds of device usage, and the percentage of weight reduction increased with the given time.

Exploring private sector perspectives on barriers and facilitators in availing tuberculosis care cascade services: a qualitative study from the Indian state

IntroductionThe private sector plays an important role in tuberculosis (TB) elimination by providing access to quality TB care services like diagnosis and treatment, advocacy for preventive measures, innovation to address challenges in TB elimination, vaccines etc. The study aims to understand the perspectives of private practitioners on patients’ TB care cascade to reinforce existing interventions by assuring the quality of care to TB patients.MethodsThe study utilized a qualitative design through in-depth interviews of private practitioners and was conducted in Ranchi and Purbi Singhbhum District of Jharkhand State from March-August 2021. The pilot-tested, semi-structured, open-ended interview guide questionnaire collected information from private practitioners on various aspects of the TB care cascade. The data from the provider interviews were transcribed into multiple codes and themes on the TB program. An inductive analysis was carried out with a focus on content credibility to eliminate bias. Ethical approval was received from the Institutional Ethics Committee of the Indian Institute of Public Health Gandhinagar (IIPHG), India. Written consent was taken from the private practitioners involved in the study. ResultIn-depth interviews of 17 private practitioners reveal various factors contributing to delays in TB care cascades, especially delay in access to TB diagnosis and TB Care, delay in providing treatment once after diagnosis and poor adherence to the TB treatment. According to the perception of private practitioners, there was an array of client, provider and system side factors affecting the TB care cascade gaps positively and negatively. Positive aspects mainly emerged from interviews: strong governance, consistent supply chain management, innovative PPP models and financial schemes reducing out-of-pocket expenditure (OOPE). Various factors affecting the TB care cascade negatively include awareness among the patient, socio-economic status, approach and decision-making power of providers, adverse effects of drugs, staff capacity building, etc. ConclusionsEngaging private practitioner in TB elimination efforts is critical to achieving global targets and reducing the burden of TB. The study helps to determine geography-specific barriers and facilitators of the TB care cascade to achieve the aim of providing universal access to TB healthcare with the inclusion of private practitioners.

A Bayesian Federated Learning Framework With Online Laplace Approximation.

Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to share data. Most existing FL methods train local models separately on different clients, and then simply average their parameters to obtain a centralized model on the server side. However, these approaches generally suffer from large aggregation errors and severe local forgetting, which are particularly bad in heterogeneous data settings. To tackle these issues, in this paper, we propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. On the server side, a multivariate Gaussian product mechanism is employed to construct and maximize a global posterior, largely reducing the aggregation errors induced by large discrepancies between local models. On the client side, a prior loss that uses the global posterior probabilistic parameters delivered from the server is designed to guide the local training. Binding such learning constraints from other clients enables our method to mitigate local forgetting. Finally, we achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.