Data Messages Research Articles

The Intrinsic Structure of High-Dimensional Data According to the Uniqueness of Constant Mean Curvature Hypersurfaces

In this paper, we study the intrinsic structures of high-dimensional data sets for analyzing their geometrical properties, where the core message of the high-dimensional data is hiding on some nonlinear manifolds. Using the manifold learning technique with a particular focus on the mean curvature, we develop new methods to investigate the uniqueness of constant mean curvature spacelike hypersurfaces in the Lorentzian warped product manifolds. Furthermore, we extend the uniqueness of stochastically complete hypersurfaces using the weak maximum principle. For the more general cases, we propose some non-existence results and a priori estimates for the constant higher-order mean curvature spacelike hypersurface.

A Review on Dynamic Buffer Traffic Condition Protocol in Telemedicine

Various MAC-protocol has been adopted over the years in Telemedicine, also known as Wireless Body Area Network system (WBANs), to enhance the proper transmission of the busy congestion of data message. However, these techniques could not coordinate traffic congestion in the receiver node, which could make the receiver nodes experience an "active mode" most of the time compared to the transmit node. This is dangerous to the network system because of uncontrollable energy usage. In this protocol, the intelligent sensor is strategically located around or implanted in the human body for the collection of human body physiological parameters. WBANs experienced some limitations such as latency and excessive consumption of energy which may hinder the lifetime maximization of the system if not taken care of properly. In this work, four elements are responsible for carrying-out only traffic data, and they are implemented by using the highest priority sensor nodes within a short period used for communicating to the Buffer Traffic Condition (BTC) discussed and the traffic measurement to mitigate active-mode interval in the receiving phase of the improved superframe structure of IEEE 802.15.6. Furthermore, meaningful information about the superframe to mitigate the busy traffic and enhance this protocol was also discussed as its possible functions with the help of an adaptive system. No article depicts the analysis of the work on the dynamic buffer traffic condition scheme, and this proposed scheme can improve on the existing one.

Fries

A computing job in a big data system can take a long time to run, especially for pipelined executions on data streams. Developers often need to change the computing logic of the job such as fixing a loophole in an operator or changing the machine learning model in an operator with a cheaper model to handle a sudden increase of the data-ingestion rate. Recently many systems have started supporting runtime reconfigurations to allow this type of change on the fly without killing and restarting the execution. While the delay in reconfiguration is critical to performance, existing systems use epochs to do runtime reconfigurations, which can cause a long delay. In this paper we develop a new technique called Fries that leverages the emerging availability of fast control messages in many systems, since these messages can be sent without being blocked by data messages. We formally define consistency in runtime reconfigurations, and develop a Fries scheduler with consistency guarantees. The technique not only works for different classes of dataflows, but also works for parallel executions and supports fault tolerance. Our extensive experimental evaluation on clusters show the advantages of this technique compared to epoch-based schedulers.

Blockchain in Digital Twins-Based Vehicle Management in VANETs

The purpose of this exploration of blockchain in vehicle management based on Digital Twins in Vehicular Adhoc Networks (VANETs) is to further improve intelligent transportation in smart cities. In view of the complexity of pedestrians in the real road network, the Digital Twins (DTs) technology is used to map the traffic situation in the real road network to the virtual space. Furthermore, the concrete interaction of vehicle data information is stored and transmitted by using blockchain technology. Finally, the DTs model of vehicle-mounted Ad Hoc network based on blockchain is constructed, and its performance is analyzed by simulation. The results suggest that the model algorithm adopted in this work shows a lower average delay time, its data message delivery rate is basically stable at 80%, the data message leakage rate is basically stable at approximately 10%, and the communication overhead does not exceed 700 bytes. Therefore, the in-vehicle self-organizing network model constructed in this work shows high network security performance while ensuring low latency performance, enabling information to interact more efficiently. Therefore, it can provide an experimental basis for the intelligent development and safety performance improvement of the transportation field of smart cities.

Spread Unsourced Random Access With an Iterative MIMO Receiver

This work is dedicated to the unsourced random access (URA) with multiple-antenna receiver. We utilize a preamble-payload packet format where the preamble, selected from a pool shared between all active users, serves as a temporary activity detection identifier. The payload carries the main portion of the message data which is encoded for error correction, permuted, and scrambled. The compressed-sensing (CS) preamble receiver that estimates the active preambles and the respective user channels is followed by a payload receiver which performs several iterations of data and channel estimation and parallel interference cancellation. Our numerical and analytic results demonstrate that the presented system outperforms the related work in terms of the achievable numbers of active users and signal-to-noise ratios (SNR)s supported by the system.

Design of topic priority based on industrial publish-subscribe system

As the size of the industrial systems increases, more and more topic messages need to be published and subscribed to in industrial scenarios such as microgrids and manufacturing industries. A priority scheduling algorithm based on the hotness of message topics is proposed to address the problem of efficient distribution of messages with different topics in microgrids. It is an algorithm that gives different priority permissions according to the number of topics, so that in the face of a large number of different topic messages, the data messages with a higher number or urgency can be transmitted first, solving the problem of port blocking when the server distributes the messages. At the same time, to prevent low priority data from sending timeout or waiting for a long time, a time period is set up, a low priority transmission mechanism is set up within a specific time period to ensure that the system can send data to customers in a timely and accurate manner. The results show that the priority design in this paper can well solve the power scheduling problem of multi-target data transmission and ensure the transmission efficiency of messages in the microgrid system.

A Visual Data Storytelling Framework

While the consumption of visual information becomes a daily commodity integrated into our lives, data visualisation is dominated by dashboards and charts. The main contribution of this article is an advanced way to visualise data as a data story. We converged paradigms from digital storytelling, serious games, and data visualisation to turn data into useful insights. The creation, management, and analysis of data have been increasingly given more attention in industry and professional practices. However, the potential of packaging data and analytic results into easily digestible and visually explorable content intended for non-professional audiences has not yet been investigated to its full extent. We contributed towards overcoming the gap between data analytics and data presentation. By integrating a story-like environment and entertainment into data visualisation, we explore the possibilities of efficiently communicating data and insights to general audiences in a casual context. We present this modular approach to customising messages for visual data storytelling from an information and communication perspective, including a test prototype developed to illustrate our data storytelling framework.

The Big Data Analysis and Visualization of Mass Messages under “Smart Government Affairs” Based on Text Mining

Based on the records of people’s political inquiries, comments from public sources on the Internet and the data of the relevant departments’ responses to some people’s messages, this study uses text analysis, text feature extraction, model building, text mining, and other evaluation methods to study and evaluate the three aspects of government services: analysis of public comments, mining of hot issues and evaluation of replies, which aims to prompt the government to understand the needs of the people quickly and solve the relevant problems in a timely and effective manner. The results show that the final classification accuracy using BERT is 3.4% and 1.8% higher than that using TF-IDF and Word2vec, respectively. Multi-classification of message data was realized by BERT combined with the LinearSVC algorithm, and the crowd message was accurately divided into seven types of problems, with an accuracy of 96.7%. It is intended to be transferred to relevant departments for processing. For problems related to people’s livelihood, law, economy, and other aspects, different departments should take countermeasures to solve them and achieve systematic, departmental, and regional coordination. This will enhance the ability of government platforms to deal with problems. Through the definition of hot indexes, hot issues mining can timely find the outstanding problems reflected by the masses. At the same time, the feedback evaluation system can comprehensively evaluate the work of relevant departments from the perspectives of relevance, completeness, and interpretability. Big data analysis technology based on text mining is a feasible way to solve the difficulties of text data analysis. The analysis model constructed in this study is suitable for mining and analyzing unstructured data with short text features, and the results can provide guidance for government decision-making.

Spotted Hyena Optimization and Simulated Annealing-Based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs

An accurate localization technique is considered as the significant entity in vehicular ad hoc networks (VANETs) for facilitating emergency message data transmission in diversified critical safety applications. In VANETs, the system of global positioing is generally used for estimating the position of the vehicles in the network for attaining neighborhood awareness in the event of warning message dissemination. However, the existence of green foliages, buildings, indoor parking lots and urban streen canyons introduces NLOS situation that introduces unwanted errors that crumbles the degree of data dissemination in emergency situations. In this paper, spotted hyena and simulated annealing optimization algorithm (SHSAOA)-based positioning scheme was proposed for precise estimation of NLOS nodes. İt included the advantages of improved simulated annealing (SA) integrated into SHOA for establishing better balance between the process of exploitation and exploration in the search space. This positioning approach generated candidate solutions by deriving the merits of the trajectory-based charateristics of SA throughout the algorithmic development process in order to improve the local optimization process. This proposed SHSAOA utilized the distance infotmation that are associated with the vehicle trajectory, number of vehicles and error in distance information for assessing the precise location of the NLOS nodes in the network. The simulation results of the proposed SHSAOA scheme confirmed minimized localization error with maximized accuracy in transmission, warning message transmission rate, channel utilization degree and neighborhood awareness degree with different vehicular density and NLOS nodes.

Estimating Driver Response Rates to Variable Message Signage at Seattle-Tacoma International Airport

We apply Bayesian Linear Regression to estimate the response rate of drivers to variable message signs at Seattle-Tacoma International Airport, or SEA. Our approach uses vehicle speed and flow data measured at the entrances of the arrival and departure-ways of the airport terminal, and sign message data. Depending on the time of day, we estimate that between 5.5 and 9.1% of drivers divert from departures to arrivals when the sign reads “departures full, use arrivals”, and conversely, between 1.9 and 4.2% of drivers divert from arrivals to departures. Though we lack counterfactual data (i.e., what would have happened had the diversionary treatment not been active), adopting a causal model that encodes time dependency with prior distributions rate can yield a measurable effect.

Quantifying DDS-cerberus network control overhead

Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of subscribers and publisher nodes from three to eighteen. By categorizing the network traffic from these nodes into either data message, security, or discovery+ with Quality of Service (QoS) best effort and reliable, the mean security traffic from DDS-C has minimal impact to Data Distribution Service (DDS) operations compared to other network traffic. The results reveal that applying DDS-C to a representative distributed network robotics operating system network does not impact performance.

HOW STANDARDS (SUCH AS XML) ACCOMPLISH ELECTRONIC AUTHENTICATION IN WEB SERVICES

Paper documents and “wet” signatures (made with ink) have given way to electronic data messages. This has led to problems with regard to the authentication of electronic “documents”. The present article explores some solutions in the shape of standards, eXtended Markup Language (XML) as a standardised format for data and the distinction between the (modern) Protocol and (traditional) Paper points of view in this regard.

The Hybrid Similar Neighborhood Robust Factorization Machine Model for Can Bus Intrusion Detection in the In-Vehicle Network

Controller area network (CAN) is the most commonly used bus technology for In-vehicle network and uses multicast communication without corresponding security measures. Therefore, the message data field is vulnerable to tampering and other attacks. Recent machine learning-based intrusion detection methods for CAN bus messages only use the information contained in the message data field and do not take into account the contribution made by the neighboring information of CAN bus messages. In addition, previous models considered the data domain information of CAN bus messages as separate features and did not consider the unique weight of each feature, as well as the second-order interaction information between features. Therefore, we propose a novel intrusion detection model, The Hybrid Similar Neighborhood Robust Factorization Machine Model (HSNRFM), for detecting anomalies in CAN bus messages to address the shortcomings and problems of the previous models. To be able to incorporate the contribution of similar neighborhood information and learn the unique weight parameters of each feature in the model decision process, as well as additional second-order interaction information between features, the HSNRFM model solves the above problem using a similarity calculation method and a factorization machine model. Comprehensive experimental results are compared on real vehicle datasets. The HSNRFM model has AUC values of 0.9216 and 0.901 and AUPR values of 0.9194 and 0.9018 on two real datasets, respectively. And the results show that our proposed HSNRFM model has excellent detection efficiency for intrusion detection of CAN bus messages.

Real-Time Charging Risk Assessment for Electric Vehicles Based on Improved Broad BP-AHP

In the research of the electric vehicle charging safety evaluation model, the key problem is to determine the influencing factors of charging safety scientifically and ensure the accuracy and real-time performance of the model evaluation. This article takes the real-time charging message data of electric vehicles as the research object, analyzes and establishes the membership model of charging safety influencing factors, and proposes a real-time charging risk assessment method for electric vehicles based on the improved broad back-propagation and analytic hierarchy process (BBP-AHP) evaluation method. First, the characteristic membership degree of electric vehicle charging message data is calculated. Then, the improved broad BP neural network based on the compression factor is established to obtain the evaluation results of several experts, and the AHP model is optimized. Finally, the charging safety influencing factor system is constructed by the AHP to evaluate the charging safety of the real-time message. According to the accuracy comparison of the AHP model and the improved broad BP-AHP model, the experimental results show that the improved broad BP-AHP model can more accurately evaluate the safety risk of the electric vehicle real-time charging.

Analisis Forensik Aplikasi TikTok Pada Smartphone Android Menggunakan Framework Association of Chief Police Officers

TikTok is the most popular social media app that ranks sixth in the world in January 2022 according to We Are Media & Hootsuite. Along with the increasing number of uses of this application, the negative impacts it causes are also increasing, ranging from fraud, cyberbullying, to the spread of fake news (hoax). This study focuses on the negative impact of defamation. The purpose of the research is to plan and implement a digital forensics process using the Association of Chief Police Officers (ACPO) framework to raise evidence of defamation cases on the TikTok application. The forensic process is carried out in the form of static forensics on cases that are intentionally made in the form of simulating TikTok postings using an android smartphone. This research combines the ACPO forensics framework with the Magnet Axiom forensics tools. The combination of the two succeeded in raising 77% of evidence in the form of data messages, videos, and hashtags. Where these data have been previously defined as initial data posted in the simulation process

On the Design and Implementation of the External Data Integrity Tracking and Verification System for Stream Computing System in IoT.

Data integrity is a prerequisite for ensuring data availability of IoT data and has received extensive attention in the field of IoT big data security. Stream computing systems are widely used in the field of IoT for real-time data acquisition and computing. However, the real-time, volatility, suddenness, and disorder of stream data make data integrity verification difficult. According to the survey, there is no mature and universal solution. To solve this issue, we constructed a data integrity verification algorithm scheme of the stream computing system (S-DIV) by utilizing homomorphic message authentication code and pseudo-random function security assumption. Furthermore, based on S-DIV, an external data integrity tracking and verification system is constructed to track and analyze the message data stream in real time. By verifying the data integrity of message during the whole life cycle, the problem of data corruption or data loss can be found in time, and error alarm and message recovery can be actively implemented. Then, we conduct the formal security analysis under the standard model and, finally, implement the S-DIV scheme in simulation environment. Experimental results show that the scheme can guarantee data integrity in an acceptable time without affecting the efficiency of the original system.

Educational Data Mining by Optimally Fusing Shallow and Deep Features.

In order to meet the requirement of designing the educational curriculum and extract the relevant data toward students, we in this paper utilize the deep learning architecture to study the personalized understanding and educational feature extraction algorithm of the high school and college students. Aiming at the insufficiency of deep collaborative filtering recommendation algorithm that can only discover the paths that have attracted students' attention, the requirements of intelligent course are proposed by us. First, the word vector permutation discourse poke model is extracted to produce audible vectors of message data, a well-designed learning algorithm is proposed subsequently to extract and fuse multiple heterogeneous educational data. Then, the SVD algorithm program is used to reduce the range of feature vectors. And thus, the model is completely functional and we identify unified channels and users. The method of combining hand-extracted low-level features with high-level human gesture features (image visual gate features and image deep features) automatically extracted by our adopted deep network is used to identify students' various emotions. The conclusive product is a flat semantic description that effectively detects copying and objection. Experiments are performed on a generic dataset, and we use the old-fashioned manual birth method and two well-known data sets VGG16 and fine-tune AlexNet.

Secure and flexible message-based communication for mobile apps within and across devices

In modern mobile platforms, message-based communication is afflicted by data leakage attacks, through which untrustworthy apps access the transferred message data. Existing defenses are overly restrictive, as they block all suspicious message exchanges, thus preventing any app from receiving messages. To better secure message-based communication, we present a model that strengthens security, while also allowing untrusted-but-not-malicious apps to execute their business logic. Our model, HTPD, introduces two novel mechanisms: hidden transmission and polymorphic delivery. Sensitive messages are transmitted hidden in an encrypted envelope. Their delivery is polymorphic: as determined by the destination’s trustworthiness, it can be delivered no data, raw data, or encrypted data. To allow an untrusted destination to operate on encrypted data deliveries, HTPD integrates homomorphic and convergent encryption. We concretely realize HTPD as PoliCC, a plug-in replacement of Android Inter-Component Communication (ICC) middleware. PoliCC mitigates three classic Android data leakage attacks, while allowing untrusted apps to perform useful operations on delivered messages. Our evaluation shows that PoliCC supports secure message-based communication within and across devices by trading off performance costs, programming effort overheads, and security11This article is a revised and extended version of our prior paper, published in the 17th EAI International Conference on Security and Privacy in Communication Networks (SecureComm 2021) (Liu et al., 2021).

Availability of Automatic Identification System (AIS) Based on Spectral Analysis of Mean Time to Repair (MTTR) Determined from Dynamic Data Age

Good marine practice and proper operation of navigation, radio navigation, and radio communication systems are nowadays of key importance for marine navigation safety. This applies to merchant vessels and navy ships and effectively monitoring unmanned vehicles along a set route. Technological progress has contributed to developing equipment for ships and unmanned vehicles, which are fitted out with devices of the automatic identification system (AIS). One of the issues in AIS operation is limited-service availability, which manifests itself in the presence of incomplete data for the navigation parameters sent by radio, compressed in dynamic data messages. This results in the unusability of the system information for ships equipped with an AIS transponder. This paper aims to develop an AIS service availability model based on the mean time of incomplete navigation parameter occurrence in AIS data messages and to present the test results in the time and frequency domain using a mathematical method—Fast Fourier Transform. The study results refer to five basic navigation parameters and are indicative of a high service availability index—over 0.99 for three out of the five navigation parameters tested. Data recorded by a ship’s system receiver were the key source of practical knowledge concerning the limitations of AIS service availability. The experiment revealed interruptions in regular data inflow from navigation devices. In effect, a description was provided of a functional relationship based on a spectral analysis of the frequencies of the times occurring between service repair (time to repair—TTR), and the use of the model to analyze other variables was proposed.

Multi authority Ciphertext-Policy Attribute-Based encryption for security enhancement in cloud storage unit

Multi Authority Ciphertext-Policy Attribute-Based Encryption (MCP-ABE) can be developed aimed at enabling efficient secure operation in this research. The proposed methodology is developed to achieve two main characteristics such as to avoid multiuser illegal share their private key and provide data owner to access the data and flexibility to change their access policy. The proposed methodology is providing high security of multi authority with different traceable and dynamic policy updating procedure. The proposed method is implemented in Java and analyzed with performance metrices such as key size, computation time and functionality. Compared to traditional cryptographic methods, attribute-based encryption has a number of benefits, including its adaptable and fine-grained control of access. Additionally, it is independent of key sharing and management methods that may protect against security attacks, a method of undermining encryption. The proposed methodology is protecting the users with different conditions such as data protection, fraud prevention, message detection and general attack resistance. To validate the performance of the proposed methodology, it can be contrasted with the conventional methods such as Ciphertext-Policy Attribute-Based Signcryption with Outsourced Designcryption (CP-OABSC) and revocable signature (CP-ABSC) with Ciphertext-Policy Attribute-Based Signcryption With Accountable and Verifiable Outsourced Designcryption (CP-ABSc-AVODs) respectively.