Salt Lake City and 17 other Utah cities are planning to construct the largest ultrahigh-speed data network in the country using fiber optic cables. The project to complete a direct fiber optic connection to homes is considered by The New York Times as one of the most ambitious of its type in the world.

What difference would it make to be able to unlock ocean data at scale? How would deploying hundreds of marine sensing platforms improve marine weather predictability and accuracy? A company named Sofar is answering some of those questions these days due to their capacity to use real-time data to improve ... [continued]

The post How Networks Of Ocean Sensors Can Improve Marine Weather Predictability appeared first on CleanTechnica.

As more businesses move to the cloud, they are becoming increasingly vulnerable to cloud network security threats. Here are the top three threats and how to avoid them: Data breaches One of the most common and devastating cloud security threats is data breaches. These can occur when hackers gain access to a company’s cloud-based data, …

The Top 3 Cloud Network Security Threats (And How to Avoid Them) Read More »

When it comes to protecting sensitive information like financial data, personal information, and intellectual property, encryption has become a must. By scrambling data through the use of algorithms, only those with access to decryption keys are able to read what's being secured. Encrypted traffic has fulfilled its intended mission: to lock down data. But, could it simultaneously be helping bad actors slip by undetected? And could encrypted traffic actually make it harder for network defenders to spot threats before it's too late? To find out, we sat down with Phil Owens, VP of customer solutions at Stamus Networks. Phil believes… [Continue Reading]

Fr. John announces some new missions, highlights the potential for growth within home school networks, and plugs a growing online homeschooling program. Great news during these challenging days!

Monica Yousef is an attorney with Sheppard, Mullin, Richter & Hampton and the Co-Chair of the Orthodox Christian Attorney Network. In conjunction with the Christian Legal Society, the OCAN is planning their second annual conference and would love to see all Orthodox lawyers attend. Find out more HERE.

Ken Liu joins us to talk about the Orthodox Christian Attorney Network and the upcoming conference in conjunction with the Christian Legal Society.

Bobby Maddex interviews Donna Haddad and Monica Youssef about the upcoming Orthodox Christian Attorney Network Conference on Saturday, October 17th. Donna and Monica share how past years' conferences have benefited them both personally and professionally. This year the free virtual conference features Archbishop Michael Dahulich, Hon. Stephanos Bibas, and Steven Christoforou. Find out more and register for this amazing conference here!

Bobby Maddex interviews Matthew Namee about the upcoming OCAN Conference, which takes place on November 6th. Listeners can learn more and register for the conference here!

Bobby Maddex interviews Joan Berg and Jesse Roberts about the upcoming Orthodox Christian Attorney Network National Conference in Newport Beach, CA. This year's conference will be held from October 7-8 and will feature keynote speaker, The Honorable Gregory Katsas. To learn more about this conference, to register, and to find details on financial assistance, please visit the OCAN website.

Bobby Maddex interviews Kenneth Liu, the Executive Director of the Orthodox Christian Attorney Network, about some new developments at the organization that listeners will want to learn more about and even perhaps support.

Personalisation in marketing works because personal connections matter. That extends beyond your customers, too. Personal connections include the relationships you have with your suppliers, your stakeholders, your employees, industry peers… the list goes on.

Neural network learning approach for color image restoration has been discussed in this paper and one of the possible solutions for restoring images has been presented. Here neural network weights are considered as regularization parameter values instead of explicitly specifying them. The weights are modified during the training through the supply of training set data. The desired response of the network is in the form of estimated value of the current pixel. This estimated value is used to modify the network weights such that the restored value produced by the network for a pixel is as close as to this desired response. One of the advantages of the proposed approach is that, once the neural network is trained, images can be restored without having prior information about the model of noise/blurring with which the image is corrupted.

Small and Medium Enterprises (SMEs) face new challenges in the global market as customers require more complete and flexible solutions and continue to drastically reduce the number of suppliers. SMEs are trying to address these challenges through cooperation within collaborative enterprise networks (CENs). Human aspects constitute a fundamental issue in these networks as people, as opposed to organizations or Information Technology (IT) systems, cooperate. Since there is a lack of empirical studies on the role of human factors in IT-supported collaborative enterprise networks, this paper addresses the major human aspects encountered in this type of organization. These human aspects include trust issues, knowledge and know-how sharing, coordination and planning activities, and communication and mutual understanding, as well as their influence on the business processes of CENs supported by IT tools. This paper empirically proves that these aspects constitute key factors for the success or the failure of CENs. Two case studies performed on two different CENs in Switzerland are presented and the roles of human factors are identified with respect to the IT support systems. Results show that specific human factors, namely trust and communication and mutual understanding have to be well addressed in order to design and develop adequate software solutions for CENs.

Collaborative Filtering(CF) is a well-known technique in recommender systems. CF exploits relationships between users and recommends items to the active user according to the ratings of his/her neighbors. CF suffers from the data sparsity problem, where users only rate a small set of items. That makes the computation of similarity between users imprecise and consequently reduces the accuracy of CF algorithms. In this article, we propose a clustering approach based on the social information of users to derive the recommendations. We study the application of this approach in two application scenarios: academic venue recommendation based on collaboration information and trust-based recommendation. Using the data from DBLP digital library and Epinion, the evaluation shows that our clustering technique based CF performs better than traditional CF algorithms.

This paper presents a sequential edge swap (SQES) mechanism to design a robust network for a healthcare system utilising energy and communication range of nodes. Two operations: sequential degree difference operation (SQDDO) and sequential angle sum operation (SQASO) are performed to enhance the robustness of network. With equivalent degrees of nodes from the network's centre to its periphery, these operations build a robust network structure. Disaster attacks that have a substantial impact on the network are carried out using the network information. To identify a link between the malicious and disaster attacks, the Pearson coefficient is employed. SQES creates a robust network structure as a single objective optimisation solution by changing the connections of nodes based on the positive correlation of these attacks. SQES beats the current methods, according to simulation results. When compared to hill-climbing algorithm, simulated annealing, and ROSE, respectively, the robustness of SQES is improved by roughly 26%, 19% and 12%.

The current advertising push methods have low accuracy and poor advertising conversion effects. Therefore, a Weibo marketing advertising push method based on social network data mining is studied. Firstly, establish a social network graph and use graph clustering algorithm to mine the association relationships of users in the network. Secondly, through sparsisation processing, the association between nodes in the social network graph is excavated. Then, evaluate the tightness between user preferences and other nodes in the social network, and use the TF-IDF algorithm to extract user interest features. Finally, an attention mechanism is introduced to improve the deep learning model, which matches user interests with advertising domain features and outputs push results. The experimental results show that the push accuracy of this method is higher than 95%, with a maximum advertising click through rate of 82.7% and a maximum advertising conversion rate of 60.7%.

TCP BBR is one of the most powerful congestion control algorithms. In this article, we provide a comprehensive review of BBR analysis, expanding on existing knowledge across various fronts. Utilising ns3 simulations, we evaluate BBR's performance under diverse conditions, generating graphical representations. Our findings reveal flaws in the probe's RTT phase duration estimation and unequal bandwidth sharing between BBR and CUBIC protocols. Specifically, we demonstrated that the probe's RTT phase duration estimation algorithm is flawed and that BBR and CUBIC generally do not share bandwidth equally. Towards the end of the article, we propose a new improved version of TCP BBR which minimises these problems of inequity in bandwidth sharing and corrects the inaccuracies of the two key parameters RTprop and cwnd. Consequently, the BBR' protocol maintains very good fairness with the Cubic protocol, with an index that is almost equal to 0.98, and an equity index over 0.95.

This paper presents a Scalable Video Coding (SVC) system over multicast Software-Defined Networking (SDN), which focuses on, transmission management for the sender-receiver model. Our approach reduces bandwidth usage by allowing the receiver to select various video resolutions in a multicast group, which helps avoid a video freezing issue during bandwidth congestion. Moreover, the SVC Multiple Sessions Transmission Bandwidth thresholds Quantised Level Balance (SVC-MST BWQLB) routes different layers of the SVC stream using distinct paths and reduces storage space and bandwidth congestion problems in different video resolutions. The experimental results show that the proposed model provides better display quality than the traditional Open Shortest Path First (OSPF) routing technique. Furthermore, it reduced transmission delays by up to 66.64% for grouped resolutions compared to SVC-Single Session Transmission (SVC-SST). Additionally, the modified Real-time Transport Protocol (RTP) header and the sorting buffer for SVC-MST are proposed to deal with the defragmentation problem.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.

A number of studies have applied social network analysis (SNA) to show that the patterns of social interaction between software developers explain important organisational outcomes. However, these insights are based on a single network relation (i.e., uniplex social ties) between software developers and do not consider the multiple network relations (i.e., multiplex social ties) that truly exist among project members. This study reassesses the understanding of software developer networks and what it means for performance in software development settings. A systematic review of SNA studies between 1990 and 2020 across six digital libraries within the IS and management science domain was conducted. The central contributions of this paper are an in-depth overview of SNA studies to date and the establishment of a research agenda to advance our knowledge of the concept of multiplexity on how a multiplex perspective can contribute to a software developer's coordination of tasks and performance advantages.

The supply chains of the Canadian forest products industry are largely dependent on accurate demand forecasts. The USA is the major export market for the Canadian forest products industry, although some Canadian provinces are also exporting forest products to other global markets. However, it is very difficult for each province to develop accurate demand forecasts, given the number of factors determining the demand of the forest products in the global markets. We develop multi-layer feed-forward artificial neural network (ANN) models for demand forecasting of the Canadian forest products industry. We find that the ANN models have lower prediction errors and higher threshold statistics as compared to that of the traditional models for predicting the demand of the Canadian forest products. Accurate future demand forecasts will not only help in improving the short-term profitability of the Canadian forest products industry, but also their long-term competitiveness in the global markets.

This research introduces a hybrid honeypot architecture to bolster security within software-defined networks (SDNs). By combining low-interaction and high-interaction honeypots, the proposed solution effectively identifies and mitigates cyber threats, including port scanning and man-in-the-middle attacks. The architecture is structured into multiple modules that focus on detecting open ports using Vilhala honeypots and simulating targeted and random attack scenarios. This hybrid approach enables comprehensive monitoring and detailed packet-level analysis, providing enhanced protection against advanced online threats. The study also conducts a comparative analysis of different attack detection methods using tools like KFSensor and networking shell commands. The results highlight the hybrid honeypot system's efficacy in filtering malicious traffic and detecting security breaches, making it a robust solution for safeguarding SDNs.

Smart home devices are vulnerable to various attacks, including distributed-denial-of-service (DDoS) attacks. Current detection techniques face challenges due to nonlinear thought, unusual system traffic, and the fluctuating data flow caused by human activities and device interactions. Identifying the baseline for 'normal' traffic and suspicious activities like DDoS attacks from encrypted data is also challenging due to the encrypted protective layer. This work introduces a concept called synoptic crow search with recurrent transformer network-based DDoS attack detection, which uses the synoptic weighted crow search algorithm to capture varying traffic patterns and prioritise critical information handling. An adaptive recurrent transformer neural network is introduced to effectively regulate DDoS attacks within encrypted data, counting the historical context of the data flow. The proposed model shows effective performance in terms of low false alarm rate, higher detection rate, and accuracy.

To better explore the application of unsupervised learning neural networks in psychological interventions for college students, this study investigates the relationships among latent psychological variables from the perspective of neural networks. Firstly, college students' psychological crisis and intervention systems are analysed, identifying several shortcomings in traditional psychological interventions, such as a lack of knowledge dissemination and imperfect management systems. Secondly, employing the Human-Computer Interaction (HCI) approach, a structural equation model is constructed for unsupervised learning neural networks. Finally, this study further confirms the effectiveness of unsupervised learning neural networks in psychological interventions for college students. The results indicate that in psychological intervention for college students. Additionally, the weightings of the indicators at the criterion level are calculated to be 0.35, 0.27, 0.19, 0.11 and 0.1. Based on the results of HCI, an emergency response system for college students' psychological crises is established, and several intervention measures are proposed.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

In order to shorten the integration and sharing time of English teaching resources, a MOOC English online and offline mixed teaching resource integration model based on convolutional neural networks is proposed. The intelligent integration model of MOOC English online and offline hybrid teaching resources based on convolutional neural network is constructed. The intelligent integration unit of teaching resources uses the Arduino device recognition program based on convolutional neural network to complete the classification of hybrid teaching resources. Based on the classification results, an English online and offline mixed teaching resource library for Arduino device MOOC is constructed, to achieve intelligent integration of teaching resources. The experimental results show that when the regularisation coefficient is 0.00002, the convolutional neural network model has the best training effect and the fastest convergence speed. And the resource integration time of the method in this article should not exceed 2 s at most.

In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high.

The study aims to review the literature on mobile wallet payment and align research trends using a systematic literature review with bibliometric and network visualisation analysis over two decades. It uses bibliometric analysis of the literature research retrieved from the Web of Science database. The study period was from 2001 to 2021, with 1,134 research papers. It also provides the indicators like citation trends, cited reference patterns, authorship patterns, subject areas published on the mobile wallet, top contributing authors, and highly cited research articles using the database. Furthermore, network visualisation analysis, like the co-occurrence of author keywords and keywords plus terms, has also been examined using VOSviewer software. The bibliometric analysis shows that the Republic of China dominates mobile wallet payment, and India is a significant contributor. Furthermore, the constructions of the network map using a co-citation analysis and bibliographic coupling shows an interesting pattern of mobile wallet payment.

A hybrid network system for mitigating errors due to jamming attacks in cognitive radio networks (CRNs) is named LeNet deep stacked autoencoder (LDSAE) and is developed. In this exploration, the sensing stage and decision-making are considered. The sensing unit is composed of four steps. First, the detected signal is forwarded to filtering progression. Here, BPF is utilised to filter the detected signal. The filtered signal is squared in the second phase. Third, signal samples are combined and jamming attacks occur by including false energy levels. Last, the attack is maliciously affecting the FC decision in the fourth step. On the other hand, FC initiated the decision-making and also recognised jamming attacks that affect the link amidst PU and SN in decision-making stage and it is accomplished by employing LDSAE-based trust model where the proposed module differentiates the malicious and selfish users. The analytic measures of LDSAE gained 79.40%, 79.90%, and 78.40%.

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.