Overload Problem Research Articles

New Continual Federated Learning System for Intrusion Detection in SDN‐Based Edge Computing

ABSTRACTSoftware Defined Networking (SDN) is an open network approach that has been proposed to address some of the main problems with traditional networks. However, SDN faces cybersecurity issues. To provide a network defense against attacks, an Intrusion Detection System (IDS) needs to be updated and included into the SDN architecture on a regular basis. Machine learning methods have proved effective in detecting intrusions in SDN. Moreover, these techniques pose the problem of significant computational overload and the absence of regular updates when new cyber‐attacks appear. To address these issues, we propose a new SDN‐based cloud intrusion detection system called Continual Federated Learning (CFL). In CFL, we modify the classical federated learning process by granting a more important and dynamic role to each participating client. On the one hand, it can trigger this process whenever a new type of intrusion is detected. On the other hand, once the new model has been identified, the customer can decide whether or not to deploy it in his network. In addition, to verify the accuracy of the CFL system, we have formally specified it by a communication protocol. This specification organizes the exchanges between the different communicating entities involved in the CFL. To verify the accuracy of this specification, we described it using the PROMELA language and checked with the associated SPIN tool. On the experimental side, we deployed this specification of the CFL system in an SDN computing environment. We defined different scenarios, and we proposed that each client decides locally to deploy or not the newly obtained intrusion detection model. The decision is based on a modified metric where we integrate the severity of the intrusions. Experimental results using private local datasets show that the proposed CFL system can efficiently and accurately detect new types of intrusions while preserving client confidentiality. Thus, it can be considered a promising system for SDN‐based edge computing.

Graph and Sequential Neural Networks in Session-based Recommendation: A Survey

Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.

Research on the application of load balancing strategy in IEC61850 communication of large-scale new energy monitoring system

Abstract This paper proposes an optimization scheme to achieve high throughput and stability of the system through efficient thread management and load-balancing strategies. Firstly, based on the IEC 61850 standard framework, a distributed architecture is constructed, which uses the advantages of multi-core processors for task distribution to improve data processing capabilities and system response speed. Secondly, in terms of thread management, prioritizing key operations ensures the timely processing of important data and reduces latency. At the same time, an asynchronous processing mechanism is introduced, so that the control and data can work in parallel, avoiding the occurrence of blockage. Then, the load-balancing strategy reasonably distributes the tasks to different processing nodes, avoiding the problem of single-point overload. Finally, the data caching mechanism and high-speed data bus are specially strengthened in the design to support the efficient transmission of large amounts of real-time data.

Independence and synergy of spatial attention in the two visual systems of jumping spiders.

By selectively focusing on a specific portion of the environment, animals can solve the problem of information overload, toning down irrelevant inputs and concentrating only on the relevant ones. This may be of particular relevance for animals such as the jumping spider, which possess a wide visual field of almost 360deg and thus could benefit from a low-cost system for sharpening attention. Jumping spiders have a modular visual system composed of four pairs of eyes, of which only the two frontal eyes (the anteromedial eyes, AMEs) are motile, whereas the other secondary pairs remain immobile. We hypothesised that jumping spiders can exploit both principal and secondary eyes for stimulus detection and attentional shift, with the two systems working synergistically. In experiment 1, we investigated the attentional responses of AMEs following a spatial cue presented to the secondary eyes. In experiment 2, we tested for enhanced attention in the secondary eyes' visual field congruent with the direction of the AMEs' focus. In both experiments, we observed that animals were faster and more accurate in detecting a target when it appeared in a direction opposite to that of the initial cue. In contrast with our initial hypothesis, these results would suggest that attention is segregated across eyes, with each system working on compensating the other by attending to different spatial locations.

Modeling Emergency Traffic Using a Continuous-Time Markov Chain

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated.

Personalized multi-head self-attention network for news recommendation

With the rapid explosion of online news and user population, personalized news recommender systems have proved to be efficient ways of alleviating information overload problems by suggesting information which attracts users in line with their tastes. Exploring relationships among words and news is critical to structurally model users’ latent tastes including interested domains, while selecting informative words and news can directly reflect users’ interests. Most of the current studies do not provide an effective framework that combines distilling users’ interested latent spaces and explicit points systematically. Moreover, introducing more advanced techniques to merely chase accuracy has become a universal phenomenon. In this study, we design a Personalized Multi-Head Self-Attention Network (PMSN) for news recommendation, which combines multi-head self-attention network with personalized attention mechanism from both word and news levels. Multi-head self-attention mechanism is used to model interactions among words and news, exploring latent interests. Personalized attention mechanism is applied by embedding users’ IDs to highlight informative words and news, which can enhance the interpretability of personalization. Comprehensive experiments conducted using two real-world datasets demonstrate that PMSN efficiently outperforms state-of-the-art methods in terms of recommendation accuracy, without complicated structure design and exhausted even external resources consumption. Furthermore, visualized case study validates that attention mechanism indeed increases the interpretability.

Algorithmic Recommender Systems

Abstract Despite their ethical challenges, recommender systems (RS) are widely endorsed as a necessary solution to the problem of information overload. After clarifying how the harmful effects of information overload can be characterized in distinct ways, I explore the often overlooked potential benefits of abundant online spaces. I argue that these spaces afford valuable opportunities to experience spontaneous freedom. I then put forth a more comprehensive evaluation of the role RS should assume in algorithmically structuring the online space. This evaluation aims to reconcile the dual objectives of mitigating the harms of information overload and preserving opportunities to experience spontaneous freedom.

What motivates knowledge sharing? Evaluating the quality of answer contribution in online Q&A communities

The Online Q&A community provides platforms for Internet users to exchange and share knowledge. Its rapid development has caused the problem of information overload, which promotes users' demand for precise and personalized information. In light of this, we innovatively analyze the correlation between knowledge quality and user behaviors. First, we propose a novel approach method to evaluate the answer quality through a machine learning approach, which applies the information adoption theory to the design of a text classification model. Hereafter, with applications of motivation crowding theory, this paper introduces the classification results of answer quality by machine learning algorithms into the empirical research model to explore the factors that motivate users in both participation and high-quality user-generated content creation. Results show that both extrinsic and intrinsic factors determine the quality of knowledge contributors' answers. Further, certain external interventions (monetary rewards) can crowd out the effects of knowledge contributors’ intrinsic motivations (knowledge self-efficacy). It enriches the research on user knowledge contribution behavior in online Q&A communities and also provides theoretical guidance and suggestions for community operation.

The role of web applications in the development of multilingual competence in CLIL courses in higher education

The purpose of the work is to determine the role of web technologies to ensure the formation of multilingual competence within the CLIL approach. For this purpose, a survey was conducted among students of various specialties, however, with a special emphasis on students of technical universities. The survey was conducted among 58 students. Questionnaires were used, including questions about the use of web applications in various forms of education. The results of the survey showed the high efficiency of using web applications in CLIL courses for the formation of both language competence and subject knowledge. The CLIL method combined with modern web applications proved to be an effective tool in the students' learning process. The implementation of the program of this approach allows not only the problem of educational overload, but also the strengthening of the general development of students, including their language skills. In the conclusions, it is noted that for the successful implementation of the CLIL program, it is necessary to provide students with essential terms and to acquire a foreign language and the availability of trained teaching staff.

The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture

With the explosive growth of information on the internet, users are increasingly facing the problem of information overload, making precise news and ad recommendations an important area of research. While traditional recommendation algorithms can meet user needs to some extent, they still have limitations in dealing with complex and changing user behaviors and dynamic content environments. This paper addresses the shortcomings of existing news and ad recommendation systems by proposing an intelligent recommendation algorithm based on an end-to-end large language model architecture. Firstly, we utilize the BERT model as the foundation, leveraging its powerful text representation capabilities to achieve deep semantic understanding of news and ad content, thereby capturing more detailed content features. Secondly, we apply prompt learning to fine-tune the BERT model, designing specific prompts for the model to better understand the implicit needs and preferences of users. Finally, we integrate these steps into an end-to-end architecture, enabling the model to achieve automated learning and optimization throughout the entire process from input to output, thus improving the precision and efficiency of recommendations. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in the task of news and ad recommendation, not only enhancing the accuracy and relevance of recommendations but also effectively improving the model's interpretability and flexibility. This research explores new possibilities for the application of large language models in recommendation systems.

Distribution-aware hybrid noise augmentation in graph contrastive learning for recommendation

The recommender systems are one of the most effective big data tools for solving the information overload problem, but data sparsity greatly affects its performance. However, most of the existing graph-based contrastive learning methods perturb the original graph structure for data augmentation, which may lead to semantic loss and subsequently degrade the recommendation performance. Meanwhile, studies have shown that improving the uniformity of data distribution is more effective than data augmentation using graph perturbation. In this paper, to enhance the uniformity of data distribution, we propose Distribution-Aware Hybrid Noise augmentation in graph contrastive learning for Recommendation (DAHNRec). Specifically, we design distribution-aware hybrid noise augmentation (DAHN) method to generate noise suitable for the users and items, instead of simply utilizing random noise following uniform distributions. By applying DAHN, the model can learn the appropriate noise distribution during the training process to enhance the uniformity of the embedding space. Additionally, we propose Balanced Bayesian Personalized Ranking (BBPR) as the loss function for recommendation tasks to improve difference characterizing between positive and negative samples. This improves the ranking tasks’ performance. Furthermore, we conduct extensive experiments based on public benchmark datasets. The results indicate that DAHNRec outperforms several existing methods. Furthermore, DAHNRec is a general recommendation model based on user–item interactions, which can be used to improve the recommendation performance in various real-world scenarios, such as e-commerce platforms and social media.

MOOCs video recommendation using low-rank and sparse matrix factorization with inter-entity relations and intra-entity affinity information

PurposeThe serious information overload problem of MOOCs videos decreases the learning efficiency of the students and the utilization rate of the videos. There are two problems worthy of attention for the matrix factorization (MF)-based video learning resource recommender systems. Those methods suffer from the sparsity problem of the user-item rating matrix, while side information about user and item is seldom used to guide the learning procedure of the MF. MethodTo address those two problems, we proposed a new MOOCs video resource recommender LSMFERLI based on Low-rank and Sparse Matrix Factorization (LSMF) with the guidance of the inter-Entity Relations and intra-entity Latent Information of the students and videos. Firstly, we construct the inter-entity relation matrices and intra-entity latent preference matrix for the students. Secondly, we construct the inter-entity relation matrices and intra-entity affinity matrix for the videos. Lastly, with the guidance of the inter-entity relation and intra-entity affinity matrices of the students and videos, the student-video rating matrix is factorized into a low-rank matrix and a sparse matrix by the alternative iteration optimization scheme. ConclusionsExperimental results on dataset MOOCcube indicate that LSMFERLI outperforms 7 state-of-the-art methods in terms of the HR@K and NDCG@K(K = 5,10,15) indicators increased by an average of 20.6 % and 21.0 %, respectively.

A Hybrid News Recommendation Approach Based on Title–Content Matching

Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news articles that users have historically browsed to construct user interest models. However, this method ignores the phenomenon of “title–content mismatching” in news articles, which leads to the lack of precision in user interest modeling. Therefore, a hybrid news recommendation method based on title–content matching is proposed in this paper: (1) An interactive attention network is employed to model the correlation between title and content contexts, thereby enhancing the feature representation of both; (2) The degree of title–content matching is computed using a Siamese neural network, constructing a user interest model based on title–content matching; and (3) neural collaborative filtering (NCF) based on factorization machines (FM) is integrated, taking into account the perspective of the potential relationships between users for recommendation, leveraging the insensitivity of neural collaboration to news content to alleviate the impact of title–content mismatching on user feature modeling. The proposed model was evaluated on a real-world dataset, achieving an nDCG of 83.03%, MRR of 81.88%, AUC of 85.22%, and F1 Score of 35.10%. Compared to state-of-the-art news recommendation methods, our model demonstrated an average improvement of 0.65% in nDCG and 3% in MRR. These experimental results indicate that our approach effectively enhances the performance of news recommendation systems.

Recommender Systems for Teachers: A Systematic Literature Review of Recent (2011–2023) Research

Recommender Systems (RSs) have recently emerged as a practical solution to the information overload problem users face when searching for digital content. In general, RSs provide their respective users with specialized advice and guidance in order to make informed decisions on the selection of suitable digital content. This paper is a systematic literature review of recent (2011–2023) publications on RSs designed and developed in the context of education to support teachers in particular—one of the target groups least frequently addressed by existing RSs. A body of 61 journal papers is selected and analyzed to answer research questions focusing on experimental studies that include RS evaluation and report evaluation results. This review is expected to help teachers in better exploiting RS technology as well as new researchers/developers in this field in better designing and developing RSs for the benefit of teachers. An interesting result obtained through this study is that the recent employment of machine learning algorithms for the generation of recommendations has brought about significant RS quality and performance improvements in terms of recommendation accuracy, personalization and timeliness.

Guest Editorial for the Special Issue “New Trends in Algorithms for Intelligent Recommendation Systems”

Currently, the problem of information overload, a term popularized by Alvin Toffler in his book Future Shock [...]

CTITF: A tensor factorization model with constrained bidirectional user trust and implicit feedback for context-aware recommender systems

Recommender systems offer an efficient solution to the problem of information overload, which is exacerbated by the rapid expansion of data. Context-aware recommender systems (CARS) have become prominent, incorporating contextual information to provide more desirable recommendations. While the dimension of contextual information rises, the complexity of algorithms increases as well, highlighting the data sparsity problem. In this paper, we propose CTITF, a novel tensor factorization model that incorporates constrained user bidirectional trust and implicit feedback. First, CTITF distinguishes the roles of trust networks in terms of the directionality of trust relationships: the trustor and the trustee. CTITF then constructs trust influences and biases for both roles to linearly represent user preferences. Furthermore, CTITF improves the rating prediction function and recommendation objective function based on CP factorization. Finally, CTITF filters out trust relationships where users do not have the same history items. It also uses contextual information to limit the amount of implicit feedback, addressing the issue of high time complexity. Experiments on two real-world datasets illustrate that the proposed model outperforms previous TF-based models and social recommender systems in terms of rating prediction accuracy. In addition, the CTITF model reduces the training time by 50.28% compared to the trustTF model, which also uses trust.

EFEKTIVITAS PENDIDIKAN KESEHATAN MENGGUNAKAN VIDEO TERHADAP KEPATUHAN PEMBATASAN CAIRAN PADA PASIEN HEMODIALISA DI KLINIK PANDAONI MEDIKA JAKARTA

Chronic kidney disease is a metabolic disease that can be seen in a decrease in kidney function. Patients with chronic kidney failure who do hemodialysis have a lot of fluid overload problems that cause an increase in IDWG. Patients who have excess fluids need to limit fluids. One effort to increase adherence is to provide education in advance to increase knowledge about the disease by providing health education using video. The purpose of this study was to determine the effectiveness of health education using video on compliance with fluid restrictions in hemodialysis patients at the Klinik Pandaoni Medika Jakarta. This study used a quantitative approach with a quasi-experimental design with two control groups for pre- and post-tests. The sampling technique used was purposive sampling on a total sample of 78 respondents. The analysis used is a paired sample t-test and an independent t-test. After the intervention was carried out and the results obtained, the intervention group experienced an average decrease indicating the influence of health education using video (p = 0.000), while in the control group the average compliance value increased with a value of p = 0.002, which indicated the influence of health education using video with a bad influence on the level of adherence to fluid restriction in the control group. There were differences in adherence to fluid restriction in the intervention group and the control group (p = 0.000).

Experimental and theoretical research on the temperature evolution law of overcurrent fault wires

AbstractTo investigate the problems of overload and excessive thermal insulation associated with building electrical fires caused by wires, a theoretical model of wire heat transfer is established, and the pyrolysis and combustion phenomena of the insulation layer are analyzed. The results showed that the temperature evolution of the wire underwent three stages: constant temperature, insulation heating, and high‐temperature pyrolysis. The insulation layer experiences bulging, exhausting, carbonization, dripping, and burning in sequence, and insulation layer dripping requires at least 160 A of current. As the current increases, the temperature increase rate of the wire increases gradually, and the fusing time of the wire gradually decreases. Under the same current, 160°C is the turning point at which the temperature increases. The temperature increase rate of the copper wire is greater than that of the aluminum alloy wire, and the temperature increase rate of the bare wire is greater than that of the insulated wire. The fusing time of an aluminum alloy wire is less than that of a copper wire, and the fusing time of a bare wire is less than that of an insulated wire. The research results provide theoretical guidance for the prevention and investigation of building electrical fires.

A Blocking Method for Overload-Dominant Cascading Failures in Power Grid Based on Source and Load Collaborative Regulation

Adjusting generator output and cutting off load can effectively solve the problem of continuous overload and disconnection of power lines caused by power flow transfer. To suppress large-scale power outages caused by overload-dominant cascading failures, a blocking method for overload-dominant cascading failures in the power grid based on source and load collaborative regulation is proposed. The shortest path algorithm was used to identify loads and generators with shorter electrical distances from overloaded lines. Under the premise of meeting the static safety constraints of the power grid, considering the regulation of generator output and the removal of interruptible loads, a multiobjective cascading failure blocking model is established with the minimum overall control cost of the system and the minimum probability coefficient of line failure. Use the NSGA-II algorithm to solve the model and obtain the optimal plan for adjusting generator output and cutting off load. Through example verification, the proposed method can effectively alleviate the phenomenon of line overload, thereby blocking the continuation of overload-dominant cascading failures.

Collaborative scheduling method of active-reactive power for rural distribution systems with a high proportion of renewable energy

Large-scale distributed renewable energy connected to the rural distribution network has given birth to a new rural distribution system with a high proportion of new energy typical characteristics, and the optimal scheduling of the new rural distribution system has become an important issue to ensure the safe and stable operation of the power grid. This paper proposes a method of active-reactive power collaborative optimization scheduling for rural power distribution system with a high proportion of renewable energy. Firstly, the active support capability evaluation model is established, and the active power support capability and reactive power support capability of rural power distribution system are quantitatively evaluated, which provides data basis and boundary conditions for the scheduling part. Then, considering power-loss cost, distribution network operation cost, and penalty cost, a method of active-reactive power collaborative optimization scheduling for rural power distribution systems with a high proportion of renewable energy is proposed. Finally, the active support capability evaluation and regulation platform of the rural power distribution system is built to provide technical support services for the safe and stable operation of the rural power distribution system. Given the problems of overload and overvoltage faced by rural power distribution systems with a high proportion of renewable energy, this paper aims to solve the key technical challenges of optimization and regulation of new rural power distribution systems. The results show that the optimized control method proposed in this paper has better security and economy, and is conducive to promoting the construction and operation of the new rural power distribution system.