Update Rule Research Articles

Cyber Security Threats to Iran and its Countermeasures: Defensive and Offensive Cyber Strategies

Iran's convoluted geopolitical status, its centrality to locale-specific wars, plus the shifting balance of the global order make cyber security a key issue worldwide in addition to traditional security challenges. Iran is an appealing target for cyber enemies due to its numerous security vulnerabilities, which include ransomware infection, scams, as well as complicated malware. Iran has taken a flexible stance, via collaboration, authorized, and intellectual tactics to counter these concerns. Forming cyber security teams, utilizing the latest innovations, and hiring competent employees are all examples of efforts in cyber defense. Legislative structures are essential for bringing criminals before the law and discouraging unlawful behaviour, but the dynamic nature of cyber threats makes it difficult to update rules. The goal is to keep information security and safety measures under check. The identification and mitigation of cyber dangers necessitate collaboration with various nations, global organizations, and corporations. Iran's cyberspace involvement shapes societal sentiment and upholds the will of the people, with both advantageous and hazardous repercussions. To bolster its application and balance its powerful adversaries, it employs cyber assaults in conjunction with conventional asymmetrical combat strategies. What is Iran's main cyber security concerns and challenges, and how is Iran addressing as well as tackling them, is the question that this study aims to answer.

Anchor-guided global view reconstruction for multi-view multi-label feature selection

In multi-view multi-label learning (MVML), the accuracy of feature weights is pivotal for establishing feature order. However, conventional MVML methods often struggle with integrating distinct information from multiple views effectively, leading to unclear segmentation and potential noise introduction. To address this challenge, this paper proposes an anchor-based latent representation method for global view learning in MVML. Specifically, we encode inherent information from each view to derive a candidate multi-view representation. Anchors extracted from both the candidate view and the global view are then constrained to approximate equality in the latent space. Furthermore, a carefully designed view matrix serves as supplement, seamlessly integrated into the reconstruction process to augment information. The convergence of results is subsequently validated using multiplicative update rules. Experimental findings showcase the superior performance of our proposed method across various multi-view datasets.

Alternating minimization for generalized rank-1 matrix sensing: sharp predictions from a random initialization

Abstract We consider the problem of estimating the factors of a rank-$1$ matrix with i.i.d. Gaussian, rank-$1$ measurements that are nonlinearly transformed and corrupted by noise. Considering two prototypical choices for the nonlinearity, we study the convergence properties of a natural alternating update rule for this non-convex optimization problem starting from a random initialization. We show sharp convergence guarantees for a sample-split version of the algorithm by deriving a deterministic one-step recursion that is accurate even in high-dimensional problems. Notably, while the infinite-sample population update is uninformative and suggests exact recovery in a single step, the algorithm—and our deterministic one-step prediction—converges geometrically fast from a random initialization. Our sharp, non-asymptotic analysis also exposes several other fine-grained properties of this problem, including how the nonlinearity and noise level affect convergence behaviour. On a technical level, our results are enabled by showing that the empirical error recursion can be predicted by our deterministic one-step updates within fluctuations of the order $n^{-1/2}$ when each iteration is run with $n$ observations. Our technique leverages leave-one-out tools originating in the literature on high-dimensional $M$-estimation and provides an avenue for sharply analyzing complex iterative algorithms from a random initialization in other high-dimensional optimization problems with random data.

Two-stage strategy update rule based on learning cost in weak prisoner's dilemma.

When players are dissatisfied with their actual payoffs, they will change the actuality by learning strategy of neighbors. The more effort players put in, the more likely they are to succeed in learning. Inspired by this, this paper proposes a two-stage strategy update rule based on learning cost. The players first decide whether to learn strategy according to the updating willingness. If the players imitate the strategy of neighbors, they need to pay the learning cost. Results show that for the well-mixed population, if the updating willingness is homogeneous and remains unchanged, reducing the updating willingness or increasing the learning cost can extend the life cycle of cooperators. If the updating willingness is heterogeneous and dynamically adjusted based on the difference between the actual payoff and the expected payoff, increasing aspiration value and learning cost promotes cooperation. For the structured population, if the updating willingness is homogeneous and remains unchanged, the moderate learning cost is beneficial for cooperators to resist the temptation of defection, and reducing updating willingness makes the system maintain cooperation within a larger parameter range. If the updating willingness is heterogeneous and dynamically adjusted, the larger learning cost and the appropriate aspiration value promote cooperation. This study highlights the complex dynamics of cooperation in paid strategy learning, contributing to the theory of cooperation in the evolutionary game.

Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty

This research aims to develop an enhanced Fuzzy Logic Framework for Financial Distress Prediction to improve corporate decision-making under uncertainty. The primary objective is to address limitations in traditional fuzzy logic models, such as static rule bases and lack of adaptability to dynamic financial conditions. To achieve this, a time-dependent fuzzy logic system is proposed, incorporating real-time financial data and adaptive learning mechanisms to improve predictive accuracy over time. The research design involves creating a dynamic fuzzy rule base, assigning weights to rules based on predictive performance, and optimizing membership functions and rule weights using real-time data. The methodology applies the proposed framework to financial indicators such as liquidity, profitability, and leverage, with a numerical example demonstrating the system's effectiveness in predicting financial distress. The results show that the model can accurately predict financial distress levels, with a predicted distress value of 0.588 compared to an actual value of 0.6. The model’s ability to update rule weights and optimize predictions over time represents a significant improvement over static fuzzy logic models. This research fills a critical gap in financial distress prediction by introducing a dynamic, adaptive fuzzy logic framework that evolves with real-time data. The model offers significant implications for both academics and industry, providing a tool for more accurate risk assessment in volatile financial environments. However, further research is needed to refine the model’s computational efficiency and test its long-term predictive capabilities across different industries

Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

Designing Decentralized Systems with High Survivability Inspired by Altruistic Social Interactions of Vampire Bats

Altruism is a key concept in the design of decentralized systems with high survivability. We focus on a community of vampire bats to reveal how intra-group altruism produces group-wide survivability. Although these bats die within three days if food is unavailable, they can survive for over 10 years by developing a highly sophisticated social community in which they share food. This food-sharing behavior occurs not only among blood relatives, but also among unrelated individuals through self-organizing social relationships based on grooming behavior. We propose a simple network model that focuses on the relationship between food sharing and grooming. We performed simulations under periodic, stationary, and irregular feeding environments, and found that suitable update rules for social relationships depend on the type of environment. Our findings provide insights into how decentralized systems with high survivability can be designed based on altruism.

Two-dimensional data partitioning for non-negative matrix tri-factorization

As a two-sided clustering and dimensionality reduction paradigm, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention in machine learning and data mining researchers due to its excellent performance and reliable theoretical support. Unlike Non-negative Matrix Factorization (NMF) methods applicable to one-sided clustering only, NMTF introduces an additional factor matrix and uses the inherent duality of data to realize the mutual promotion of sample clustering and feature clustering, thus showing great advantages in many scenarios (e.g., text co-clustering). However, the existing methods for solving NMTF usually involve intensive matrix multiplication, which is characterized by high time and space complexities, that is, there are limitations of slow convergence of the multiplicative update rules and high memory overhead. In order to solve the above problems, this paper develops a distributed parallel algorithm with a 2-dimensional data partition scheme for NMTF (i.e., PNMTF-2D). Experiments on multiple text datasets show that the proposed PNMTF-2D can substantially improve the computational efficiency of NMTF (e.g., the average iteration time is reduced by up to 99.7% on Amazon) while ensuring the effectiveness of convergence and co-clustering.

A Review of Improved Methods for Ant Colony Optimization in Path Planning

_ Ant colony optimization is an intelligent evolutionary algorithm that has been widely used in the field of path planning, and the research on its improved methods is proliferating. In this paper, the improvement methods of single and hybrid ant colony optimization in path planning are reviewed in terms of initial pheromone concentration, heuristic function, state transition rule, pheromone update rule, and parameter optimization, respectively. The future research directions of ant colony optimization have been prospected in terms of theoretical aspects, parameter setting and optimization, integration with other algorithms, and multiobjectives. Keywords ant colony optimization; path planning; single ant colony optimization; hybrid ant colony optimization; improvement methods

Elementary Cellular Automata with Memory

Standard cellular automata (CA) are ahistoric (memoryless), that is, the new state of a cell depends on the neighborhood configuration of only the preceding time step. This article introduces an extension to the standard framework of CA by considering automata with memory capabilities. While the update rules of the CA remain unaltered, a history of all past iterations is incorporated into each cell by a weighted mean of all its past states. The historic weighting is defined by a geometric series of coefficients based on a memory factor (alpha). A study is made of the effect of historic memory on the spatio-temporal and difference patterns of elementary (one-dimensional, two states, nearest neighbors) CA starting with states assigned at random.

Efficient Resource Management in Cloud Environments: A Modified Feeding Birds Algorithm for VM Consolidation

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers.

Stream label distribution learning processing via broad learning system

Label distribution learning (LDL) is designed for label ambiguity problem which widely exists in tasks like image classification and sentiment analysis. Nevertheless, most existing LDL methods adopt a batch-wise processing strategy that is not applicable to stream data issues, like video semantic segmentation and object tracking in auto-driving applications. For these scenarios where dynamic new data is generated continuously, stream LDL processing can be applied to reflect the changes in time and improve the accuracy of online prediction. This paper proposes a novel broad learning system (BLS) based online label distribution learning framework (SLD_BLS). Broad learning system was adopted as the baseline model because its incremental weight updating scheme, excellent efficiency, and outstanding performance can process streaming data in dynamically changing environments. However, there are several challenges to adapt BLS for LDL: 1) BLS's update rule cannot process LDL data; 2) BLS cannot show the mapping relationship between feature space and label space; 3) BLS neglects the inter-label relationships. To tackle these challenges, 1) a new weight update rule for LDL is designed; 2) a manifold regularization is applied to exploit the feature space manifolds; 3) an explicit label collaborating approach is introduced to positively bootstrapping the model training. Extensive experiments on 13 label distribution datasets indicated the performance of SLD_BLS outperforms 9 state-of-the-art LDL models on most datasets with significant reductions in training time across 6 metrics.

Developing Countries and Joint Statement Initiatives at the WTO: Damned if You Join, Damned if You Don't?

ABSTRACTLimited progress in World Trade Organization (WTO) multilateral trade negotiations has led to calls to expand plurilateral processes through which groups of member states can negotiate new agreements. These calls have manifested in Joint Statement Initiatives (JSIs), covering issues such as investments and e‐commerce. To their proponents, JSIs offer a path to update rules and move towards flexible multilateralism. However, critics argue that JSIs marginalize developing countries and reinforce the dominance of advanced economies. Notwithstanding this criticism, a growing number of developing countries are joining these initiatives. Through data collected from in‐depth interviews with officials from 60 WTO member states, this article examines the drivers for growing JSI membership among developing countries. It illustrates that many are joining JSIs to avoid being excluded from agreements that might become binding in the future. While such membership is driven by fear of exclusion and the preference to ‘be in the room’, the negotiation processes of the JSIs often mean that developing countries have little actual influence over the negotiation outcomes. The article concludes that JSIs represent a significant change in the organization of multilateral trade negotiations and enable larger economies to restore their ‘go‐it‐alone power’ in multilateral trade governance.

Hierarchical Noncooperative Dynamical Systems Under Intragroup and Intergroup Incentives

A framework for hierarchical noncooperative systems with dynamic agents is proposed. In the characterized framework, agents in each group are incentivized by a corresponding group manager who represents the benefits of group utility via an intra-group incentive mechanism. The coefficients in intra-group incentive functions are characterized as the group manager's strategy in this paper. The update rules that can be adopted by the group managers are proposed based on the local state and the payoff information with continual and intermittent observation on the state of the agents from the other groups. Sufficient conditions under which the trajectory of agents' state converges towards the group Nash equilibrium are derived for the proposed update rules. Furthermore, to improve the social welfare of the entire system, we propose an inter-group incentive scheme in the group managers level for a system governor to bring agents' state to a target equilibrium. To deal with the uncertain information on agents' personal payoff functions for the system governor, we present sufficient conditions to guarantee the convergence of agents' state to the target equilibrium. Three numerical examples are provided to illustrate the efficacy of our approach.

D-Bot: Database Diagnosis System using Large Language Models

Database administrators (DBAs) play an important role in managing database systems. However, it is hard and tedious for DBAs to manage vast database instances and give timely response (waiting for hours is intolerable in many online cases). In addition, existing empirical methods only support limited diagnosis scenarios, which are also labor-intensive to update the diagnosis rules for database version updates. Recently large language models (LLMs) have shown great potential in various fields. Thus, we propose D-Bot , an LLM-based database diagnosis system that can automatically acquire knowledge from diagnosis documents, and generate reasonable and well-founded diagnosis report (i.e., identifying the root causes and solutions) within acceptable time (e.g., under 10 minutes compared to hours by a DBA). The techniques in D-Bot include ( i ) offline knowledge extraction from documents, ( ii ) automatic prompt generation (e.g., knowledge matching, tool retrieval), ( iii ) root cause analysis using tree search algorithm, and ( iv ) collaborative mechanism for complex anomalies with multiple root causes. We verify D-Bot on real benchmarks (including 539 anomalies of six typical applications), and the results show D-Bot can effectively identify root causes of unseen anomalies and significantly outperforms traditional methods and vanilla models like GPT-4.

Distributed multi-robot source term estimation with coverage control and information theoretic based coordination

In this paper, we introduce a novel coordination strategy for a group of autonomous robots tasked with estimating the source term of an airborne chemical release. This strategy integrates distributed Bayesian filtering, coverage control, information-theoretic sampling, and proximity constraint handling, forming an efficient and fully distributed coordination protocol. In the proposed framework, each robot employs a consensus-based belief update rule, allowing it to adaptively incorporate information from neighbouring robots to ensure a unified belief across the network. The overall control action is designed to maximise information gain while maintaining network connectivity and minimising communication requirements during movement between sampling points. Extensive numerical simulations are conducted to analyse the performance of the proposed strategy, which demonstrate significant performance improvements compared to popular filtering practices and advanced path-planning strategies. The simulation study is also designed to substantiate the design choices of the proposed coordination strategy and to emphasise its advantages.

РОЗРОБКА ЕФЕКТИВНОГО ТА ТОЧНОГО МЕТОДУ ВІДСТЕЖЕННЯ ОБʼЄКТІВ НА ВІДЕО НА МОБІЛЬНИХ ПРИСТРОЯХ

One of the top research topics in computer vision area is visual object tracking. Main goal is to obtain the target object’s location in the first video frame of a given video sequence. The recent innovations of deep neural networks, specifically Siamese networks has significant impact on visual object tracking. In spite of high accuracy and high results in academic benchmarks, there are drawbacks in current state-of-the-art approaches in particular compute-intensive and large memory footprint that cannot satisfy the performance requirements of real-world applications. The aim of this paper is to design a new lightweight framework for resource-efficient and accurate visual object tracking. To add, a new tracker of efficiency benchmark and protocol were introduced. Efficiency is defined in terms of both energy consumption and execution speed on edge devices. New dual template representation for object model adaptation was developed. The first template, static, fixes the original visible appearance and thus prevents deviation and, as a result, failures caused by adaptation. The other is dynamic; the state reflects the current conditions of assembly and the appearance of its object. Unlike STARK, which incorporates additional timing information by introducing a separate estimation prediction head, we introduce parameter-free module similarity as a template update rule optimized from the latest network. We show that the learned convex combination of two patterns is effective for tracking on multiple tests. A lightweight tracker was proposed, which includes functions, dual representation of patterns and pixel-by-pixel merged blocks in its compact network. The resulting FEAR-XS tracker runs at 205 FPS on the iPhone 11, which is 4.2 times faster than LightTrack and 26.6 times faster than Ocean, with high accuracy on many tests – no state-of-the-art tracker is more accurate and faster than any FEAR tracker. In addition, the algorithm is highly energy efficient.

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm.

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

Mixed-integer quadratic optimization and iterative clustering techniques for semi-supervised support vector machines

Among the most famous algorithms for solving classification problems are support vector machines (SVMs), which find a separating hyperplane for a set of labeled data points. In some applications, however, labels are only available for a subset of points. Furthermore, this subset can be non-representative, e.g., due to self-selection in a survey. Semi-supervised SVMs tackle the setting of labeled and unlabeled data and can often improve the reliability of the results. Moreover, additional information about the size of the classes can be available from undisclosed sources. We propose a mixed-integer quadratic optimization (MIQP) model that covers the setting of labeled and unlabeled data points as well as the overall number of points in each class. Since the MIQP’s solution time rapidly grows as the number of variables increases, we introduce an iterative clustering approach to reduce the model’s size. Moreover, we present an update rule for the required big-M values, prove the correctness of the iterative clustering method as well as derive tailored dimension-reduction and warm-starting techniques. Our numerical results show that our approach leads to a similar accuracy and precision than the MIQP formulation but at much lower computational cost. Thus, we can solve larger problems. With respect to the original SVM formulation, we observe that our approach has even better accuracy and precision for biased samples.

Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates.

Deep neural feedforward networks are effective models for a wide array of problems, but training and deploying such networks presents a significant energy cost. Spiking neural networks (SNNs), which are modeled after biologically realistic neurons, offer a potential solution when deployed correctly on neuromorphic computing hardware. Still, many applications train SNNs offline, and running network training directly on neuromorphic hardware is an ongoing research problem. The primary hurdle is that back-propagation, which makes training such artificial deep networks possible, is biologically implausible. Neuroscientists are uncertain about how the brain would propagate a precise error signal backward through a network of neurons. Recent progress addresses part of this question, e.g., the weight transport problem, but a complete solution remains intangible. In contrast, novel learning rules based on the information bottleneck (IB) train each layer of a network independently, circumventing the need to propagate errors across layers. Instead, propagation is implicit due the layers' feedforward connectivity. These rules take the form of a three-factor Hebbian update a global error signal modulates local synaptic updates within each layer. Unfortunately, the global signal for a given layer requires processing multiple samples concurrently, and the brain only sees a single sample at a time. We propose a new three-factor update rule where the global signal correctly captures information across samples via an auxiliary memory network. The auxiliary network can be trained a priori independently of the dataset being used with the primary network. We demonstrate comparable performance to baselines on image classification tasks. Interestingly, unlike back-propagation-like schemes where there is no link between learning and memory, our rule presents a direct connection between working memory and synaptic updates. To the best of our knowledge, this is the first rule to make this link explicit. We explore these implications in initial experiments examining the effect of memory capacity on learning performance. Moving forward, this work suggests an alternate view of learning where each layer balances memory-informed compression against task performance. This view naturally encompasses several key aspects of neural computation, including memory, efficiency, and locality.