Critical Nodes Evaluation in Large-Scale Software Based on Static Structure and Runtime Information

Visual information consists of static and dynamic properties. How is their representation organized in the visual system? Static information has been associated with ventral temporal regions while dynamic information with lateral and dorsal regions. Investigating the representation of static and dynamic information is complicated by the correlation between static and dynamic information within continuous visual input. Here, we used two-stream deep convolutional neural networks (DCNNs) to separate static and dynamic features in quasi-naturalistic videos and to investigate their neural representations. The first DCNN stream was trained to represent static features by recognizing action labels using individual video frames, and the second DCNN stream was trained to encode dynamic features by recognizing actions from optic flow information that describes changes across different frames. To investigate the representation of these different types of features in the visual system, we used representational similarity analysis to compare the neural network models to the neural responses in different visual pathways of 14 human participants (six females). First, we found that both static and dynamic features are encoded across all visual pathways. Second, we found that distinct visual pathways represent overlapping as well as unique static and dynamic visual information. Finally, multivariate analysis revealed that ventral and dorsal visual pathways share a similar posterior-to-anterior gradient in the representation of static and dynamic visual features.

Consistency-constrained unsupervised video anomaly detection framework based on Co-teaching

With the continuous development of the smart grid, the power wireless private communication network carries more and more services, and the network structure is becoming more and more complex. In order to accurately identify the critical nodes in the power wireless private communication network, this paper studies the critical node identification of communication networks based on complex network theory. Firstly, the network model is constructed based on the network topology of the power wireless private communication network, and the degree centrality and the betweenness centrality of the node are analyzed and used as node evaluation index. The information entropy weight method is used to calculate the objective weight of the evaluation index, and the importance degree of each node is calculated by the TOPSIS method to complete the critical node identification of the power wireless private communication network. The simulation results show that the critical node identification method of power wireless private communication network based on complex network theory can provide technical support for improving the operation and management of power wireless private communication network. At the same time, by setting redundant backup settings and security protection for critical nodes, the network efficiency and reliability of the power wireless private communication network can be improved.

In order to pursue their objectives, the groups of agents seem to be well organized according to predefined structures and unambiguously defined concepts. At the same time, the interaction presents some dynamic aspects, that is, the virtual organizations (VO) undergo a life cycle from the objectives formation to the task division, task allocation, plans formation, negotiations, contracts formation, and the final VO dissolvement or evolvement under exceptions. Although E-institutions provide facilitation to the former issues, the facilitation of VO in different stages is still blurred. Hence, this paper proposes a hybrid E-institution model including the static organization structure aspect and the dynamic aspect, provide assistance to the agent interactions. On the one hand, the E-institutions are modelled statically to make the agent cooperation trustable and predictable in terms of ontologies, behavior norms, and organization structure. On the other hand, the E-institutions keep repositories of instantiated (dynamic) information, provide services to facilitate the dynamic VO in different stages. Also, this paper presents explicitly formal descriptions of the static organization structure, dynamic information and institutional services to different VO stages.

There is limited understanding of the information captured by deep spatiotemporal models in their intermediate representations. For example, while evidence suggests that action recognition algorithms are heavily influenced by visual appearance in single frames, no quantitative methodology exists for evaluating such static bias in the latent representation compared to bias toward dynamics. We tackle this challenge by proposing an approach for quantifying the static and dynamic biases of any spatiotemporal model, and apply our approach to three tasks, action recognition, automatic video object segmentation (AVOS) and video instance segmentation (VIS). Our key findings are: (i) Most examined models are biased toward static information. (ii) Some datasets that are assumed to be biased toward dynamics are actually biased toward static information. (iii) Individual channels in an architecture can be biased toward static, dynamic or jointly encode a combination static and dynamic information. (iv) Most models converge to their culminating biases in the first half of training. We then explore how these biases affect performance on dynamically biased datasets. For action recognition, we propose StaticDropout, a semantically guided dropout that debiases a model from static information toward dynamics. For AVOS, we design a better combination of fusion and cross connection layers compared with previous architectures.

Prefabricated buildings face greater and different prefabricated components throughout their entire lifecycle, leading to a significant increase in management difficulty. This article proposes an evaluation method based on a multi-layer complex network model combined with an improved topsis. Firstly, by combining relevant regulations, literature, and engineering experience, the factors affecting the connection of components in prefabricated building are identified and the relationships between these factors are clarified to construct a multi-layered complex network model. Secondly, complex network theory is applied to calculate and analyze the importance evaluation indicators of the model nodes. Finally, the nodes are evaluated using the entropy weight optimization topsis method, and key nodes are selected based on the comprehensive importance evaluation value, and simulation verification is carried out by attacking the nodes. A specific model is constructed and analyzed using a building in Shenzhen, Guangdong Province as an example. The study shows that:(1) By analyzing key importance evaluation metrics such as node degree, betweenness centrality, and closeness centrality, the critical nodes identified for the project are "G2," "G1," "S24," and "Q1"; (2) According to the comprehensive evaluation results using the improved topsis method, quality issues are the core cause of connection problems in prefabricated components, with the construction phase being the peak period for such issues; (3) The critical nodes play a significant role in maintaining the coordination and robustness of multi-layer complex networks, and the failure of these critical nodes undermines the network's cohesion and synergy. This study provides new insights and methods for the evaluation and management of prefabricated construction.

River drainage networks are important landscape features that have been studied for several decades from a range of geomorphological and hydrological perspectives. However, identifying the most vital (critical) nodes on river networks and analyzing their relationships with geomorphic and climatic properties have not yet been extensively addressed in the literature. In this study, we use an algorithm that determines the set of critical nodes whose removal results in maximum network fragmentation and apply it to various topologies of simulated and natural river networks. Specifically, we consider simulated river networks obtained from optimal channel network (OCN) approach as well as extracted networks from several natural basins across the United States. Our results indicate a power-law relationship between the number of connected node pairs in the remaining network and the number of removed critical nodes. We also investigate the characteristics of sub-basins resulted from the removal of critical nodes and compare them with those of central nodes (in the context of betweenness centrality) for both natural basins and OCNs with varying energy exponent γ to understand vulnerability and resilience of river networks under potential external disruptions.

Fog computing as an extension of the cloud based infrastructure, provides a better computing platform than cloud computing for mobile computing, Internet of Things, etc. One of the problems is how to make full use of the resources of the fog so that more requests of applications can be executed on the edge, reducing the pressure on the network and ensuring the time requirement of tasks. The high mobility of fog nodes also has a great impact on the task completion time and user satisfaction. Thus, a general IoT-Fog-Cloud computing architecture with a contract-based resource sharing mechanism is proposed in this paper. The contract establishment problem of resource sharing mechanism among fog clusters is modeled as a sealed-bid bilateral auction in order to take full advantage of the fog resources and ensure that more tasks could be executed on the fog. Then, we propose a scheduling method based on functional domain construction to mitigate the influence of mobility of fog nodes. It includes the selection of critical fog nodes and the construction of fog function domains based on spectral clustering. The selection of critical fog nodes is used to find the best fog nodes in each fog cluster with respect to the betweenness centrality, computing performance and communication delay to the IoT nodes. The critical nodes are responsible for building the functional domains of the remaining fog nodes in each fog cluster. Functional domain construction is used to determine the set of fog nodes contained in the corresponding functional domain. Finally, through extensive simulation experiments, the performance difference between the proposed method and the other four methods in terms of average service time, average utilization of fog nodes, success rate of tasks, average WLAN delay and the average cost of successful tasks are evaluated. Results show that our method generally outperforms the other four methods in these metrics.

The study investigated the effects of position (upper, below, right-hand, and left-hand side of the screen) for VDT dynamic display and other factors including speed (150, 200, 250, and 300 wpm), display type of dynamic information (leading, flash and RSVP), , and polarity combination of dynamic/static(black-on-white/black-onwhite, white-on-black/white-on-black, black-onwhite/ white-on-black, and white-on-black/black-on-white) on subjects’ comprehension for reading static and dynamic display information. The result of this research showed that position and type of dynamic information had no significant effect on subjects’ comprehension for dynamic and static information. Polarity combination and speed also had no significant effect on subjects’ comprehension for static information; however, the two factors had significant effects on subjects’ comprehension for dynamic information. When display speed was set at 200 wpm, subjects’ comprehension was better than with at speed settings. When the dynamic/static display was set to be the same polarity combination (black-on-white/black-onwhite), subjects’ performance was better than other polarity combinations.

Moving to smaller libraries can be considered as a relevant task when porting software systems to limited-resource devices (e.g., hand-helds). Library miniaturization will be particularly effective if based on both dynamic (keeping into account dependencies exploited during application execution in a given user profile) and static (keeping into account all possible dependencies) information. This paper presents distributed software architecture, based on Web services, to collect dynamic information at run-time, and an approach for miniaturization of libraries, exploiting both dynamic and static information with the aim of reducing the memory requirements of executables. New, smaller libraries are identified via hierarchical clustering and genetic algorithms; clustering produces a first initial solution, then optimized by multi-objective genetic algorithms. The approach has been applied to medium size open source software systems such as Samba and MySQL, allowing to effectively produce smaller, loosely coupled libraries, and to reduce the memory requirements of each application.

With the increase of software system complexity, the traditional measurements can not meet the requirements, for the reason that the developers need control the software quality effectively and guarantee the normal operation of software system. Hence how to measure the complexity of large-scale software system has been a challenge problem. In order to solve this problem, the developers have to obtain a good method to measure the complexity of software system first. Only through this work, the software quality and the software structure could be controlled and optimized. Note that the complex network theory has offered a new theoretical understanding and a new perspective to solve this kind of complexity problem, this work discusses the complexity phenomenon in large-scale software system. Based on this, some complexity measurements of large-scale software system are put forward from static structure and dynamic structure perspectives. Furthermore, we find some potential complexity characteristics in large-scale software networks through the numerical simulations. The proposed measurement methods have a guiding significance on the development for today's large-scale software system. In addition, this paper presents a new technique for the structural complexity measurements of large-scale software system

In the context of digital transformation driven by information technology, traditional project approval processes often fail to meet the efficiency demands of modern enterprises due to complex procedures, poor information flow, and excessive manual intervention. Existing studies show limitations in identifying critical nodes and coordinating resource and time optimization, and they lack effective mechanisms for dynamic optimization under uncertainty. To address these challenges, this paper proposes a comprehensive optimization method for project approval processes based on critical node identification and resource optimization. The approval process is modeled as a directed acyclic graph (DAG) through the construction of a timelabeled network data structure. Forward and backward time calculations combined with float time analysis are applied to identify critical paths and nodes. A multi-dimensional evaluation model is developed to assess node importance for accurate critical node detection. Furthermore, a multi-level resource management strategy is designed to balance workloads by adjusting non-critical activities and optimizing resource utilization. Experimental results demonstrate that the proposed method effectively identifies critical paths and nodes, achieving a 10.1% reduction in total project duration and a 15.6% improvement in resource utilization, showing strong applicability in practical engineering project management.

In recent years we extended Shannon static statistical information theory to dynamic processes and established a Shannon dynamic statistical information theory, whose core is the evolution law of dynamic entropy and dynamic information. We also proposed a corresponding Boltzmman dynamic statistical information theory. Based on the fact that the state variable evolution equation of respective dynamic systems, i.e. Fokker-Planck equation and Liouville diffusion equation can be regarded as their information symbol evolution equation, we derived the nonlinear evolution equations of Shannon dynamic entropy density and dynamic information density and the nonlinear evolution equations of Boltzmann dynamic entropy density and dynamic information density, that describe respectively the evolution law of dynamic entropy and dynamic information. The evolution equations of these two kinds of dynamic entropies and dynamic informations show in unison that the time rate of change of dynamic entropy densities is caused by their drift, diffusion and production in state variable space inside the systems and coordinate space in the transmission processes; and that the time rate of change of dynamic information densities originates from their drift, diffusion and dissipation in state variable space inside the systems and coordinate space in the transmission processes. Entropy and information have been combined with the state and its law of motion of the systems. Furthermore we presented the formulas of two kinds of entropy production rates and information dissipation rates, the expressions of two kinds of drift information flows and diffusion information flows. We proved that two kinds of information dissipation rates (or the decrease rates of the total information) were equal to their corresponding entropy produc-tion rates (or the increase rates of the total entropy) in the same dynamic system. We obtained the formulas of two kinds of dynamic mutual informations and dynamic channel capacities reflecting the dynamic dissipation characteristics in the transmission processes, which change into their maximum—the present static mutual information and static channel capacity under the limit case where the proportion of channel length to informa-tion transmission rate approaches to zero. All these unified and rigorous theoretical for-mulas and results are derived from the evolution equations of dynamic information and dynamic entropy without adding any extra assumption. In this review, we give an overview on the above main ideas, methods and results, and discuss the similarity and difference between two kinds of dynamic statistical information theories.

The compiler is able to detect the data dependencies in an application and is able to analyze the specific sections of code for parallelization potential. However, all of these techniques provided by a compiler are usually applied at compile time, so they rely on static analysis, which is insufficient for achieving maximum parallelism and desired application scalability. These compiler techniques should consider both the static information gathered at compile time and dynamic analysis captured at runtime about the system to generate a safe parallel application. On the other hand, runtime information is often speculative. Solely relying on it doesn't guarantee maximal parallel performance. So collecting information at compile time could significantly improve the runtime techniques performance. The goal is achieved in this research by introducing new techniques proposed for both compiler and runtime system that enable them to contribute with each other and utilize both static and dynamic analysis information to maximize application parallel performance. In the proposed framework, a compiler can implement dynamic runtime methods in its parallelization optimizations and a runtime system can apply static information in its parallelization methods implementation. The proposed techniques are able to use high-level programming abstractions and machine learning to relieve the programmer of difficult and tedious decisions that can significantly affect program behavior and performance.

Events consist of objects in motion. When objects move, their opaque surfaces reflect light and produce both static image structure and dynamic optic flow. The static and dynamic optical information co-specify events. Patients with age-related macular degeneration (AMD) and amblyopia cannot identify static objects because of weakened image structure. However, optic flow is detectable despite blurry vision because visual motion measurement uses low spatial frequencies. When motion ceases, image structure persists and might preserve properties specified by optic flow. We tested whether optic flow and image structure interact to allow event perception with poor static vision. AMD (Experiment 1), amblyopic (Experiments 2 and 3), and normally sighted observers identified common events from either blurry (Experiments 1 and 2) or clear images (Experiment 3), when either single image frames were presented, a sequence of frames was presented with motion masks, or a sequence of frames was presented with detectable motion. Results showed that with static images, but no motion, events were not perceived well by participants other than controls in Experiment 3. However, with detectable motion, events were perceived. Immediately following this and again after five days, participants were able to identify events from the original static images. So, when image structure information is weak, optic flow compensates for it and enables event perception. Furthermore, weakened static image structure information nevertheless preserves information that was once available in optic flow. The combination is powerful and allows events to be perceived accurately and stably despite blurry vision.