Update Function Research Articles

An Image Encryption Algorithm Based on Improved Lifting-Like Structure and Cross-Plane Zigzag Transform

With the increasing development of information technology, image information security has received more and more attention. Furthermore, image encryption is one of the most powerful methods to ensure image information security and plays a vital role in modern society. This paper proposes an image encryption algorithm based on an improved lifting-like structure and cross-plane zigzag transform. First, to overcome the problem of simple original lifting-like structure and single update function. We modularize the original image and add new update functions and prediction functions to handle the information between different modules. Our proposed structure has higher encryption quality and can better spread the information between each image pixel. Secondly, we propose a cross-plane zigzag transform, which can effectively eliminate the correlation between adjacent elements of the original image by applying the zigzag transform to the replacement between different planes, overcoming the disadvantage that the traditional zigzag transform is limited in scanning mode and challenging to resist brute-force attack. The experimental results show that compared with other state-of-the-art schemes, this scheme has higher information entropy, can well resist selective plaintext attacks and differential attacks, and has better security.

Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space

This paper proposes a fully decentralized federated learning (FL) scheme for Internet of Everything (IoE) devices that are connected via multi-hop networks. Because FL algorithms hardly converge the parameters of machine learning (ML) models, this paper focuses on the convergence of ML models in function spaces. Considering that the representative loss functions of ML tasks e.g, mean squared error (MSE) and Kullback-Leibler (KL) divergence, are convex functionals, algorithms that directly update functions in function spaces could converge to the optimal solution. The key concept of this paper is to tailor a consensus-based optimization algorithm to work in the function space and achieve the global optimum in a distributed manner. This paper first analyzes the convergence of the proposed algorithm in a function space, which is referred to as a meta-algorithm, and shows that the spectral graph theory can be applied to the function space in a manner similar to that of numerical vectors. Then, consensus-based multi-hop federated distillation (CMFD) is developed for a neural network (NN) to implement the meta-algorithm. CMFD leverages knowledge distillation to realize function aggregation among adjacent devices without parameter averaging. An advantage of CMFD is that it works even with different NN models among the distributed learners. Although CMFD does not perfectly reflect the behavior of the meta-algorithm, the discussion of the meta-algorithm's convergence property promotes an intuitive understanding of CMFD, and simulation evaluations show that NN models converge using CMFD for several tasks. The simulation results also show that CMFD achieves higher accuracy than parameter aggregation for weakly connected networks, and CMFD is more stable than parameter aggregation methods.

Multi-objective equilibrium optimizer: framework and development for solving multi-objective optimization problems

ABSTRACT This paper proposes a new Multi-Objective Equilibrium Optimizer (MOEO) to handle complex optimization problems, including real-world engineering design optimization problems. The Equilibrium Optimizer (EO) is a recently reported physics-based metaheuristic algorithm, and it has been inspired by the models used to predict equilibrium state and dynamic state. A similar procedure is utilized in MOEO by combining models in a different target search space. The crowding distance mechanism is employed in the MOEO algorithm to balance exploitation and exploration phases as the search progresses. In addition, a non-dominated sorting strategy is also merged with the MOEO algorithm to preserve the population diversity and it has been considered as a crucial problem in multi-objective metaheuristic algorithms. An archive with an update function is used to uphold and improve the coverage of Pareto with optimal solutions. The performance of MOEO is validated for 33 contextual problems with 6 constrained, 12 unconstrained, and 15 practical constrained engineering design problems, including non-linear problems. The result obtained by the proposed MOEO algorithm is compared with other state-of-the-art multi-objective optimization algorithms. The quantitative and qualitative results indicate that the proposed MOEO provides more competitive outcomes than the different algorithms. From the results obtained for all 33 benchmark optimization problems, the efficiency, robustness, and exploration ability to solve multi-objective problems of the MOEO algorithm are well defined and clarified. The paper is further supported with extra online service and guideline at https://premkumarmanoharan.wixsite.com/mysite.

Rotational Cryptanalysis of MORUS

MORUS is one of the finalists of the CAESAR competition. This is an ARX construction that required investigation against rotational cryptanalysis. We investigated the power of rotational cryptanalysis against MORUS. We show that all the operations in the state update function of MORUS maintain the rotational pairs when the rotation distance is set to a multiple of the sub-word size. Our investigation also confirms that the rotational pairs can be used as distinguishers for the full version of MORUS if the constants used in MORUS are rotational-invariant. However, the actual constants used in MORUS are not rotational-invariant. The introduction of such constants in the state update function breaks the symmetry of the rotational pairs. Experimental results show that rotational pairs can be used as distinguishers for only one step of the initialization phase of MORUS. For more than one step, there are not enough known differences in the rotational pairs of MORUS to provide an effective distinguisher. This is due to the XOR-ing of the constants that are not rotational-invariant. Therefore, it is unlikely for an adversary to construct a distinguisher for the full version of MORUS by observing the rotational pairs.

Perfect Trees: Designing Energy-Optimal Symmetric Encryption Primitives

Energy efficiency is critical in battery-driven devices, and designing energyoptimal symmetric-key ciphers is one of the goals for the use of ciphers in such environments. In the paper by Banik et al. (IACR ToSC 2018), stream ciphers were identified as ideal candidates for low-energy solutions. One of the main conclusions of this paper was that Trivium, when implemented in an unrolled fashion, was by far the most energy-efficient way of encrypting larger quantity of data. In fact, it was shown that as soon as the number of databits to be encrypted exceeded 320 bits, Trivium consumed the least amount of energy on STM 90 nm ASIC circuits and outperformed the Midori family of block ciphers even in the least energy hungry ECB mode (Midori was designed specifically for energy efficiency).In this work, we devise the first heuristic energy model in the realm of stream ciphers that links the underlying algebraic topology of the state update function to the consumptive behaviour. The model is then used to derive a metric that exhibits a heavy negative correlation with the energy consumption of a broad range of stream cipher architectures, i.e., the families of Trivium-like, Grain-like and Subterranean-like constructions. We demonstrate that this correlation is especially pronounced for Trivium-like ciphers which leads us to establish a link between the energy consumption and the security guarantees that makes it possible to find several alternative energy-optimal versions of Trivium that meet the requirements but consume less energy. We present two such designs Trivium-LE(F) and Trivium-LE(S) that consume around 15% and 25% less energy respectively making them the to date most energy-efficient encryption primitives. They inherit the same security level as Trivium, i.e., 80-bit security. We further present Triad-LE as an energy-efficient variant satisfying a higher security level. The simplicity and wide applicability of our model has direct consequences for the conception of future hardware-targeted stream ciphers as for the first time it is possible to optimize for energy during the design phase. Moreover, we extend the reach of our model beyond plain encryption primitives and propose a novel energy-efficient message authentication code Trivium-LE-MAC.

Artificial Intelligence Technology in Electric Automation Monitoring System of Power Plant

With the development of large-capacity and high-parameter units, the degree of electrical automation in power plants is becoming more and more important. Due to the relatively backward technology of traditional electrical monitoring, it is more human monitoring, and the level of automation is low. Nowadays, communication network technology has been rapidly developed, and machinery and equipment have become more intelligent. These have laid a good technical foundation for the reform and innovation of electrical automation monitoring systems in power plants. This article first studies the development status of the electrical automation monitoring system, and then uses the fieldbus and Ethernet technology to carry out the configuration mode and construction of the electrical automation system. Then, this article designs the basic functions, advanced application functions and software parts of the system. Finally, this paper tests the running time and update function of the system dynamic module. The test result shows that the running time of the dynamic module meets the requirements of the system, and the update function is relatively fast.

Рекламное агентство: особенности работы в конкурентной цифровой среде

The article is devoted to changes in the advertising market in the field of relationships between its main subjects. The author systematizes the problems that have intensified in connection with sanctions, restrictions and the global crisis caused by the global course of the pandemic 2020–2021. The research focuses on the advertising agency, its new role and functionality. Particular attention is paid to the agency’s work in the most dynamic segment of the advertising market – digital, and new tools, formats and technologies used in agency work. The study reveals a set of problems that arise in the digital environment and are associated, on the one hand, with man-made causes and features of information technologies 3.0, which are not so easy to adapt to solve marketing problems, and on the other hand, with the problems of communication management and updated agency functionality. Such a characteristic feature as transparency, which initially, at the dawn of the digital communication era, was a serious competitive advantage compared to classic advertising media, has now almost disappeared. This limits the possibilities for cooperation between advertisers, advertising distributors, and advertising producers. The movement towards transparency should be supported both at the macro-level of the industry and at the micro-scale of the individual agency, in order to help the agency business maintain its key role in the advertiser’s business and not push it to directly cooperate with advertising carriers, which will lead to a loss of content quality and media placement. In digital communications, the differentiation between the tasks of consistently strengthening the brand value and the tactics of quick advertising for immediate purchase is very serious, since these tasks are solved by completely different – not overlapping-tools. The authors consider integrated marketing in the digital environment as the implementation of the agency’s functions of planning, selecting promotion channels in accordance with the tasks set, budgeting, monitoring efficiency and building work with the subjects of the communication market.

Twitter as an innovation process with damping effect

In the existing literature about innovation processes, the proposed models often satisfy the Heaps’ law, regarding the rate at which novelties appear, and the Zipf’s law, that states a power law behavior for the frequency distribution of the elements. However, there are empirical cases far from showing a pure power law behavior and such a deviation is mostly present for elements with high frequencies. We explain this phenomenon by means of a suitable “damping” effect in the probability of a repetition of an old element. We introduce an extremely general model, whose key element is the update function, that can be suitably chosen in order to reproduce the behaviour exhibited by the empirical data. In particular, we explicit the update function for some Twitter data sets and show great performances with respect to Heaps’ law and, above all, with respect to the fitting of the frequency-rank plots for low and high frequencies. Moreover, we also give other examples of update functions, that are able to reproduce the behaviors empirically observed in other contexts.

Reinforcement Learning-Based Multihop Relaying: A Decentralized Q-Learning Approach.

Conventional optimization-based relay selection for multihop networks cannot resolve the conflict between performance and cost. The optimal selection policy is centralized and requires local channel state information (CSI) of all hops, leading to high computational complexity and signaling overhead. Other optimization-based decentralized policies cause non-negligible performance loss. In this paper, we exploit the benefits of reinforcement learning in relay selection for multihop clustered networks and aim to achieve high performance with limited costs. Multihop relay selection problem is modeled as Markov decision process (MDP) and solved by a decentralized Q-learning scheme with rectified update function. Simulation results show that this scheme achieves near-optimal average end-to-end (E2E) rate. Cost analysis reveals that it also reduces computation complexity and signaling overhead compared with the optimal scheme.

Certificateless Aggregate Signature Scheme with Conditional Privacy Protection in Vehicle Networking

Real-time road navigation information update in vehicle networking plays an extremely important role in safe driving and alleviating traffic congestion, but how to protect user privacy from leakage is a major challenge when updating real-time navigation information. In order to solve this problem effectively, a certificateless aggregate signature scheme with real-time navigation information update function is proposed, which is suitable for vehicle networking. In the scheme, when the navigation company needs to access the data, the fog node aggregates the signature message of the vehicle broadcast and uploads it to the trusted center, and then feeds it back to the navigation company after batch verification by the trusted center. The trusted center generates temporary pseudonyms for vehicle users, realizes the anonymity of user identity, and meets the requirements of conditional privacy protection. The aggregate signature technology is used to reduce the computing and communication overhead. Finally, based on the discrete logarithm difficulty problem in the elliptic curve, it is proved that the scheme satisfies the existence and can not be forged under the adaptive selection message attack. The results of numerical analysis show that the scheme has some advantages in terms of computational cost.

Synaptic metaplasticity for image processing enhancement in convolutional neural networks

Synaptic metaplasticity is a biological phenomenon shortly defined as the plasticity of synaptic plasticity, meaning that the previous history of the synaptic activity determines its current plasticity. This phenomenon interferes with some of the underlying mechanisms that are considered important in memory and learning processes, such as long-term potentiation and long-term depression. In this work, we provide an approach to include metaplasticity in convolutional neural networks to enhance learning in image classification problems. This approach consists of including metaplasticity as a weight update function in the backpropagation stage of convolutional layers. To validate this proposal, we have been used eight different award-winning convolutional neural networks architectures: LeNet-5, AlexNet, GoogLeNet, VGG16, VGG32, ResNet50, DenseNet121 and DenseNet169; trained with four different popular datasets for benchmarking: MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100. Experimental results show that there is a performance enhancement for each of the convolution neural network architectures in all the datasets used.

Evaluation of NOAA National Water Model Parameter Calibration in Semi-Arid Environments Prone to Channel Infiltration

AbstractThe NOAA National Water Model (NWM), maintained and executed by the NOAA National Weather Service (NWS) Office of Water Prediction, provides operational hydrological guidance throughout the Contiguous United States. Based on the WRF-Hydro model architecture developed by the National Center for Atmospheric Research (NCAR), the NWM was recently modified for semi-arid domains, by permitting it to explicitly resolve infiltration from ephemeral channels into the underlying channel bed as an added model sink term. To analyze the added value of channel infiltration in semi-arid environments, we calibrated NWM v2.1 (with the channel infiltration function) to 56 independent basins in the western CONUS, following identical calibration methods as the pre-operational NWM v2.1 (not including channel infiltration). Calibration of the model consists of two parts, including 1) calibration of channel infiltration only with other parameters set to the calibrated parameters used for pre-operational NWM v2.1 and 2) calibration of all parameters including channel infiltration with settings otherwise equivalent to the calibration of NWM v2.1. The calibrated channel-infiltration enhanced NWM improves predictive skill compared to the control NWM in 85% of evaluated basins, for the calibration period. The current NWM settings for physical processes and the biases of the calibration scheme limit model performance in semi-arid environments. To explore whether channel infiltration paired with an alternative calibration scheme could address these limitations, NWM v2.1 was calibrated with a new objective function in selected basins. We found that this updated objective function could ameliorate model biases in some semi-arid environments.

Graph-LSTM with Global Attribute for Scene Graph Generation

Lots of machine learning tasks require dealing with graph data, and among them, scene graph generation is a challenging one that calls for graph neural networks’ potential ability. In this paper, we present a definition of graph neural network (GNN) consists of node, edge and global attribute, as well as their corresponding update and aggregate functions. Based on this, we then propose a realization of GNN model called Graph-LSTM and use it in scene graph generation. The model first extracts the item features in the image as the initial states of the node-LSTM representing subject/object and edge-LSTM representing predicate. Two LSTMs update the states via LSTM’s timestep and aggregate information via message passing. Repeat the update-aggregate until convergence. Meanwhile, the tag feature, i.e., the generated probability distribution of image’s semantic concepts is sent to the LSTM through a semantic compositional network (SCN). The SCN-LSTM is trained in an ensemble style, and hence allows the tag feature to serve as the global attribute providing context information to all individuals. The LSTMs’ final states are input to inference modules and generate the triplet (subject, predicate, object) of the scene graph. Experimental results show that Graph-LSTM outperforms the Message Passing and the attention Graph Covolutional Network methods, proving the effectiveness of the proposed scheme.

The Precise Relationship Between Model for End-Stage Liver Disease and Survival Without a Liver Transplant.

Scores from the Model for End-Stage Liver Disease (MELD), which are used to prioritize candidates for deceased donor livers, are widely acknowledged to be negatively correlated with the 90-day survival rate without a liver transplant. However, inconsistent and outdated estimates of survival probabilities by MELD preclude useful applications of the MELD score. Using data from all prevalent liver waitlist candidates from 2016 to 2019, we estimated 3-day, 7-day, 14-day, 30-day, and 90-day without-transplant survival probabilities (with confidence intervals) for each MELD score and status 1A. We used an adjusted Kaplan-Meier model to avoid unrealistic assumptions and multiple observations per person instead of just the observation at listing. We found that 90-day without-transplant survival has improved over the last decade, with survival rates increasing >10% (in absolute terms) for some MELD scores. We demonstrated that MELD correctly prioritizes candidates in terms of without-transplant survival probability but that status 1A candidates' short-term without-transplant survival is higher than that of MELD 40 candidates and lower than that of MELD 39 candidates. Our primary result is the updated survival functions themselves. We calculated without-transplant survival probabilities for each MELD score (and status 1A). The survival function is an invaluable tool for many applications in liver transplantation: awarding of exception points, calculating the relative demand for deceased donor livers in different geographic areas, calibrating the pediatric end-stage liver disease score, and deciding whether to accept an offered liver.

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

Early-, Late-, and Very Late-Term Prediction of Target Lesion Failure in Coronary Artery Stent Patients: An International Multi-Site Study

The main intervention for coronary artery disease is stent implantation. We aim to predict post-intervention target lesion failure (TLF) months before its onset, an extremely challenging task in clinics. This post-intervention decision support tool helps physicians to identify at-risk patients much earlier and to inform their follow-up care. We developed a novel machine-learning model with three components: a TLF predictor at discharge via a combination of nine conventional models and a super-learner, a risk score predictor for time-to-TLF, and an update function to manage the size of the at-risk cohort. We collected data in a prospective study from 120 medical centers in over 25 countries. All 1975 patients were enrolled during Phase I (2016–2020) and were followed up for five years post-intervention. During Phase I, 151 patients (7.6%) developed TLF, which we used for training. Additionally, 12 patients developed TLF after Phase I (right-censored). Our algorithm successfully classifies 1635 patients as not at risk (TNR = 90.23%) and predicts TLF for 86 patients (TPR = 52.76%), outperforming its training by identifying 33% of the right-censored patients. We also compare our model against five state of the art models, outperforming them all. Our prediction tool is able to optimize for both achieving higher sensitivity and maintaining a reasonable size for the at-risk cohort over time.

Nonlinear finite element analysis of PBL shear connectors in hybrid structures

The load-bearing capacity of perfobond Leisten (PBL) shear connectors in hybrid structures is represented by the shear-friction effect between the shear surfaces caused by the interlocking of aggregates when the fracture surface of the concrete dowel slips under an external load. To carry out an accurate numerical simulation of the bearing capacity and shear transmission mechanism of the PBL shear connectors, based on the secondary development function of the user subroutine, user-defined field variable subroutine (USDFLD), the effective simulation of the transverse shear expansion effect of the concrete dowel is achieved. A comparison with the results of the test data shows that the concrete damage plasticity (CDP) model demonstrates good robustness and convergence for the prediction and simulation of the damage characteristics of concrete structures. Based on the user subroutine USDFLD's automatic update function of material points and element integration points to field variables, this model effectively simulated the functional gradient model of the concrete dilatancy angle, the load on the shear connectors is always transmitted from the near load end to the far one, and the bearing capacity is clearly affected by the boundary conditions.

A sequential niche multimodal conformational sampling algorithm for protein structure prediction.

Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy. A sequential niche multimodal conformational sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm overcome high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high-energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins, 24 CASP13 and 19 CASP14 FM targets. Results show that SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta restrained by distance (Rosetta-dist), SNfold achieves higher average TM-score and improves the sampling efficiency by more than 100 times. On several CASP FM targets, SNfold also shows good performance compared with four state-of-the-art servers in CASP. As a plug-in conformational sampling algorithm, SNfold can be extended to other protein structure prediction methods. The source code and executable versions are freely available at https://github.com/iobio-zjut/SNfold. Supplementary data are available at Bioinformatics online.

Generalized stability conditions for host–parasitoid population dynamics: Implications for biological control

Discrete-time models are the traditional approach for capturing population dynamics of insects living in the temperate regions of the world. We revisit classical discrete-time models of host–parasitoid population dynamics and provide novel results on the stability of the population dynamics. Discrete-time host–parasitoid models are characterized by update functions that connect the population densities from one year to the next, and a host escape response — the fraction of hosts escaping parasitism each year. For a general class of models we show that the stability can be simply characterized in terms of two quantities: the rate at which the host equilibrium changes with the host’s growth rate, and the sensitivity of the host’s escape response to the host density. Interestingly, stability is more likely to arise when the escape response is a decreasing function of the host density rather than an increasing function. Moreover, if the host’s escape response only depends on the parasitoid population density then the stability condition is further simplified to the host equilibrium density being an increasing function of the host’s reproduction rate. We interpret several mechanisms known for stabilizing host–parasitoid population dynamics in the context of these generalized stability conditions. Next, we introduce a hybrid approach for obtaining the update functions by solving ordinary differential equations that mechanistically capture the ecological interactions between the host and the parasitoid. This hybrid approach is used to study the suppression of host density by a parasitoid. Our analysis shows that when the parasitoid attacks the host at a constant rate, then the host density cannot be suppressed beyond a certain point without making the population dynamics unstable. In contrast, when the parasitoid’s attack rate increases with increasing host density (Type III functional response), then the host population density can be suppressed to arbitrarily low levels while maintaining system stability. These results have important implications for biological control where parasitoids are introduced to eliminate a pest that is the host species for the parasitoid.

A genetic timing scheduling model for urban traffic signal control

As urban traffic condition is diverse and complicated, maximizing the use of urban traffic signal control system to solve traffic issues becomes one of the hot and promising topics. Especially, how to control traffic signals at multiple intersections is a key challenge. To speed up urban traffic flow, this research presents a genetic timing scheduling model (GTSM) for urban traffic signal control. GTSM constructs cellular automata to update the timing cycles of traffic signals at multiple intersections, where state update functions are formulated to coordinate traffic signals. In addition, a proposed genetic optimization algorithm (GOA) in GTSM optimizes the timing cycles of traffic signals at multiple intersections in the dynamic timing optimization. The experimental results on urban road networks show that the performance of our model is excellent in various scenarios to control traffic signals to speed up urban traffic flow.