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- 1,208
- Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization752
A parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization that maintains two collaborative archives simultaneously and develops a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status.
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- CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation241
This paper explores the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training, and proposes a novel WSSS framework called CLIP-ES.
- Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM215
This paper proposes a novel speech-text multimodal LLM architecture called Freeze-Omni, holding great potential to conduct speech-to-speech dialogue based on a multimodal LLM under the condition of a frozen LLM, avoiding the catastrophic forgetting problem caused by limited data and training resources.
- Dynamic Multiobjectives Optimization With a Changing Number of Objectives212
A dynamic two-archive evolutionary algorithm is implemented which maintains two co-evolving populations simultaneously which are complementary to each other: one concerns more about the convergence while the other concerns moreabout the diversity.
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- speechocean762: An Open-Source Non-Native English Speech Corpus for Pronunciation Assessment152
This paper introduces a new open-source speech corpus, consisting of 5000 English utterances from 250 non-native speakers, where half of the speakers are children, designed for pronunciation assessment use.
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- Achieving balance between proximity and diversity in multi-objective evolutionary algorithm147
A dynamic neighborhood multi-objective evolutionary algorithm based on hyper volume indicator (DNMOEA/HI), which benefits from both Pareto dominance and hypervolume indicator based frameworks is presented.
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- From cloud to edge134
This study performs a first-of-its-kind measurement study on a leading public edge platform that has been densely deployed in China and quantitatively reveals the status quo of today's public edge platforms, and provides crucial insights towards developing and operating future edge services.
- 128
- R-Metric: Evaluating the Performance of Preference-Based Evolutionary Multiobjective Optimization Using Reference Points126
This paper suggests a systematic way to adapt the existing metrics to quantitatively evaluate the performance of a preference-based evolutionary multiobjective optimization algorithm using reference points using multicriterion decision making approach.
- Evolutionary Many-Objective Optimization Based on Adversarial Decomposition124
This paper develops an adversarial decomposition method for many-objective optimization, which leverages the complementary characteristics of different subproblem formulations within a single paradigm.
- CF-ViT: A General Coarse-to-Fine Method for Vision Transformer121
A coarse-to-fine vision transformer (CF-ViT) to relieve computational burden while retaining performance in this paper, motivated by two important observations in modern ViT models: The coarse-grained patch splitting can locate informative regions of an input image and network inference in a two-stage manner.
- SelfMatch: Robust semisupervised time‐series classification with self‐distillation118
A feature extractor for TSC is designed, called ResNet–LSTMaN, responsible for feature and relation extraction, and the experimental results show that SelfMatch achieves excellent SSL performance on 35 widely adopted UCR2018 data sets, compared with a number of state‐of‐the‐art semisupervised and supervised algorithms.
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- A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation110
An ensemble model based on the synthetic minority over-sampling technique (SMOTE) and a classifier optimisation technique is proposed for personal credit risk evaluation that is more effective at processing credit data problems compared to the other classification models examined in this study.
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- A dual-population paradigm for evolutionary multiobjective optimization100
A dual-population paradigm (DPP) that uses two separate and co-evolving populations to deal with convergence and diversity simultaneously and paves an avenue to integrate Pareto- and decomposition-based techniques in a single paradigm.
- Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective Optimization94
The concept of incomplete preference lists is introduced into the stable matching model to remedy the loss of population diversity and an adaptive mechanism is developed to automatically set the length of the incomplete preference list for each solution according to its local competitiveness.
- Class-specific soft voting based multiple extreme learning machines ensemble90
A SpaCSSV-ELM method is proposed by transforming the weight optimization problem to a sparse coding problem, which uses the sparse representation technique for maintaining classification performance with less nonzero weight coefficients.
- Distributed coordinative transaction of a community integrated energy system based on a tri-level game model81
The effectiveness of the proposed tri-level game model and distributed solution algorithm was verified by case study, which confirmed that all stakeholders could benefit from the proposed transaction mechanism.
- FEMOSAA81
A novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime, and a new method to search for the knee solutions, which can achieve a balanced tradeoff.
- Integration of Preferences in Decomposition Multiobjective Optimization79
A systematic way to incorporate the DM’s preference information into the decomposition-based evolutionary multiobjective optimization methods and a nonuniform mapping scheme by which the originally evenly distributed reference points on a canonical simplex can be mapped to new positions close to the aspiration-level vector supplied by the DM.
- A general framework for evolutionary multiobjective optimization via manifold learning74
A general framework that applies advanced manifold learning techniques in EMO and can interpolate new candidate solutions that obey the geometric property of the PS manifold, as well as identify the neighborhood relationship in this low-dimensional representation.
- Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multiobjective Optimization Using Reference Points73
The experimental results demonstrate that preference incorporation in EMO does not always lead to a desirable approximation of SOI if the DM’s preference information is not well utilized, nor does the DM elicit invalid preference information, which is not uncommon when encountering a black-box system.
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- Learning paradigm based on jumping genes: A general framework for enhancing exploration in evolutionary multiobjective optimization72
A general learning paradigm based on Jumping Genes is proposed to enhance the exploration ability of multiobjective evolutionary algorithms and demonstrate that the performance of a baseline algorithm can be significantly improved by the proposed paradigm.
- Inclusive GAN: Improving Data and Minority Coverage in Generative Models70
This work formalizes the problem of minority inclusion as one of data coverage, and proposes to improve data coverage by harmonizing adversarial training with reconstructive generation by developing an extension that allows explicit control over the minority subgroups that the model should ensure to include.
- DeepSQLi: deep semantic learning for testing SQL injection69
A deep natural language processing based tool, dubbed DeepSQLi, is proposed, to generate test cases for detecting SQLi vulnerabilities, equipped with the ability to learn the semantic knowledge embedded in SQLi attacks, allowing it to translate user inputs into a new test case, which is potentially more sophisticated.
- FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software at Runtime.65
A novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime, and a new method to search for the knee solutions, which can achieve a balanced tradeoff.
- New agent-based proactive migration method and system for Big Data Environment (BDE)65
New agent-based proactive migration method and system for Big Data Environment (BDE) with service discovery and key frames selection strategy, which has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device.
- Interactive Decomposition Multiobjective Optimization Via Progressively Learned Value Functions63
This paper develops an interactive framework for the decomposition-based EMO algorithm to lead a DM to the preferred solutions of her/his choice, which consists of three modules, i.e., consultation, preference elicitation, and optimization.
- Multi-Objective Differential Evolution with Adaptive Control of Parameters and Operators63
Experimental results confirm the superior performance of the proposed adaptive DE algorithm, referred to as Adap-MODE, when compared to two state-of-the-art baseline approaches, and to its static and partially-adaptive variants.
- Adaptive weights generation for decomposition-based multi-objective optimization using Gaussian process regression61
This paper proposes an adaptive method to periodically regenerate the weight vectors for decomposition-based multi-objective algorithms according to the geometry of the estimated Pareto front, and proves the effectiveness of the proposed adaptive weights generation method.
- PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis59
PyABSA, a modularized framework built on PyTorch for reproducible ABSA, is presented, which supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis.
- Bayesian network based label correlation analysis for multi-label classifier chain59
This paper employs Bayesian network (BN) to model the label correlations and proposes a new BN-based CC method (BNCC), which derives the label order for constructing CC model by applying topological sorting on the nodes of the optimized BN.
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- A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy57
A novel fingerprint liveness detection method based on DCNN and voting strategy, which performs better than handcraft feature and optimize the process of feature extraction and classifier training simultaneously, and keep highly real-time performance at the same time.
- BiLO-CPDP54
This paper proposes a tool, dubbed BiLO-CPDP, which is the first of its kind to formulate the automated CPDP model discovery from the perspective of bi-level programming, and champions better prediction performance than all other 21 existing C PDP techniques on 70% of the projects, while being overwhelmingly superior to Auto-Sklearn and its single-level optimization variant on all cases.
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- Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid53
To design a multi-microgrid power system, an intelligent multi- microgrid energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques, which shows that the proposed method can effectively learn different preferences.
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- An interpretable RNA foundation model for exploring functional RNA motifs in plants52
This study introduced PlantRNA-FM, a high-performance and interpretable RNA FM specifically designed for plants, which achieves superior performance in plant RNA biology tasks and enables the discovery of functional RNA sequence and structure motifs across transcriptomes.
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- A knee-point-based evolutionary algorithm using weighted subpopulation for many-objective optimization52
An algorithm which uses a weighted subpopulation knee point is proposed, which is used to divide the whole population into a number of subpopulations, and the knee point of each subpopulation guides other solutions to search.
- Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D49
A new AOS mechanism for multi-objective evolutionary algorithm based on decomposition (MOEA/D) where the dynamic Thompson sampling (DYTS) is applied to adapt the bandit learning model to a non-stationary setup.
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- Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization44
A diversity maintenance strategy for AR to balance convergence and diversity during evolution process is proposed and shows a good balance among convergence, uniformity and spread.
- A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments43
A hierarchical multioutput Gaussian process is proposed to capture the correlation among data collected from different time steps with a linearly increased number of hyperparameters to jump start the optimization in the new environment with a very limited computational budget.
- Batched Data-Driven Evolutionary Multiobjective Optimization Based on Manifold Interpolation43
A framework for implementing batched data-driven evolutionary multiobjective optimization (EMO), so general that any off-the-shelf EMO algorithms can be applied in a plug-in manner, which is featured with a faster convergence and a stronger resilience to various Pareto-optimal front shapes.
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- Personalized search for social media via dominating verbal context41
The dominating set obtained from verbal context is proposed and a revised ranking method for measuring the relevance of a resource to an issued query, a discovered context and an extracted user profile is presented.
- Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons40
This paper empirically compare six representative visualisation techniques for the exploratory analysis of different Pareto front approximation sets obtained by four state-of-the-art evolutionary multi-objective optimisation algorithms on the classic DTLZ benchmark test problems and finds that visual comparisons follow the No-Free-Lunch theorem.
- EVOLVING EXTREME LEARNING MACHINE PARADIGM WITH ADAPTIVE OPERATOR SELECTION AND PARAMETER CONTROL39
In this paradigm, a Differential Evolution (DE) variant, which can online select the appropriate operator for offspring generation and adaptively adjust the corresponding control parameters, is proposed for optimizing the network.
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- Containment control of stochastic multiagent systems with semi‐Markovian switching topologies37
The containment control of stochastic multiagent systems with semi‐Markov switching topologies is investigated and sufficient conditions to achieve the containment control in the asymptotic mean square sense are obtained in a form of linear matrix inequalities.
- Progressive Preference Learning: Proof-of-Principle Results in MOEA/D36
This paper uses the state-of-the-art MOEA/D as the baseline and develops its interactive version that is able to find solutions preferred by the DM in a progressive manner, and implements an approximated value function, which models the DM’s preference information from the scoring results.
- Interactive Evolutionary Multiobjective Optimization via Learning to Rank34
This article develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner that is so general that any existing EMO algorithm can be applied in a plug-in manner.
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- Designing Semantic Web Processes: The Wsdl-S Approach34
This chapter presents a solution for both the problems based on WSDL-S regarding dynamic binding of partner services and the growing requirement to build complex processes which may include Web Services supplied by the different partners.
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- Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms33
Three reference point based algorithms can improve the search efficiency and quality of preferred solutions and provide insights in algorithm selection for multi-objective reservoir optimization problems.
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- A grid-based fitness strategy for evolutionary many-objective optimization32
To strengthen selection pressure and refine comparison level, three hierarchical grid-based criterions are incorporated into fitness to establish a completer order among individuals to balance convergence and diversity in fitness for many-objective optimization problems.
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- A vector angles-based many-objective particle swarm optimization algorithm using archive31
A vector angles-based many-objective particle swarm optimization algorithm using archive is proposed to make the external archive getting a balance between convergence and diversity, based on the values of vector angles of each solution.
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- Security testing of web applications30
This paper proposes an automatic security test case generation approach to detect SQL injection vulnerabilities for web applications, following a search-based software engineering (SBSE) paradigm, and proposes a novel fitness function that evaluates the similarity between the SQL statements produced by feeding user inputs in the system under test and a known malicious SQL statement.
- Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution30
This paper takes the differential evolution (DE) as the baseline algorithm for proof-of-concept study, and evaluates and compares four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and alsoHow well they approximate the parameter versus the empiricalperformance landscapes.
- A Formal Model for Multi-objective Optimisation of Network Function Virtualisation Placement29
This paper develops a formal model for the NFV placement problem based on queuing theory using the popular NSGA-II as the optimiser and the effectiveness of the proposed model is validated through a series of proof-of-concept experiments.
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- An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-objective Optimization27
An improved version of C-TAEA is proposed, featuring an improved update mechanism of two co-evolving archives and an adaptive mating selection mechanism to promote a better collaboration between co- Evolving archives.
- Necessary and sufficient conditions for leader-following consensus of multi-agent systems with random switching topologies27
This paper designs a leader-following consensus of general linear multi-agent systems with random switching topologies, where the dwell time in each topology consists of a fixed part and random part, and the topology switching signal in random part is modeled by a semi-markov process.
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- Frozen Large Language Models Can Perceive Paralinguistic Aspects of Speech26
This work is the first to explore how to induce a frozen LLM to understand more than just linguistic content from speech inputs in a general interaction setting, and is able to produce higher quality and more empathetic responses to expressive speech prompts compared to several baselines.
- Surrogate assisted evolutionary algorithm for medium scale multi-objective optimisation problems26
A Gaussian process surrogate model assisted EA for medium-scale expensive multi-objective optimisation problems with up to 50 decision variables is proposed and a subset selection method is developed to choose a couple of promising candidate solutions for actual objective function evaluations thus to update the training dataset.
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- A new predictive method supporting streaming data with hybrid recurring concept drifts in process industry23
An Online Autoregression with Deep Long-Short-Term Memory (OAR-DLSTM) method for the hybrid recurring concept drifts of process industry streaming data is proposed and the predicted results are more accurate, and the effectiveness of the method is verified.
- Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results23
The experimental results demonstrate that the reference point based EMO method can be viable for problems with a massively large number of objectives, given an appropriate choice of the distance measure.
- Variable Interaction in Multi-objective Optimization Problems23
This study uncovers nontrivial variable interaction structures for the ZDT and DTLZ benchmark problems which were thought to be either separable or non-separable.
- Discriminating Human-authored from ChatGPT-Generated Code Via Discernable Feature Analysis22
A discriminative feature set is developed that yields high accuracy in distinguishing ChatGPT-generated code from human-authored code in binary classification tasks and a dataset cleansing strategy is introduced that extracts pristine, high-quality code datasets from open-source repositories, thereby achieving exceptional accuracy in code authorship attribution tasks.
- Quality Indicators for Preference-Based Evolutionary Multiobjective Optimization Using a Reference Point: A Review and Analysis21
It is shown that an achievement scalarizing function value is not always consistent with the distance from a solution to the reference point in the objective space and the ranking of preference-based EMO algorithms depends on the choice of quality indicators.
- Architecture Disentanglement for Deep Neural Networks21
This paper introduces neural architecture disentanglement (NAD), which learns to disentangle a pre-trained DNN into sub-architectures according to independent tasks, forming information flows that describe the inference processes.
- Evolutionary Alternating Direction Method of Multipliers for Constrained Multiobjective Optimization With Unknown Constraints20
This framework tackles CMOPs with unknown constraints by reformulating the original problem into an additive form of two subproblems, each of which is allotted a dedicated evolutionary population, and its salient features include faster convergence and enhanced resilience to various Pareto front shapes.
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- Ant-based distributed constrained steiner tree algorithm for jointly conserving energy and bounding delay in ad hoc multicast routing18
A distributed constrained Steiner tree algorithm is described, which jointly conserves energy and bounds delay for multicast routing in ad hoc networks and strives to minimize the sum of transmission power of non-leaf nodes, subject to the given maximum hop count constraint.
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- A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model17
The paper proposes an A-ResNet model to improve ResNet, and the residual attention module with shortcut connection is introduced to enhance the focus on the target object and the dropout layer is introduction to prevent the overfitting phenomenon.
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- A Parallelizable Lattice Rescoring Strategy with Neural Language Models17
The proposed rescoring strategy obtains comparable recognition performance and generates more compact lattices than a competitive baseline method, and the parallel rescoring method offers more flexibility by simplifying the integration of PyTorch-trained neural LMs for lattice rescoring with Kaldi.
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- Data preprocessing and modelling of electronically-controlled automotive engine power performance using kernel principal components analysis and least squares support vector machines16
Experimental results show that KPCA+LS-SVM can really improve the training time and accuracy of an engine model.
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- DaNuoYi: Evolutionary Multitask Injection Testing on Web Application Firewalls14
An automatic injection testing tool that simultaneously generates test inputs for multiple types of injection attacks on a WAF and generates more valid test inputs than its state-of-the-art single-task counterparts and the context-free grammar-based injection construction.
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- Adversarial example generation via genetic algorithm13
This paper proposes a more efficient attention based genetic algorithm adversarial attack method, called AGA, which uses attention mechanism to pay more attention to the important tokens and utilize the multi-membered strategy to accelerate the search procedure.
- EduChain: A Blockchain-Based Education Data Management System13
EduChain, a novel heterogeneous blockchain-based system for managing educational data, is introduced and demonstrates superior performance in key areas such as information verification, error traceback, and data security, thereby significantly improving the integrity and trustworthiness of educational data management.
- RETRACTED ARTICLE: Analysis of complex cognitive task and pattern recognition using distributed patterns of EEG signals with cognitive functions13
A technique for analyzing the distribution pattern of EEG signals with cognitive functions utilizing the variational pattern recognition based on brain computation to derive signals from a group of adults performing several complex works is provided.
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- A many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism12
- Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics12
The trained Xiwu model is significantly outperformed the benchmark model on the HEP knowledge question-and-answering and code generation, with the hope of surpassing GPT-4 as it evolves with the development of open-source models.
- Multi-objective Reinforcement Learning Based Multi-microgrid System Optimisation Problem12
An intelligent multi-microgrid energy management method based on the multi-objective reinforcement learning (MORL) techniques leads to a Pareto optimal set, demonstrating the performance of the MORL and verifying the viability of the proposed approach.
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- PFARN: Pyramid Fusion Attention and Refinement Network for Multiscale Ship Detection in SAR Images11
A novel detection algorithm named pyramid fusion attention and refinement network (PFARN) is proposed, which achieves 98.1% average precision (AP) for all and 57.4% AP for tiny ships in the SSDD datasets, and 94.2% AP for all and 68.0% AP for tiny ships in the HRSID datasets, which is satisfactory for the detection of multiscale ships.
- An Extended Multi-Attributive Border Approximation Area Comparison Method for Emergency Decision Making with Complex Linguistic Information11
A new emergency decision-making model is proposed in this study based on double hierarchy hesitant linguistic term sets (DHHLTSs) and the multi-attributive border approximation area comparison (MABAC) method and the criteria weights are determined objectively with a maximum comprehensive method.
- Black-box adversarial attack via overlapped shapes11
This paper proposes a concept of applying techniques similar to those used within evolutionary-art to generated adversarial images to address the extreme vulnerabilities to attacks generated by adversarial examples.
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- An High-precision FFT Frequency Offset Estimation Algorithm based on Interpolation and Binary Search11
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- IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs10
This work proposes a novel method for accelerating self-attention at inference time that works with pretrained Transformer models out-of-the-box without requiring retraining and experiments using this method to accelerate various long-sequence Transformers.
- A Bichannel Transformer with Context Encoding for Document-Driven Conversation Generation in Social Media10
A bichannel transformer with context encoding (BCTCE) for document-driven conversation that yields significant improvements over the state-of-the-art baselines on most of the evaluation metrics, and the generated responses of BCTCE are more informative and more relevant to dialogues than baselines.
- 3D‐QSAR, molecular docking, and ONIOM studies on the structure–activity relationships and action mechanism of nitrogen‐containing bisphosphonates10
A series of N‐BPs inhibitors of human FPPS (hFPPS) were investigated using a combination of three‐dimensional quantitative structure–activity relationship (3D‐QSAR), molecular docking, and three‐layer ONIOM studies, and a good correlation between the predicted and experimental activities was yielded.
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- Improving fast-slow Encoder based Transducer with Streaming Deliberation9
This paper aims to improve the recognition accuracy of the fast-slow encoder based transducer while keeping its latency low by integrating a streaming deliberation model that leverages partial hypotheses from the streaming fast encoder and implicitly learns to correct recognition errors.
- Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems9
By using local optima network as a proxy of fitness landscapes, this paper proposed to leverage graph data mining techniques to conduct qualitative and quantitative analyses to explore the latent topological structural information embedded in those landscapes.
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- Improving Irregularly Sampled Time Series Learning with Time-Aware Dual-Attention Memory-Augmented Networks9
The proposed DAMA-Net can leverage both time irregularity, multi-sampling rates and global temporal patterns information inherent in IASS-MTS so as to learn more effective representations for improving prediction performance.
- Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study9
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- Inter-language prosodic style modification experiment using word impression vector for communicative speech generation9
To confirm the language independency of a communicative prosody generation from input word impression vector, communicative Mandarin speech is synthesized using prosodic characteristics of communicative Japanese speech using prosode characteristics of one-word “n” utterances.
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- Predictor‐based H∞ leader‐following consensus of stochastic multi‐agent systems with random input delay8
A new predictor‐based controller is designed based on the reduction approach to guarantee H∞ leader‐following consensus is achieved in mean square sense and sufficient conditions are obtained in a form of linear matrix inequalities.
- A Survey on Big Data Analytics Technologies8
The objective of this paper is to analyze the typical big data analysis technologies, find out the characteristics and applicative scenarios, and then provide the reference for big data processing of all industries.
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- DIRECT: Video Mashup Creation via Hierarchical Multi-Agent Planning and Intent-Guided Editing7
This work forms video mashup creation as a Multimodal Coherency Coherency Satisfaction Problem (MMCSP) and proposes the DIRECT framework, a hierarchical multi-agent framework that significantly outperforms state-of-the-art baselines in both objective metrics and human subjective evaluation.
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- Bridging Sequence-Structure Alignment in RNA Foundation Models7
OmniGenome, an RNA FM trained to align RNA sequences with respect to secondary structures through structure-contextualized modelling, achieves state-of-the-art performance on RNA and DNA benchmarks, even without any training on DNA genomes.
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- CEB-Collaboratively Multi-Granularity Interest Scheduling Algorithm for Loading Large WebBIM Scene7
First, edge computing is introduced into WebBIM online visualization, and the demand on visualizing large-scale BIM scene on mobile web browsers is increasing rapidly.
- Routing-Led Placement of VNFs in Arbitrary Networks7
Empirical results show that the proposed routing-led algorithm can produce significantly better solutions to large problem instances on a range of datacentre topologies and is preferable to the traditional placement based algorithm.
- Using molecular fingerprints and unsupervised learning algorithms to find simulants of chemical warfare agents7
This study can effectively help researchers to find the best approximate simulant to a certain extent and combines molecular fingerprint and unsupervised learning algorithm to develop a simulants selection framework.
- An Analysis of Source Context Dependency in Neural Machine Translation7
A numerical measurement of source context dependency in the NMT models is proposed and the behaviors of the N MT decoder with this measurement under several circumstances show that this measurement is an appropriate estimate for source context Dependency and consistent over different domains.
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- Two-Stage Channel Estimation Algorithm for TD-SCDMA System Employing Multi-Cell Joint Detection7
Simulation results prove the validity of the proposed algorithm for estimation accuracy upgrade, contributing to 10.2 dB and 6.8 dB BER performance improvement for the outdoor-to-indoor A channel and Vehicular A channel respectively.
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- On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study6
By conducting exploratory analysis on these landscapes with fine-grained visualizations and dedicated FLA metrics, this paper observed a similar landscape topography across a wide range of models, datasets, and fidelities, and shed light on the mechanism behind the success of several popular methods in HPO.
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- Long-term and short-term memory networks based on forgetting memristors6
A forgetting memristor model with controllable decay rate is proposed, the short-term memory and long- term memory of the long-term and short- Term memory (LSTM) network based on forgetting Memristor is suggested, and the conversion speed from short-terms memory network to long-Term memory network is controllables.
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- Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity6
A Quality-Aware Feature Matching IQA metric (QFM-IQM) is proposed that employs contrastive learning to remove harmful features from the upstream task and achieves superior performance to the state-of-the-art NR-IQA.
- To Estimate Performance of Artificial Neural Network Model Based on Terahertz Spectrum: Gelatin Identification as an Example6
It is found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data.
- Nearly tight approximation algorithm for (connected) Roman dominating set6
A greedy algorithm for MinRDS with a guaranteed performance ratio at most H(δmax+1), which is connected to the minimum connected Roman dominating set problem.
- Decomposition multi-objective optimisation6
Ke Li is a UKRI Future Leaders Fellow and a Senior Lecturer (Associate Professor) in Computer Science at the University of Exeter and his current research interests include the evolutionary multi-objective optimisation, automatic problem solving, machine learning and applications in searchbased software engineering and water engineering.
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- User Interests Driven Collaborative Cloud-Edge-Browser Architecture for WebBIM Visualization6
This study introduces edge computing into the existing cloud-browser mode for an online Web3D framework and shows that the proposed method can reduce the networking transmission delay and improve the user experience of accessing large-scale WebBIM scenes.
- Search-Based Software Engineering for Self-Adaptive Systems: One Survey, Five Disappointments and Six Opportunities6
This paper provides the first systematic and comprehensive survey exclusively on SBSE for SASs, covering 3,740 papers in 27 venues from 7 repositories, which eventually leads to several key statistics from the most notable 73 primary studies in this particular field of research.
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- OpenTOS: Open-source System for Transfer Learning Bayesian Optimization5
This work introduces OpenTOS, an open-source system designed for transfer learning in Bayesian optimization, allowing users to build different algorithms by choosing algorithmic components, similar to assembling LEGO blocks.
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- Unsupervised Discriminative Learning of Sounds for Audio Event Classification5
On several audio event classification benchmarks, a fast and effective alternative is shown that pre-trains the model unsupervised, only on audio data and yet delivers on-par performance with ImageNet pre-training.
- Neural Language Modeling with Implicit Cache Pointers5
A cache-inspired approach is proposed for neural language models (LMs) to improve long-range dependency and better predict rare words from long contexts and is more effective on rare words.
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- [Quantitative structure activity relationship models based on heuristic method and gene expression programming for the prediction of the pK(a) values of sulfa drugs].5
The two QSPR models are successful in predicting pK(a) during the discovery of new drugs and providing theory information for studying the new drugs.
- A deformable model-based minimal path segmentation method for kidney MR images5
The automatic minimal path segmentation method is fast, accurate, and robust and it can be applied not only for kidney images but also for other organs.
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- Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective4
This work advocate a novel perspective to rethink performance analysis—modeling the configuration space as a structured “landscape” using GraphFLA, an open-source, graph data mining empowered fitness landscape analysis (FLA) framework.
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- KiteRunner: Language-Driven Cooperative Local-Global Navigation Policy with UAV Mapping in Outdoor Environments4
KiteRunner is a language-driven cooperative local-global navigation strategy that combines UAV orthophoto-based global planning with diffusion model-driven local path generation for long-distance navigation in open-world scenarios and innovatively leverages real-time UAV orthophotography to construct a global probability map.
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- A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part I-Past and Future4
This two-part survey series uses MOEA/D as the representative of decomposition-based EMO to review the up-to-date development in this area, and systematically and comprehensively analyze its research landscape.
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- Towards Fairness-Aware Ranking by Defining Latent Groups Using Inferred Features4
This paper takes the Semantic Scholar dataset released in TREC 2020 Fairness Ranking Track as a case study, and infer and extract multiple fairness related dimensions of author identity including gender and location to construct groups and proposes a fairness-aware re-ranking algorithm.
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- Enhancing Constrained Multi-Objective Optimization via Knowledge Transfer and Co-Evolution3
Comprehensive experiments across 46 benchmark test functions and 10 real-world problems, comparing EKTEMO with nine state-of-the-art algorithms, demonstrate its superior performance in terms of inverted generational distance and hypervolume, as confirmed by Wilcoxon rank-sum test results.
- ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field3
It is shown that modeling per-point provenance during the NeRF optimization enriches the model with information on triangulation leading to improvements in novel view synthesis and uncertainty estimation under the challenging sparse, unconstrained view setting against competitive baselines.
- Research on vibration pattern recognition based on phase‐sensitive optical time domain reflectometry and voting fully convolution neural networks3
A method that combines phase‐sensitive optical time domain reflectometry with deep learning to construct new voting fully convolution neural networks (VoteFCNs) is proposed and can be input with data of random size and requires less parameters so that the training speed can be improved greatly.
- L1-Norm Robust Regularized Extreme Learning Machine with Asymmetric C-Loss for Regression3
An asymmetric C-loss function (called AC-loss) is proposed, which is non-convex, bounded, and relatively insensitive to noise, and achieves better generalization performance compared to other state-of-the-art algorithms, especially when noise exists in the datasets.
- 3
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- Image Style Transfer Based on Dynamic Convolutional Manifold Alignment of Halo Attention3
The experimental results show that the proposed arbitrary style transfer algorithm can generate high-quality stylized images, achieving values of 33.861, 2.516, and 3.602 for ArtFID, style loss, and content loss, respectively.
- Towards Robust Text Retrieval with Progressive Learning3
Extensive experiments conducted on C-MTEB and DuReader demonstrate that PEG surpasses state-of-the-art embeddings in retrieving true positives, highlighting its significant potential for applications in LLMs.
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- An Image Processing Method: Edge-guided Directional Walk Mean Smoothing3
A novel image post-processing technique termed edge-guided directional walk mean smoothing (EDWMS), it can effectively calculate the mean of pixels along different directions then obtain denoised image with sharp edge and experiments show that the proposed EDWMS outperforms compared algorithms in both edge sharpness and color saturation.
- 3
- 3
- Collaborative Intelligence Analysis for Industrial Control Systems Threat Profiling3
This work developed dozens of honeypots to collect ICS-related attack data and proposed a novel framework to analyze the current threat landscape, which presents a considerable performance in terms of efficiency and effectiveness.
- 3
- Recent advances in semantic computing and personalization3
This special issue aims to investigate how high-level semanics are extracted and exploited from web and social data sources and state-of-the-art data mining techniques and how personalized models are facilitated and supported by underlyng semantics.
- 3
- Adaptive Two-Level Matching-Based Selection for Decomposition Multi-Objective Optimization3
This paper proposes an adaptive two-level stable matching-based selection for decomposition multi-objective optimization and develops an adaptive mechanism to automatically set the length of preference list for each solution according to its local competitiveness.
- A new effective branch-and-bound algorithm to the high order MIMO detection problem3
A new convex reformulation is proposed which gets the maximized average objective value as the lower bound estimator in the branch-and-bound scheme and achieves a high total efficiency.
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- JGBL paradigm3
A Jumping Gene Based Learning (JGBL) paradigm is proposed to enhance the exploration ability of NSGA-II and only operates on the non-dominated solutions which are eliminated in the environmental selection procedure due to the low quality of crowded distance.
- 3
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- 3
- Cell search scheme for long-term evolution of TD-SCDMA system3
Simulations show that the FD method is better than the code division multiplexed (CD) method in the time-division-synchronou code-division multiple access (TD-SCDMA) LTE system.
- 3
- 2
- Self-Supervised Learning for Multi-Modal Medical Image Segmentation2
To address the shortcomings of underutilized multi-modal features in self-supervised learning methods for medical images, a self-supervised learning method considering multi-modal complementary information is proposed, named SLeM.
- Global Motion Corresponder for 3D Point-Based Scene Interpolation Under Large Motion2
This paper introduces Global Motion Corresponder (GMC), a novel approach that robustly handles large motion and achieves smooth transitions and enables extrapolation capabilities where other baseline methods cannot.
- Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach2
The Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series, and excels in urban mobility pattern modeling and long-term prediction.
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- Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in MEC Systems Interconnected by Metro Optical Networks2
Evaluation results show that the BiVNE algorithm can effectively enhance the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously.
- 2
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- 2
- FPGA-based multi-channel thermocouple temperature measurement system2
Aiming at the high and low temperature test requirements of spacecraft before being sent into space, this paper designs and implements a multi-channel thermocouple temperature measurement system based on FPGA, which solves the problem of thermoc coupling cold junction compensation better.
- Automated Few-Shot Time Series Forecasting based on Bi-level Programming2
A BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bi-level programming perspective that helps boost the base-learner to mitigate the small data challenge and proactively searches for the optimal hyperparameter configurations for both base- and meta-learners.
- Short term photovoltaic power prediction based on FWA-CNN-GRU neural network2
It is shown that FWA-CNN-GRU model has higher prediction accuracy and can provide a theoretical basis for the operation and regulation of power grid.
- 2
- 2
- Art-Attack: Black-Box Adversarial Attack via Evolutionary Art2
This paper proposes a gradient-free attack by using a concept of evolutionary art to generate adversarial examples that iteratively evolves a set of overlapping transparent shapes that achieves a higher attack success rate on all three baseline models.
- Pushing the Limits of Respiration Sensing with Reconfigurable Metasurface2
This work proposes a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference, and employs a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering.
- 2
- 2
- 2
- 2
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- 2
- Design and Implementation of Hybrid CORDIC Algorithm Based on Phase Rotation Estimation for NCO2
A kind of hybrid CORDIC algorithm based on phase rotation estimation applied in numerical controlled oscillator (NCO) is proposed, which reduces part phase rotation and add-subtract unit, so that it decreases delay.
- 2
- 2
- 2
- An Efficient High Dimensional Cluster Method and its Application in Global Climate Sets2
A spatial clustering algorithm that cures the problem of dimensionality in high dimensional clustering, and is applied to a 100-year global climate dataset and partition the global surface into sub areas under various spatial granularities.
- 2
- Improved fast SPIHT algorithm2
The improved algorithm modifies the SPIHT algorithm by use of two concepts-minimum threshold and minimum exported bit, changing the original scanning order, and Experimental results show that it reduces both the memory requirement and the time consumption.
- 1
- Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization1
This work sheds light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization, and proposes a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance.
- 1
- 1
- Research and Simulation of Multi-objective Optimization of Urban Rail Train Automatic Driving System1
- Analysis of Roll Dynamics With Computer Vision1
Development of a roll test system instrumented with accelerometers and high-speed cameras to imitate lateral dynamics that the tool could undergo while drilling in a controlled laboratory environment and artificial intelligence (AI) and computer vision techniques demonstrated could open new possibilities for fit-for-basin virtual qualification of new drilling tools and for post-run root cause analysis of drilling tool failures.
- 1
- 1
- 1
- 1
- How Good Are Deep Generative Models for Solving Inverse Problems?1
This study evaluates recent diffusion-based, GAN-based, and IMLE-based methods on three inverse problems, i.e., super-resolution, colourization, and image decompression, and finds that the IMLE-based CHIMLE method outperforms other methods in terms of producing valid solutions and reliable uncertainty estimates.
- 1
- BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework1
This study proposes B OOST A UG, a flexible framework which reconsiders the role of the language 020 model in text augmentation and emphasizes the augmentation instance filtering rather than 022 generation and evaluates B OOST A UG on both sentence-level text classification and aspect-based sentiment classification.
- 1
- Research on Data Quality Evaluation and Comprehensive Analysis Decision-Making under the Integration of Regulation and Control1
A method for evaluating the quality of operation data in substations based on electrical physics knowledge and manual data discrimination is proposed, and an online data mining and risk early warning technology system under the integration of regulation and control is developed.
- 1
- 1
- Latency-aware secure transmission in MEC-assisted HSR communication systems1
A novel joint iterative optimization algorithm is proposed that jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency.
- Joint optimization of BS-VS association and power control in secure HSR communication systems1
Simulation results show that the proposed algorithms for secure transmission for high-speed railway (HSR) communication system have good convergence.
- Empirical study of correlations in the fitness landscapes of combinatorial optimization problems1
Two alternative approaches to empirically investigate the potential existence of structural similarity among different fitness landscapes are proposed and it is exciting to find some evidence of the existence of similarity not only for a given problem with various dimensions but also across different problems.
- 1
- 1
- 1
- 1
- 1
- Research on distribution network fault location based on binary particle swarm optimization1
This paper constructs a binary particle swarm optimization (PSO) algorithm model to realize fault location of distribution network, and uses MATLAB to verify the simulation example and the results show that the algorithm has the advantages of good convergence, high stability and excellent fault tolerance.
- Analysis and Research of Distributed network Crawler based on Cloud Computing Hadoop Platform1
This paper presents analysis and research of distributed network crawler based on cloud computing Hadoop platform, which transforms the traditional desktop task processing into the network based task processing.
- Design and Analysis of Underactuated Robotic Hand Suitable for Desktop Grasping1
An underactuated robotic hand (SDG-UA hand) suitable for tabletop gripping is proposed, which can realize the parallel and self-adaptive grasping by using guide-bar mechanism.
- 1
- Construction of Data Acquisition and Statistical Analysis Model based on Big Data Processing1
The paper presents construction of data acquisition and statistical analysis model based on big data processing, which will bring great business value to enterprises.
- 1
- 1
- Analyze Soft Measurement Technology Based Intelligent Algorithm in Sewage Treatment1
This paper presents a high quality research strategy of on-line real-time control of Genetic Algorithm, Artificial Neural Networks and Support Vector Machine.
- A Novel Time-Frequency Analysis Approach for Nonstationary Time Series Using Multiresolution Wavelet1
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- 1
- 1
- 1
- 1
- Bayesian Matrix Factorization for Electricity Load Imputation–
Experimental results on three real-world electricity load datasets are presented to show the superiority of the proposed Bayesian matrix factorization (BMF)-based imputation method against five benchmark algorithms.
- HICnet: A two-stage framework based on region partitioning for predicting HIC values from car point clouds–
This work represents the front-end of a vehicle as a 3D point cloud and introduces HICnet, a framework built upon a PointNet++ backbone for geometric feature encoding, augmented with multilayer perceptrons to integrate auxiliary physical attributes.
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- Short-term prediction of wind power based on fusion of traditional convolution and coding–
A deep learning-based power prediction model for wind power clusters is proposed, which synthetically uses Transformer and Graph Convolutional Neural Network to extract and fuse the features of meteorological data of wind power clusters.
- Structure-aware Hybrid-order Similarity Learning for Multi-view Unsupervised Feature Selection–
This paper proposes a novel MUFS method, termed Structure-aware Hybrid-order sImilarity learner for multi-viEw unsupervised Feature Selection (SHINE-FS), which constructs a hybrid-order similarity graph that captures both local and global structures, thereby revealing the intrinsic data structure to enhance feature selection.
- Multi-Task Prediction Method for User Behavior Utilizing Transformers–
Experimental results indicate that LogSeqTrans is highly effective in multi-task prediction and capturing complex sequence patterns.
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- Joint Blocklength and Power Optimization in High-Speed Railway Relay Communication Systems–
The problem of maximizing the minimum user throughput by jointly optimizing the transmission packet length and the transmission power control of the relay device is investigated and a low-complexity algorithm is proposed to obtain the solution to the joint optimization problem.
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- Design and Implementation of Data Service Middleware in Earthquake Precursor Instrument Platform–
A data service middleware solution based on B/S architecture for the interaction between earthquake precursor signal observation equipment and remote access terminals on the earthquake precursor instrument platform achieves compatible and universal deployment of heterogeneous terminals, and achieves the ideal function of data servicemiddleware.
- Robust Multi-objective Optimization for BEESM Based on Improved Climbing Algorithm–
A robust multi-objective optimization design method of BEESM based on an improved hill-climbing algorithm that can reduce the computational cost and improve the robustness of the Motor based on improving the performance of the motor.
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- Investigation and Research on the Use of the App of the National Anti Fraud Center–
The project studies the problems existing in the anti fraud App since its launch, such as the imprecise mode setting, the lack of in-depth publicity and education, and the low download rate, and puts forward corresponding solutions to these problems so as to help the App serve people better.
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- The Influence of Python on Logical Thinking and Analysis of Teaching Countermeasures–
The characteristics and advantages of Python language are analyzed, the computer network course is taken as an example, and the countermeasure analysis is put forward to carry out Python programming teaching.
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- Automatic Detection of the Underwater Stationary Artificial Torpedo-shaped Target Based on SAS Image–
Aiming at the problems of speckle noise, seafloor background scattering interference and lack of texture feature in SAS images, an underwater torpedo-shaped artificial target detection algorithm based on Meanshift filtering and Ostu algorithm was proposed.
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- Analysis and Application of Mapreduce Architecture and Working Principle–
The analysis and application of the MapReduce architecture and working principle is described in detail to complete the parallel processing of the massive data.
- The Design and Implementation of Virtual Roaming in Yunnan Diqing Tibetan traditional Villages–
Why and how to use virtual reality technology to better solve the above problems are analyzed, and take the Yunnan Diqing Tibetan traditional dwellings as the specific example to explore.
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- Novel Application of DaaS and Hadoop Technology in Big Data Cloud Computing Platform–
The paper presents novel application of DaaS and Hadoop technology in big data cloud computing platform and users can easily in the application on the development and operation of big data processing.
- Innovation Model Analysis of Data Flow Calculation and Storage Technology based on Cloud Computing–
The paper describes big data acquisition and processing key technology based on data mining and presents innovation model analysis of data flow calculation and storage technologybased on cloud computing.
- Study on Storage life predicting Method of Missile equipment–
The results show that the 2 χ optimizing method is a better fitting algorithm for storage life prediction of missile equipment.
- Public Transportation Bus Trajectory Prediction using Least-Squares Support Vector Machine–
The study shows that the predicted results using the estimated model from LS-SVM are good agreement with the actual test results, and the model can be used for bus arriving time forecasting.
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- Simulation Experiment of Physical Energy Based on Data Mining Technology–
This paper analyzes college students' physical situation and understood the university student sports technological learning makes physical development problems, to guide the student to carry on the sports practice, constantly strengthen and improve the physical development and physical quality.
- Investigations of the Energy-Saving Technology of a Cloud Computing Data Center–
The energy consumption distribution of current data center is shown, some effective energy-saving measures to reduce the energy consumption are presented and an effective way to optimize the air distribution of the data center such as sealing the cold passageway is raised.
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- A Randomized Response Protocol Based on Homomorphic Encryption–
A novel protocol using homomorphic encrypted system is proposed in this paper that uses the Paillier encryption algorithm to encrypt respondents answer and can effectively compute the sum of respondents answer with better privacy protection.
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- Study on Mechanical Automation with Design of Digital Function Generator–
The principle of digital wave form synthesizer is introduced and the specific realization method of the digital waveform synthesizer circuit is explained, and parameter selection of the subsequent simulation processing circuit with 125 KHZ sine signal is provided.
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- Design and Realization of Vehicle Exhaust Gas Measurer Based on CPLD–
A novel vehicle exhaust gas measurer based on CPLD can meet expectant request and design cost is reduced 18% and development cycle decrease to 15 month, the results show.
- Semi-supervised Clustering with Feature Weighting–
Experiment is carried out not only on the single-language data sets, but also the Sino-British data set in the labled document containing only Chinese or English language to verify the validity of the semi-supervised clustering idea.
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- Capture of meaningful faces from motion images–
This paper proposes a method using FFT and DWT to analyze the face definition in motion images, and experimental results show that DWT base on Bior3.1 is the most efficient.
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- Constrained Power Control to Enhance the Capacity of Slotted DS-CDMA Cellular Systems–
An analytical framework is presented to analyze the number of users that can be supported by the proposed constrained power control scheme for direct sequence code division multiple access (DS-CDMA) systems supporting voice and data services and shows that the total interference power can be approximated by the log-normal distribution.
- GIS information organization based on the Semantic Geospatial Web–
This work proposes the information organization framework of the GIS semantic data according to the architecture of the Semantic Web, that is, the ontology, the metadata and the data source, and presents a novel method to disambiguate geographical name based on theOntology of the place.
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- Effects of Natural Language Complexity on Student Performance in Object-Oriented Domain Analysis–
Whether there is more to teaching students how to identify object-oriented (OO) concepts in domain analysis than has perhaps previously been appreciated is examined.
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- Research on the Networking Exploitation Using of Information Resources in District——Demonstration Analysis of Network Information Resources Construction in Guangzhou–
Digitalization of feature resource, resource integration and consciousness of utilizing resource are considered to be aspects that restrict the development of networking exploitation and utilization of information resources in Guangzhou, and a series of measure are listed to improve current situation.
- An enhanced parallel downloading algorithm for BT–
This paper addresses the downloading mechanism of BitTorrent by using neighbours in P2P networks to resolve the redundant copies and to optimise the download speed by using an algorithm to improve its performance.
- [The implementation of sharing treatment planning system based on DICOM].–
Three DICOM objects of RT S. S, RT Plan, RT Image are designed to implement the information sharing of different TPS, and to implemented the information exchanging and sharing of the radiotherapy planning systems of different manufacturers.
- Transform domain smart antennas algorithm for MAI suppression–
A novel transform domain algorithm combined with parameter estimation for MAI suppression is proposed and it converges faster with the same Bit Error Rate(BER) performance.
- [A telemedical consultation system based on multicast].–
A scheme of the telemedical consultation application based on the multicast technology has been designed and the features are: unrestrained joining in and quitting, simultaneous control over the transport information of doctor and patient, extending, and simulating consultation scene.
- A modified chaotic associative memory system for gray-scale images.–
A novel covariance learning rule for multivalue patterns is proposed and applied in memorization of gray-scale images based on modified GCM model (S-GCM) and analysis of the retrieval results are given.
- Optimization of rewriting and complexity of rewriting–
This paper divides non-ambiguous and left-linear term rewriting systems into three subclasses: variable-more, variable-equal, and variable-fewer, and proposes optimal strategies for the two former subclasses and an approximate strategy for the last one.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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