Redundancy Elimination Research Articles

MG+: Towards Efficient Context Inconsistency Detection by Minimized Link Generation

ABSTRACTSelf‐adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location‐aware navigation or resource‐sensitive suggestions. However, due to inherent noises in the process of sensing and interpreting environmental information, there is a growing demand for guarding the consistency of collected contexts to avoid application misbehaviour and, at the same time, minimize extra costs. Existing work attempted to achieve this by speeding up the kernel constraint checking module inside the consistency guarding process. Most of these efforts were spent on reusing previous checking results or parallelizing the checking process, but they all leave one central step of constraint checking, that is, link generation, untouched. In this step, the checking engine provides reasons to explain the violation of constraints under check. It occupies a substantial part of the total time cost. Focusing on this key link generation step, we proposed MG, which deploys a rigourous analysis to automatically identify and avoid redundancy in the link generation without harming any correctness of the checking results. MG has been proven sound (always guaranteeing correctness) and complete (entirely removing redundancy). Moreover, based on our observation that MG's redundancy elimination also assists another core step of constraint checking to reduce unnecessary computation further, we additionally enhance MG with an escape‐condition optimization to escape unnecessary evaluation of truth values to further improve the efficiency of constraint checking in an aspect other than link generation. We call it MG+ for distinguishing. Our experiments with synthesized and real‐world consistency constraints reported that, compared with existing work, MG eliminates all link redundancy (83% to 0%), and based on it, MG+ further reduces significant truth value calculations (e.g., 49.74% reduction when combined with ECC and Con‐C). Generally, MG brought 14–500 speed‐ups in link generation, and MG+ further made 1.2–1.9 speed‐ups in truth value evaluation. Altogether, MG reduced the total constraint checking time up to 45.4%, and MG+ reduced it up to 61.0%.

Translating Four-Character Structures in Chinese Literary Works on Traditional Architecture—A Case Analysis of Canal Towns South of the Yangtze and Folk Houses South of the Yangtze

Traditional Chinese architecture boasts a unique style and profound cultural connotations. Accordingly, the related literary works attain value for international dissemination. The four-character structures in these works serve as concise expressions for describing architectural details and design concepts. However, due to their linguistic implicitness, grammatical structure diversity, and rich cultural images, their translation into English requires multidimensional shift, redundancy elimination, and the avoidance of cultural defaults. This research has developed an effective translation workflow that leverages the synergistic strengths of Large Language Models (LLMs) and the human translator. By strategically employing tailored methods and techniques, it addresses the challenges in the translation process. Analysis suggests: It is recommended that the technique of shift be employed to address the implicit content and complex grammatical structures in translating four-character structures in Chinese literary works on traditional architecture into English. Besides, the selective application of combination and omission can help deal with information redundancy. Furthermore, it is advisable to apply foreignization and a combined method of literal translation and paraphrasing in conveying cultural images. These approaches will, to a certain extent, convert the untranslatability of four-character structures into translatability, enhancing the dissemination of the essence of traditional Chinese architecture.

К ассоциативной типологии в контексте изучения языка как достояния человека

The article discusses a number of fundamental for the development of associative typology propositions connected with systemogenesis of language as a human species- specific phenomenon. Using the example of the psychological meanings of lexical units that make up the ethnopolitically marked paradigm of the “national”, the author reflects on the patterns in the functioning of a living language, which are revealed on associative- verbal models of the Russian Language Personality (RLP) and can serve as the basis for the development of an associative typology of language. The article considers the problem of ontology and functional motivation of associative (active) grammar (as an inherent property of a linguistic personality) on a semantic basis. The hypothesis of associative grammar as a factor contributing to the integrative formation of meaningful units in the course of their being recruited for the inclusion into the speech flow through elimination of redundancy is proposed for consideration. It is stated that different functionally specific types of techniques aimed at elimination of redundancy and discovered via experiment can be considered as a subject for analysis in associative typology in the light of whole-systemic comparative analyses of network models of an average (national) linguistic personality. They can be described in terms of associative dominants (accentuations of a linguistic personality) and statistically formalized using vector models represented as psychoglosses and /or chronoglosses (in current diachrony).

PEO-Store: Delegation-Proof Based Oblivious Storage With Secure Redundancy Elimination

PEO-Store: Delegation-Proof Based Oblivious Storage With Secure Redundancy Elimination

GSAAN with War Strategy Optimization Algorithm for Cyber Security in a 5G Wireless Communication Network

In this paper, the graph sample and aggregate-attention network with war strategy optimization algorithm for cyber security in the 5G wireless communication network (CS-5GWCN-GSAAN-WSOA) is proposed in 5G mobile networks to identify cyber threats. Initially, the input data are amassed from the 5G-NIDD dataset. Then the input data are fed to preprocessing, here redundancy elimination and missing value replacement are performed utilizing the contrast limited adaptive histogram equalization filtering (CLAHEF) method. After preprocessing, optimal features are selected by the stochastic gradient boosting based recursive feature elimination process. The selected features are fed to the graph sample and aggregate-attention network (GSAAN) to detect cyber-attacks in 5G wireless communication. Generally, the GSAAN approach does not adopt any optimization techniques for determining optimal parameters and guaranteeing accurate attack detection. Therefore, the war strategy optimization algorithm (WSOA) contemplates to optimize the GSAAN weight parameters. The CS-5GWCN-GSAAN-WSOA method is activated on Python. The CS-5GWCN-GSAAN-WSOA method attains 22.51%, 20.35%, and 35.54% higher accuracy, 19.45%, 27.80%, and 18.93% higher sensitivity analysed with existing models, like cyber security attack identification in 5G wireless systems utilizing machine learning (CS-5GWCN-SVM), enhanced dropping attacks detection in 5G networks depending on deep learning models (CS-5GWCN-KNN), and cyber security issues in 5G enabled attack detection through deep learning (CS-5GWCN-RNN), respectively.

UKRAINIAN PHD STUDENTS' ATTITUDES TOWARD AI LANGUAGE PROCESSING TOOLS IN THE CONTEXT OF ENGLISH FOR ACADEMIC PURPOSES

Mastering academic writing skills in English is essential for future researchers. At present, AI language processing tools provide high-quality, accessible, and fast assistance for translation, editing, and stylistic enhancement of scientific texts. However, their use within English for Academic Purposes (EAP) courses generates mixed reactions among educators and raises ethical concerns. Our study aimed to explore the predominant perceptions of AI language processing tools by PhD students of the National Academy of Sciences of Ukraine (NASU) from the viewpoint of their integration into the academic English course taught in the first year of their PhD studies. The study involved 52 PhD students from various NASU institutes. They completed a survey with both closed-ended and open-ended questions regarding their previous and expected use of online translators, writing enhancement tools, and ChatGPT for research writing purposes. The results of the survey show that NASU PhD students have extensive experience with online translators, but are less familiar with writing enhancement tools and less certain about their potential use in the future. Almost a third of the respondents expressed reservations about using ChatGPT for academic writing due to academic integrity concerns. Most of the respondents (66%) agree that the basics of ethical use of AI writing and editing tools should be incorporated into EAP courses. One subgroup of the participants (n = 11) took part in a small-scale additional intervention focused on writing enhancement tools. They were asked to apply Grammarly, QuillBot, and ChatGPT to edit their course projects (presentations of the current state of their dissertation research) and compare these tools according to various criteria. The feedback provided by this subgroup indicates that they were most satisfied with the quality of editing provided by ChatGPT but found Grammarly and QuillBot easier to use and more suitable for superficial grammar checks. We found out that the AI tools helped participants achieve improvements primarily in such aspects as the use of articles, punctuation, use of prepositions, and elimination of redundancy. The study has significant pedagogical implications, promoting the wider use of AI tools in the context of teaching English for Academic Purposes and addressing appropriate teaching techniques and methods.

Utilizing Heuristics and Metaheuristics for Solving the Set Covering Problem

A basic combinatorial optimization problem, the Set Covering Problem (SCP) finds extensive use in computer science, operations research, and logistics, among other domains. The SCP’s goal is to determine the smallest number of subsets, or sets, needed to cover every element precisely once given a finite set of items and a collection of subsets of these elements. Numerous practical uses for the SCP exist, such as crew scheduling, truck routing, and facility locating. This paper focuses on obtaining feasible solutions, applying to the obtained solutions constructive heuristics (CH), followed by a redundancy elimination procedure to remove unnecessary sets. To further optimize the quality of the solution, a local search method is also implemented based on the First and Best improvement algorithms. Additionally, the Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Search (VNS) metaheuristics are designed and implemented. Each implemented heuristic underwent testing across 42 instances, with the average deviation from the optimal solution calculated for each instance. The GRASP heuristic demonstrated the most favorable performance, achieving a maximum deviation of 2.26% from the optimal solution, while the VNS approach yielded a maximum deviation of 11.46% from the optimal solution at its best.

Utilizing Heuristics and Metaheuristics for Solving the Set Covering Problem

A basic combinatorial optimization problem, the Set Covering Problem (SCP) finds extensive use in computer science, operations research, and logistics, among other domains. The SCP’s goal is to determine the smallest number of subsets, or sets, needed to cover every element precisely once given a finite set of items and a collection of subsets of these elements. Numerous practical uses for the SCP exist, such as crew scheduling, truck routing, and facility locating. This paper focuses on obtaining feasible solutions, applying to the obtained solutions constructive heuristics (CH), followed by a redundancy elimination procedure to remove unnecessary sets. To further optimize the quality of the solution, a local search method is also implemented based on the First and Best improvement algorithms. Additionally, the Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Search (VNS) metaheuristics are designed and implemented. Each implemented heuristic underwent testing across 42 instances, with the average deviation from the optimal solution calculated for each instance. The GRASP heuristic demonstrated the most favorable performance, achieving a maximum deviation of 2.26% from the optimal solution, while the VNS approach yielded a maximum deviation of 11.46% from the optimal solution at its best.

A prospective study of the proteome of equine pre-implantation embryo.

The present study was conducted to investigate the global proteome of 8-day-old equine blastocysts. Follicular dynamics of eight adult mares were monitored by ultrasonography and inseminated 24 h after the detection of a preovulatory follicle. Four expanded blastocysts were recovered, pooled, and subjected to protein extraction and mass spectrometry. Protein identification was conducted based on four database searches (PEAKS, Proteome Discoverer software, SearchGUI software, and PepExplorer). Enrichment analysis was performed using g:Profiler, Panther, and String platforms. After the elimination of identification redundancies among search tools (at three levels, based on identifiers, peptides, and cross-database mapping), 1977 proteins were reliably identified in the samples of equine embryos. Proteomic analysis unveiled robust metabolic activity in the 8-day equine embryo, highlighted by an abundance of proteins engaged in key metabolic pathways like the TCA cycle, ATP biosynthesis, and glycolysis. The prevalence of chaperones among highly abundant proteins suggests that regulation of protein folding, and degradation is a key process during embryo development. These findings pave the way for developing new strategies to improve equine embryo media and optimize invitro fertilization techniques.

The role of link redundancy and structural heterogeneity in network disintegration

While link redundancy has long been acknowledged as a critical factor in network robustness, current approaches frequently neglect the inherent heterogeneity of structure, thereby falling short of optimal network disintegration. This study introduces the novel Neighborhood Dissimilarity Community Heterogeneity (NDCH) framework, which systematically investigates redundancy elimination from a new perspective of neighborhood dissimilarity, while concurrently integrating structural heterogeneity derived from community structure. Extensive experiments on both synthetic and diverse real-world networks reveal that strategies developed under NDCH framework markedly outperform existing state-of-the-art methods in network disintegration, with performance improvements reaching up to 60.151% and 31.000% for Schneider R and the critical removal fraction fc, respectively. Notably, additional analysis consistently indicates low correlations (Kendall’s Tau < 0.6) and distinct value distributions between NDCH-based strategies and the state-of-the-art approaches. In summary, the novel framework underscores the significance of both redundancy and structural heterogeneity when devising network disintegration strategies, offering a substantial leap forward in enhancing network robustness against malicious attacks and various disruptions, especially in infrastructure networks.

The Distributed Multi Constrained Channel Access Mechanism By Using Framework For Mobile Adhoc Network

The Mobile Ad hoc Network (MANET) is an autonomous wireless communication system, and does not have any fixed infrastructure. The Network loads which arise due to application evolving using MANET environment has to balance using routing protocol. Networks are increasingly resource intensive to efficiently manage the bandwidth &amp; Energy in the group communication on basis of addressing the routing issues to provide the enhanced functionality of the network on basis of channel utilization and channel conditioning against load balancing. In this research, proposed a novel unified framework for Distributed multi constrained Channel Access Mechanism for mobile Adhoc Network on inclusion of Scheduled Throughput Discriminant Multi-Data synchronization Strategies for Channel Allocation in Mobile Adhoc Network on various traffic patterns, Multi Trajectory Particle Swarm Optimization towards Optimal Velocity Control against load balancing and band width controlling and finally Self Adaptive Redundancy Elimination Clustering and Distributed Load Bandwidth Management Constraints for Mobile Ad Hoc Network.

Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning

We introduce Sauron, a filter pruning method that eliminates redundant feature maps of convolutional neural networks (CNNs). Sauron optimizes, jointly with the loss function, a regularization term that promotes feature maps clustering at each convolutional layer by reducing the distance between feature maps. Sauron then eliminates the filters corresponding to the redundant feature maps by using automatically adjusted layer-specific thresholds. Unlike most filter pruning methods, Sauron requires minimal changes to typical neural network optimization because it prunes and optimizes CNNs jointly, which, in turn, accelerates the optimization over time. Moreover, unlike with other cluster-based approaches, the user does not need to specify the number of clusters in advance, a hyperparameter that is difficult to tune. We evaluated Sauron and five state-of-the-art filter pruning methods on four medical image segmentation tasks. This is an area where little attention has been paid to filter pruning, but where smaller CNN models are desirable for local deployment, mitigating privacy concerns associated with cloud-based solutions. Sauron was the only method that achieved a reduction in model size of over 90% without deteriorating substantially the performance. Sauron also achieved, overall, the fastest models at inference time in machines with and without GPUs. Finally, we show through experiments that the feature maps of models pruned with Sauron are highly interpretable, which is essential for medical image segmentation.

Proposed fog computing-enabled conceptual model for semantic interoperability in internet of things

Semantic interoperability has emerged as a key barrier amidst the major developments and challenges brought about by the rapid expansion of internet of things (IoT) applications. Establishing interoperability is essential for IoT systems to function optimally, especially across diverse organizations. Despite extensive research in achieving semantic interoperability, dynamic interoperability, a vital facet, remains inadequately addressed. This paper addresses this gap by presenting a fog-based conceptual model designed to facilitate dynamic semantic interoperability in IoT. The model incorporates a single-tier fog layer, providing the necessary processing capabilities to achieve this goal. The study conducts a comprehensive literature review on semantic interoperability, emphasizing latency, bandwidth, total cost, and energy consumption. Results demonstrate the proposed double skin façade (DSF) model’s remarkable 88% improvement in service delay over IoT-SIM and Open IoT, attributed to its efficient load-offloading mechanism and optimized fog layer, offering a 50% reduction in service delay, power consumption, and 86% reduction in network usage compared to existing approaches through data redundancy elimination via pre-processing at the fog layer.

A fragmentation-aware redundancy elimination scheme for inline backup systems

Data deduplication is a widely employed technique in backup systems to enhance storage efficiency by eliminating duplicate chunks. Delta compression is a technique that complements deduplication by removing redundant data between similar chunks. However, when integrated into deduplication-based backup systems, delta compression can considerably decrease backup throughput due to the additional I/Os for fetching base chunks. Moreover, data deduplication can lead to chunk fragmentation, causing continuous chunks in a backup to become fragmented. We observe that the number of additional I/Os for fetching base chunks when delta compression is applied depends on the degree of chunk fragmentation. When chunk fragmentation is not significant, delta compression can improve storage efficiency without significantly impacting backup throughput. Based on this observation, we propose a redundancy elimination approach called Fragmentation-aware Redundancy Elimination (FaRE) for in-line backup systems.The main idea behind FaRE is the combined use of three techniques: fragmentation estimation for assessing the degree of chunk fragmentation, sequential deduplication to significantly reduce chunk fragmentation by only deduplicating against chunks with sequential layout when fragmentation is severe, and local redundancy elimination to greatly enhance storage efficiency by performing both deduplication and delta compression when chunk fragmentation is not significant. Our evaluation results demonstrate that FaRE achieves higher storage efficiency and restoration performance compared to traditional approaches that only adopt deduplication for redundancy elimination, while achieving comparable backup throughput due to the limited number of additional I/Os required.

A Class Specific Feature Selection Method for Improving the Performance of Text Classification

Recently, a significant amount of research work has been carried out in the field of feature selection. Although these methods help to increase the accuracy of the machine learning classification, the selected subset of features considers all the classes and may not select recommendable features for a particular class. The main goal of our paper is to propose a new class-specific feature selection algorithm that is capable of selecting an appropriate subset of features for each class. In this regard, we first perform class binarization and then select the best features for each class. During the feature selection process, we deal with class imbalance problems and redundancy elimination. The Weighted Average Voting Ensemble method is used for the final classification. Finally, we carry out experiments to compare our proposed feature selection approach with the existing popular feature selection methods. The results prove that our feature selection method outperforms the existing methods with an accuracy of more than 37%.

Energy Efficient Localization Technique Using Multilateration for Reduction of Spatially and Temporally Correlated Data in RFID System

RFID plays a vital role in data communication in multidimensional WSNs as it collects vast amounts of redundant data. The physical phenomena constitute the correlated observations in the space domain and generate spatial correlation. Periodic observations of sensor nodes result in a temporal correlation in the data. Reducing these spatio-temporal correlations in RFID surveillance data is necessary for the smooth functioning of the network. This paper proposes a Voronoi diagram-based spatio-temporal data redundancy elimination approach for RFID systems having multiple readers so only one reader will read every RFID tag depending on the distance between the tag and the center of the Minimum Enclosing Circle of the Voronoi cell to which the reader belongs. This approach eliminates spatial redundancy in the gathered data. Reading the RFID tags at regular time intervals larger than a chosen threshold value minimized temporal redundancy. In contrast to existing methods, the proposed technique is free from any false positive and false negative errors, with no loss of data and every tag being read by only one reader. Simulation of the proposed approach also established its superiority to the existing techniques in terms of these performance parameters.

Enhanced dynamic fine‐grained popularity‐based caching algorithm for ICN‐based edge computing networks

AbstractIn this letter, we propose an Enhanced Dynamic Fine‐Grained Popularity‐based Caching (ED‐FGPC) algorithm to efficiently cache static contents in ICN‐based edge computing networks. The content popularity changes with time. Thus, the ED‐FGPC algorithm considers the number of incoming interests, the practical file size, and the available cache size to adjust the content popularity threshold. This allows dynamic adjustment of the content popularity threshold. Additionally, ED‐FGPC incorporates a single bit called cached bit (CB) in the header field of the content. This eliminates caching redundancy in the on‐path routers. The cache eviction policy jointly considers the content popularity and the average content access timestamp for content replacement. Based on the content access pattern, this allows finer adjustment of the content eviction policy. We use analytical and mathematical modeling to derive the effectiveness and efficiency of our contribution. Results present a better hit ratio due to dynamic adjustment of the content popularity threshold and the elimination of caching redundancy from the on‐path routers.

Cognitive simulation along with neural adaptation explain effects of suggestions: a novel theoretical framework.

Hypnosis is an effective intervention with proven efficacy that is employed in clinical settings and for investigating various cognitive processes. Despite their practical success, no consensus exists regarding the mechanisms underlying well-established hypnotic phenomena. Here, we suggest a new framework called the Simulation-Adaptation Theory of Hypnosis (SATH). SATH expands the predictive coding framework by focusing on (a) redundancy elimination in generative models using intrinsically generated prediction errors, (b) adaptation due to amplified or prolonged neural activity, and (c) using internally generated predictions as a venue for learning new associations. The core of our treatise is that simulating proprioceptive, interoceptive, and exteroceptive signals, along with the top-down attenuation of the precision of sensory prediction errors due to neural adaptation, can explain objective and subjective hypnotic phenomena. Based on these postulations, we offer mechanistic explanations for critical categories of direct verbal suggestions, including (1) direct-ideomotor, (2) challenge-ideomotor, (3) perceptual, and (4) cognitive suggestions. Notably, we argue that besides explaining objective responses, SATH accounts for the subjective effects of suggestions, i.e., the change in the sense of agency and reality. Finally, we discuss individual differences in hypnotizability and how SATH accommodates them. We believe that SATH is exhaustive and parsimonious in its scope, can explain a wide range of hypnotic phenomena without contradiction, and provides a host of testable predictions for future research.

Enabling Efficient Deep Learning on MCU with Transient Redundancy Elimination

Enabling Efficient Deep Learning on MCU with Transient Redundancy Elimination

Exploiting Sparse Self-Representation and Particle Swarm Optimization for CNN Compression.

Structured pruning has received ever-increasing attention as a method for compressing convolutional neural networks. However, most existing methods directly prune the network structure according to the statistical information of the parameters. Besides, these methods differentiate the pruning rates only in each pruning stage or even use the same pruning rate across all layers, rather than using learnable parameters. In this article, we propose a network redundancy elimination approach guided by the pruned model. Our proposed method can easily tackle multiple architectures and is scalable to the deeper neural networks because of the use of joint optimization during the pruning procedure. More specifically, we first construct a sparse self-representation for the filters or neurons of the well-trained model, which is useful for analyzing the relationship among filters. Then, we employ particle swarm optimization to learn pruning rates in a layerwise manner according to the performance of the pruned model, which can determine optimal pruning rates with the best performance of the pruned model. Under this criterion, the proposed pruning approach can remove more parameters without undermining the performance of the model. Experimental results demonstrate the effectiveness of our proposed method on different datasets and different architectures. For example, it can reduce 58.1% FLOPs for ResNet50 on ImageNet with only a 1.6% top-five error increase and 44.1% FLOPs for FCN_ResNet50 on COCO2017 with a 3% error increase, outperforming most state-of-the-art methods.