Space Complexity Research Articles

Parameter-Free and Scalable Incomplete Multiview Clustering With Prototype Graph.

Multiview clustering (MVC) seamlessly combines homogeneous information and allocates data samples into different communities, which has shown significant effectiveness for unsupervised tasks in recent years. However, some views of samples may be incomplete due to unfinished data collection or storage failure in reality, which refers to the so-called incomplete multiview clustering (IMVC). Despite many IMVC pioneer frameworks have been introduced, the majority of their approaches are limited by the cubic time complexity and quadratic space complexity which heavily prevent them from being employed in large-scale IMVC tasks. Moreover, the massively introduced hyper-parameters in existing methods are not practical in real applications. Inspired by recent unsupervised multiview prototype progress, we propose a novel parameter-free and scalable incomplete multiview clustering framework with the prototype graph termed PSIMVC-PG to solve the aforementioned issues. Different from existing full pair-wise graph studying, we construct an incomplete prototype graph to flexibly capture the relations between existing instances and discriminate prototypes. Moreover, PSIMVC-PG can directly obtain the prototype graph without pre-process of searching hyper-parameters. We conduct massive experiments on various incomplete multiview tasks, and the performances show clear advantages over existing methods. The code of PSIMVC-PG can be publicly downloaded at https://github.com/wangsiwei2010/PSIMVC-PG.

Quantum enhanced solution method for platform clustering grouping based on quantum K-means

The paper proposes a quantum enhanced solution method based on quantum K-means for platform clustering and grouping in joint operations campaigns. The method first calculates the number of categories for platform clustering based on the determined number of task clusters, and sets the number of clustering categories in the classical K-means algorithm. By using the location information of the tasks, the clustering center points are calculated and derived. Secondly, the Euclidean distance is used as an indicator to measure the distance between the platform data and each cluster center point. The platform data are quantized and transformed into their corresponding quantum state representations. According to theoretical derivation, the Euclidean distance solution is transformed into the quantum state inner product solution. By designing and constructing a universal quantum state inner product solution quantum circuit, the Euclidean distance solution is completed. Then, based on the sum of squared errors of the clustering dataset, the corresponding quantum circuits are constructed through calculation and deduction. The experimental results show that compared with the classical K-means algorithm, the proposed method not only effectively solves the platform clustering and grouping problem on such action scales, but also significantly reduces the time and space complexity of the algorithm .

New Scalable and Efficient Online Pairwise Learning Algorithm.

Pairwise learning is an important machine-learning topic with many practical applications. An online algorithm is the first choice for processing streaming data and is preferred for handling large-scale pairwise learning problems. However, existing online pairwise learning algorithms are not scalable and efficient enough for large-scale high-dimensional data, because they were designed based on singly stochastic gradients. To address this challenging problem, in this article, we propose a dynamic doubly stochastic gradient algorithm (D2SG) for online pairwise learning. Especially, only the time and space complexities of O (d) are needed for incorporating a new sample, where d is the dimensionality of data. This means that our D2SG is much faster and more scalable than the existing online pairwise learning algorithms while the statistical accuracy can be guaranteed through our rigorous theoretical analysis under standard assumptions. The experimental results on a variety of real-world datasets not only confirm the theoretical result of our new D2SG algorithm, but also show that D2SG has better efficiency and scalability than the existing online pairwise learning algorithms.

A Nonaugmented Method for the Minimal Observability of Boolean Networks.

This article proposes a nonaugmented method for investigating the minimal observability problem of Boolean networks (BNs). This method can be applied to more general BNs and reduce the computational and space complexity of existing results. First, unobservable states concerning an unobservable BN are classified into three categories using the vertex-colored state transition graph, each accompanied by a necessary and sufficient condition for determining additional measurements to make them distinguishable. Then, an algorithm is designed to identify the additional measurements that would render an unobservable BN observable using the conditions. Next, to determine the minimum added measurements, a necessary and sufficient condition and an algorithm based on a constructed matrix are presented. Finally, the results obtained are compared with existing literature and illustrated with examples.

Deep Supervised Multi-View Learning with Graph Priors.

This paper presents a novel method for supervised multi-view representation learning, which projects multiple views into a latent common space while preserving the discrimination and intrinsic structure of each view. Specifically, an apriori discriminant similarity graph is first constructed based on labels and pairwise relationships of multi-view inputs. Then, view-specific networks progressively map inputs to common representations whose affinity approximates the constructed graph. To achieve graph consistency, discrimination, and cross-view invariance, the similarity graph is enforced to meet the following constraints: 1) pairwise relationship should be consistent between the input space and common space for each view; 2) within-class similarity is larger than any between-class similarity for each view; 3) the inter-view samples from the same (or different) classes are mutually similar (or dissimilar). Consequently, the intrinsic structure and discrimination are preserved in the latent common space using an apriori approximation schema. Moreover, we present a sampling strategy to approach a sub-graph sampled from the whole similarity structure instead of approximating the graph of the whole dataset explicitly, thus benefiting lower space complexity and the capability of handling large-scale multi-view datasets. Extensive experiments show the promising performance of our method on five datasets by comparing it with 18 state-of-the-art methods.

A secure authenticated image encryption scheme based on elliptic curve cryptography

The picture archiving and communication system is a powerful mechanism to store and securely transmit images and reports. The development of a cost-effective encryption algorithm in terms of time and space complexity is needed. Our main goal is to protect the content and integrity of the image being transmitted. We implement an efficient authenticated encryption algorithm based on the Lehmer Random Number Generator, the Sophie Germain Prime Generator and Elliptic Curve Cryptography to transmit the digitally preserved images (including medical) which serves our purpose. Our method achieves randomness along with authentication with the help of random number generator and Elliptic Curve Cryptography which is unique compared to the existing schemes. The performance metrics of the proposed system stay on top of the state-of-the-art techniques that were recently derived. We obtained negative correlation coefficient values of −0.000242, −0.001349, −0.001313 for vertical, horizontal, and diagonal coefficients respectively. This makes our scheme better as it is difficult to find any relationship between the original and encrypted image. On average, our method uses only 10 seconds for encryption using a 521-bit curve.

High-performance unified modular multiplication algorithm and hardware architecture over G(2m)

The finite field Modular Multiplier (MM) over GF (2m) based on the National Institute of Standards and Technology (NIST) recommended polynomials can be used as a critical component in many cryptosystems. But much of the research suffers from the problems of only supporting a single curve or being very inefficient. In this paper, we present a KA-based unified algorithm (hybrid field size) and its corresponding architecture for high-performance implementation of the systolic modular multiplier over GF (2m), which can support all the NIST recommended polynomials. A number of efficient techniques have been explored and used to realize a high-performance implementation of the multiplier. First, we propose a three-term KA based MM approach and make it fit for systolic implementation. Second, we propose a configurable, unified irreducible polynomial that can support all five curves recommended by NIST and the corresponding modular reduction method. Third, the corresponding systolic structure is also designed in accordance with (1) and (2) to improve efficiency, and we adopt register sharing and computation unit sharing methods to reduce the space complexity of the proposed structure. Additionally, parallel computation of two163-width MMs is employed to improve resource utilization further. From the synthesis results on ASIC, it is shown that the proposed multipliers have significantly lower latency and higher performance than the existing designs. e.g., the proposed structure could achieve up to a 73 % reduction in area-delay product (ADP) over the existing structures.

Cucconi Feature Extracted Random Decision Forest Classification for Efficient Sentiment Analysis

Sentiment is the method of conveying the opinion by the customer for particular product e-commerce website. Sentiment analysis is otherwise termed as opinion mining that identifies the polarity of extracted opinions. Nowadays, opinion mining concept received large interest by many researchers to identify the polarity of the statements. A number of scholars have conducted their research on efficient sentiment analysis with better accuracy. But the space complexity and time complexity performance did not reduced using existing classification techniques. To deal with these issues, CFDFC (Cucconi Feature Extracted Random Decision Forest Classification) Approach is presented. The primary aim of CFDFC Approach is to perform effective sentiment analysis with improved accuracy and reduced time complexity. The number of user review statement is collected from the input dataset. The proposed CFDFC Technique comprises of pre-processes, feature extractions, and classifications. Pre-processes eliminate stop words and stem words from user reviews. After pre-processing, the features (i.e. keywords) extraction process is performed to minimize the dimensionality and time consumption for opinion classification. This work uses cucconi projective feature extractions in CFDFC to squeeze out opinion words from reviews where cucconi projective feature extraction is generally a dimensionality reduction technique used for identifying the projections in multidimensional data. Finally, the classification process is carried out using random decision forest classifier. The random decision forest classifier uses the ID3 DT (decision tree) as a weak learner to classify the review statements with the extracted features. By using the voting scheme, random decision forest classifications finally classify instances with higher accuracy. This again is useful to boost the performance of sentiment analysis. The comprehensive experimental assessment is carried out using various parameters including accuracy, error rates, recall values , time and space complexities with regard to the number of review statements gathered from the dataset.The statistical results obtained show that the proposed CFDFC Technique achieves remarkable accuracy, recall, and minimal time complexity than the current methods.

A secured energy aware resource allocation and task scheduling based on improved cuckoo search algorithm and deep reinforcement learning for e-healthcare applications

Healthcare industries have begun using modern technologies and tools like cloud computing these days. When cloud computing and assisted technologies are used in the healthcare domain the storage cost improves along with the quality and time availability. The present work makes use of cryptography based architecture for improving the overall security aspects. However, the tasks of the user take a longer duration for the execution to complete when the needed resources are unavailable on the main server. ICSA (Improved Cuckoo Search Algorithm) with DRL (Deep Reinforcement Learning) to solve this issue. The main goal of this work is to increase overall security and reduce execution times of jobs. The objective also includes using underutilized resources. Scheduling jobs, Binary In-order Traversal Trees with weights are used. Subsequently, DRL algorithm has been used to decrease the space complexity by splitting the individual resources. There will be idle resources in a state space that are used in allocation of tasks. The scheduled tasks will then do a search on the resources based on the ICSA algorithm. The server will then distribute the resources to the action area after an optimal resource has been chosen and assigned to the job. DRL for resource allocation so that the inactive resource usage is minimized and the execution time is reduced. Finally, MTAC-IBEA (multi tenant authentication control with improved blowfish encryption algorithm) for decrypt health data. The outcomes of simulation demonstrate that the suggested method offers optimum work schedules, resource allocations, and security in e-healthcare systems.

Simulation of quantum optics by coherent state decomposition

We introduce a framework for simulating quantum optics by decomposing the system into a finite rank (number of terms) superposition of coherent states. This allows us to define a resource theory, where linear optical operations are “free” (i.e., do not increase the rank), and the simulation complexity for an m-mode system scales quadratically in m, in stark contrast to the Hilbert space dimension. We outline this approach explicitly in the Fock basis, relevant in particular for Boson sampling, where the simulation time (space) complexity for computing output amplitudes, to arbitrary accuracy, scales as O(m2 2 n ) [O(m2 n )] for n photons distributed among m modes. We additionally demonstrate that linear optical simulations with the n photons initially in the same mode scales efficiently, as O(m2 n). This paradigm provides a practical notion of “non-classicality,” i.e., the classical resources required for simulation. Moreover, by making connections to the stellar rank formalism, we show this comes from two independent contributions, the number of single-photon additions and the amount of squeezing.

Algorithm 1038: KCC: A MATLAB Package for k -Means-based Consensus Clustering

Consensus clustering is gaining increasing attention for its high quality and robustness. In particular, k -means-based Consensus Clustering (KCC) converts the usual computationally expensive problem to a classic k -means clustering with generalized utility functions, bringing potentials for large-scale data clustering on different types of data. Despite KCC’s applicability and generalizability, implementing this method such as representing the binary dataset in the k -means heuristic is challenging and has seldom been discussed in prior work. To fill this gap, we present a MATLAB package, KCC, that completely implements the KCC framework and utilizes a sparse representation technique to achieve a low space complexity. Compared to alternative consensus clustering packages, the KCC package is of high flexibility, efficiency, and effectiveness. Extensive numerical experiments are also included to show its usability on real-world datasets.

Model checking combined trust and commitments in Multi-Agent Systems

Trust and social commitments have been studied with different objectives for communication in Multi-Agent Systems (MASs) separately. The purpose of this paper is to present the first logical framework to explore the relationship between two key concepts in autonomous MASs, namely trust and commitments among agents, along with its model checking algorithms. An analysis is performed on this relationship, with a specific emphasis on the perspectives of semantics and model checking. The analysis is carried out within the framework of Trust Computation Tree Logic with Commitments (CTLC) logic, an extended logic built on top of the standard branching temporal Computation Tree Logic (CTL) by including modalities for reasoning about commitments and their fulfillment. Moreover, the study employs a trust operator to facilitate trust reasoning. We propose thus Trust Computation Tree Logic with Commitments (TCTLC), a new logic able to express properties about trust and commitments simultaneously. Such rich properties cannot be expressed in other languages. We introduce the semantics using the extended formalism of interpreted systems. We propose some postulates and proofs that show how we can reason about combined trust and commitments in the logic proposed. New Binary Decision Diagram (BDD)-based algorithms for our logic are presented and implemented as additional libraries of the Model Checker for Multi-Agent Systems (MCMAS), the default model checker of MASs. Furthermore, we prove that although TCTLC extends CTL, its model checking algorithm remains polynomial (P-complete) with respect to the size of the model and the length of the formula. Similarly, the space complexity for concurrent programs also remains unchanged, which is polynomial (PSPACE-complete) with respect to the size of the components within these programs. Finally, to evaluate the proposed technique, we report the experimental results using an industrial case study and compare the results with those obtained on relevant benchmarks.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows.

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

Robust DTW-based entropy fuzzy clustering of time series

AbstractTime series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures.

Algorithmic Complexity Attacks on Dynamic Learned Indexes

Learned Index Structures (LIS) view a sorted index as a model that learns the data distribution, takes a data element key as input, and outputs the predicted position of the key. The original LIS can only handle lookup operations with no support for updates, rendering it impractical to use for typical workloads. To address this limitation, recent studies have focused on designing efficient dynamic learned indexes. ALEX, as the first and one of the representative dynamic learned index structures, enables dynamism by incorporating a series of design choices, including adaptive key space partitioning, dynamic model retraining, and sophisticated engineering and policies that prioritize read/write performance. While these design choices offer improved average-case performance, the emphasis on flexibility and performance increases the attack surface by allowing adversarial behaviors that maximize ALEX's memory space and time complexity in worst-case scenarios. In this work, we present the first systematic investigation of algorithmic complexity attacks (ACAs) targeting the worst-case scenarios of ALEX. We introduce new ACAs that fall into two categories, space ACAs and time ACAs, which target the memory space and time complexity, respectively. First, our space ACA on data nodes exploits ALEX's gapped array layout and uses Multiple-Choice Knapsack (MCK) to generate an optimal adversarial insertion plan for maximizing the memory consumption at the data node level. Second, our space ACA on internal nodes exploits ALEX's catastrophic cost mitigation mechanism, causing an out-of-memory (OOM) error with only a few hundred adversarial insertions. Third, our time ACA generates pathological insertions to increase the disparity between the actual key distribution and the linear models of data nodes, deteriorating the runtime performance by up to 1, 641× compared to ALEX operating under legitimate workloads.

Ensemble model for accuracy prediction of protein secondary structure

<span>Predicting a protein’s secondary structure is crucial for understanding the working of proteins. Despite advancements over the years, the top predictors have achieved only 80% Q8 accuracy when sequence profile information is the sole input. An ensemble approach is proposed using convolutional neural network (CNN) and a classifier known as support vector machine (SVM) on both the partial and the whole CullPDB datasets. The protein secondary structure (PSS) has a complex hierarchical structure, as well as the ability to take into account the reliance between neighbouring labels. A detailed experiment yielding high levels of Q8 accuracy with scores of 97.91%, 85.13%, and 78.02% using 20%, 80%, and 100% respectively of the protein residues on the new predicted dataset CullPDB6133 which is better than the accuracies predicted by similar models. The proposed methodology highlights the use of CNN as a general framework, for efficiently predicting eight-state (Q8) accuracy of secondary protein structures with a low time and space complexity.</span>

HISTORY OF ORIGIN, ADVANTAGES AND DISADVANTAGES, VECTORS OF APPLICATION OF THE DIAPHONIZATION METHOD: CURRENT STATE OF THE PROBLEM.

The article deals with the method of diaphonization, which consists in clarification and staining of tissues, organs and whole organisms for long-term storage in the form of preparations as one of the modern methods of morphological research and, in general, a powerful tool for studying the internal structure and topography of organisms. The authors have analyzed the history of the diaphonization method, its advantages and disadvantages. Its advantages include the ability to study the internal anatomy without destroying the object, detailing structures, preserving samples for further research, and the possibility of combining with other modern research methods. Despite its advantages, the diaphonization method also has certain limitations and disadvantages (possible deformation of structures, loss of color, time and space complexity, and limitations in application to certain types of organisms). With its three main application vectors (clinical, experimental, and botanical), the diaphonization method is an essential tool for researchers in many fields, which continues to evolve and find new applications, improving the overall scientific picture of understanding the structure of organisms and contributing to scientific progress. The article also highlights the importance and prospects of combining the diaphonization method with other techniques to determine a more accurate picture of the relative location and structural features of certain tissues, organs, and systems.

The Effects of Spatial Complexity on Narrative Experience in Space-Adaptive AR Storytelling.

A critical yet unresolved challenge in designing space-adaptive narratives for Augmented Reality (AR) is to provide consistently immersive user experiences anywhere, regardless of physical features specific to a space. For this, we present a comprehensive analysis on a series of user studies investigating how the size, density, and layout of real indoor spaces affect users playing Fragments, a space-adaptive AR detective game. Based on the studies, we assert that moderate levels of traversability and visual complexity afforded in counteracting combinations of size and complexity are beneficial for narrative experience. To confirm our argument, we combined the experimental data of the studies (n=112) to compare how five different spatial complexity conditions impact narrative experience when applied to contrasting room sizes. Results show that whereas factors of narrative experience are rated significantly higher in relatively simple settings for a small space, they are less affected by complexity in a large space. Ultimately, we establish guidelines on the design and placement of space-adaptive augmentations in location-independent AR narratives to compensate for the lack or excess of affordances in various real spaces and enhance user experiences therein.

SIN: Semantic Inference Network for Few-Shot Streaming Label Learning.

Streaming label learning aims to model newly emerged labels for multilabel classification systems, which requires plenty of new label data for training. However, in changing environments, only a small amount of new label data can practically be collected. In this work, we formulate and study few-shot streaming label learning (FSLL), which models emerging new labels with only a few annotated examples by utilizing the knowledge learned from past labels. We propose a meta-learning framework, semantic inference network (SIN), which can learn and infer the semantic correlation between new labels and past labels to adapt FSLL tasks from a few examples effectively. SIN leverages label semantic representation to regularize the output space and acquires labelwise meta-knowledge based on gradient-based meta-learning. Moreover, SIN incorporates a novel label decision module with a meta-threshold loss to find the optimal confidence thresholds for each new label. Theoretically, we illustrate that the proposed semantic inference mechanism could constrain the complexity of hypotheses space to reduce the risk of overfitting and achieve better generalizability. Experimentally, extensive empirical results and ablation studies demonstrate the performance of SIN is superior to the prior state-of-the-art methods on FSLL.

RSFIN: A Rule Search-based Fuzzy Inference Network for performance prediction of configurable software systems

Many modern software systems provide numerous configuration options to users and different configurations often lead to different performances. Due to the complex impact of a configuration on the system performance, users have to experimentally evaluate the performance for different configurations. However, it is practically infeasible to exhaust the almost infinite configuration space. To address this issue, various approaches have been proposed for performance prediction based on a limited number of configurations and corresponding performance measurements. Many of such efforts attempt to achieve a reasonable trade-off between experiment effort and prediction accuracy. In this paper, we propose a novel performance prediction model using a Rule Search-based Fuzzy Inference Network (RSFIN) based on ANFIS and NAS. One intuition is that, in systems, similar configurations produce similar performance. We experimentally validate this intuition based on data and introduce a configuration space under entropy. This view suggests the use of RSFIN to capture hidden distributions in configuration space. We implement and evaluate RSFIN using eleven real-world configurable software systems. Experimental results show that RSFIN achieves a better trade-off between measurement effort and prediction accuracy compared to other algorithms. In addition, the results also confirm that the evaluation of configuration space complexity based on data entropy is beneficial.