Code Patterns Research Articles

Specific neural coding of fMRI spiking neural network based on time coding

Specific neural coding is the key to achieving advanced cognitive function in a bio-brain, which can form an identifying coding pattern for external stimulation. The performance of specific neural coding depends extremely on brain-like models. However, the bio-interpretability of the topology of a brain-like model is still insufficient. The purpose of this paper is to investigate a more biological interpretative brain-like model verified by the performance of specific neural coding. In this study, we used the topology constrained by human brain functional magnetic resonance imaging (fMRI) to construct a new spiking neural network (SNN) as a brain-like model called fMRI-SNN. In the fMRI-SNN, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with time-delay. Then, we investigated the specific neural coding of fMRI-SNN, and discussed its mechanism. Our results indicated that: (i) fMRI-SNN has obvious specific neural coding based on time coding for different external stimulations. Furthermore, our discussion on relevance analysis implies that the intrinsic element of specific neural coding is synaptic plasticity. (ii) The specific neural coding of fMRI-SNN outperforms that of scale-free SNN and small-world SNN. Furthermore, our discussion on dynamic topological characteristics implies that the network topology is an element that impacts the performance level of the specific neural coding. (iii) Taking a speech recognition task as a case study, the performance of fMRI-SNN outperforms that of scale-free SNN and small-world SNN in terms of speech recognition accuracy.

Deep learning-based counter anti-forensic of GAN-based attack in HEVC compressed domain using coding pattern analysis

Double compression detection is one of the most popular techniques for the authentication of images and videos in the sphere of multimedia forensics. Several forensic methods have been suggested revealing video forgery, particularly high-efficiency video codec (HEVC) double compression detection. Even though forensic methods can effectively validate the integrity of videos, they become more intricate while practicing anti-forensic (AF) on manipulated videos. In this study, we present an innovative counter-anti-forensic (CAF) approach to detect anti-forensically misguided HEVC double compressed videos with the same coding parameters using coding pattern analysis. We mainly focus on employing generative adversarial networks (GANs) attacks to reconstruct artifacts that appear in compressed videos for deceiving forensic methods. In the proposed CAF method, we apply AF attacks twice in primary and secondary compressions and analyze the coding pattern changes to build a CAF method. We extract syntax information, and introduce new syntax map representations to improve distinguishability between single and double-compressed sequences. Additionally, we apply Gaussian and median filtering as AF attacks to show the robustness of the proposed method. The proposed deep learning-based CAF method shows better performance and can overwhelm state-of-the-art works.

Irreversibility of symbolic time series: A cautionary tale.

Many empirical time series are genuinely symbolic: Examples range from link activation patterns in network science, to DNA coding or firing patterns in neuroscience, to cryptography or combinatorics on words. In some other contexts, the underlying time series is actually real valued, and symbolization is applied subsequently, as in symbolic dynamics of chaotic systems. Among several time series quantifiers, time series irreversibility-the difference between forward and backward statistics in stationary time series-is of great relevance. However, the irreversible character of symbolized time series is not always equivalent to the one of the underlying real-valued signal, leading to some misconceptions and confusion on interpretability. Such confusion is even bigger for binary time series-a classical way to encode chaotic trajectories via symbolic dynamics. In this paper we aim to clarify some usual misconceptions and provide theoretical grounding for the practical analysis-and interpretation-of time irreversibility in symbolic time series. We outline sources of irreversibility in stationary symbolic sequences coming from frequency asymmetries of nonpalindromic pairs which we enumerate, and prove that binary time series cannot show any irreversibility based on words of length m<4, thus discussing the implications and sources of confusion. We also study irreversibility in the context of symbolic dynamics, and clarify why these can be reversible even when the underlying dynamical system is not, such as the case of the fully chaotic logistic map.

Research on smart contract vulnerability detection method based on domain features of solidity contracts and attention mechanism

The smart contract, a self-executing program on the blockchain, is key to programmable finance. However, the rise of smart contract use has also led to an increase in vulnerabilities that attract illegal activity from hackers. Traditional manual approaches for vulnerability detection, relying on domain experts, have limitations such as low automation and weak generalization. In this paper, we propose a deep learning approach that leverages domain-specific features and an attention mechanism to accurately detect vulnerabilities in smart contracts. Our approach reduces the reliance on manual input and enhances generalization by continuously learning code patterns of vulnerabilities, specifically detecting various types of vulnerabilities such as reentrancy, integer overflow, forced Ether injection, unchecked return value, denial of service, access control, short address attack, tx.origin, call stack overflow, timestamp dependency, random number dependency, and transaction order dependency vulnerabilities. In order to extract semantic information, we present a semantic distillation approach for detecting smart contract vulnerabilities. This approach involves using a syntax parser, Slither, to segment the code into smaller slices and word embedding to create a matrix for model training and prediction. Our experiments indicate that the BILSTM model is the best deep learning model for smart contract vulnerability detection task. We looked at how domain features and self-attentiveness mechanisms affected the ability to identify 12 different kinds of smart contract vulnerabilities. Our results show that by including domain features, we significantly increased the F1 values for 8 different types of vulnerabilities, with improvements ranging from 7.35% to 48.58%. The methods suggested in this study demonstrate a significant improvement in F1 scores ranging from 4.18% to 38.70% when compared to conventional detection tools like Oyente, Mythril, Osiris, Slither, Smartcheck, and Securify. This study provides developers with a more effective method of detecting smart contract vulnerabilities, assisting in the prevention of potential financial losses. This research provides developers with a more effective means of detecting smart contract vulnerabilities, thereby helping to prevent potential financial losses.

The Generalized Integrated Interleaved Zipper Codes With Anchor Decoding

Constructions of high-performance hard-decision decodable error correction codes are important for high-speed communication systems. In this paper, we present the generalized integrated interleaved (GII) zipper codes, in which multiple zipper codes are coupled together by the constraint of the GII code. The resulting codes are referred to as GII-zipper codes. Firstly, since the performances of GII-zipper codes are sensible to miscorrections, we propose an enhanced anchor decoder (AD) which uses the hard-decision results during the GII decoding to assign anchor reliability to reduce miscorrections. We then analyze the size and multiplicity of the minimum-sized stall patterns (MSSPs) of the GII-zipper codes. The analytical results show that the size of the MSSPs of the GII-zipper codes is larger that of the zipper codes. Finally, we present extensive simulation results to show the performance advantages of the GII-zipper codes. The results show that, when decoded with the original AD, the GII-zipper codes perform better than the comparable zipper codes and we can obtain further performance gain by the enhanced AD. Particularly, a GII-staircase code with a rate of 0.846 can achieve 0.74 dB from capacity at a bit error rate (BER) of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-15</sup> .

Development of a hybrid cost-based risk integrity assessment model for burst failure of pipeline systems with interacting corrosion defects

Burst is one of the primary causes for the failures of pipelines with corrosion defects. In this study, a hybrid cost-based risk integrity assessment model (CBRIA) was developed to schedule the maintenance plans during the entire service period for the pipeline systems consisting of multiple corroded segments with either the single or interacting defects: the finite element method (FEM) and the probabilistic methodologies were coupled to estimate the burst capacity and the corresponding failure probabilities, motivated on capturing the non-linear behaviors of structural response; Subsequently, they were embedded into the multi-object genetic algorithm (MOGA) to search the optimal solutions with two goals, mitigation of the failure risk and minimization of the life cycle cost. After validated against the burst tests and traditional risk analysis methodologies, the CRBIA model was applied to a case study. The performances of the CBRIA model in different cases were evaluated. For the single pipeline segment, this model yielded reasonable schedules in capturing the failure evolution characteristics. For pipeline systems with multi-segments, an iteration scheme with the constraint-dependent coding pattern of chromosomes was developed into the CBRIA model to supervise the solution searching, where the inner mechanism between the constraint and the model performances was discussed in depth. Furtherly, a parametric study was performed to explore the effects of the structural response on the failure risk. Besides, the growth rate on corrosion depth had more remarkable effects on the failure risk than the wall thickness, mainly attributed to the nature of larger uncertainties.

On the relative value of imbalanced learning for code smell detection

SummaryMachine learning‐based code smell detection (CSD) has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for CSD, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques for machine learning‐based CSD, we examine 31 imbalanced learning techniques with seven classifiers to build CSD models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed‐rank test and Cliff's . The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over‐sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best‐performing imbalanced learning techniques and the top‐3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

The relationality of geopolitical codes: the example of the Belt and Road Initiative

ABSTRACT The concept of the geopolitical code is a useful tool to understand the multi-faceted nature of a country’s foreign policy, the way in which global patterns of geopolitics are an aggregation of geopolitical codes, and moments of geopolitical transition from one pattern of codes to another. We address the nature of geopolitical codes through the lens of instrumental and associative power, and the geography of its modalities. Especially, we consider the combined logics of power expressed as economic networks and territorial sovereignty. To illustrate our argument, we briefly consider a number of regional responses to China’s Belt and Road Initiative (BRI). The geopolitical codes of Russia, India, Japan, the European Union, and the United States are discussed. By considering the process of the relationality of geopolitical codes constructed in response to the BRI we see an intensification in relations and an increasing scope that suggests the BRI is driving an ongoing geopolitical transition that will usher in a new geopolitical world order.

Housing instability: Exploring socioecological influences on the health of birthing people.

To describe how socioecological influences of housing instability affect pregnancy health among birthing and postpartum people. We used the socioecological framework to guide this exploratory descriptive study using semi-structured, in-depth interviews. We purposively recruited birthing people in the southern mid-Atlantic region. Seventeen one-time, semi-structured interviews were conducted between February 2020 and December 2021 with English-speaking unstably housed participants ≥18 years old, currently pregnant, or recently postpartum. Qualitative and quantitative content approaches were used to analyse transcribed interviews. Dedoose software was used to identify code patterns and refine the codebook until group consensus. The team examined code patterns, explored meaning in text and codified code-generated categories to describe experiences. Majority (82.4%) of participants were African Americans between 22 and 41 years, and most were postpartum (76.5%). Participants described multiple forms of housing instability, reasons for losing housing, challenges with finding housing and strategies for finding housing. Participants did not describe housing instability as a barrier to receiving prenatal care. Building and sustaining individual relationships and social support were prominent factors affecting their housing challenges. Participants also reported a lack of obstetric provider inquiry about housing status during pregnancy. Many reported that challenges with housing triggered mental health issues, especially depression. Nurses and other obstetric providers are key points of contact in the prenatal care setting for assessing housing stability. Additionally, refining social structures and funding support services within communities and prenatal health systems should be a strategy for future programme and policy planning improvement. This study highlights critical areas for consideration when addressing social determinants for birthing people and reinforces the need for more comprehensive assessment in the prenatal setting. Members of the public participated in this study as key informants for study interviews.

Temporal characterization of Alzheimer's Disease with sequences of clinical records.

Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD. We conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts. In a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts. We present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling. National Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535).

Convolution Operation on Pancharatnam-Berry Coding Metasurfaces in Visible Band

Coding metasurfaces bring photonic design into a new era for their convenience in manipulating electromagnetic waves in a programmable way. Different coding patterns can be further convoluted to give rise to nontrivial effects such as multiple beam steering, simultaneous control of surface and space waves, and the integration of multiple functionalities. However, previous experimental works have been limited to low frequencies. Extending convoluted coding metasurfaces to optical frequency can significantly reduce the size of structures, and therefore facilitate their applications in optical circuits. Here, we experimentally demonstrate a Pancharatnam-Berry metasurface designed by convoluting two distinct coding patterns. Such a metasurface exhibits integrated functionalities: the optical spin Hall effect and spin to orbital angular momentum conversion, which are robust over a broad visible band and a wide range of incident angles. Our work represents an important step towards multifunctional coding metasurfaces at optical frequency based on convolution operation.

An Investigation of confusing code patterns in JavaScript

Evolving software is particularly challenging when the code has been poorly written or uses confusing idioms and language constructs, which might increase maintenance efforts and impose a significant cognitive load on developers. Previous research has investigated possible sources of confusion in programs, including the impact of small code patterns (hereafter atoms of confusion) that contribute to misunderstanding the source code. Although researchers have explored atoms of confusion in code written in C, C++, and Java, different languages have different features, developer communities, and development cultures. This justifies the exploration of other languages to verify whether they also exhibit confusion-inducing patterns. In this paper we investigate the impact of atoms of confusion on understanding JavaScript code—a dynamically typed language whose popularity is growing in the most diverse application domains. We present the results of a mixed-methods research comprising a mining software repositories (MSR) study, two experiments, and a set of interviews with practitioners. Our MSR effort shows that atom candidates are frequent and used intensively in 72 popular open-source JavaScript projects: four atom candidates appear in 90% of them and two of them occur more than once for every 100 lines of code. This helps motivate the other three studies. The results of both experiments suggest that two code patterns that have been previously observed to confuse C programmers also confuse JavaScript programmers: the comma operator and assignments being used as values. In addition, some code patterns, such as omitted curly braces and change of literal encoding, have caused confusion in participants in one of the experiments. We discover that some JavaScript-specific elements, such as automatic semicolon insertion and object destructuring, also have the potential to cause confusion. For all these cases effect sizes were either medium or high. The interviews we conducted indicate other constructs and idioms that merit investigation in the future.

Implantable QR code subcutaneous microchip using photoacoustic and ultrasound microscopy for secure and convenient individual identification and authentication

Individual identification and authentication techniques are merged into many aspects of human life with various applications, including access control, payment or banking transfer, and healthcare. Yet conventional identification and authentication methods such as passwords, biometrics, tokens, and smart cards suffer from inconvenience and/or insecurity. Here, inspired by quick response (QR) code and implantable microdevices, implantable and minimally-invasive QR code subcutaneous microchips (QRC-SMs) are proposed to be an effective approach to carry useful and private information, thus enabling individual identification and authentication. Two types of QRC-SMs, QRC-SMs with “hole” and “flat” elements and QRC-SMs with “titanium-coated” and “non-coated” elements, are designed and fabricated to store personal information. Corresponding ultrasound microscopy and photoacoustic microscopy are used for imaging the QR code pattern underneath skin, and open-source artificial intelligence algorithm is applied for QR code detection and recognition. Ex vivo experiments under tissue and in vivo experiments with QRC-SMs implanted in live mice have been performed, demonstrating successful information retrieval from implanted QRC-SMs. QRC-SMs are hidden subcutaneously and invisible to the eyes. They cannot be forgotten, misplaced or lost, and can always be ready for timely medical identification, access control, and payment or banking transfer. Hence, QRC-SMs provide promising routes towards private, secure, and convenient individual identification and authentication.

Genetic subdivisions of follicular lymphoma defined by distinct coding and noncoding mutation patterns

AbstractFollicular lymphoma (FL) accounts for ∼20% of all new lymphoma cases. Increases in cytological grade are a feature of the clinical progression of this malignancy, and eventual histologic transformation (HT) to the aggressive diffuse large B-cell lymphoma (DLBCL) occurs in up to 15% of patients. Clinical or genetic features to predict the risk and timing of HT have not been described comprehensively. In this study, we analyzed whole-genome sequencing data from 423 patients to compare the protein coding and noncoding mutation landscapes of untransformed FL, transformed FL, and de novo DLBCL. This revealed 2 genetically distinct subgroups of FL, which we have named DLBCL-like (dFL) and constrained FL (cFL). Each subgroup has distinguishing mutational patterns, aberrant somatic hypermutation rates, and biological and clinical characteristics. We implemented a machine learning–derived classification approach to stratify patients with FL into cFL and dFL subgroups based on their genomic features. Using separate validation cohorts, we demonstrate that cFL status, whether assigned with this full classifier or a single-gene approximation, is associated with a reduced rate of HT. This implies distinct biological features of cFL that constrain its evolution, and we highlight the potential for this classification to predict HT from genetic features present at diagnosis.

Log-related Coding Patterns to Conduct Postmortems of Attacks in Supervised Learning-based Projects

Adversarial attacks against supervised learning a algorithms, which necessitates the application of logging while using supervised learning algorithms in software projects. Logging enables practitioners to conduct postmortem analysis, which can be helpful to diagnose any conducted attacks. We conduct an empirical study to identify and characterize log-related coding patterns, i.e., recurring coding patterns that can be leveraged to conduct adversarial attacks and needs to be logged. A list of log-related coding patterns can guide practitioners on what to log while using supervised learning algorithms in software projects. We apply qualitative analysis on 3,004 Python files used to implement 103 supervised learning-based software projects. We identify a list of 54 log-related coding patterns that map to six attacks related to supervised learning algorithms. Using Lo g Assistant to conduct P ostmortems for Su pervised L earning ( LOPSUL ) , we quantify the frequency of the identified log-related coding patterns with 278 open-source software projects that use supervised learning. We observe log-related coding patterns to appear for 22% of the analyzed files, where training data forensics is the most frequently occurring category.

Facile preparation of robust microgrooves based photonic crystals film for anti-counterfeiting

Photonic crystals structural colors have many advantages (e.g., environmentally friendly, nonfading, etc.), showing great potential application in some emerging fields of display, detection and anti-counterfeiting. Unfortunately, they are easily damaged because of the poor mechanical robustness origined from the weak interfacial adhesion between film substrate and nanoparticles. Although some photonic crystals show good mechanical robustness, they are often suffered from complex methods. Therefore, it is still a huge challenge to prepare photonic crystals with good mechanical robustness via facile methods. Herein, an efficient and facile strategy to prepare polycarbonate (PC)/silica nanoparticles (SNPs) photonic crystals film based on microgrooves is proposed. That is, with the help of micromilling and vacuum-assisted hot-pressor, microgrooves were prepared onto PC film surface, and SNPs can be spontaneously deposited into the as-prepared microgrooves via capillary force. Such PC/SNPs photonic crystals film exhibits excellent mechanical robustness, vivid structural color, and color can change with the viewing angle. Interestingly, such films with quick response (QR) code pattern can store information and can be used as anti-counterfeiting labels. This work proposes a facile strategy to prepare microgrooves based photonic crystals film with good mechanical robustness, which can further expand its applications in anti-counterfeiting.

Detecting vulnerabilities in IoT software: New hybrid model and comprehensive data analysis

Software vulnerabilities have always been an essential issue in cyberspace, for which many vulnerability detection techniques have been investigated. Among them, deep learning-based detection techniques have demonstrated promising detection results. However, due to the various programming patterns of developers, vulnerabilities are usually associated with the code context, such as Internet of Things (IoT) programs. Therefore, we propose a contextual embedding model to integrate three hybrid models, CLSTM, CBiLSTM (sequential structure), and CNN-BiLSTM (parallel structure), based on the code characteristics of IoT applications. To further improve the precision and robustness, we apply information augment by adding synthetic data to the real-world vulnerability data to address the severe data imbalance and facilitate neural models learning vulnerability patterns. The new method inherits the architecture of CodeBERT with multiheaded attention mechanisms and learns a richer set of vulnerable code patterns with long-range context dependencies when processing code sequence data. To assess the effectiveness of hybrid contextual embedding, we contrast the neural network models developed using the representations obtained from the three embedding methods, including CodeBERT, Word2Vec, and FastText. The experiments involve IoT applications and well-known open-source APIs. The results show that the hybrid model built based on the neural features outperforms the non-hybrid model in vulnerability detection.

THE ACCEPTANCE OF AN EDUCATIONAL INTEGRATED DEVELOPMENT ENVIRONMENT TO LEARN PROGRAMMING FUNDAMENTALS

Programming is an important course for any IT or engineering-related course. However, previous research shows that students face difficulties in learning programming due to its abstract concepts. This study aims to evaluate the acceptance of a developed Integrated Development Environment (IDE), namely C-SOLVIS which is a web-based application that specifically intends to facilitate the teaching and learning of the C programming fundamentals in Malaysian tertiary education. The C-SOLVIS integrates problem-solving into a program development environment for the C language. The goal is to guide the users in problem-solving and help them write C programs based on problem-solving algorithms. The Rapid Application Development (RAD) Model was employed in the C-SOLVIS development process. Based on this model, the requirement planning phase was carried out through the triangulation technique by applying qualitative approaches comprising a literature review supported by semi-structured interviews, document reviews, and content validation by expert programming lecturers. Subsequently, the design of the application was accomplished through the iterative prototyping process which was then followed by the application construction. Then, the C-SOLVIS is deployed to be used by several programming lecturers to evaluate its usability by adopting a quantitative method using the System Usability Scale (SUS) questionnaire. The study has discovered several suitable techniques and designs for the problem-solving and program development environment. For the problem-solving environment, the Computational Thinking (CT) concepts were applied which were supported by the Input-Process-Output (IPO) Model through Scientific Instructions and Inquiries. Meanwhile, the program development environment was designed and developed based on frame-based programming using a set of developed Code Patterns. The C-SOLVIS evaluation using the SUS instrument has yielded a SUS mean score of 86.07. This score is interpreted by SUS as an A grade that indicates C-SOLVIS as a highly usable application and thus is accepted for C programming learning. Hence, the development process of the C-SOLVIS can be used as a guideline for educational software development, especially in the field of programming education.

Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

Spontaneous Activity Patterns in Human Attention Networks Code for Hand Movements.

Recent evidence suggests that, in the absence of any task, spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. These "resting-state" activity patterns may underlie the maintenance and consolidation (replay) of information states coding for ecological stimuli and behaviors. In this study, we examine whether replay patterns occur in resting-state activity in association cortex grouped into high-order cognitive networks not directly processing sensory inputs or motor outputs. Fifteen participants (7 females) performed four hand movements during an fMRI study. Three movements were ecological. The fourth movement as control was less ecological. Before and after the task scans, we acquired resting-state fMRI scans. The analysis examined whether multivertex task activation patterns for the four movements computed at the cortical surface in different brain networks resembled spontaneous activity patterns measured at rest. For each movement, we computed a vector of r values indicating the strength of the similarity between the mean task activation pattern and frame-by-frame resting-state patterns. We computed a cumulative distribution function of r 2 values and used the 90th percentile cutoff value for comparison. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, rest-task pattern correlation was more likely for less ecological movement in the ventral attention network. These findings show that spontaneous activity patterns in human attention networks code for hand movements.SIGNIFICANCE STATEMENT fMRI indirectly measures neural activity noninvasively. Resting-state (spontaneous) fMRI signals measured in the absence of any task resemble signals evoked by task performance both in topography and inter-regional (functional) connectivity. However, the function of spontaneous brain activity is unknown. We recently showed that spatial activity patterns evoked by visual and motor tasks in visual and motor cortex, respectively, occur at rest in the absence of any stimulus or response. Here we show that activity patterns related to hand movements replay at rest in frontoparietal regions of the human attention system. These findings show that spontaneous activity in the human cortex may mediate the maintenance and consolidation of information states coding for ecological stimuli and behaviors.