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In recent years, Automated Program Repair (APR) has been extensively studied in academia and even drawn wide attention from the industry. However, APR techniques can be extremely time consuming since (1) a large number of patches can be generated for a given bug, and (2) each patch needs to be executed on the original tests to ensure its correctness. In the literature, various techniques (e.g., based on learning, mining, and constraint solving) have been proposed/studied to reduce the number of patches. Intuitively, every patch can be treated as a software revision during regression testing; thus, traditional Regression Test Selection (RTS) techniques can be leveraged to only execute the tests affected by each patch (as the other tests would keep the same outcomes) to further reduce patch execution time. However, few APR systems actually adopt RTS and there is still a lack of systematic studies demonstrating the benefits of RTS and the impact of different RTS strategies on APR. To this end, this article presents the first extensive study of widely used RTS techniques at different levels (i.e., class/method/statement levels) for 12 state-of-the-art APR systems on over 2M patches. Our study reveals various practical guidelines for bridging the gap between APR and regression testing, including: (1) the number of patches widely used for measuring APR efficiency can incur skewed conclusions, and the use of inconsistent RTS configurations can further skew the conclusions; (2) all studied RTS techniques can substantially improve APR efficiency and should be considered in future APR work; (3) method- and statement-level RTS outperform class-level RTS substantially and should be preferred; (4) RTS techniques can substantially outperform state-of-the-art test prioritization techniques for APR, and combining them can further improve APR efficiency; and (5) traditional Regression Test Prioritization (RTP) widely studied in regression testing performs even better than APR-specific test prioritization when combined with most RTS techniques. Furthermore, we also present the detailed impact of different patch categories and patch validation strategies on our findings. [ABSTRACT FROM AUTHOR]

We introduce a novel privacy-preserving biometric authentication scheme based on irises that allows a user to enroll once at a trusted biometric certification authority (BCA) and authenticate to online service providers (SPs) multiple times without involving the BCA during the authentication. Our scheme preserves the user's biometric privacy from the SPs and transactional privacy from the BCA, while providing security against a malicious user. During the enrollment, the BCA issues a signed token that encrypts the user's biometrics. We introduce techniques enabling the SP and the user to perform secure computation of biometric matching between such encrypted biometrics and the user's biometrics captured at the authentication time. We provide a prototype implementation, a performance evaluation, and a security analysis of the protocol. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to develop, implement, and test a visuohaptic simulator designed to explore the buoyancy phenomenon for freshman engineering students enrolled in physics courses. The primary goal was to enhance students' understanding of physical concepts through an immersive learning tool. Methods: The visuo-haptic simulator was created using the VIS-HAPT methodology, which provides high-quality visualization and reduces development time. A total of 182 undergraduate students were randomly assigned to either an experimental group that used the simulator or a control group that received an equivalent learning experience in terms of duration and content. Data were collected through pre- and post-tests and an exit-perception questionnaire. Results: Data analysis revealed that the experimental group achieved higher learning gains than the control group (p = 0.079). Additionally, students in the experimental group expressed strong enthusiasm for the simulator, noting its positive impact on their understanding of physical concepts. The VIS-HAPT methodology also reduced the average development time compared to similar visuo-haptic simulators. Discussion: The results demonstrate the efficacy of the buoyancy visuo-haptic simulator in improving students' learning experiences and validate the utility of the VIS-HAPT method for creating immersive educational tools in physics. [ABSTRACT FROM AUTHOR]

We perturb a real matrix A of full column rank, and derive lower bounds for the smallest singular values of the perturbed matrix, in terms of normwise absolute perturbations. Our bounds, which extend existing lower-order expressions, demonstrate the potential increase in the smallest singular values and represent a qualitative model for the increase in the small singular values after a matrix has been downcast to a lower arithmetic precision. Numerical experiments confirm the qualitative validity of this model and its ability to predict singular values changes in the presence of decreased arithmetic precision. [ABSTRACT FROM AUTHOR]

Skeletal program enumeration (SPE) can generate a great number of test programs for validating the correctness of compilers or interpreters. The classic SPE generates programs by exhaustively enumerating all possible variable usage patterns into a given syntactic structure. Even though it is capable of producing many test programs, the exhaustive enumeration strategy generates a large number of invalid programs, which may waste plenty of testing time and resources. To address the problem, this article proposes a tree-based SPE technique. Compared to the state-of-the-art, the key merit of the tree-based approach is that it allows us to take the dependency information into consideration when producing test programs and, thus, make it possible to (1) directly generate non-equivalent programs and (2) apply dominance relations to eliminate invalid test programs that have undefined variables. Hence, our approach significantly saves the cost of the naïve SPE approach. We have implemented our approach into an automated testing tool, IFuzzer, and applied it to test eight different implementations of Python interpreters, including CPython, PyPy, IronPython, Jython, RustPython, GPython, Pyston, and Codon. In three months of fuzzing, IFuzzer detected 142 bugs, of which 87 have been confirmed to be previously unknown bugs, of which 34 have been fixed. Compared to the state-of-the-art SPE techniques, IFuzzer takes only 61.0% of the time cost given the same number of testing seeds and improves 5.3% source code function coverage in the same time budget of testing. [ABSTRACT FROM AUTHOR]

Image restoration is a prominent field of research in computer vision. Restoring broken paintings, especially ancient Chinese artworks, is a significant challenge for current restoration models. The difficulty lies in realistically reinstating the intricate and delicate textures inherent in the original pieces. This process requires preserving the unique style and artistic characteristics of the ancient Chinese paintings. To enhance the effectiveness of restoring and preserving traditional Chinese paintings, this paper presents a framework called Sketch-Guided Restoration Generative Adversarial Network, termd SGRGAN. The framework employs sketch images as structural priors, providing essential information for the restoration process. Additionally, a novel Focal block is proposed to enhance the fusion and interaction of textural and structural elements. It is noteworthy that a BiSCCFormer block, incorporating a Bi-level routing attention mechanism, is devised to comprehensively grasp the structural and semantic details of the image, including its contours and layout. Extensive experiments and ablation studies on MaskCLP and Mural datasets demonstrate the superiority of the proposed method over previous state-of-the-art methods. Specifically, the model demonstrates outstanding visual fidelity, particularly in the restoration of landscape paintings. This further underscores its efficacy and universality in the realm of cultural heritage preservation and restoration. [ABSTRACT FROM AUTHOR]

The principal tool of computation used in this paper is classical Gaussian quadrature on the interval [0,1], which happens to be particularly effective here. Explicit expressions are found for the derivatives of the Ramanujan integral in question, and it is proved that the latter is completely monotone on (0,\infty). As a byproduct, known series expansions for incomplete gamma functions are examined with regard to their convergence properties. The paper also pays attention to another famous integral, the Euler integral — better known as the gamma function — revitalizing a largely neglected part of the function, the part corresponding to negative values of the argument, which plays a prominent role in our work. [ABSTRACT FROM AUTHOR]

In the development of open-source software (OSS), many developers use badges to give an overview of the software and share some key features/metrics conveniently. Among various badges, quality assurance (QA) badges make up a large proportion and are the most prevalent because QA is of vital importance in software development, and ineffective QA may lead to anomalies or defects. In this paper, we focus on QA badges in open-source projects, which present quality assurance information directly and instantly, and aim to produce some interesting findings and provide practical implications. We collect and analyze 100000 projects written in popular programming languages from GitHub and conduct a comprehensive empirical study both inside and outside QA badges. Inside QA badges, we build a category classification for all QA badges based on the properties they focus on, which shows the types of QA badges developers use. Then, we analyze the frequency of the properties that QA badges focus on, and property combinations, too, which present their use status. We find that QA badges focus on various properties while developers give different preferences to different properties. The use status also differs between different programming languages. For example, projects written in C focus on Security to a great extent. Our findings also provide implications for developers and badge providers. Outside QA badges, we conduct a correlation analysis between QA badges and some software metrics that have potential relationships with code quality, contribution quality, and popularity. We find that QA badges have statistically significant correlations with various software metrics. [ABSTRACT FROM AUTHOR]

We show how a Transformer can encode hierarchical tree-like string structures by introducing a new deep learning-based framework for generating 3D biological tree models represented as Lindenmayer system (L-system) strings. L-systems are string-rewriting procedural systems that encode tree topology and geometry. L-systems are efficient, but creating the production rules is one of the most critical problems precluding their usage in practice. We substitute the procedural rules creation with a deep neural model. Instead of writing the rules, we train a deep neural model that produces the output strings. We train our model on 155k tree geometries that are encoded as L-strings, de-parameterized, and converted to a hierarchy of linear sequences corresponding to branches. An end-to-end deep learning model with an attention mechanism then learns the distributions of geometric operations and branches from the input, effectively replacing the L-system rewriting rule generation. The trained deep model generates new L-strings representing 3D tree models in the same way L-systems do by providing the starting string. Our model allows for the generation of a wide variety of new trees, and the deep model agrees with the input by 93.7% in branching angles, 97.2% in branch lengths, and 92.3% in an extracted list of geometric features. We also validate the generated trees using perceptual metrics showing 97% agreement with input geometric models. [ABSTRACT FROM AUTHOR]

Group testing is the process of pooling arbitrary subsets from a set of $ {n}$ items so as to identify, with a minimal number of tests, a “small” subset of $ {d}$ defective items. In “classical” non-adaptive group testing, it is known that when $ {d}$ is substantially smaller than $ {n}$ , $\Theta ({d}\log ({n}))$ tests are both information-theoretically necessary and sufficient to guarantee recovery with high probability. Group testing schemes in the literature that meet this bound require most items to be tested $ {\Omega }(\log ({n}))$ times, and most tests to incorporate $ {\Omega }({{n/d}})$ items. Motivated by physical considerations, we study group testing models in which the testing procedure is constrained to be “sparse.” Specifically, we consider (separately) scenarios in which 1) items are finitely divisible and hence may participate in at most $ {\gamma } \in {o}(\log ({n}))$ tests; or 2) tests are size-constrained to pool no more than $\rho \in {o}({{n/d}})$ items per test. For both scenarios, we provide information-theoretic lower bounds on the number of tests required to guarantee high probability recovery. In particular, one of our main results shows that $ {\gamma }$ -finite divisibility of items forces any non-adaptive group testing algorithm with the probability of recovery error at most $ {\epsilon }$ to perform at least $ {\gamma } {d}({ {n/d}})^{({1}-{5} {\epsilon })/ {\gamma }}$ tests. Analogously, for $ {\rho }$ -sized constrained tests, we show an information-theoretic lower bound of $ {\Omega }({n}/ {\rho })$ tests for high-probability recovery–hence in both settings the number of tests required grows dramatically (relative to the classical setting) as a function of $ {n}$. In both scenarios, we provide both randomized constructions and explicit constructions of designs with computationally efficient reconstruction algorithms that require a number of tests that is optimal up to constant or small polynomial factors in some regimes of ${{n, d,}} {\gamma }$ , and $ {\rho }$. The randomized design/reconstruction algorithm in the $ {\rho }$ -sized test scenario is universal–independent of the value of $ {d}$ , as long as $ {\rho } \in {o}({\textbf {n/d}})$. We also investigate the effect of unreliability/noise in test outcomes, and show that whereas the impact of noise in test outcomes can be obviated with a small (constant factor) penalty in the number of tests in the $ {\rho }$ -sized tests scenario, there is no group-testing procedure, regardless of the number of tests, that can combat noise in the $ {\gamma }$ -divisible scenario. [ABSTRACT FROM AUTHOR]

We present a rigorous and precise analysis of the maximum degree and the average degree in a dynamic duplication-divergence graph model introduced by Sol\'e et al. [Adv. Complex Syst., 5 (2002), pp. 43-54] in which the graph grows according to a duplication-divergence mechanism, i.e., by iteratively creating a copy of some node and then randomly alternating the neighborhood of a new node with probability p. This model captures the growth of some real-world processes, e.g., biological or social networks. In this paper, we prove that for some 0 < p < 1, the maximum degree and the average degree of a duplication-divergence graph on t vertices are asymptotically concentrated with high probability around tp and max\{ t2p-1, 1}, respectively, i.e., they are within at most a polylogarithmic factor from these values with probability at least 1-t-A for any constant A >0. [ABSTRACT FROM AUTHOR]

Connection matrices are a generalization of Morse boundary operators from the classical Morse theory for gradient vector fields. Developing an efficient computational framework for connection matrices is particularly important in the context of a rapidly growing data science that requires new mathematical tools for discrete data. Toward this goal, the classical theory for connection matrices has been adapted to combinatorial frameworks that facilitate computation. We develop an efficient persistence-like algorithm to compute a connection matrix from a given combinatorial (multi) vector field on a simplicial complex. This algorithm requires a single pass, improving upon a known algorithm that runs an implicit recursion executing two passes at each level. Overall, the new algorithm is more simple, direct, and efficient than the state-of-the-art. Because of the algorithm's similarity to the persistence algorithm, one may take advantage of various software optimizations from topological data analysis. [ABSTRACT FROM AUTHOR]

The low earth orbit (LEO) satellite constellations exhibit dynamic network topology due to the intermittent inter-satellite links (ISLs). To ensure efficient data delivery in LEO satellite networks, it is crucial to find an appropriate routing protocol that is capable of accommodating the dynamic topology. In this paper, we consider two typical IP-based and ICN-based link-state routing protocols (i.e., OSPF and NLSR, respectively) and investigate the adaptability in LEO satellite networks. Specifically, we first analyze and compare their advantages and disadvantages in LEO satellite networks from the perspectives of link state detection, link state synchronization, and packet forwarding scheme. To verify our analysis, we conduct extensive packet-level experiments on OMNeT++. Our analysis and simulation unveil several critical insights. First, IP-based OSPF (following push-based communication) outperforms the ICN-based NLSR (following pull-based communication) in terms of the routing convergence and control overhead in satellite networks. Specifically, OSPF converges faster than NLSR up to 90.8% and yields less control overhead under the Iridium constellation. Second, the ICN-based NLSR (supporting Interest aggregation and in-network caching) outperforms the IP-based OSPF in terms of the packet delivery performance, especially under the content-sharing traffic pattern (up to 206%). However, the occasional ISL failure significantly reduces the content delivery performance of NLSR under the point-to-point traffic pattern. To sum up, our study unveils the necessity of properly integrating IP-based and ICN-based link-state routing paradigms in LEO satellite networks. [ABSTRACT FROM AUTHOR]

Let G be an undirected, bounded degree graph with n vertices. Fix a finite graph H, and suppose one must remove n edges from G to make it H-minor-free (for some small constant > 0). We give an n1/2+o(1)-time randomized procedure that, with high probability, finds an H-minor in such a graph. As an application, suppose one must remove n edges from a bounded degree graph G to make it planar. This result implies an algorithm, with the same running time, that produces a K3,3- or K5-minor in G. No prior sublinear time bound was known for this problem. By the graph minor theorem, we get an analogous result for any minor-closed property. Up to no(1) factors, this resolves a conjecture of Benjamini, Schramm, and Shapira [Adv. Math., 223 (2010), pp. 2200--2218] on the existence of one-sided property testers for minor-closed properties. Furthermore, our algorithm is nearly optimal by a n) lower bound of Czumaj et al. [Random Structures Algorithms, 45 (2014), pp. 139--184]. Prior to this work, the only graphs H for which nontrivial one-sided property testers were known for H-minor-freeness were the following: H being a forest or a cycle [Czumaj et al., Random Structures Algorithms, 45 (2014), pp. 139--184], K2,k, (k2)-grid, and the k-circus [Fichtenberger et al., preprint, arXiv:1707.06126v1, 2017]. [ABSTRACT FROM AUTHOR]

Motivation The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3–4.5 Å), improvement in the map quality facilitates structure modeling. Results We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3–6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools. Availability and implementation https://github.com/kiharalab/EM-GAN , Google Colab: https://tinyurl.com/3ccxpttx. [ABSTRACT FROM AUTHOR]

Human motion segmentation (HMS) aims to segment a long human action video into a bunch of short and meaningful action clips. Existing supervised learning approaches need a large amount of training data which may be costly in real-world scenario, while most unsupervised clustering methods cannot fully explore the temporal correlations among human motions and hard to achieve promising performances. In our paper, we design a novel unsupervised framework, called Velocity-Sensitive Dual-Side Auto-Encoder (VSDA), for HMS task. Specifically, a multi-neighbor auto-encoder (MNA) is proposed to extract informative temporal features, which fully explores the local temporal patterns of human motions. In addition, a long-short distance encoding (LSE) strategy is designed. It constrains the encoded representations of close (short-distance) frames becoming similar while the representations of far-away (long-distance) frames becoming distinctive. Similarly, this strategy is also deployed on the decoded outputs as the long-short distance decoding (LSD) module. The LSE/LSD guides the learning process explicitly and implicitly to achieve the dual-side structure. Moreover, we consider the energy variations during the human motion to propose the velocity-sensitive (VS) guidance mechanism for further model improvement. VSDA leverages the temporal characteristics of human motion and derives promising HMS performance. Comprehensive experiments on six real-world human motion datasets illustrate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

The One-Tap Authentication (OTAuth) based on the cellular network is a password-less login service provided by Mobile Network Operator (MNO) through the unique communication gateway access technique. The service allows app users to quickly sign up or log in with their mobile phone numbers without entering a password. Due to its convenience, OTAuth has been widely used by various apps. However, some studies have elaborated that OTAuth services are of great drawbacks from the perspective of mobile security and identified several flawed designs, which make the MNO cannot distinguish malicious apps from normal ones and cause impersonation attacks. In this paper, we further analyze OTAuth services from the perspective of 4G and 5G cellular networks and focus on two important procedures in which the cellular network plays an important role in OTAuth services. Not surprisingly, we discover a new fundamental design flaw in determining whether the runtime environment supports OTAuth services. Moreover, we propose a mature attack paradigm by exploiting this flaw, which allows an attacker to login or register one app as a victim. To evaluate the impact of the attack, we have examined 100/90/100 Android/iOS/HarmonyOS apps for OTAuth services of 3 mainstream MNOs in China. The experimental results show that our proposed attack is applicable to almost all the apps that support OTAuth services, and affects more apps than the attacks that have been reported before. Finally, we propose several countermeasures to defend against the attack. Note that, for security’s sake, we have already reported our findings to authorized parties and received their confirmations. [ABSTRACT FROM AUTHOR]

As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of the genetics of different diseases. However, RNAseq analysis can provide information about the specific mechanisms underlying the disease and the potential targets for therapy. This research proposes a new approach to differential gene expression (DGE) analysis, which is the heart of the core-analysis phase in any RNAseq study. Based on the Drosophila Genetic Reference Panel (DGRP), the gene expression dataset is computationally analyzed in light of eye-size phenotypes. We utilized the foreach and the doParallel R packages to run the code on a multicore machine to reduce the running time of the original algorithm, which exhibited an exponential time complexity. Experimental results showed an outstanding performance, reducing the running time by 95% while using 32 processes. In addition, more candidate modifier genes for RP were identified by increasing the scope of the analysis and considering more datasets that represent different phenotype models. [ABSTRACT FROM AUTHOR]

This document is an introduction to a theme section in the journal Software & Systems Modeling. The theme section focuses on the central role of modeling in designing and explaining data-driven systems and software. It discusses the challenges and importance of data and models in the development of intelligent systems. The document also provides an overview of the MEDI conference, which serves as a platform for research in models and data engineering. The theme section includes papers on topics such as model synchronization and the evolution of programming artifacts. The document concludes with acknowledgments to the authors, reviewers, and editors involved in the theme section. [Extracted from the article]

The methods of Chinese relation extraction(CRE) based on the neural network can be divided into two categories according to the input mode(word-based and character-based). The performance of word-based models depends on the accuracy of word segmentation. Unfortunately, there are still errors in existing word segmentation tools (methods). Among the character-based models, Lattice LSTM-based models have been successful in CRE. However, such RNN-based models cannot meet the requirements of parallel computing and thus have natural drawbacks in model training and inference. There is much word polysemy in Chinese that constrains the performance of CRE. Most CRE models are built on English datasets, which often perform poorly on Chinese datasets. To address the above issues, we propose a method for CRE with the Polysemy Rethinking Mechanism. In this method, (1) we use a CNN-based architecture in which input is characters. It can incorporate word-level information through the lexicon to correct the error caused by word segmentation. (2) We propose a Polysemy Rethinking Mechanism, which can alleviate the problems caused by multiple meanings of one word by adding multiple sense information to the model. (3) Compared with the Lattice LSTM-based model, our model improves computational efficiency to gain results. We conduct experiments on two real-world datasets of CRE. The results show that our method achieves better performance than the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

A thorough understanding of the neuroanatomy of peripheral nerves is required for a better insight into their function and the development of neuromodulation tools and strategies. In biophysical modeling, it is commonly assumed that the complex spatial arrangement of myelinated and unmyelinated axons in peripheral nerves is random, however, in reality the axonal organization is inhomogeneous and anisotropic. Present quantitative neuroanatomy methods analyze peripheral nerves in terms of the number of axons and the morphometric characteristics of the axons, such as area and diameter. In this study, we employed spatial statistics and point process models to describe the spatial arrangement of axons and Sinkhorn distances to compute the similarities between these arrangements (in terms of first- and second-order statistics) in various vagus and pelvic nerve cross-sections. We utilized high-resolution transmission electron microscopy (TEM) images that have been segmented using a custom-built high-throughput deep learning system based on a highly modified U-Net architecture. Our findings show a novel and innovative approach to quantifying similarities between spatial point patterns using metrics derived from the solution to the optimal transport problem. We also present a generalizable pipeline for quantitative analysis of peripheral nerve architecture. Our data demonstrate differences between male- and female-originating samples and similarities between the pelvic and abdominal vagus nerves. [ABSTRACT FROM AUTHOR]

The purpose of a reputation system is to hold the users of a distributed application accountable for their behavior. The reputation of a user is computed as an aggregate of the feedback provided by fellow users in the system. Truthful feedback is clearly a prerequisite for computing a reputation score that accurately represents the behavior of a user. However, it has been observed that users can hesitate in providing truthful feedback because, for example, of fear of retaliation. Privacy-preserving reputation systems enable users to provide feedback in a private and thus uninhibited manner. In this survey, we propose analysis frameworks for privacy-preserving reputation systems. We use these analysis frameworks to reviewand compare the existing approaches. Emphasis is placed on blockchain-based systems as they are a recent significant development in the area. Blockchain-based privacy-preserving reputation systems have properties, such as trustlessness, transparency, and immutability, which prior systems do not have. Our analysis provides several insights and directions for future research. These include leveraging blockchain to its full potential in order to develop truly trustless systems, to achieve some important security properties, and to include defenses against common attacks that have so far not been addressed by most current systems. [ABSTRACT FROM AUTHOR]

Due to energy and bandwidth limitations of wireless sensor networks (WSNs), it is crucial that data provenance for these networks be as compact as possible. Even if lossy compression techniques are used for encoding provenance information, the size of the provenance increases with the number of nodes traversed by the network packets. To address such issues, we propose a dictionary based provenance scheme. In our approach, each sensor node in the network stores a packet path dictionary. With the support of this dictionary, a path index instead of the path itself is enclosed with each packet. Since the packet path index is a code word of a dictionary, its size is independent of the number of nodes present in the packet's path. Furthermore, as our scheme binds the packet and its provenance through an AM-FM sketch and uses a secure packet sequence number generation technique, it can defend against most of the known provenance attacks. Through simulation and experimental results, we show that our scheme outperforms other compact provenance schemes with respect to provenance size, robustness, and energy consumption. [ABSTRACT FROM AUTHOR]

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1-10 cells) spots per patient sampled in a honeycomb pattern, coregistered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x-y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural networks to predict spatial RNA patterns at the Visium spots under the hypothesis that the transformer- and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model’s ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, the results indicate that the transformer- and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on under studied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications. [ABSTRACT FROM AUTHOR]

Clustering points in a vector space or nodes in a graph is a ubiquitous primitive in statistical data analysis, and it is commonly used for exploratory data analysis. In practice, it is often of interest to "refine"" or "improve"" a given cluster that has been obtained by some other method. In this survey, we focus on principled algorithms for this cluster improvement problem. Many such cluster improvement algorithms are flow-based methods, by which we mean that operationally they require the solution of a sequence of maximum flow problems on a (typically implicitly) modified data graph. These cluster improvement algorithms are powerful, both in theory and in practice, but they have not been widely adopted for problems such as community detection, local graph clustering, semisupervised learning, etc. Possible reasons for this are the steep learning curve for these algorithms, the lack of efficient and easy-to-use software, and the lack of detailed numerical experiments on real-world data that demonstrate their usefulness. Our objective here is to address these issues. To do so, we guide the reader through the whole process of understanding how to implement and apply these powerful algorithms. We present a unifying fractional programming optimization framework that permits us to distill, in a simple way, the crucial components of all these algorithms. This also makes apparent similarities and differences among related methods. Viewing these cluster improvement algorithms via a fractional programming framework suggests directions for future algorithm development. Finally, we develop efficient implementations of these algorithms in our LocalGraphClustering Python package, and we perform extensive numerical experiments to demonstrate the performance of these methods on social networks and image-based data graphs. [ABSTRACT FROM AUTHOR]

Energy harvesting systems have emerged as an alternative to battery-operated Internet of Things (IoT) devices. To deal with frequent power outages in the absence of battery, energy harvesting systems rely on a capacitor-backed checkpoint mechanism also known as just-in-time (JIT) checkpointing. It checkpoints volatile data in nonvolatile memory (NVM) just before a power outage occurs—using the energy buffered in the capacitor—and restores the checkpointed data from NVM in the wake of the outage. While the JIT checkpointing gives an illusion that volatile data survive a power outage as if they were nonvolatile, it turns out that due to capacitor degradation, energy harvesting systems can unexpectedly fail the JIT checkpointing, losing or corrupting data across the outage. To address the problem, this article presents an operating system-driven solution called CapOS. At a high level, CapOS diagnoses the capacitor in a reactive yet safe manner. When the JIT checkpoint failure occurs, CapOS detects the capacitor degradation without causing the data corruption. To recover from such a capacitor error, CapOS electrically isolates the degraded capacitor—so that it restores its original capacitance by itself with the help of capacitor’s resilient nature—and disables the JIT checkpointing. In case, power outages occur during the capacitor isolation, CapOS leverages undo logging with interval-based checkpointing for their recovery. Once the capacitor is fully recovered, CapOS gets back to the capacitor-based JIT checkpointing. The experimental results demonstrate that CapOS can effectively address the capacitor error of energy harvesting systems at a low run-time cost, without compromising the recovery of power outages. [ABSTRACT FROM AUTHOR]

We present an ultrahigh-resolution Energy Exascale Earth System Model Land Model (uELM) for high-fidelity land simulations targeting new exascale computers. After considering modeling infrastructure compatibility and ELM software features, we designed a parallel model for the uELM development targeting the hybrid architectures of new U.S. exascale computers. We also described a function unit test framework to expedite the piecewise code porting (with compiler directives), verification, and global variable management. Furthermore, in this study, we report the development of an early uELM model using OpenACC within a function unit test framework on a pre-exascale computer, demonstrate the performance of a uLEM submodel with three-times speedup, and summarize the code-porting experience regarding global variable handling, deepcopy, memory reduction, and parallel loop reconstruction. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) is a smart-transportation system for road-side assistance and data exchange support by integrating cloud and wireless networks. ITS facilitates vehicle-to-vehicle and vehicle-to-anything (V2X) data exchanges for satisfying user demands. The rate of big data granting to the vehicular users is interrupted by the fundamental attributes such as mobility and link instability of the vehicles. To address the issues in vehicular data exchange big data, this article introduces displacement-aware service endowment scheme with the benefits of data offloading. Displacement-aware big data endowment ensures responsive availability of vehicle request information despite unfavorable location and density factors. The time congruency in V2V and V2X data exchanges are adopted for minimizing data exchange dropouts. In the data offloading phase, extraneous information and big data responses are detained based on data exchange relevance to improve congestion free big data endowment. The distinct methods work in a co-operative manner to improve big data quality of fast configuring smart vehicles to provide reliable big data in smart city environments. [ABSTRACT FROM AUTHOR]

The accuracy of the location estimation of sensors in wireless sensor networks (WSNs) is important to many applications such as environment surveillance applications. In large-scale and distributed WSNs, locations of the sensors may not be recorded during the deployment process, resulting in the need of localization techniques to help collect the sensor location information. WSNs are highly vulnerable to localization attacks since the localization techniques totally rely on the neighboring relations to determine the location of sensors. In other words, some of malicious sensor nodes can take the advantage of the neighboring relations to manipulate the localization process. In this paper, we propose two localization algorithms to overcome both common types of localization attacks, independent attacks and collusion attacks. In the first algorithm, we propose the Improved Randomized Consistency Position Algorithm to determine the location of unknown nodes using the location estimation information provided by the three random anchor nodes and using the PSO algorithm paradigm to identify the coordinates of the unknown nodes' location. In addition, the second algorithm, which is a combination of voting method, location optimization, and the PSO algorithm paradigm to identify the location of unknown nodes, is referred to as the Enhanced Attack-Resistant Secure Localization Algorithm. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed localization algorithms yield better performance comparing to the existing methods. [ABSTRACT FROM AUTHOR]

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) provide roadside communication for improving the ease of driving user information exchange. It interconnects hierarchical infrastructure units and other vehicles for real-time traffic and vehicle management applications. The growth of vehicle and information density requires concord information exchange for application responses. In this article, Vehicle-Consensus Routing Management Scheme (VCRMS) is proposed for achieving fair roadside assistance for driving users. The proposed scheme exploits the surrounding vehicle information for infrastructure selection and traffic management. The infrastructure and vehicle information are analyzed for their similarity using deep learning for extracting monotonous decisions. The current and previous decisions are used for succeeding in information selection and traffic information retrieval. This prevents unnecessary data from being congesting the traffic and vehicle management application during driver assistance. The performance shows that the proposed scheme achieves 10.7% high application response, 15.9% less information delay, and 10.7% less traffic for different vehicle velocities. [ABSTRACT FROM AUTHOR]

As one of the fundamental visual tasks in the unmanned driving area, lane detection attracts increasing attention. In practical applications, lane points are very difficult to localize because they usually appear to be sparse and incomplete due to the influence of illumination and environment. Conventional lane detection methods rely on coarse features and carefully designed postprocessing to detect the lane lines. However, these methods are usually slow, and the stability and generalization ability are unsatisfactory. In this paper, we propose a novel lane detection network – Fast-HBNet(Fast-Hybrid Branch Network), which exploits both global semantic information and spatial contexts. To enlarge receptive fields and encode more detailed information, the compound transformation is employed and the proposed hybrid branch network extracts four diverse feature maps with different receptive fields and spatial contexts. Besides, we design a Hierarchical Feature Learning (HFL) module to learn lane features from the scale, channel, and spatial levels to enhance the generalization ability of our detector. These features are further selectively coalesced to generate unified lane feature maps with large receptive fields and rich detailed information. In other words, our network can encode the global semantic information from the high-resolution feature maps and the fine-grained details in the low-resolution feature maps. Experimental results conducted on the TuSimple (2017) and CULane [Pan et al. (2018)] datasets demonstrate that the proposed Fast-HBNet outperforms numerous state-of-the-art lane detectors in both speed and accuracy. Particularly, Fast-HBNet achieves an accuracy of 96.88% on the TuSimple dataset at a speed of 76 FPS. [ABSTRACT FROM AUTHOR]

A fundamental challenge in learning is the presence of nonlinear redundancies and dependencies in the data. To address this, we propose a Fourier-based approach to characterize feature redundancies, in unsupervised learning, and feature-label dependencies, in the supervised variant of the problem. We first develop a novel Fourier expansion for functions (more generally stochastic mappings) of correlated binary random variables. This is a generalization of the standard Fourier expansion on the Boolean cube beyond product probability spaces. As an important application of this analysis, we investigate learning with feature subset selection. In the unsupervised variant of this problem, we characterize feature redundancies via the Shannon entropy and group the features into sufficiently informative and redundant. Then, we make a connection to the proposed Fourier expansion and derive an upper bound on the joint entropy. Based on that, we propose a measure to quantify feature redundancies and present an unsupervised learning algorithm. We test our method on various real-world and synthetic datasets and demonstrate improvements on conventional unsupervised feature selection techniques. Then, we investigate the supervised feature subset selection and reformulate it in the Fourier domain. Bridging the Bayesian error rate with the Fourier coefficients, we demonstrate that the Fourier expansion provides a powerful tool to characterize nonlinear feature-label dependencies. Further, we introduce a computationally efficient measure for selecting relevant features. Via a theoretical analysis, we show that our proposed measure finds provably asymptotically optimal feature subsets. Lastly, we present an algorithm based on this measure and via numerical experiments demonstrate its improvements on various supervised feature selection algorithms. [ABSTRACT FROM AUTHOR]

With the development of 5G, it is unsurprising that most of the smart devices in the Internet of Things (IoT) will be wirelessly connected with each other in the near future. This kind of lightweight, scalable and green network architecture will be well-received. In a wide variety of IoT application scenarios, sensor nodes deployed in a local space, such as a multistory building, automatically form a distributed 3D wireless IoT and it can be employed to collect and analyze environmental information. Source-location privacy protection is of great importance in these networks and however, most existing schemes focus on only planar distributed networks which are not suitable for the 3D networks. In this paper, we consider a novel trace-back attack for 3D wireless IoT and then design a source-location privacy protection scheme, named DMR-3D, to defend this kind of novel attacks. In DMR-3D, the source node first selects a set of virtual locations to indirectly choose a set of agent nodes based on the cold start sphere structure and the ellipsoid communication pipeline. Then, a sophisticated mechanism is designed based on both the connected graph and Multiple Delaunay Triangulation (MDT) structure of the network to deliver packets from the source node to the destination node via these agent nodes in a relay manner. Analysis and simulation results illustrate that the proposed scheme can effectively protect source-location privacy with a moderate increment of path stretch, time delay and data transmission amount. [ABSTRACT FROM AUTHOR]

Coflow scheduling improves data-intensive application performance by improving their networking performance. State-of-the-art online coflow schedulers in essence approximate the classic Shortest-Job-First (SJF) scheduling by learning the coflow size online. In particular, they use multiple priority queues to simultaneously accomplish two goals: to sieve long coflows from short coflows, and to schedule short coflows with high priorities. Such a mechanism pays high overhead in learning the coflow size: moving a large coflow across the queues delays small and other large coflows, and moving similar-sized coflows across the queues results in inadvertent round-robin scheduling. We propose Philae, a new online coflow scheduler that exploits the spatial dimension of coflows, i.e., a coflow has many flows, to drastically reduce the overhead of coflow size learning. Philae pre-schedules sampled flows of each coflow and uses their sizes to estimate the average flow size of the coflow. It then resorts to Shortest Coflow First, where the notion of shortest is determined using the learned coflow sizes and coflow contention. We show that the sampling-based learning is robust to flow size skew and has the added benefit of much improved scalability from reduced coordinator-local agent interactions. Our evaluation using an Azure testbed, a publicly available production cluster trace from Facebook shows that compared to the prior art Aalo, Philae reduces the coflow completion time (CCT) in average (P90) cases by $1.50\times $ ($8.00\times $) on a 150-node testbed and $2.72\times $ ($9.78\times $) on a 900-node testbed. Evaluation using additional traces further demonstrates Philae’s robustness to flow size skew. [ABSTRACT FROM AUTHOR]

Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns. [ABSTRACT FROM AUTHOR]

We introduce a framework for proving lower bounds on computational problems over distributions against algorithms that can be implemented using access to a statistical query oracle. For such algorithms, access to the input distribution is limited to obtaining an estimate of the expectation of any given function on a sample drawn randomly from the input distribution rather than directly accessing samples. Most natural algorithms of interest in theory and in practice, for example, moments-based methods, local search, standard iterative methods for convex optimization, MCMC, and simulated annealing, can be implemented in this framework. Our framework is based on, and generalizes, the statistical query model in learning theory [Kearns 1998]. Our main application is a nearly optimal lower bound on the complexity of any statistical query algorithm for detecting planted bipartite clique distributions (or planted dense subgraph distributions) when the planted clique has size O(n1/2-δ) for any constant δ > 0. The assumed hardness of variants of these problems has been used to prove hardness of several other problems and as a guarantee for security in cryptographic applications. Our lower bounds provide concrete evidence of hardness, thus supporting these assumptions. [ABSTRACT FROM AUTHOR]

In a wireless sensor network (WSN), provenance, usually considered as the tracebacks of the data packets’ acquisition and transmission, is critical for assessing data trustworthiness. However, the provenance size expands rapidly with increases in the number of packet transmission hops. Among the known provenance schemes, the dictionary based provenance (DP) scheme achieves the highest provenance compression rate up to now. Nevertheless, the average compression rate of the DP scheme decreases drastically when the WSN’s topology is not stable. To address such a disadvantage, in this paper we propose a packet path index differences based provenance (PIDP) scheme. In the PIDP scheme, the backbone paths along the gradient directions of the packet transmissions are built, and then the provenance is encoded by a selected index of a backbone path dictionaries together with a deviation from the backbone path. To further increase the provenance compression rate and decrease the computation complexity at each node, a packet path hash value based provenance (PHP) scheme is proposed as a complementary approach of the PIDP scheme, in which the provenance is encoded as the data source node ID together with a segment of the packet path’s hash value. Both the simulation and experimental results show that our proposed two schemes, the PIDP and the PHP schemes, outperform the DP scheme with respect to both provenance compression rate and energy conservation rate even when a WSN’s topology is not stable. [ABSTRACT FROM AUTHOR]

The existing publicly available datasets with pixel-level labels contain limited categories, and it is difficult to generalize to the real world containing thousands of categories. In this paper, we propose an approach to generate object masks with detailed pixel-level structures/boundaries automatically to enable semantic image segmentation of thousands of targets in the real world without manually labelling. A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from an existed dataset, and such GFN then transfers the learned segmentation knowledge to generate initial coarse object masks for the target images. These coarse object masks are treated as pseudo labels to self-optimize the GFN iteratively in the target images. Our experiments on six image sets have demonstrated that our proposed approach can generate object masks with detailed pixel-level structures/boundaries, whose quality is comparable to the manually-labelled ones. Our proposed approach also achieves better performance on semantic image segmentation than most existing weakly-supervised, semi-supervised, and domain adaptation approaches under the same experimental conditions. [ABSTRACT FROM AUTHOR]

Distributed denial of service (DDoS) flooding attacks are very harmful and difficult to prevent due to the default-on nature of both inter-domain and intra-domain routing adopted by the current Internet. Accordingly, many future Internet initiatives try to eliminate DDoS attacks by design. Among these efforts, using path identifiers (PIDs) as inter-domain routing objects has attracted much research interest. However, existing approaches either advertise PIDs throughout the Internet or use secret but forgeable (whether static or dynamic) PIDs, which makes it easy to launch DDoS flooding attacks. To address this issue, in this paper we propose K-PID that uses cryptographic (thus unforgeable) PIDs as inter-domain routing objects. In particular, an ${N}$ -bit PID is comprised of an ${n}$ -bit prefix and (${N}\,\,-\,\,{n}$) bits that are cryptographic hash over per-flow/request information and a secret number. The prefix is used for inter-domain packet forwarding and the cryptographic hash is used as a token for allowing a data packet to enter into (or, pass through) a domain. We analyze K-PID’s performance in preventing DDoS flooding attacks and implement K-PID in a prototype to verify K-PID’s feasibility. The results show that K-PID can effectively prevent DDoS flooding attacks at a low cost of reducing the forwarding capability of routers. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things uses intelligent sensors to collect the physical properties of objects placed on a large area. It provides innovative service and network functioning, such as time-bounded data delivery and efficient sensor sleep cycle management. Intelligent scheduling is crucial to assisting the situation. The periodic scheduling of radio transceivers of sensor nodes into sleep or active mode helps accomplish efficient energy consumption. Such an extensive scale network work on data collection on multihop fashion. Our proposal provides the algorithm to form the node's backbone, which task is to collect the data from sensors scatted in the area. Further, the heuristic algorithm is proposed to assist the backbone node in balancing the incoming traffic for scheduling. In Time Division Multiple Access (TDMA) driven Medium Access Control (MAC), the nodes are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. Our proposed work is named aggressive scheduling medium access control (AS-MAC). If the owner node does not have data to forward, then the corresponding slot will be aggressively scheduled by other nodes. Therefore, that slot and all consecutive slots if the corresponding owner nodes do not use will go to the needy nodes. The proposed system model presents a unique instance of interference for successfully delivering control and data packets. Therefore, a probabilistic estimation has given for the packet delivery rate for the raised specific condition. Our proposal's distinctive feature is that the performance is better than Time Division Multiple Access for any given load. Finally, the packet delivery rate, packet drop, energy consumption, and time frame requirement for a different number of sensor node has been calculated and compared with the current state-of-the-art proposals. [ABSTRACT FROM AUTHOR]

Background: A new compact superconducting synchrocyclotron single-room proton solution delivers pulsed proton beams to each spot through several irradiation bursts calculated by an iterative layer delivery algorithm. Such a mechanism results in a new beam parameter, burst switching time (BST) in the total beam delivery time (BDT) which has never been studied before. In this study, we propose an experimental approach to build an accurate BDT and sequence prediction model for this new proton solution.Methods: Test fields and clinical treatment plans were used to investigate each beam delivery parameter that impacted BDT. The machine delivery log files were retrospectively analyzed to quantitatively model energy layer switching time (ELST), spot switching time (SSWT), spot spill time (SSPT), and BST. A total of 102 clinical IMPT treatment fields' log files were processed to validate the accuracy of the BDT prediction model in comparison with the result from the current commercial system. Interplay effect is also investigated as a clinical application by comparing this new delivery system model with a conventional cyclotron accelerator model.Results: The study finds that BST depends on the amount of data to be transmitted between two sequential radiation bursts, including a machine irradiation log file of the previous burst and a command file to instruct the proton system to deliver the next burst. The 102 clinical treatment fields showed that the accuracy of each component of the BDT matches well between machine log files and BDT prediction model. More specifically, the difference of ELST, SSWT, SSPT, and BST were (- 3.1 ± 5.7)%, (5.9 ± 3.9)%, (2.6 ± 8.7)%, and (- 2.3 ± 5.3)%, respectively. The average total BDT was about (2.1 ± 3.0)% difference compared to the treatment log files, which was significantly improved from the current commercial proton system prediction (58 ± 15)%. Compared to the conventional cyclotron system, the burst technique from synchrocyclotron effectively reduced the interplay effect in mobile tumor treatment.Conclusion: An accurate BDT and sequence prediction model was established for this new clinical compact superconducting synchrocyclotron single-room proton solution. Its application could help users of similar facilities better assess the interplay effect and estimate daily patient treatment throughput. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, more and more data owners are motivated to outsource their documents to the cloud and share them with the authorized data users securely and flexibly. To protect data privacy, the documents are generally encrypted before being outsourced to the cloud and hence their searchability decreases. Though many privacy-preserving document search schemes have been proposed, they cannot reach a proper balance among functionality, flexibility, security and efficiency. In this paper, a new encrypted document retrieval system is designed and a proxy server is integrated into the system to alleviate data owner's workload and improve the whole system's security level. In this process, we consider a more practical and stronger threat model in which the cloud server can collude with a small number of data users. To support multiple document search patterns, we construct two AVL trees for the filenames and authors, and a Hierarchical Retrieval Features tree (HRF tree) for the document vectors. A depth-first search algorithm is designed for the HRF tree and the Enhanced Asymmetric Scalar-Product-Preserving Encryption (Enhanced ASPE) algorithm is utilized to encrypt the HRF tree. All the three index trees are linked with each other to efficiently support the search requests with multiple parameters. Theoretical analysis and simulation results illustrate the security and efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]