Range Search Research Articles

A Localization Algorithm Based on Global Descriptor and Dynamic Range Search

The map-based localization method is considered an effective supplement to the localization under the GNSS-denied environment. However, since the map is constituted by the dispersed keyframes, it sometimes happens that the initial position of the unmanned ground vehicle (UGV) lies between the map keyframes or is not on the mapping trajectory. In both cases, it will be impossible to precisely estimate the pose of the vehicle directly via the relationship between the current frame and the map keyframes, leading to localization failure. In this regard, we propose a localization algorithm based on the global descriptor and dynamic range search (LA-GDADRS). In specific, we first design a global descriptor shift and rotation invariant image (SRI), which improves the rotation invariance and shift invariance by the methods of coordinates removal and de-centralization. Secondly, we design a global localization algorithm for shift and rotation invariant branch-and-bound scan matching (SRI-BBS). It first leverages SRI to obtain an approximate priori position of the unmanned vehicle and then utilizes the similarity between the current frame SRI and the map keyframes SRI to select a dynamic search range around the priori position. Within the search range, we leverage the branch-and-bound scanning matching algorithm to search for a more precise pose. It solves the problem that global localization tends to fail when the priori position is imprecise. Moreover, we introduce a tightly coupled factor graph model and a HD map engine to achieve real-time position tracking and lane-level localization, respectively. Finally, we complete extensive ablation experiments and comparative experiments to validate our methods on the benchmark dataset (KITTI) and the real application scenarios at the campus. Extensive experimental results demonstrate that our algorithm achieves the performance of mainstream localization algorithms.

Fully Dynamic No-Back-Edge-Traversal Forest via 2D-Range Queries

Orthogonal range search is ubiquitous nowadays, with natural applications in databases, data mining, and text indexing. Very recently, yet another application was discovered, which is to maintain a DFS forest in a dynamic graph. In this paper, we want to extend the above recent study, by applying orthogonal range search to efficient maintenance of a BFS-like forest, called no-back-edge-traversal (NBET) forest, which refers to a spanning forest obtained from a traversal that does not create any back edge. The study of such a problem is motivated by the fact that NBET forest can be used as a strong certificate of 2-connectivity of an undirected graph, which is more general than a spanning forest obtained from a scan-first search traversal.

Improved symmetric flipped nested array for mixed near‐field and far‐field non‐circular sources localization

AbstractAt present, there are few sparse arrays used in the mixed near‐field (NF) and far‐field (FF) localization based on non‐circular (NC) signals. Inspired by the symmetric flipped nested array (SFNA) used in the existing mixed NF and FF NC source, in order to further improve the parameter estimation accuracy of the mixed NF and FF NC signal, an improved symmetric flipped nested array (ISFNA) for mixed NF and FF NC sources localization was developed. First, the uniform subarrays in the SFNA are rearranged, elements are extracted from the uniform subarrays and rearranged into ISFNA. ISFNA is more sparse, the array aperture is larger, and the array degree of freedom (DOF) is higher; second, the formula of the maximum consecutive lags of ISFNA is given; third, a special fourth‐order cumulant is used to eliminate the range parameter and then use a one‐dimensional (1‐D) spectral peak search to obtain all Directions of Arrival (DOAs). By defining the range search, the range can be obtained by bringing in estimated DOAs. Finally, the superiority of the proposed array is proved by simulation.

Discrete Salp Swarm Algorithm for symmetric traveling salesman problem.

In the Salp Swarm Algorithm (SSA), the update mechanism is inspired by the unique chain movement of the salp swarm. Numerous versions of SSA were already put forward to deal with various optimization problems, but there are very few discrete versions among them. d-opt is improved based on the 2-opt algorithm: a decreasing factor d is introduced to control the range of neighborhood search; TPALS are modified by Problem Aware Local Search (PALS) based on the characteristics of Travelling Salesman Problem (TSP); The second leader mechanism increases the randomness of the algorithm and avoids falling into the local optimal solution to a certain extent. We also select six classical crossover operators to experiment and select Subtour Exchange Crossover (SEC) and the above three mechanisms to integrate them into the SSA algorithm framework to form Discrete Salp Swarm Algorithm (DSSA). In addition, DSSA was tested on 23 known TSP instances to verify its performance. Comparative simulation studies with other advanced algorithms are conducted and from the results, it is observed that DSSA satisfactorily solves TSP.

MAPPING OF THE PENTAGONAL TOWERS OF EUROPE IN CONNECTION WITH RESEARCH OF THE "KNIGHT'S TOWER" OF THE MEDZHYBIZH FORTRESS

Our research is related to elucidating the features of the construction, existence, and modernization of the pentagonal tower of the Medzhybizh fortress (Khmelnytskyi region, Ukraine). One of the stages of our work is based on comparative analysis and the search for analogies. The search for a wide range of such objects requires systematization and a comprehensive approach. That is why, to solve this task, we launched the research project “Pentagonal Towers of Europe”. One of the directions of this project is to create a database of European pentagonal towers. Such a database is the basis of complex research and searches for an analogy for the Medzhybizh Tower in such areas as dating, construction technology, architectural specificity, roles in the fortification ensemble, and so on. In the first stages of the project, while the database is being formed, there is an opportunity to solve research questions. These include the mapping of pentagonal towers. Thus, with the help of cartographic methods, it is possible to study their geographical location. In our work, along with mapping, the pentagonal towers were systematized according to four positions (defensive complex, dating, construction specifics, architectural solutions), plus visualization and geographic location of the object. So, using the cartographic approach allows us to better understand the geographical location and distribution of the pentagonal towers, their relationships with other defensive objects. In the future, this may provide an opportunity to search for historical and geographical patterns of the existence and development of the style of fortification architecture, which was embodied in such a specific construction of pentagonal towers.

Generic Exact Combinatorial Search at HPC Scale

Exact combinatorial search is essential to a wide range of important applications, and there are many large problems that need to be solved quickly. Searches are extremely challenging to parallelise due to a combination of factors, e.g. searches are non-deterministic, dynamic pruning changes the workload, and search tasks have very different runtimes. YewPar is a C++/HPX framework that generalises parallel search by providing a range of sophisticated search skeletons.This paper demonstrates generic high performance combinatorial search, i.e. that a variety of exact combinatorial searches can be easily parallelised for HPC using YewPar. We present a new mechanism for profiling key aspects of YewPar parallel combinatorial search, and demonstrate its value. We exhibit, for the first time, generic exact combinatorial searches at HPC scale. We baseline YewPar against state-of-the-art sequential C++ and C++/OpenMP implementations. We demonstrate that deploying YewPar on an HPC system can dramatically reduce the runtime of large problems, e.g. from days to just 100s. The maximum relative speedups we achieve for an enumeration search are near-linear up to 195(6825) compute-nodes(workers), super-linear for an optimisation search on up to 128(4480) (pruning reduces the workload), and sub-linear for decision searches on up to 64(2240) compute-nodes(workers).

Range Search over Encrypted Multi-Attribute Data

This work addresses expressive queries over encrypted data by presenting the first systematic study of multi-attribute range search on a symmetrically encrypted database outsourced to an honest-but-curious server. Prior work includes a thorough analysis of single-attribute range search schemes (e.g. Demertzis et al. 2016) and a proposed high-level approach for multi-attribute schemes (De Capitani di Vimercati et al. 2021). We first introduce a flexible framework for building secure range search schemes over multiple attributes (dimensions) by adapting a broad class of geometric search data structures to operate on encrypted data. Our framework encompasses widely used data structures such as multi-dimensional range trees and quadtrees, and has strong security properties that we formally prove. We then develop six concrete highly parallelizable range search schemes within our framework that offer a sliding scale of efficiency and security tradeoffs to suit the needs of the application. We evaluate our schemes with a formal complexity and security analysis, a prototype implementation, and an experimental evaluation on real-world datasets.

Search for long-lived heavy neutral leptons and Higgs portal scalars decaying in the MicroBooNE detector

We present a search for long-lived Higgs portal scalars (HPS) and heavy neutral leptons (HNL) decaying in the MicroBooNE liquid-argon time projection chamber. The measurement is performed using data collected synchronously with the NuMI neutrino beam from Fermilab's Main Injector with a total exposure corresponding to $7.01 \times 10^{20}$ protons on target. We set upper limits at the $90\%$ confidence level on the mixing parameter $\lvert U_{\mu 4}\rvert^2$ ranging from $\lvert U_{\mu 4}\rvert^2<12.9\times 10^{-8}$ for Majorana HNLs with a mass of $m_{\rm HNL}=246$ MeV to $\lvert U_{\mu 4}\rvert^2<0.92 \times 10^{-8}$ for $m_{\rm HNL}=385$ MeV, assuming $\lvert U_{e 4}\rvert^2 = \lvert U_{\tau 4}\rvert^2 = 0$ and HNL decays into $\mu^\pm\pi^\mp$ pairs. These limits on $\lvert U_{\mu 4}\rvert^2$ represent an order of magnitude improvement in sensitivity compared to the previous MicroBooNE result. We also constrain the scalar-Higgs mixing angle $\theta$ by searching for HPS decays into $\mu^+\mu^-$ final states, excluding a contour in the parameter space with lower bounds of $\theta^2<31.3 \times 10^{-9}$ for $m_{\rm HPS}=212$ GeV and $\theta^2<1.09 \times 10^{-9}$ for $m_{\rm HPS}=275$ GeV. These are the first constraints on the scalar-Higgs mixing angle $\theta$ from a dedicated experimental search in this mass range.

Range search on encrypted spatial data with dynamic updates1

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

Semantic Segmentation of 3D Point Clouds Based on High Precision Range Search Network

Semantic segmentation for 3D point clouds plays a critical role in the construction of 3D models. Due to the sparse and disordered natures of the point clouds, semantic segmentation of such unstructured data yields technical challenges. A recently proposed deep neural network, PointNet, delivers attractive semantic segmentation performance, but it only exploits the global features of point clouds without incorporating any local features, limiting its ability to recognize fine-grained patterns. For that, this paper proposes a deeper hierarchical structure called the high precision range search (HPRS) network, which can learn local features with increasing contextual scales. We develop an adaptive ball query algorithm that designs a comprehensive set of grouping strategies. It can gather detailed local feature points in comparison to the common ball query algorithm, especially when there are not enough feature points within the ball range. Furthermore, compared to the sole use of either the max pooling or the mean pooling, our network combining the two can aggregate point features of the local regions from hierarchy structure while resolving the disorder of points and minimizing the information loss of features. The network achieves superior performance on the S3DIS dataset, with a mIoU declined by 0.26% compared to the state-of-the-art DPFA network.

Privacy-Preserving Spatio-Temporal Keyword Search for Outsourced Location-Based Services

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this article, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption, linear encryption and RSA encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.

PD-FAC: Probability Density Factorized Multi-Agent Distributional Reinforcement Learning for Multi-Robot Reliable Search

This letter presents a new range of multi-robot search for a non-adversarial moving target problems, namely multi-robot reliable search (MuRRS). The term ‘reliability’ in MuRRS is defined as the expectation of a predefined utility function over the probability density function (PDF) of the target’s capture time. We argue that MuRRS subsumes the canonical multi-robot efficient search (MuRES) problem, which minimizes the target’s expected capture time, as its special case, and offers the end user with a wide range of objective selection options. Since state-of-the-art algorithms are usually targeting the MuRES problem, and cannot offer up-to-standard performance to the various MuRRS objectives, we, thereby, propose a probability density factorized multi-agent distributional reinforcement learning method, namely PD-FAC, as a unified solution to the MuRRS problem. PD-FAC decomposes the PDF of the multi-robot system’s overall value distribution into a set of individual value distributions and guarantees that any reliability objective defined as a function of the overall system’s value distribution can be linearly approximated by the same reliability metric defined over the agent’s individual value distribution. In this way, the individual global maximum (IGM) principle is satisfied for all the pre-defined reliability metrics. It means that when each reinforcement learning agent is executing the individual policy, which maximizes its own reliability metric, the system’s overall reliability performance is also maximized. We evaluate and compare the performance of PD-FAC with state of the arts in a range of canonical multi-robot search environments with satisfying results, and also deploy PD-FAC to a real multi-robot system for non-adversarial moving target search.

Faster compressed quadtrees

Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first show how a compact representation of quadtrees using O(1) bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy-path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.

The Fine-Grained Complexity of Multi-Dimensional Ordering Properties

We define a class of problems whose input is an n-sized set of d-dimensional vectors, and where the problem is first-order definable using comparisons between coordinates. This class captures a wide variety of tasks, such as complex types of orthogonal range search, model-checking first-order properties on geometric intersection graphs, and elementary questions on multidimensional data like verifying Pareto optimality of a choice of data points. Focusing on constant dimension d, we show that any such k-quantifier, d-dimensional problem is solvable in $$O(n^{k-1} \log ^{d-1} n)$$ time. Furthermore, this algorithm is conditionally tight up to subpolynomial factors: we show that assuming the 3-uniform hyperclique hypothesis, there is a k-quantifier, $$(3k-3)$$ -dimensional problem in this class that requires time $$\Omega (n^{k-1-o(1)})$$ . Towards identifying a single representative problem for this class, we study the existence of complete problems for the 3-quantifier setting (since 2-quantifier problems can already be solved in near-linear time $$O(n\log ^{d-1} n)$$ , and k-quantifier problems with $$k>3$$ reduce to the 3-quantifier case). We define a problem Vector Concatenated Non-Domination $$\mathsf {VCND}_d$$ (Given three sets of vectors X, Y and Z of dimension d, d and 2d, respectively, is there an $$x \in X$$ and a $$y \in Y$$ so that their concatenation $$x \circ y$$ is not dominated by any $$z \in Z$$ , where vector u is dominated by vector v if $$u_i \le v_i$$ for each coordinate $$1 \le i \le d$$ ), and determine it as the “unique” candidate to be complete for this class (under fine-grained assumptions).

Resource Search in HPC Systems using Levy Flights

Parallel applications represented by Directed Acyclic Graphs (DAGs) as Parallel Task Graphs (PTGs) requiring high execution times with large amounts of storage, are executed on High Performance Computing (HPC) Systems such as clusters. For the execution of these applications, a scheduler performs the scheduling and allocation of the resources contained in the HPC System. One of the activities of the scheduler is the search for idle resources that are geographically dispersed in the clusters to schedule and allocate them to the tasks. The search for idle resources in the clusters is a process that consumes time and system resources due to the geographical distances that must be traveled and the repetitive and permanent execution within the system. A sequential search for resources causes the scheduling and allocation of resources in the HPC System to be slowed down and paused, and increases the waiting times of the tasks that remain in the queue. The techniques that shorten the location times of resource and perform more exhaustive searches in dispersed geographic spaces can reduce the times generated by sequential searches. The open access paper: Scheduling in Heterogeneous Distributed Computing Systems Based on Internal Structure of Parallel Tasks Graphs with Meta-Heuristics presents the Array Method, a scheduler for scheduling and allocation resources in an HPC System. Array Method uses a sequential search process for the idle resources that are geographically dispersed in the clusters and save their location and characteristics in an array, which is updated every time idle resources are located in the clusters. Considering the above, this paper presents a search for idle resources using Levy random walks, a technique ´ used for searching resources in large geographical spaces; this technique avoids sequential node-by-node searches of each cluster, and promote short and long range searches over the entire geographical extent of the HPC Systems. To obtain experimental results, sequential resource search algorithm versus Levy random walks with the synthetic loads, different ´ clusters and different numbers of resources per cluster as proposed in the open access paper aforementioned, are used. Obtained results show Levy random walks ´ locates more idle resources in less time, optimizes the times of the resource searches in the clusters and update the array of available resources more frequently. With more idle resources found, the total execution times of the tasks are reduced.

Geometric Searchable Encryption Without False Positive And Its Applications

Abstract As a prominent cryptographic tool, geometric searchable encryption (GSE) can be applied in many scenarios, such as location-based services (LBS), social networks and vehicle networks. Unfortunately, most of existing searchable encryption schemes supporting the functionality of geometric range searches suffer from false positives, which will lead people to make a wrong decision and further raise some serious consequences such as financial loss. In addition, some of them are designed under a symmetric system, which is not enough flexible deployed in LBS since in a symmetric system only a private key holder creates ciphertext, whereas in a public-key system anyone who holds a public key can produce ciphertext. In this paper, we intend to design a novel GSE scheme without any false positive under a public-key system supporting arbitrary geometric area searches, which is able to guarantee an accurate query result. Toward this goal, we develop a novel technique in handling the relation between a point and any convex polygon in combination with an inner product encryption, which is able to support arbitrary convex polygon range searches without any false positive. A comprehensive experiment demonstrates that, compared with the known schemes, our scheme possesses a 100% accuracy as well as an acceptable efficiency in the sense that it can guarantee that all files retrieved by users are exactly matched ones. Finally, we provide two practical examples of our GSE scheme: privacy-preserving friend-nearby notification with a common point of interest and privacy-preserving parking monitor and guiding system.

EAFS: An Efficient, Accurate, and Forward Secure Searchable Encryption Scheme Supporting Range Search

Forward privacy in existing searchable symmetric encryption approaches generally supports only keyword searches, rather than range searches. In addition, existing approaches that support range search over encrypted datasets have limitations associated with efficiency, accuracy, and security. Seeking to address these limitations, we formally propose the definition of the forward privacy for secure range searches. Also, we demonstrate how three widely used cryptographic tools&#x2014; order-preserving encryption (OPE), pseudorandom function, and one-time pad&#x2013;can be leveraged to design an efficient, accurate, and forward secure (EAFS) searchable encryption scheme supporting range search in encrypted numerical databases. In the proposed EAFS scheme, a trapdoor only matches the last data record that satisfies the search range, and the other results are found iteratively using the previous result. The chain-like search and the embedded ciphertexts of OPE simultaneously guarantee its efficiency, forward privacy, and accuracy. We also use the simulation-based method to demonstrate that our scheme is secure. Then, we implement EAFS in Microsoft Azure and evaluate its performance. The evaluation findings demonstrate that our proposed scheme supports secure range search practically and efficiently.

Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models.

Multiphase CT scanning of the liver is performed for several clinical applications; however, radiation exposure from CT scanning poses a nontrivial cancer risk to the patients. The radiation dose may be reduced by determining the scan range of the subsequent scans by the location of the target of interest in the first scan phase. The purpose of this study is to present and assess an automatic method for determining the scan range for multiphase CT scans. Our strategy is to first apply a CNN-based method for detecting the liver in 2D slices, and to use a liver range search algorithm for detecting the liver range in the scout volume. The target liver scan range for subsequent scans can be obtained by adding safety margins achieved from Gaussian liver motion models to the scan range determined from the scout. Experiments were performed on 657 multiphase CT volumes obtained from multiple hospitals. The experiment shows that the proposed liver detection method can detect the liver in 223 out of a total of 224 3D volumes on average within one second, with mean intersection of union, wall distance and centroid distance of 85.5%, 5.7mm and 9.7mm, respectively. In addition, the performance of the proposed liver detection method is comparable to the best of the state-of-the-art 3D liver detectors in the liver detection accuracy while it requires less processing time. Furthermore, we apply the liver scan range generation method on the liver CT images acquired from radiofrequency ablation and Y-90 transarterial radioembolization (selective internal radiation therapy) interventions of 46 patients from two hospitals. The result shows that the automatic scan range generation can significantly reduce the effective radiation dose by an average of 14.5% (2.56 mSv) compared to manual performance by the radiographer from Y-90 transarterial radioembolization, while no statistically significant difference in performance was found with the CT images from intra RFA intervention (p = 0.81). Finally, three radiologists assess both the original and the range-reduced images for evaluating the effect of the range reduction method on their clinical decisions. We conclude that the automatic liver scan range generation method is able to reduce excess radiation compared to the manual performance with a high accuracy and without penalizing the clinical decision.

Intelligent Tourism Information Service Model considering Tourist Experience in the Environment of Internet of Things.

Due to the narrow range of route search and the single choice of the optimal route during the peak period of tourists' tourism, resulting in a long walking time and relatively high route cost, an intelligent tourism information service model considering tourists' experience is proposed in the environment of Internet of things. Firstly, the overall architecture of the Internet of things environment is designed to obtain the characteristic data of the carrying capacity of scenic spots, including tourism resources, psychology, ecology, and economic carrying capacity, based on which the tourist perception experience model is constructed; different service functions such as in-depth mining of Web content, personalized information push, and data information conversion under the environment of Internet of things and the construction of intelligent tourism information service model are realized. The experimental results show that the passenger flow prediction error of the designed model is small and has a good prediction effect of tourist attraction selection demand.

A fast and flexible attribute-based searchable encryption scheme supporting multi-search mechanism in cloud computing

How to use efficient search mechanisms to realize data sharing while ensuring data security has become an important research topic at present. In this paper, we propose a new searchable algorithm based on LSSS (Linear Secret Sharing Scheme) access policy, which is superior to previous work in computational and storage efficiency. Our scheme supports various search mechanisms, such as exact search, boolean search and range search mechanism. We use 0,1-coding theory to reduce the number of the numeric keywords, thus improving the range search mechanism. We also prove that our scheme is secure against the selectively chosen-keyword attack (SCKA) in the generic bilinear group model. Furthermore, we evaluate the performance of our scheme by theoretical analysis and experimental simulation, and the result show that our scheme achieves significant improvement in both computational and storage efficiency. To enhance the security and practicality of our scheme, we also deploy our scheme on a private chain and conduct extensive experiments to demonstrate its practical performance.