Hash Table Research Articles

A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.

CCECGP: causal consistency model of edge–cloud collaborative based on grouping protocol

At present, most causal consistency models based on cloud storage can no longer meet the needs of delay-sensitive applications. Moreover, the overhead of data synchronization between replicas is too high. This paper proposes a causal consistency model of edge–cloud collaborative based on grouping protocol. The model is based on the edge–cloud collaboration architecture, partitions cloud data centers and groups edge nodes by distributed hash tables, and stores a subset of the complete data set in nodes located at the edge of the network, thereby realizing partial geo-replication in edge–cloud collaboration environment. At the same time, we design a group synchronization algorithm called Imp_Paxos, so that the update only needs to be synchronized to the main group, which reduces the visibility delay of the update and decreases the data synchronization overhead. Besides, a sort timestamp is proposed in this paper, which generates different timestamps according to the type of update to track causality, keeping the amount of metadata managed in a relatively stable and low state. Therefore, the proposed model reduces the overhead of metadata for system management and improves throughput quantity of system. Experiments show that our model performs well in terms of throughput, operation latency, and update visibility latency compared with existing causal consistency models.

DHash: Dynamic Hash Tables With Non-Blocking Regular Operations

Once started, existing hash tables cannot change their pre-defined hash functions, even if the incoming data cannot be evenly distributed to the hash table buckets. In this paper, we present DHash, a type of hash table for shared memory systems, that can change its hash function and rebuild the hash table on the fly, without noticeably degrading its service. The major technical novelty of DHash stems from an efficient distributing mechanism that can atomically distribute every node when rebuilding, without locking the corresponding hash table buckets. This not only enables non-blocking lookup, insert, and delete operations, but more importantly, makes DHash independent of the implementation of hash table buckets, such that DHash allows programmers to select the set algorithms that meet their requirements best from a variety of existing lock-free and wait-free set algorithms. Evaluations show that DHash can efficiently change its hash function on the fly. Moreover, when rebuilding, DHash consistently outperforms the state-of-the-art hash tables in terms of throughput and response time of concurrent operations, at different concurrency levels, and with different operation mixes and average load factors.

Analog Integrated Circuit Topology Synthesis With Deep Reinforcement Learning

This article presents a novel deep-reinforcement-learning-based method for topology synthesis of analog-integrated circuits, especially operational amplifiers (OpAmps). It behaves like a human designer, who learns from trials, derives design knowledge and experience, and evolves gradually to finally figure out optimal manners to construct proper circuit topologies that meet design specifications. Essential design rules are defined and applied to set up the specialized environment for reinforcement learning in order to reasonably construct circuit topologies with building blocks as the basic components. Our proposed method can not only handle large-size circuit designs but also generate creative circuit topologies. The produced circuit topologies are verified by the simulation-in-loop sizing. In order to improve the evaluation efficiency, hash table and symbolic analysis techniques are utilized to significantly reduce the number of the produced topologies to be sized during the synthesis process. Compared with the state-of-the-art approaches, our proposed method significantly improves the synthesis efficiency by consuming only several hours on average to produce a trustworthy solution. Our experimental results demonstrate its sound efficiency, strong reliability, and wide applicability.

A Learned Bloom Filter-Assisted Scheme for Packet Classification in Software-Defined Networking

Traditional routing technologies based on a single IP address domain faces the challenge to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. Tuple space search (TSS) used in SDN supports fast rule updates but low-speed packet classification. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines LBF and TSS to promote classification speed by avoiding invalid hash table accesses. Specifically, LBF consists of multiple RNN models and one support Bloom filter (SBF), in which the learned models are trained with the positive and negative sets, and used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from learned models, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of hash checking. Our experimental results show that the proposed algorithm saves more memory space than Tuple space pruning (TSP) given the same false positive rate. The proposed algorithm is competitive in terms of the number of memory accesses, while achieving almost one order of magnitude improvement on pre-processing time over NeuroCuts which is an advanced decision tree classifier.

A Hybrid Loop Closure Detection Method Based on Brain-Inspired Models

Various hippocampal-entorhinal-based models are used to construct brain-inspired simultaneous localization and mapping (SLAM) systems. Loop closure detection (LCD) is a critical process of SLAM systems for robots to relocalize themselves and correct accumulative errors. The existing LCD methods of brain-inspired SLAM systems cannot solve well with challenging or large-scale environments by hand-crafted features and brute force search strategy. In this article, we propose a hybrid LCD method, which is based on the convolutional neural network (CNN) features and the fly’s locality-sensitive hashing algorithm (Fly-LSH). CNN features can improve the reliability of image matching results, and the Fly-LSH, a nearest neighbor search method derived from the fruit fly olfactory circuit, is used to accelerate the image processing. We use multiple hash tables to make a balance between the image matching time and the accuracy of loop closures. The proposed method provides a general approach for SLAM systems to process visual cues, and has application in a hippocampal-entorhinal-based SLAM system. The experimental results verify that the proposed method enables the system to build cognitive maps with better robustness and efficiency.

Reservoirs Optimal Operation Based on Reinforcement Learning

Reinforcement learning has been widely used to solve the problem of optimal operation of a single reservoir. However, the research on the combination of reinforcement learning and optimal operation of reservoir groups is still blank. The complexity of reservoir groups brings difficulties to the application of reinforcement learning. In order to solve the complex problem of optimal operation of the reservoir group, Q-learning combined with the penalty method is used to generate the optimal operation scheme of the reservoir group by exploring the optimization Q value of the operation sequence. The experimental results show that this method can achieve the optimal solution in theory. Furthermore, Q-learning combined with a feasible direction method is used to establish time feasible state table and state-feasible action hash table according to the constraints of the discrete four-reservoirs problem, so that the Q-learning algorithm can shorten the optimization time and obtain the optimal solution.

Observation of an Alternate Charge‐Polarization State in a One‐Dimensional Pt−Pt−I Chain Compound with a Bulky Pendant Ligand

AbstractA one‐dimensional (1D) halogen‐bridged dinuclear‐metal complex (MMX‐chain) exhibits various electronic states based on a mixed‐valence metal‐dimer system. This report deals with the synthesis and physical properties of a new MMX‐chain with a bulky pendant ligand, Pt2(mcc‐HexCS2)4I (mcc‐HexCS2=trans‐4‐(methoxycarbonyl)cyclohexanedithiocarboxylate). The steric hindrance caused by the bulky substituent induces a strain in its 1D chain, achieving at ambient condition the first pure alternate charge‐polarization (ACP) state (−Pt2+−Pt3+−I−−Pt3+−Pt2+−I−−), a kind of spin‐Peierls state, as confirmed by X‐ray diffraction and its conducting and magnetic properties. The strain effect is also manifested as a very large linear thermal expansion along the chain direction, which is quite different from conventional MMX‐chains without a bulky ligand. The design of low‐dimensional materials with ligand variations is expected to lead to the emergence of new electron–lattice coupled electronic states.

Observation of an Alternate Charge-Polarization State in a One-Dimensional Pt-Pt-I Chain Compound with a Bulky Pendant Ligand.

A one-dimensional (1D) halogen-bridged dinuclear-metal complex (MMX-chain) exhibits various electronic states based on a mixed-valence metal-dimer system. This report deals with the synthesis and physical properties of a new MMX-chain with a bulky pendant ligand, Pt2 (mcc-HexCS2 )4 I (mcc-HexCS2 =trans-4-(methoxycarbonyl)cyclohexanedithiocarboxylate). The steric hindrance caused by the bulky substituent induces a strain in its 1D chain, achieving at ambient condition the first pure alternate charge-polarization (ACP) state (-Pt2+ -Pt3+ -I- -Pt3+ -Pt2+ -I- -), a kind of spin-Peierls state, as confirmed by X-ray diffraction and its conducting and magnetic properties. The strain effect is also manifested as a very large linear thermal expansion along the chain direction, which is quite different from conventional MMX-chains without a bulky ligand. The design of low-dimensional materials with ligand variations is expected to lead to the emergence of new electron-lattice coupled electronic states.

Spin and charge density waves in quasi-one-dimensional KMn6Bi5

AMn6Bi5 materials (A = Na, K, Rb and Cs) consisting of unique Mn-cluster chains emerge as a new family of superconductors with the suppression of their antiferromagnetic (AFM) order under high pressures. Here, we report transverse incommensurate spin density waves (SDWs) for the Mn atoms with a propagating direction along the chain axes as a ground state for KMn6Bi5 by single crystal neutron diffraction. The SDWs have a refined amplitude of ~2.46 Bohr magnetons for the Mn atoms in the pentagons and ~0.29 Bohr magnetons with a large standard deviation for Mn atoms in the center between the pentagons. AFM dominate both the nearest-neighbor Mn-Mn interactions within the pentagon and next-nearest-neighbor Mn-Mn interactions out of the pentagon (along the propagating wave). The SDWs exhibit both local and itinerant characteristics probably formed by a cooperative interaction between local magnetic exchange and conduction electrons. A significant magnetoelastic effect during the AFM transition, especially along the chain direction, has been demonstrated by temperature-dependent x-ray powder diffraction. Single crystal x-ray diffraction below the AFM transition revealed satellite peaks originating from charge density waves along the chain direction with a q-vector twice as large as the SDW one, pointing to a strong real space coupling between them. Our work not only manifests a fascinating interplay among spin, charge, lattice and one dimensionality to trigger intertwined orders in KMn6Bi5 but also provides important piece of information for the magnetic structure of the parent compound to understand the mechanism of superconductivity in this new family.

HybriDC: A Resource-Efficient CPU-FPGA Heterogeneous Acceleration System for Lossless Data Compression.

Lossless data compression is a crucial and computing-intensive application in data-centric scenarios. To reduce the CPU overhead, FPGA-based accelerators have been proposed to offload compression workloads. However, most existing schemes have the problem of an imbalanced resource utilization and a poor practicability. In this paper, we propose HybriDC, an adaptive resource-efficient CPU-FPGA heterogeneous acceleration system for lossless data compression. Leveraging complementary advantages of the heterogeneous architecture, HybriDC provides a universal end-to-end compression acceleration framework with application compatibility and performance scalability. To optimize the hardware compression kernel design, we build a performance-resource model of the compression algorithm taking into account the design goal, compression performance, available resources, etc. According to the deduced resource-balanced design principle, the compression algorithm parameters are fine-tuned, which reduces 32% of the block RAM usage of the LZ4 kernel. In the parallel compression kernel implementation, a memory-efficient parallel hash table with an extra checksum is proposed, which supports parallel processing and improves the compression ratio without extra memory. We develop an LZ4-based HybriDC system prototype and evaluate it in detail. Our LZ4 compression kernel achieves state-of-the-art memory efficiency, 2.5-4× better than existing designs with comparable compression ratios. The evaluation of total resource utilization and end-to-end throughput demonstrates the excellent scalability of HybriDC. In power efficiency, the four-kernel HybriDC prototype achieves a threefold advantage over the standard LZ4 algorithm.

A Synthesis of Pulse Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

Seasonal influenza (also known as flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to the WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of the influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results indicate that optimal vaccination campaigns with compromise tradeoffs between the two conflicting objectives can be designed effectively using CENSGA, providing policymakers with a number of alternatives to accommodate the best strategies. The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns.

An efficient hash map based technique for mining high average utility itemset

An efficient hash map based technique for mining high average utility itemset

High‐frequency k‐mer counting at low memory footprint

Genomics data analysis requires efficient tools to address the vast amount of data generated by current next-generation sequencing technologies. K-mer counting works face difficulties in balancing high memory overhead with statistical precision. We designed a high-frequency k-mer statistical computation based on the Space Saving algorithm and a novel hash table structure, which reduces the memory overhead by 46% while ensuring high computational efficiency.

GPU implementations of deflate encoding and decoding

SummaryDeflate coding is a very popular lossless data compression method used in zlib, gzip (GNU zip), and zip, which performs the LZSS compression algorithm with Huffman coding. Deflate encoding and decoding involve sequential operations and their parallel acceleration using a GPU is quite hard. The main purpose of this paper is to present GPU implementations for encoding and decoding of Deflate coding. For efficient GPU implementations of Deflate coding, we have used multiple small hash tables for finding matching subsequences in the dictionary by multiple threads in parallel and applied the Single Kernel Soft Synchronization (SKSS) technique to fully utilize GPU computing resources. We have also adopted Huffman coding with gap arrays to accelerate parallel Huffman decoding. We have evaluated the performance of our GPU implementations using an NVIDIA A100 GPU and compared them with parallel/sequential Deflate encoding and decoding on the Intel X86 multicore CPUs using multiple threads/a single thread. Our GPU implementation of Deflate decoding is 1.66x–8.33x faster than the multiple thread implementation and 4.13x–36.56x faster than the single thread implementation.

Young's modulus of the different crystalline phases of poly (l-lactic acid)

Young's modulus of α′- and α-crystals of poly (l-lactic acid) (PLLA), more precisely, of aggregates of isotropically arranged lamellae, has been estimated based on dynamic-mechanical analysis of sets of isotropic film samples containing largely different though well-defined amounts of crystals. Evaluation of the modulus of elasticity of these film samples yielded the dependence of Young's modulus as a function of the enthalpy-based crystallinity, increasing with the crystal fraction in the assessed range, from zero to about 75% crystallinity. Extrapolation towards 100% crystallinity suggests values of Young's modulus of around 3.7 and 4.6 GPa for isotropic aggregates of α′- and α-crystals, respectively, being only slightly higher than the modulus of the unaged glassy amorphous phase of 3.0 GPa. Noting the inherent anisotropy of the crystal modulus, suggested in the literature, the average modulus determined in this work seems to be controlled by weaker interchain secondary bonding but not the modulus in chain direction. Great effort has been undertaken to minimize errors by keeping the lamellar thickness in samples of different crystallinity constant, and by providing evidence for independence of the moduli on the spherulitic superstructure.

Electric Field-Controlled Peptide Self-Assembly through Funnel-Shaped Two-Dimensional Nanopores.

Self-assembly of biomolecules is critical for the realization of biological functions. Thus, the precise control of self-assembly has great significance in the design of biochips and biomedical agents. In this report, we design a Y-shaped funnel on a two-dimensional (2D) heterostructure, called 2D funnel, based on monolayered polyaniline carbon nitride (C3N) and boron carbide (BC3), and study its application in the self-assembly state regulation of the peptide oligomer, using Aβ16-21 as the representative model. Structurally, the 2D funnel is composed of three regions: channel area, triangle area, and barrier area. The channel and triangle areas show higher binding affinity to the peptide than that of the barrier area, which leads to the confinement of the peptide in the 2D funnel. Our results show that when an external electric field is applied along the 2D funnel, the oligomer is driven to migrate across the funnel. Its trajectory is confined inside the narrow channel area, which effectively causes peptide dissociation into the individual peptide chains. Then, when the external electric field is turned off, the separated peptide chains spontaneously assemble in the triangle area and tend to reunite. Our present findings propose a novel heterostructure platform, which enables the manipulation of the self-assembly state of peptides by switching the electric field, which could guide the design and fabrication of nanodevices for sensing and sequencing applications.

Rethinking Fine-Grained Measurement From Software-Defined Perspective: A Survey

Network measurement provides operators an efficient tool for many network management tasks such as performance diagnosis, traffic engineering and intrusion prevention. However, with the rapid and continuous growth of traffic speed, it needs more computing and memory resources to monitor traffic in per-flow or per-packet granularity. Sample-based measurement systems (e.g., NetFlow, sFlow) have been developed to perform coarse-grained measurement, but they may miss part of records, especially for mice flows, which are important for some network management tasks (e.g., anomaly detection, performance diagnosis). To address these issues, data streaming algorithms such as hash tables and sketches have been introduced to balance the trade-off among accuracy, speed, and memory usage. In this article, we present a systematic survey of various data structures, algorithms and systems which have been proposed in recent years to perform fine-grained measurement for high-speed networks. We organize these methods and systems from a software-defined perspective. In particular, we abstract fine-grained network measurement into three-layer architecture. We introduce the responsibility of each layer and categorize existing state-of-the-art works into this architecture. Finally, we conclude the article and discuss the future directions of fine-grained network measurement.

Learning Binary Hash Codes Based on Adaptable Label Representations.

The goal of supervised hashing is to construct hash mappings from collections of images and semantic annotations such that semantically relevant images are embedded nearby in the learned binary hash representations. Existing deep supervised hashing approaches that employ classification frameworks with a classification training objective for learning hash codes often encode class labels as one-hot or multi-hot vectors. We argue that such label encodings do not well reflect semantic relations among classes and instead, effective class label representations ought to be learned from data, which could provide more discriminative signals for hashing. In this article, we introduce Adaptive Labeling Deep Hashing (AdaLabelHash) that learns binary hash codes based on learnable class label representations. We treat the class labels as the vertices of a K -dimensional hypercube, which are trainable variables and adapted together with network weights during the backward network training procedure. The label representations, referred to as codewords, are the target outputs of hash mapping learning. In the label space, semantically relevant images are then expressed by the codewords that are nearby regarding Hamming distances, yielding compact and discriminative binary hash representations. Furthermore, we find that the learned label representations well reflect semantic relations. Our approach is easy to realize and can simultaneously construct both the label representations and the compact binary embeddings. Quantitative and qualitative evaluations on several popular benchmarks validate the superiority of AdaLabelHash in learning effective binary codes for image search.

BIIoVT: Blockchain-Based Secure Storage Architecture for Intelligent Internet of Vehicular Things

Today, the rapid growth of vehicles connected to the Internet enables provides various services to consumers, including traffic management, traffic safety, and entertainment. Vehicular ad-hoc network is one of the most prominent and emerging technologies on the Internet of Vehicular Things (IoVT). This technology offers to fulfill requirements such as robust information exchange, and infotainment among vehicles for the smart city environment. Still, it has some challenges such as centralization, storage, security, and privacy because all city vehicular networks send vehicles and road-related information data directly to the cloud. This article proposes BIIoVT: Blockchain-based secure storage architecture for Intelligent Internet of Vehicular Things to mitigate the above-mention issues. Blockchain provides security and privacy at each city's vehicular networks and decentralized storage at the cloud layer with a distributed hash table. It also examines how the vehicular network offers a secure platform. The validation results of the proposed architecture show an outstanding balance of secure storage and efficiency for the IoVT compared to existing methods.