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The extraction of the structural features of materials is fundamental for investigating novel properties in fields such as electronic information and biochemistry. However, existing experimental methods have limitations in analyzing material structures with sufficient depth. Therefore, rapid and accurate extraction and analysis of structural features from atomic coordinates obtained through simulation calculations are crucial for advancing the exploration of new material properties. Herein, we propose an approach for extracting the structural features of materials by combining the holographic matrix method with Bayesian optimization and tensor flow operations. The proposed algorithm efficiently classifies and statistically analyzes cluster structures within materials. Experimental validation conducted on a system comprising 8000 atoms demonstrated a correct recognition rate exceeding 99.213%. Moreover, the algorithm achieved an average recognition time of approximately 1. 8 1 ± 0. 0 3 s. The proposed analytical framework exhibits scalability and robustness, establishing an algorithmic foundation for future advancements in big data analytics for complex materials. [ABSTRACT FROM AUTHOR]

During the packaging process, it is essential to detect the steel stamping characters inside the box to identify any missing or repeated characters. Currently, manual detection suffers from low efficiency and a high false detection rate. To address these challenges, a steel stamping character recognition and analysis system based on machine vision has been developed. The enhanced YOLOv7 detection method was employed for character identification, complemented by a statistical analysis approach to achieve automated judgment and detection. To address the issue of size disparity between large and small characters, a small size anchor box and a larger detection head were integrated. Furthermore, modifications were made to the output structure of the YOLOv7 prediction network to enhance multi-scale detection capabilities. The inclusion of the location attention convolution module bolstered global feature extraction, thereby enhancing the detection accuracy of similar characters. Moreover, the utilization of a hash table was used to improve the efficiency of mapping steel stamping character recognition sequences. The experimental results demonstrate that the enhanced model achieves an accuracy of 99.83%, with a processing efficiency of 10.5 ms per single frame. These findings align with the performance criteria for automatic recognition and analysis of steel stamping characters. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today's era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more. [ABSTRACT FROM AUTHOR]

We revisit the problem of building static hash tables on the GPU and present an efficient implementation of bucketed hash tables. By decoupling the probing scheme from the hash table in-memory representation, we offer an implementation where the number of probes and the bucket size are the only factors limiting performance. Our analysis sweeps through the hash table parameter space for two probing schemes: cuckoo and iceberg hashing. We show that a bucketed cuckoo hash table (BCHT) that uses three hash functions outperforms alternative methods that use iceberg hashing and a cuckoo hash table that uses a bucket size of one. At load factors as high as 0.99, BCHT enjoys an average probe count of 1.43 during insertion. Using three hash functions only, positive and negative queries require at most 1.39 and 2.8 average probes per key, respectively.

Persistent memory (PMem) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery. Especially with the release of Intel Optane DC Persistent Memory Modules, we have witnessed a flourish in (re)designing persistent hash indexes. However, most of them are focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how the proposed hash indexes perform under a unified testing framework and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of several state-of-the-art hash tables including CCEH, Dash, PCLHT, Clevel, Viper, Halo, SOFT, and Plush, with the second-generation PMem hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PMem bandwidth, CPU instructions, and NUMA) affect the performance of PMem-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts and suggest desirable optimizations and directions for the future development of PMem-based hash tables. [ABSTRACT FROM AUTHOR]

Nowadays, everybody needs to secure his/her activities. Existing levels of cyber-criminals need technology for detecting malicious activity. This work proposes a real-time Hardware IDS implemented on FPGA and an algorithm for classifying features from network traffic through the network interface card (NIC). It minimizes search time for extracting statistical features from connection records stored in connection queues to memory references. Therefore, it can detect most internal and external network attacks. A decision tree classifier is used as an inference engine and gives a high detection rate of 99.93%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories. Software-defined networks (SDN) support the processing of data and restrict the transmission of duplicate values. It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead. Existing State of the art schemes suffer fromcomputational overhead due to deterministic or randomtree-based tags generation which further increases as the file size grows. This paper presents an efficient file-level de-duplication scheme (EFDS) where the cost of creating tags is reduced by employing a hash table with key-value pair for each block of the file. Further, an algorithm for hash table-based duplicate block identification and storage (HDBIS) is presented based on fingerprints that maintain a linked list of similar duplicate blocks on the same index.Hash tables normally have a consistent time complexity for lookup, generating, and deleting stored data regardless of the input size. The experiential results show that the proposed EFDS scheme performs better compared to its counterparts. [ABSTRACT FROM AUTHOR]

Modern computer systems can use different types of hardware acceleration to achieve massive performance improvements. Some accelerators like FPGA and dedicated GPU (dGPU) need optimized data structures for the best performance and often use dedicated memory. In contrast, APUs, which are a combination of a CPU and an integrated GPU (iGPU), support shared memory and allow the iGPU to work together with the CPU on pointer-based data structures. First, we develop an approach for dGPU to accelerate queries in libcuckoo and robin-map and when looking at accelerating insert, updates and erase operations in the original libcuckoo using OneAPI on an APU. We evaluate the dGPU against the CPU variants and our dGPU approach adapted for the CPU and also in a hybrid context by using longer keys on the CPU and shorter keys on the dGPU. In comparison with the original libcuckoo algorithm, our dGPU approach achieves a speed-up of 2.1, and in comparison with the robin-map a speed-up of 1.5. For hybrid workloads, our approach is efficient if long keys are processed on the CPU and short keys are processed on the dGPU. By processing a mixture of 20% long keys on the CPU and 80% short keys on dGPU, our hybrid approach has a 40% higher throughput than the CPU only approach. In addition, we develop a hybrid APU approach for insert, update and erase operations in the original libcuckoo structure focusing on shared memory with iGPU accelerated look-ups of the positions for insert, update and erase operations. [ABSTRACT FROM AUTHOR]

In order to improve the handling stability of active four-wheel steering vehicles, a nonlinear model predictive controller is presented, which can guarantee that the actual sideslip angle and yaw rate can track the ideal sideslip angle and the ideal yaw rate through control of the front and rear wheel angles. A nonlinear static tyre model connected with a linear dynamic model is adopted to describe the vehicle dynamics. Furthermore, the tyre model is replaced by a map in the optimization problem of nonlinear model predictive control. The introduction of maps can reduce the online computational time by a trade-off between the computational burden of CPU and the storage burden of ROM. Simulation results in CarSim indicate that the proposed controller can follow the outputs of the ideal reference model, reduce the computational burden, and improve the handling stability of the active four-wheel steering vehicles effectively. [ABSTRACT FROM AUTHOR]

Neural radiance fields (NeRFs) combining machine learning with differentiable rendering have arisen as one of the most promising approaches for novel view synthesis and depth estimates. However, NeRFs only applies to close-range static imagery and it takes several hours to train the model. The satellites are hundreds of kilometers from the earth. Satellite multi-view images are usually captured over several years, and the scene of images is dynamic in the wild. Therefore, multi-view satellite photogrammetry is far beyond the capabilities of NeRFs. In this paper, we present a new method for multi-view satellite photogrammetry of Earth observation called remote sensing neural radiance fields (RS-NeRFs). It aims to generate novel view images and accurate elevation predictions quickly. For each scene, we train an RS-NeRF using high-resolution optical images without labels or geometric priors and apply image reconstruction losses for self-supervised learning. Multi-date images exhibit significant changes in appearance, mainly due to cars and varying shadows, which brings challenges to satellite photogrammetry. Robustness to these changes is achieved by the input of solar ray direction and the vehicle removal method. NeRFs make it intolerable by requiring a very long time to train an easy scene. In order to significantly reduce the training time of RS-NeRFs, we build a tiny network with HashEncoder and adopted a new sampling technique with our custom CUDA kernels. Compared with previous work, our method performs better on novel view synthesis and elevation estimates, taking several minutes. [ABSTRACT FROM AUTHOR]

The index method of in-memory key value storage determines the time performance and space overhead of the system, which is a key factor for improvement and optimization. Hash index provides access operations in O（1） time complexity, but it generates storage conflicts and causes access performance degradation. To this end, this paper proposed a bitmap-based hash optimization method for key value storage, which can avoid storage conflicts and improve access performance. This method divided the keys with common prefixes into the same block to reduce the storage space of keys, used a hierarchical bitmap structure in the block, where the global bitmap represented the suffix part of all keys to avoid storage conflicts, and the summary bitmap supported quick positioning and acceleration for range queries. The experimental results show that the optimized hash index can achieve high throughput and good concurrency performance on various workloads, and the memory consumption is greatly reduced compared with existing schemes. [ABSTRACT FROM AUTHOR]

Log analysis plays an important role in the stable operation of computer system. However, logs are usua-lly unstructured, which is not conducive to automatic analysis. How to categorize logs and turn them into structured data automatically is of great practical significance. In this paper, LDmatch algorithm is proposed, which imple-ments a log pattern extracting algorithm based on word matching rate. Traditional log matching algorithms use one-to-one word matching method in similarity calculation, while the proposed LDmatch algorithm calculates the simi-larity between logs according to the longest common subsequence (LCS) of words contained in two logs, and classi-fies logs based on the LCS. LDmatch algorithm can also get real-time log template and update. In addition, the pat-tern warehouse of the algorithm uses a data structure based on hash table for storage, which refines the classification of logs and reduces the times of comparison during log matching, thus improving the matching efficiency of the algorithm. In order to verify the advantages of the algorithm, it is applied to the open source data set and the actual log data set generated by the CNGrid. A variety of other log pattern extraction algorithms are used for comparison and experimental results are obtained. Finally, the advantages of the algorithm in accuracy, robustness and efficiency are proven.

Abstract Background Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading. Results This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades $$\sim 90 \%$$ ∼ 90 % cache references and achieves $$\times 4.3$$ × 4.3 speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves $$\times 80$$ × 80 speedup consequently. Conclusions In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future.

Chaotic systems, especially hyper-chaotic systems are suitable for digital image encryption because of their complex properties such as pseudo randomness and extreme sensitivity. This paper proposes a new color image encryption algorithm based on a hyper-chaotic system constructed by a tri-valued memristor. The encryption process is based on the structure of permutation-diffusion, and the transmission of key information is realized through hyper-chaotic synchronization technology. In this design, the hash value of the plaintext image is used to generate the initial key the permutation sequence with the Hash table structure based on the hyper-chaotic sequence is used to implement pixel-level and bit-level permutation operations. Hilbert curves combining with the ciphertext feedback mechanism are applied to complete the diffusion operation. A series of experimental analyses have been applied to measure the novel algorithm, and the results show that the scheme has excellent encryption performance and can resist a variety of attacks. This method can be applied in secure image communication fields. [ABSTRACT FROM AUTHOR]

Cisco IOS (Internet operating system) is a special operating system of Cisco router.Due to the limitation of hardware conditions, it pays more attention to the performance and ignores the system security in the design, which makes it unable to effectively detect the attack of return address oriented programming (ROP).Aiming at the defects of traditional ROP protection technology in Cisco IOS protection, a method based on return address memory hash verification is proposed, which can effectively detect the ROP attack on Cisco IOS and capture the attack code.By analyzing the advantages and disadvantages of the existing protection mechanisms against ROP attacks, on the basis of the idea of compact shadow memory protection, the traditional sha-dow memory storage mode is transformed into a hash based memory search mode, and the record of the return address memory pointer is added as the index of hash search, which improves the efficiency of shadow me-mory search and can resist shadow memory tampering caused by memory leakage.Based on the Dynamips virtualization platform, the CROPDS system is designed and implemented, and the method is verified effectively.Compared with the previous methods, it improves the generality and perfor-mance, and can capture the shellcode of attack execution.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neural radiance fields (NeRFs) combining machine learning with differentiable rendering have arisen as one of the most promising approaches for novel view synthesis and depth estimates. However, NeRFs only applies to close-range static imagery and it takes several hours to train the model. The satellites are hundreds of kilometers from the earth. Satellite multi-view images are usually captured over several years, and the scene of images is dynamic in the wild. Therefore, multi-view satellite photogrammetry is far beyond the capabilities of NeRFs. In this paper, we present a new method for multi-view satellite photogrammetry of Earth observation called remote sensing neural radiance fields (RS-NeRFs). It aims to generate novel view images and accurate elevation predictions quickly. For each scene, we train an RS-NeRF using high-resolution optical images without labels or geometric priors and apply image reconstruction losses for self-supervised learning. Multi-date images exhibit significant changes in appearance, mainly due to cars and varying shadows, which brings challenges to satellite photogrammetry. Robustness to these changes is achieved by the input of solar ray direction and the vehicle removal method. NeRFs make it intolerable by requiring a very long time to train an easy scene. In order to significantly reduce the training time of RS-NeRFs, we build a tiny network with HashEncoder and adopted a new sampling technique with our custom CUDA kernels. Compared with previous work, our method performs better on novel view synthesis and elevation estimates, taking several minutes.

• Modern packet classification algorithms need to support both high speed search and update operations. • Implementation on GPU to further improve the performance of the proposed H- HashTree scheme. • The proposed H-HashTree performs better than state-of-the-art algorithms, CutTSS and TabTree in both searches and updates. • The H-HashTree is suitable for frequent updates needed in SDN environment. Packet classification is an important functionality of modern routers/switches, needed in packet forwarding, Quality of Service (QoS), firewall etc. In order to better utilize routers on the Internet, Software Defined Network (SDN) decouples control plane from data plane to fulfill centralized management. Based on OpenFlow standards, packet classification in SDN is designed for multi-field rules which are more complex than traditional 5-tuple rules. In the paper, we propose a novel packet classification algorithm, called hierarchical hash tree (H-HashTree), based on the two IP address fields and the 7 exact-match fields to partition rules into groups. An extended Bloom filter is also proposed to accelerate search process by skipping groups in the hash tree. To further improve the performance, H-HashTree is implemented on GPU. We tested on 100K rules including synthesized rules containing characteristics of ACL, FW, and IPC with different wildcard ratios in exact-match fields, and real OpenFlow rules from Open vSwitch. Compared with the existing state-of-the-art algorithms, CutTSS and TabTree [19] [18] , H-HashTree achieves the best performance on both search and update speeds. H-HashTree achieves 1.17-13.9 and 2.48-12.7 times faster in search speed and 2.03-6.0 and 1.87-4.53 times faster in rule updates from synthesized rulesets than CutTSS and TabTree, respectively. On the GPU platform, H-HashTree can achieve up to 114 MPPS in search speed and less than 0.04 usec/rule in rule updates. [ABSTRACT FROM AUTHOR]

Background: Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading. Results: This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades ∼ 90 % cache references and achieves × 4.3 speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves × 80 speedup consequently. Conclusions: In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future. [ABSTRACT FROM AUTHOR]

We consider the problem of defending a hash table against a Byzantine attacker that is trying to degrade the performance of query, insertion and deletion operations. Our defense makes use of resource burning (RB)—the verifiable expenditure of network resources—where the issuer of a request incurs some RB cost. Our algorithm, Depth Charge , charges RB costs for operations based on the depth of the appropriate object in the list that the object hashes to in the table. By appropriately setting the RB costs, our algorithm mitigates the impact of an attacker on the hash table's performance. In particular, in the presence of a significant attack, our algorithm incurs a cost which is asymptotically less that the attacker's cost. [ABSTRACT FROM AUTHOR]

Chaotic systems, especially hyper-chaotic systems are suitable for digital image encryption because of their complex properties such as pseudo randomness and extreme sensitivity. This paper proposes a new color image encryption algorithm based on a hyper-chaotic system constructed by a tri-valued memristor. The encryption process is based on the structure of permutation-diffusion, and the transmission of key information is realized through hyper-chaotic synchronization technology. In this design, the hash value of the plaintext image is used to generate the initial key the permutation sequence with the Hash table structure based on the hyper-chaotic sequence is used to implement pixel-level and bit-level permutation operations. Hilbert curves combining with the ciphertext feedback mechanism are applied to complete the diffusion operation. A series of experimental analyses have been applied to measure the novel algorithm, and the results show that the scheme has excellent encryption performance and can resist a variety of attacks. This method can be applied in secure image communication fields.

Many journaling file systems currently use non-volatile memory-express (NVMe) solid-state drives (SSDs) as external journal devices to improve the input and output (I/O) performance. However, when facing microwrite workloads, which are typical of many applications, they suffer from severe I/O fluctuations and the NVMe SSD utilization is extremely low. The experimental results indicate that this phenomenon arises mainly because writing back data to backend file systems on hard disk drives is much slower than journal writing, causing journal writing to frequently freeze because of the two-phase mechanism. We, therefore, propose a merging-in-memory (MIM) acceleration architecture to stabilize and boost the I/O performance for such journaling file systems. MIM employs an efficient data structure of hash-table-based multiple linked lists in memory, which not only merges random microwrites into sequential large blocks to speed up writebacks but also provides additional gains in terms of reducing the frequency of write addressing and object opening and closing. Using a prototype implementation in Ceph FileStore, we experimentally show that MIM not only eliminates severe fluctuations but also improves the I/O operations per second by roughly 1×–12× and reduces the write latency by 75%–98%. [ABSTRACT FROM AUTHOR]

Summary: Algorithms utilize an index‐based aligning strategy, like a hash table, which typically entails the seed‐and‐extend method and is a time‐consuming task. Here, we developed a hash‐based search algorithm based on the SSAHA method without the use of seed‐and‐extend to conduct search and alignment faster than previous methods with multiple processors. In the proposed method by using the overlapping method in query and reference sequences, the accuracy and sensitivity increased. Further, the speed also increased by creating a hash table for the reference sequence when it was placed in the memory. Furthermore, by evaluating three datasets of different sequences in size and volumes, the effect of the created piece lengths as well as the effect of multiple processors on each dataset was evaluated indicating not only appeasing the time issue in alignment but also improving the mapping speed compared to the BLAST and SSAHA algorithms. [ABSTRACT FROM AUTHOR]

Screen contents have become a popular image type driven by the growing market for transferring display screen between devices, especially mobile devices. Due to the ultra-high quality display featured in most of nowadays mobile devices, lossless screen content coding (SCC) is usually required or preferred. Mobile devices also require ultra-low power consumption in all tasks including SCC. To address these issues, this paper proposes an ultra-low coding complexity technique based on string matching for high efficiency lossless SCC. The technique covers three major coding phases of fast searching, prediction, and entropy coding. Condensed hash table (CHT) based fast searching is proposed to speed-up reference string searching process. Coplanar prediction (CP) and predictor-dependent residual (PDR) are presented to first efficiently predict an unmatchable pixel using multiple neighboring pixels and then further reduce the entropy of prediction residuals. To achieve a good trade-off between coding complexity and efficiency, 4-bit-aligned variable length code (4bVLC) and byte-aligned multi-variable-length-code (BMVLC) are proposed to code the prediction residuals and three string matching parameters, respectively. For 184 screen content images commonly used, compared with X265 and PNG in the default configuration and lossless mode, the proposed technique achieves 35.67% less total compressed bytes with only 0.96% encoding and 1.54% decoding runtime, and 10.04% less total compressed bytes with only 6.83% encoding and 24.32% decoding runtime, respectively. The proposed technique also outperforms X265 and PNG in all other configurations. For twelve HEVC-SCC CTC images, compared with PNG in fast, default and slow configurations and X265 in ultrafast and default configurations, the proposed technique shows significant advantage with both high coding efficiency and ultra-low coding complexity. [ABSTRACT FROM AUTHOR]

This paper proposes an image encryption algorithm based on hash table structure scrambling and DNA substitution. The algorithm uses the classical `scrambling-diffusion' process, and the pseudo-random sequence used in each process is generated by the hyper-chaotic Chen system. Firstly, in the process of scrambling, two sequences with no repeated values are constructed by using the closed hash method in the hash table structure for chaotic sequences, and the plain image is scrambled twice according to the two sequences. Then, in the process of diffusion, DNA coding and decoding rules and DNA substitution rules are made according to the chaotic sequences. The image is dynamically encoded, substituted and decoded with DNA. In this way, while modifying the gray value of image pixels, it also destroys the strong correlation between adjacent pixels. Through a series of verifications, it is concluded that the algorithm not only has good encryption effect, high security and large key space, but also has the advantages of high key sensitivity and strong anti-attack capability.

In order to solve the problem that the topological relationship between the triangular facets is lost when the 3D model is converted to STL files，in the process of reading and analyzing STL files, a fast topology reconstruction algorithm based on half-edges structure and hash table is proposed. In the process of reading data, a point table without repeat position information is established through a hash table, and a collection containing half-edges in which no adjacency facets are added is maintained therein. According to the set and topology algorithm, the topological relationship of the facets is improved, and the topological relationship of the facets is quickly established in the process of reading data.

Clustering algorithms are the essential tools in the target metagenomics, used to perform the taxonomic profiling of microbial communities. In the present study, an algorithmic tool called hash‐based exact alignment (HBEA) clustering algorithm is presented, which uses exact pairwise global alignment algorithm to improve the cluster quality and creates a hash table for extraction of cluster representatives. The algorithm is de novo based and uses the general de facto 97% sequence similarity score to cluster the sequences. Our experimental investigation on various types of datasets with distinct parameters and attributes showed that HBEA produces better operational taxonomic unit (OTU) clusters and computational complexity than other algorithms. [ABSTRACT FROM AUTHOR]

Modern microprocessors contain several special function units (SFUs) such as specialized arithmetic units, cryptographic processors, etc. In recent times, applications such as cloud computing, web-based search engines, and network applications are widely used, and place new demands on the microprocessor. Hashing is a key algorithm that is extensively used in such applications. Hashing can reduce the complexity of search and lookup from O(N) to O(N/n), where n bins are used. Hashing is typically performed in software. Thus, implementing a hardware-based hash unit on a modern microprocessor would potentially increase performance significantly. In this article, we propose a novel hardware hash unit (HU) design for use in modern microprocessors, at the microarchitecture level and at the circuit level. First, we present the design of the HU at the microarchitecture level. We simulate the HU to compare its performance with a software-based hash implementation. We demonstrate a significant speedup (up to 15×) for the HU. Furthermore, the performance scales elegantly with increasing database size and application diversity, without increasing the hardware cost. Second, we present the circuit design of the HU for use in modern microprocessors, using a 45nm technology. Our proposed hardware hash unit is based on the use of a content-addressable memory (CAM) to implement each bin of the hash table. We simulate the HU circuit and compare it with a traditional CAM design. We demonstrate an average power reduction of 5.48× using the HU over the traditional CAM. Also, we show that the HU can operate at a maximum frequency of 1.39 GHz (after accounting for process, voltage and temperature (PVT) variations and accounting for wiring parasitics). Furthermore, we present the delay, power and area trade-offs of the HU design with varying hash table sizes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Motivation : Calculating the frequency of occurrence of each substring of length k in DNA sequences is a common task in many bioinformatics applications, including genome assembly, error correction, and sequence alignment. Although the problem is simple, efficient counting of datasets with high sequencing depth or large genome size is a challenge. Results : We propose a robust and efficient method, CHTKC, to solve the k-mer counting problem with a lock-free hash table that uses linked lists to resolve collisions. We also design new mechanisms to optimize memory usage and handle situations where memory is not enough to accommodate all k-mers. CHTKC has been thoroughly tested on seven datasets under multiple memory usage scenarios and compared with Jellyfish2 and KMC3. Our work shows that using a hash-table-based method to effectively solve the k-mer counting problem remains a feasible solution. [ABSTRACT FROM AUTHOR]

Community structure discovery can help us better understand the capabilities and functions of the network. However, many existing methods have failed to identify nodes in communities accurately. In this paper, we proposed a heuristic community detection method based on node similarities that are computed by assigning different edge weight influence factors based on different neighbor types of nodes. Concretely, by arbitrarily choosing a pair of nodes, we firstly found out the common neighbor nodes of the node pair and their corresponding neighbor nodes. Then, different edge weight influence factors are assigned according to the impact of different types of neighbor nodes on node similarity. Finally, the similarities between a pair of nodes are calculated by the proportion of various edge weight influence factors related to the node pair. Along the direction, a hash table based data storage and retrieval strategy with a lower conflict rate is introduced to hash the edge information into a ternary bucket structure that can be merged according to the same starting node. This operation can reduce the time complexity of the data query to a constant level, and realize the parallel computing of node similarity. When obtaining similarity of node pair, we merged nodes into communities by a heuristic hierarchical clustering. And, the resulting community structure is detected until all node similarities are calculated. With the help of the comparison tests of different methods based on the benchmark networks that have ground-truth communities, the proposed method for community detection provides better performance in both identification accuracy and time efficiency.

As a common biological sequence, DNA sequences contain important information. The discovery of frequent patterns in DNA sequences can help to study the evolution, function and variation of genes. The findings are of great significance to genetic and mutation analysis, analysis of disease causes and treatment of diseases. Traditional methods of frequent pattern discovery need to scan DNA sequences multiple times. To overcome this shortcoming, this article proposes a new method to discover frequent patterns from DNA sequences. This method is based on a two-level nested hash table data structure and set operation. All frequent patterns and their positions in DNA sequences can be found by scanning DNA sequences only once. Experimental results show that this method can correctly recognize all the frequent patterns in DNA sequences and their locations. The method can also be applied to discover frequent patterns in RNA, protein or other biological sequences.