High-performance Search Research Articles

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back-propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSW-NB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.

Adopting Critical Success Factors (CSF) Methods to Achieve High Management Performance on Dairy Farms

It is known that the agribusiness sector in Brazil is made up of several companies operating in different business models in their environment. They operate in production side as no cyclical and the market side as cyclical, such as milk and dairy products. It is, however, characterized by dynamic and complex processes and competitive events, applied technologies, specialized labor problems, with risks and uncertainties inherent to the environment. Dairy farming is a relevant segment of this sector, both in terms of representativeness and wealth generation. Therefore, dairy farms need to be aware of innovations, and be effective in the pursuit of productive performance and profitability. The present study aims to evaluate the Critical Success Factors (CSF) involved in the search for high productive performance and profitability in dairy farms assisted by technical and managerial consulting of an agribusiness alpha company. The research has a qualitative approach and a descriptive conclusive character. Multiple case studies were carried out, and the units of analysis are 6 farms, their respective leaders (owners and managers), and consultants specialized in dairy farming from the company Alfa de Consultoria. The results demonstrate that the CSFs that promote high performance in dairy farms are: financial management; leadership and people management; process management; employee training and development; adequate sizing of the project; search for continuous improvement; management of indices and technical indicators; ensure animal comfort; technical assistance and consultancy.

Ship pipe route design based on NSGA-III and multi-population parallel evolution

Ship pipe route design (SPRD) aims to generate pipe layouts while considering various objectives and constraints in a confined 3D space, leading to extremely high complexity. To solve the problem of SPRD, traditional methods often adopt weighted summation of sub-objectives, introduce penalty functions to convert it to a single-objective optimization problem. However, these methods tend to yield a single optimal pipe route design pattern, and some existing multi-objective pipe routing algorithms do not have high search performance or sufficient treatment of constraints. Based on the grid-space decomposition model, this paper establishes a multi-objective pipe routing optimization model with several sub-objectives including path length, bends number, path energy, number of air-pockets, and number of violated bending distance, which also considers pipe routing for different diameters and interface direction requirements. A ship pipe route design framework based on the NSGA-III (Non-dominated Sorting Genetic algorithm III) is proposed, and its embedded pathfinding algorithm can explore pipe route using heuristic information and failure retry strategy, which greatly reduces the algorithm complexity compared to the classical algorithms such as A* and LEE and also facilitates parallel implementation. By employing OpenMP technology to achieve parallel evolution of multiple populations, more Pareto optimal solutions can be obtained in approximately the same timeframe as single population evolution. Finally, through comparative experiments using simulated cases, the feasibility and advancement of the proposed method are verified.

A pairwise ranking estimation model for surrogate-assisted evolutionary algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) have attracted considerable attention for reducing the computation time required by an EA on computationally expensive optimization problems. In such algorithms, a surrogate model estimates the solution evaluation with a low computing cost and is used to obtain promising solutions to which the accurate evaluation with an expensive computation cost is then applied. This study proposes a novel pairwise ranking surrogate model called the Extreme Learning-machine-based DirectRanker (ELDR). ELDR integrates two machine learning models: extreme learning machine (ELM) and DirectRanker (DR). ELM is a single-layer neural network capable of fast learning, whereas DR uses pairwise learning to rank using a neural network developed mainly for information retrieval. To investigate the effectiveness of the proposed surrogate model, this study first examined the estimation accuracy of ELDR. Subsequently, ELDR was incorporated into a state-of-the-art SAEA and compared with existing SAEAs on well-known real-valued optimization benchmark problems. The experimental results revealed that ELDR has a high estimation accuracy even on high-dimensional problems with a small amount of training data. In addition, the SAEA using ELDR exhibited a high search performance compared with other existing SAEAs, especially on high-dimensional problems.

Design of Ultracompact Content Addressable Memory Exploiting 1T-1MTJ Cell

Content addressable memories (CAMs) are a promising category of computing-in-memory (CiM) elements that can perform highly parallel and efficient search operations for routers, pattern matching, and other data-intensive applications. Various magnetic tunnel junction (MTJ) based content addressable memory (CAM) designs have been proposed to realize zero standby power and high performance search. However, due to the relatively small tunnel magneto-resistance (TMR) ratio, MTJ based CAMs require extra transistors and differential MTJ branches to distinguish between the parallel and anti-parallel resistance states, resulting in significant area and energy overhead. In this paper, we propose a device-circuit co-design approach for an ultra-compact CAM design by only exploiting an 1T-1MTJ structure in each cell. We propose a 2-step search scheme to enable the parallel in-memory search operation across the proposed CAM array, and demonstrate the sufficient sensing margin of the array in a successful search operation. Evaluation results suggest that our proposed 1T-1MTJ based CAM design improves 179X/301X area efficiency compared with the state-of-the-art 15T-4MTJ/20T-6MTJ CAM design. Application benchmarking on hyperdimensional computing (HDC) inference shows a 54.6X/12.8X speedup compared with GPU/20T-6MTJ CAM based approaches.

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.

Computational Investigation of the Aerodynamics of a Wheel Installed on a Race Car with a Multi-Element Front Wing

The search for high aerodynamic performance of a race car is one of the main aspects of the design process. The flow around the basic body shape is complicated by the presence of the rotating wheels. This is especially true in race cars on which the wheels are not shrouded, where the effects on the flow field are considerable. Despite this, few works have focused on the flow around the rotating wheels. In this paper, CFD techniques were used to provide a detailed analysis of the flow structures generated by the interaction between a multielement inverted wing and the wheel of an open-wheel race car. In the first part, the CFD approach was validated for the isolated wheel case by comparing the results with experimental and numerical data from the literature. The wheel was analyzed both in stationary and unsteady flow conditions. Then, the CFD model was adopted to study the interaction of the flow structures between the wheel with the real grooves on the tire and the front wing of a Formula 1 car. Three different configurations were considered in order to differentiate the individual effects. The discussions were supported by the values of the aerodynamic performance coefficients and flow contours.

Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms.

Sepsis is a life-threatening condition with a high mortality rate. Early prediction and treatment are the most effective strategies for increasing survival rates. This paper proposes a neural architecture search (NAS) model to predict the onset of sepsis with a low computational cost and high search performance by applying a genetic algorithm (GA). The proposed model shares the weights of all possible connection nodes internally within the neural network. Externally, the search cost is reduced through the weight-sharing effect between the genotypes of the GA. A predictive analysis was performed using the Medical Information Mart for Intensive Care III (MIMIC-III), a medical time-series dataset, with the primary objective of predicting sepsis onset 3 h before occurrence. In addition, experiments were conducted under various prediction times (0–12 h) for comparison. The proposed model exhibited an area under the receiver operating characteristic curve (AUROC) score of 0.94 (95% CI: 0.92–0.96) for 3 h, which is 0.31–0.26 higher than the scores obtained using the Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Simplified Acute Physiology Score (SAPS) II scoring systems. Furthermore, the proposed model exhibited a 12% improvement in the AUROC value over a simple model based on the long short-term memory neural network. Additionally, it is not only optimally searchable for sepsis onset prediction, but also outperforms conventional models that use similar predictive purposes and datasets. Notably, it is sufficiently robust to shape changes in the input data and has less structural dependence.

Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time

BackgroundWith the precision of the mass spectrometry (MS) going higher, the MS file size increases rapidly. Beyond the widely-used open format mzML, near-lossless or lossless compression algorithms and formats emerged in scenarios with different precision requirements. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focus more on lossless compression rate, computation-oriented formats concentrate as much on decoding speed as the compression rate.ResultsHere we introduce “Aird”, an opensource and computation-oriented format with controllable precision, flexible indexing strategies, and high compression rate. Aird provides a novel compressor called Zlib-Diff-PforDelta (ZDPD) for m/z data. Compared with Zlib only, m/z data size is about 55% lower in Aird average. With the high-speed decoding and encoding performance of the single instruction multiple data technology used in the ZDPD, Aird merely takes 33% decoding time compared with Zlib. We have downloaded seven datasets from ProteomeXchange and Metabolights. They are from different SCIEX, Thermo, and Agilent instruments. Then we convert the raw data into mzML, mgf, and mz5 file formats by MSConvert and compare them with Aird format. Aird uses JavaScript Object Notation for metadata storage. Aird-SDK is written in Java, and AirdPro is a GUI client for vendor file converting written in C#. They are freely available at https://github.com/CSi-Studio/Aird-SDK and https://github.com/CSi-Studio/AirdPro.ConclusionsWith the innovation of MS acquisition mode, MS data characteristics are also constantly changing. New data features can bring more effective compression methods and new index modes to achieve high search performance. The MS data storage mode will also become professional and customized. ZDPD uses multiple MS digital features, and researchers also can use it in other formats like mzML. Aird is designed to become a computing-oriented data format with high scalability, compression rate, and fast decoding speed.

Study on multi-objective search performance of production plan optimization in manufacturing field

There is a great demand for multi-objective optimization of production plans at the high-mix low-volume production of the manufacturing industry, and the importance of multi-objective numerical optimization technology will increase more and more in the future. In this research, we have developed a control method that can realize a wide range of Pareto solution at high speed for two object functions; cost and productivity, which are important in manufacturing fields but have a trade-off relationship. The key of present technique is a technology that can obtain a wide range of Pareto solutions, which are difficult to solve by each technology alone, by appropriately controlling a technology that has high search performance but is single objective optimization and a technology that can perform multi-objective optimization but has low search performance. This paper presents an example of solving a standard multi-objective optimization problem and the results of applying it to setup optimization problems which are often found in actual manufacturing fields.

Publisher Correction: Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Publisher Correction: Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometry based glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

Background and Objective: Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process the Big Data. Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System are used. In this paper, a lightweight and adaptable spatial data index for big data have discussed, which have used to process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of spatial ordering system for various spatial inquiries. Methods: In this section, the different type of approaches are used which helps to understand the procedure to develop an efficient system by involving the methods like efficient and scalable method for processing Top-k spatial Boolean Queries, Efficient query processing in Geographic web search engines. Geographic search engine query processing combines text and spatial data processing technique & Top-k spatial preference Queries. In this work, the implementation of all the methods is done for comparative analysis. Results and Discussion: The execution of algorithm gives results which show the difference of performance over different data types. Three different graphs are presented here based on the different data inputs indexing and data types. Results show that when the number of rows to be executed increases the performance of geohash decreases, while the crucial point for change in performance of execution is not visible due to sudden hike in number of rows returned. Conclusion: The query processing have discussed in geographic web search engines. In this work a general framework for ranking search results based on a combination of textual and spatial criteria, and proposed several algorithms for efficiently executing ranked queries on very large collections have discussed. The integrated of proposed algorithms into an existing high-performance search engine query processor and works on evaluating them on a large data set and realistic geographic queries. The results shows that in many cases geographic query processing can be performed at about the same level of efficiency as text-only queries.

CRISPR-SE: a brute force search engine for CRISPR design.

CRISPR is a revolutionary genome-editing tool that has been broadly used and integrated within novel biotechnologies. A major component of existing CRISPR design tools is the search engines that find the off-targets up to a predefined number of mismatches. Many CRISPR design tools adapted sequence alignment tools as the search engines to speed up the process. These commonly used alignment tools include BLAST, BLAT, Bowtie, Bowtie2 and BWA. Alignment tools use heuristic algorithm to align large amount of sequences with high performance. However, due to the seed-and-extend algorithms implemented in the sequence alignment tools, these methods are likely to provide incomplete off-targets information for ultra-short sequences, such as 20-bp guide RNAs (gRNA). An incomplete list of off-targets sites may lead to erroneous CRISPR design. To address this problem, we derived four sets of gRNAs to evaluate the accuracy of existing search engines; further, we introduce a search engine, namely CRISPR-SE. CRISPR-SE is an accurate and fast search engine using a brute force approach. In CRISPR-SE, all gRNAs are virtually compared with query gRNA, therefore, the accuracies are guaranteed. We performed the accuracy benchmark with multiple search engines. The results show that as expected, alignment tools reported an incomplete and varied list of off-target sites. CRISPR-SE performs well in both accuracy and speed. CRISPR-SE will improve the quality of CRISPR design as an accurate high-performance search engine.

Human visual search follows a suboptimal Bayesian strategy revealed by a spatiotemporal computational model and experiment

There is conflicting evidence regarding whether humans can make spatially optimal eye movements during visual search. Some studies have shown that humans can optimally integrate information across fixations and determine the next fixation location, however, these models have generally ignored the control of fixation duration and memory limitation, and the model results do not agree well with the details of human eye movement metrics. Here, we measured the temporal course of the human visibility map and performed a visual search experiment. We further built a continuous-time eye movement model that considers saccadic inaccuracy, saccadic bias, and memory constraints. We show that this model agrees better with the spatial and temporal properties of human eye movements and predict that humans have a memory capacity of around eight previous fixations. The model results reveal that humans employ a suboptimal eye movement strategy to find a target, which may minimize costs while still achieving sufficiently high search performance.

IGD: high-performance search for large-scale genomic interval datasets.

Databases of large-scale genome projects now contain thousands of genomic interval datasets. These data are a critical resource for understanding the function of DNA. However, our ability to examine and integrate interval data of this scale is limited. Here, we introduce the integrated genome database (IGD), a method and tool for searching genome interval datasets more than three orders of magnitude faster than existing approaches, while using only one hundredth of the memory. IGD uses a novel linear binning method that allows us to scale analysis to billions of genomic regions. https://github.com/databio/IGD. Supplementary data are available at Bioinformatics online.

Semantic Service Search in IT Crowdsourcing Platform: A Knowledge Graph-Based Approach

Understanding user’s search intent in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, searching for services accurately on crowdsourcing platforms is still difficult, because these platforms do not contain enough information to support high-performance search. To solve these problems, we build and leverage a knowledge graph named ITServiceKG to enhance search performance of crowdsourcing IT services. The main ideas are to (1) build an IT service knowledge graph from Wikipedia, Baidupedia, CN-DBpedia, StuQ and data in IT service crowdsourcing platforms, (2) use properties and relations of entities in the knowledge graph to expand user query and service information, and (3) apply a listwise approach with relevance features and topic features to re-rank the search results. The results of our experiments indicate that our approach outperforms the traditional search approaches.

ER-TCAM: A Soft-Error-Resilient SRAM-Based Ternary Content-Addressable Memory for FPGAs

Static random access memory (SRAM)-based ternary content-addressable memory (TCAM) on field-programmable gate arrays (FPGAs) is used for packet classification in software-defined networking (SDN) and OpenFlow applications. SRAMs implementing TCAM contents constitute the major part of a TCAM design on FPGAs, which are vulnerable to soft errors. The protection of SRAM-based TCAMs against soft errors is challenging without compromising critical path delay and maintaining a high search performance. This brief presents a lowcost and low-response-time technique for the protection of SRAM-based TCAMs. This technique uses simple, single-bit parity for fault detection which has a minimal critical path overhead. This technique exploits the binary-encoded TCAM table maintained in SRAM-based TCAMs for update purposes to implement a low-response-time error-correction mechanism at low cost. The error-correction process is carried out in the background, allowing lookup operations to be performed simultaneously, thus maintaining a high search performance. The proposed technique provides protection against soft errors with a response time of 293 ns, whereas maintaining a search rate of 222 million searches per second on a 1024 × 40 size TCAM on Artix-7 FPGA.

The mystery of high performance – mediation by entrepreneurial orientation and organizational citizenship behavior

The search for high performance is one of the leading themes in management science with a number of frameworks developed over the decades. The purpose of the paper is to investigate if the effect of high performance organization (HPO) characteristics has a direct effect on actual organizational performance or if the effect is mediated by organizational citizenship behavior (OCB) and entrepreneurial orientation (EO). Previous research states that the framework of HPO characteristics answers the question of ‘what’ high performers do, but does not answer the question of ‘how’ they do it, suggesting a mediating effect. The study attempts to establish which of the pair of OCB and EO (if any) has a stronger mediating effect. The hypotheses were tested using a cross-sectional research design with a random and representative sample of 406 enterprises. The research was carried out using the technique of personal interview (CAPI). Established scales were used to measure high performance indicators, organizational citizenship behavior, entrepreneurial orientation, and organizational performance. The conceptual framework with mediated effects was tested using a structural equations modeling approach. Research results indicate that there is a strong direct positive relationship between high performance indicators and organizational effectiveness. Moreover, there is a strong positive effect of high performance indicators on entrepreneurial orientation and organizational citizenship behavior. However, the effect of those two constructs on organizational effectiveness is rather weak, and, in the case of OCB, negative. That leads to the conclusion that the direct effect of high performance indicators is stronger, although the positive mediation by entrepreneurial orientation is statistically significant. The study contributes to the scientific debate in at least three ways. First, it confirms that high performance characteristics have a strong effect on actual performance. Second, it points to entrepreneurial orientation as a partial mediator in that relationship. Finally, it uncovers the very strong effect of high performance characteristics on organizational citizenship behavior. The research results also allow one to formulate practical recommendations. They mainly consider enhancing OCB, EO, and performance by skillful use of high performance factors. The study is novel in its approach to use OCB and entrepreneurial orientation as mediators in the effect of high performance characteristics on organizational performance, as no such attempts were made in the past.