Memory Utilization Research Articles

Urban traffic light scheduling for pedestrian–vehicle mixed-flow networks using discrete sine–cosine algorithm and its variants

This study addresses the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks. A macroscopic model, which strikes an appropriate balance between pedestrians’ needs and vehicle drivers’ needs, is employed to describe the traffic light scheduling problem in a scheduling framework. The objective of this problem is to minimize the total network-wise delay time of vehicles and pedestrians within a given finite-time window, which is crucial to avoid traffic congestion in urban road networks. To achieve this objective, the present study first uses a well-known optimization solver called GUROBI to obtain the optimal solution by converting the problem into mixed-integer linear programming. The obtained results indicate the computational inefficiency of the solver for large network sizes. To overcome this computational inefficiency, three novel metaheuristic methods based on the sine–cosine algorithm are proposed. These methods are denoted by discrete sine–cosine algorithm, discrete sine–cosine algorithm with local search operator, and discrete sine–cosine algorithm with local search operator and memory utilization inspired by harmony search. Each of these methods is developed hierarchically by taking the advantages of previously developed method(s) in terms of a better search process to provide more accurate solutions and a better convergence rate. To validate all these proposed metaheuristics, extensive computational experiments are carried out using the real traffic infrastructure of Singapore. Moreover, various performance measures such as statistical optimization results, relative percentage deviation, computational time, statistical analysis, and convergence behavior analysis have been employed to evaluate the performance of algorithms. The comparison of the proposed SCA variants is done with GUROBI solver and other metaheuristics namely, harmony search, firefly algorithm, bat algorithm, artificial bee colony, genetic algorithm, salp swarm algorithm, and harris hawks optimization. Overall comparison analysis concludes that the proposed methods are very efficient to solve the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks with different network sizes and prediction time horizons.

Past-future preferences for hedonic goods and the utility of experiential memories

ABSTRACT Recent studies have suggested that while both adults and children hold past-future hedonic preferences – preferring painful experiences to be in the past and pleasurable experiences to lie in the future – these preferences are abandoned when the quantity of pain or pleasure under consideration is greater in the past than in the future. We examined whether such preferences might be affected by the utility people assign to experiential memories, since the recollection of events can itself be pleasurable or aversive, and we examined the developmental trajectory of the value that people assign to experiential memories of past painful experiences. Using a task in which we manipulated hypothetical memory loss in a series of brief vignettes, we found that for some adults, but not for children, the disutility attached to the recollection of painful past events outweighed the disutility of living through future painful events. Between middle childhood and adulthood, experiential memory appears to assume a more important role in determining the value that people assign to past experiences and in mitigating bias toward the future.

Cuckoo Search Augmented MapReduce for Predictive Scheduling With Big Stream Data

Handling an information stream is a basic report for streaming application. There were numerous strategies which help during Bigdata streaming, however it can't deal with the tremendous information. To advance the productivity with least time intricacy, a Cuckoo Search Augmented Map Reduce for Predictive Scheduling (CSA-MRPS) system is presented. This technique incorporates cycles in preprocessing and prescient booking for stream information examination. In preprocessing, nonstop information streams are discretized utilizing Khiops and it begins from the constant time spans, consolidates the closest time as indicated by the Chi-square worth with lesser time intricacy. MapReduce work is applied to discretized information for prescient investigation utilizing Multi-Objective Ranked Cuckoo Search Optimization (MRCSA). It characterize the target capacities for the handling units, for example, CPU time, memory utilization, transfer speed use and energy utilization. Thus, CSA-MRPS Mechanism predicts the asset upgraded preparing unit with high position through the planning system.

Multilevel Thresholding for Image Segmentation Using Mean Gradient

Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically impossible to implement these algorithms in a low-capacity hardware application where computational power and memory utilization are of great concern. In this study, a simple yet effective and noniterative global and bilevel thresholding technique is proposed. It uses the concept of image gradient vector to binarize or segment the image into three clusters. In addition, a parametric preprocessing approach is also proposed that can be used in image restoration applications. Evidences from the experiments from both visual and standard evaluation metrics show that the proposed methods perform exceptionally well. The proposed global thresholding outperforms the formidable Otsu thresholding technique.

Frequent Itemset Generation Using Association Rule Mining Based on Hybrid Neural Network Based Billiard Inspired Optimization

The concept based on data mining has drawn considerable attention from various database professionals and research scholars. The progression of computer-based advancements, namely database management and data storage has facilitated the storage of large data and the data mining approaches are employed to gain valuable information from huge databases. Recently, several techniques to association rule mining (ARM) and frequent itemset mining (FIM) have been established; yet the efficiency based on execution time and scalability continues to be seen as a significant limitation that results in poor solution quality. Therefore, it is necessary to enhance the consistency that signifies the total number of frequently discovered frequent itemsets. This paper proposes three different phases namely the pre-processing phase, FIM phase and ARM phase. In the first pre-processing phase, the Twitter databases are pre-processed and converted into a suitable format for FIM. Here, the tweets are converted into related feature sets and items. In the second FIM phase, an improved Apriori algorithm is 1utilized in mining and extracting the frequent Then in the final phase, an adaptive billiard inspired optimization (ABIO) algorithm which is the integration of neural network (NN) optimization algorithm and billiard inspired optimization (BIO) algorithm is proposed for the optimal generation of association rules with minimum support and confidence from the huge itemsets. Finally, the recent tweets based on covidvaccine, BTSlivestreaming, KFC, McDonald’s as well as lockdown achieved using the hashtag is evaluated for various performance measures, like precision, recall, [Formula: see text]-measure, execution time and memory utilization. Also, comparative analyses are performed to evaluate the efficiency of the proposed technique.

Measuring Memory Lapses and Their Impact on Daily Life: Results From Two Daily Diary Studies.

Memory lapses (e.g., forgetting a medication) are common for most people, yet past methods of assessment relied upon retrospective reports from long recall windows. Recently, researchers have incorporated daily diary methods to capture memory lapse frequency closer to the experience in real-world environments. This study describes the utility of the Daily Memory Lapses Checklist using data from two 14-day diary studies (Combined Sample = 467; 66% women). Frequency and impact (i.e., irritation and interference) of prospective and retrospective memory lapses were assessed at both individual- and daily levels. Across studies, memory lapses occurred on more than one-third of assessment days. Retrospective lapses were reported more frequently than prospective; however, both lapses had a similar impact. The Daily Memory Lapses Checklist represents a flexible measure that separates the occurrence of a memory lapse from its impact on daily life: metrics that will enhance our understanding of daily experiences of cognitive functioning.

Responsible Remembering and Forgetting in Younger and Older Adults

ABSTRACT Although older adults are often concerned about instances of forgetting, forgetting can be a useful feature of our memory system. Specifically, strategically forgetting less important information can benefit memory for goal-relevant information (i.e., responsible remembering and responsible forgetting). In two experiments, we presented younger and older adults with a list of words (either unrelated words or items to bring on a camping trip) with a cue indicating whether participants (“You”) or their “Friend” was responsible for remembering each item. Results revealed that both younger and older adults engaged in responsible remembering and forgetting by better remembering items they were responsible for remembering, indicating a strategic utilization of their limited memory capacity. Additionally, regardless of age and the cue indicating who was responsible for remembering each item, participants used importance to guide the encoding and retrieval of information. Thus, people may be able to engage strategic cognitive mechanisms to maximize memory utility for important, goal-relevant information, and responsible forgetting can enhance memory utility in both younger and older adults by using importance to drive memory and reduce consequences for forgetting.

An Efficient Full Hardware Implementation of Extended Merkle Signature Scheme

This paper presents a full hardware implementation of the eXtended Merkle Signature Scheme (XMSS), a NIST approved and IETF RFC specified post-quantum cryptography (PQC) algorithm. An optimized node traversal is proposed to enable efficient memory utilization without compromising the computational latency of the L-tree and Merkle tree construction, which are two key components used for the compression of the Winternitz One-Time Signature (WOTS) public key in XMSS. The computation of the authentication path during signature generation has also been significantly sped up by our proposed hardware implementation of the Buchmann, Dahmen, and Schneider (BDS) algorithm. Our implementation has completely avoided the use of block random-access memory, which is known to be vulnerable to side-channel attacks. The memory requirement has been highly optimized for implementation with small flip-flop chains and register counters as pointers for fast data access. To the best of our knowledge, this is the first full hardware implementation of all three <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">key generation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signing</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">verification</i> operations of XMSS. The design has been prototyped and evaluated on a 28 nm FPGA platform to demonstrate its performance improvements over the most efficient software and hardware/software co-design methods reported to date. Specifically, it increases the computational efficiency of the best reported XMSS implementation for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">key generation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signature generation</i> by about 20% and 50%, respectively. It can also run at 10% higher clock speed than the fastest hardware implementation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signature verification</i> in FPGA with 8% lower hardware resource utilization.

Intermediate data placement and cache replacement strategy under Spark platform

Spark is widely used due to its high performance caching mechanism and high scalability, which still causes uneven workloads and produces useless intermediate caching results when faced with data-intensive applications. A data placement strategy based on an improved reservoir sampling algorithm is proposed to solve the problem of intermediate data tilt in the shuffle stage of Spark. Compared with the traditional sampling algorithm, the amount of intermediate data is accumulated while sampling. The data skew measurement model is used to classify data into skewed data, and non-skewed and coarse-grained, and fine-grained placement algorithms are designed. To further improve Spark's system memory utilization and cache hit rate, an adaptive cache replacement algorithm is proposed to maximize cache gain. We analyze the operational dependencies and propose a cache gain model. Compared with the traditional method, the two known and unknown job arrival rates are considered separately to obtain an online adaptive cache replacement strategy that maximizes cache gain. Experimental results show that our data placement strategy effectively reduces Spark applications' execution time and improves the load balance of reduce tasks. Meanwhile, the proposed adaptive cache replacement strategy effectively reduces Spark's average completion time and improves the memory utilization and cache hit rate.

Smartphone-Based Photogrammetry Assessment in Comparison with a Compact Camera for Construction Management Applications

Close-range photogrammetry (CRP) has proven to be a remarkable and affordable technique for data modeling and measurements extraction in construction management applications. Nevertheless, it is important to aim for making CRP more accessible by using smartphones on-site directly without a pre-calibration procedure. This study evaluated the potential of smartphones as data acquisition tools in comparison with compact cameras based on the quality and accuracy of their photogrammetric results in extracting geometrical measurements (i.e., surface area and volume). Two concrete specimens of regular shapes (i.e., beam and cylinder) along with an irregular-shaped sand pile were used to conduct this study. The datasets of both cameras were analyzed and compared based on lens distortions, image residuals, and projections multiplicity. Furthermore, the photogrammetric models were compared according to various quality criteria, processing time, and memory utilization. Though both cameras were not pre-calibrated, they both provided highly accurate geometrical estimations. The volumetric estimation error ranged from 0.37% to 2.33% for the compact camera and 0.67% to 3.19% for the smartphone. For surface area estimations, the error ranged from 0.44% to 0.91% for the compact camera and 0.50% to 1.89% for the smartphone. Additionally, the smartphone data required less processing time and memory usage with higher applicability compared with the compact camera. The implication of these findings is that they provide professionals in construction management with an assessment of a more direct and cost-effective 3D data acquisition tool with a good understanding of its reliability. Moreover, the assessment methodology and comparison criteria presented in this study can assist future research in conducting similar studies for different capturing devices in construction management applications. The findings of this study are limited to small quantification applications. Therefore, it is recommended to conduct further research that assesses smartphones as a photogrammetric data acquisition tool for larger construction elements or tracking ongoing construction activities that involve measurements estimation.

Performance Impact of Type-I Virtualization on a NewSQL Relational Database Management System

For more than 40 years, the relational database management system (RDBMS) and the atomicity, consistency, isolation, durability (ACID) transaction guarantees provided through its use have been the standard for data storage. The advent of Big Data created a need for new storage approaches that led to NoSQL technologies, which rely on basic availability, soft-state, eventual consistency (BASE) transactions. Over the last decade, NewSQL RDBMS technology has emerged, providing the benefits of RDBMS ACID transaction guarantees and the performance and scalability of NoSQL databases. The reliance on virtualization in IT has continued to grow, but an investigation of current academic literature identified a void regarding the performance impact of virtualization of NewSQL databases. To help address the lack of research in this area, a quantitative experimental study was designed and carried out to answer the central research question, "What is the performance impact of Type-I virtualization on a NewSQL RDBMS?" VMware ESXi virtualization software, NuoDB RDBMS, and OLTP-Bench software were used to execute a mixed-load benchmark. Performance metrics were collected comparing bare metal and virtualized environments, and the data analyzed statistically to evaluate five hypotheses related to CPU utilization, memory utilization, disk and network input-output (I/O) rates, and database transactions per second. Findings indicated a negative performance impact on CPU and memory utilization, as well as network I/O rates. Performance improvements were noted in disk I/O rates and database transactions-per-second.

An interactive practice platform of English mobile teaching in colleges and universities based on open API

In order to overcome the problems of long response time, high CPU and memory usage, this paper proposes a new interactive platform based on open API. On the basis of open API protocol, the whole framework of B/S structure design platform is introduced. The platform hardware mainly includes three main components: microprocessor, memory, programmer, etc. through feedback elite teaching optimisation algorithm and open API protocol program, students are graded to complete the design of open API College English mobile teaching interactive practice platform. The experimental results show that the maximum response time is reduced to 0.85, and the CPU utilisation and memory utilisation are reduced to 56.4 and 64.2 when the number of users is the largest, so the evaluation of platform learning investment effect is better.

An Adaptive Offloading Mechanism for Mobile Cloud Computing: A Niching Genetic Algorithm Perspective

The fast evolution of mobile applications demonstrates the growing need for more resources and processing power on mobile devices. Mobile Cloud Computing (MCC) combines cloud computing with mobile devices, enabling sophisticated and resource-intensive applications to operate on mobile devices regardless of their performance limits (e.g., battery life, memory utilization, and computation). Overcoming these constraints is accomplished using a well-known technique called computation offloading, which entails offloading heavy processing to resourceful servers and getting the results from these servers. Many studies have been done on mobile code offloading, with the goal of avoiding the stated limits and reducing execution time or battery consumption through single- or multiple-site offloading, so that people can use their phones and tablets more. However, the majority of existing techniques make offloading choices based on profile data, which implies a stable network environment and forces an object to be offloaded to the same site. As a consequence of these challenges, this research proposes a novel strategy for enhancing the multisite offloading mechanism by combining a Niching Genetic Algorithm (NGA) with a Markov Decision Process (MDP). MDP is used to determine the most optimal location for each application&#x2019;s modules to be executed. GA was used to determine the optimal transition probability for components operating on several sites. To aid in initial population selection, the proposed model employs a niche model in the form of a context-based clearing (CBC) technique to improve the variability of the genes inside the chromosome in order to minimize their association. The simulation results reveal that the proposed technique consumes little power and executes quickly while determining the optimal offloading decision with lower generation numbers.

Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of efficiency, memory utilization, and scalability.

Learning Depth Estimation From Memory Infusing Monocular Cues: A Generalization Prediction Approach

Depth estimation from a single image is a challenging task, yet this field has a promising prospect in automatic driving and augmented reality. However, the prediction accuracy is degraded significantly when the trained network is transferred from the training dataset to real scenarios. To solve this issue, we propose MonoMeMa, a novel deep architecture based on the human monocular cue, which means humans can perceive depth information with one eye through the relative size of objects, light and shadow, etc. based on previous visual experience. Our method simulates the process of the formation and utilization of human monocular visual memory, including three steps: Firstly, MonoMeMa perceives and extracts real-world objects feature vectors (encoding). Then, it maintains and replaces the extracted feature vector over time (storing). Finally, MonoMeMa combines query objects feature vectors and memory to inference depth information (retrieving). According to the simulation results, our model shows the state-of-the-art results on the KITTI driving dataset. Moreover, MonoMema exhibits remarkable generalization performance when our model is migrated to other driving datasets without any finetune.

Mobility Induced Multi-Hop LEACH Protocol in Heterogeneous Mobile Network

Heterogeneous mobile networks are a vital cue for Internet of Things (IoT) networks and their applications. Therefore, an optimal routing protocol is essential for the heterogeneous network to realize energy efficiency and robustness. However, designing an optimal routing protocol is more complex due to uneven energy depletion and dynamic event generation. The complexity further increases with the node mobility. In this context, the multi-hop routing approach with a controlled mobility pattern provides an energy efficient network by improving memory and storage utilization. This work proposes a new energy-efficient model for heterogeneous network, considering the effect of mobility and residual energy of nodes for cluster head selection in multi-hop-LEACH (M-LEACH). The performance of the proposed mobility induced multi-hop LEACH (Mob M-LEACH) protocol is further compared with existing baseline methods. The robustness of the network is evaluated considering static and dynamic environments for multiple network technologies. The simulation results show that the proposed method reduces network latency by 25% and enhances the throughput by 75%, improving energy consumption and network lifetime when compared with the state-of-art methods.

LBRO: Load Balancing for Resource Optimization in Edge Computing

Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user’s preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users’ preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.

Impact of Block Data Components on the Performance of Blockchain-Based VANET Implemented on Hyperledger Fabric

Blockchain, a vital technology in today&#x2019;s era, changed the way we share, manage and exchange our data in a centralized way to decentralized architecture. With the increasing demand for Blockchain, various platforms are available to implement public, private, consortium, or permissioned, permissionless Blockchain. Hyperledger, an open-source, permissioned, distributed ledger-based Blockchain, was hosted by Linux. This paper explores Hyperledger Fabric Private Blockchain Network (HFPBN). The architecture of HFPBN with its components and transaction flow is explored in detail. The Blockchain in HFPBN comprises multiple blocks that are linked to each other. The block elements are discussed in detail with their type and size, and after that, the total size of the block depending upon various parameters is calculated. Further, one application of Blockchain, i.e., Vehicular Ad-hoc Networks (VANETs), is discussed in this paper as a case study. The VANET application is being implemented on the Hyperledger Fabric platform. Formulas showing the dependency of various parameters like endorsement policy, number of transactions, and number of reads and writes on block size are derived and shown in their relationship through the graph for the VANET system. The impact of block size on various performance parameters like throughput, latency, memory, and CPU utilization for the VANET system is then analyzed using Hyperledger Caliper. An optimal required value of throughput and latency is achieved for Blockchain-based VANET. Also, the Hyperledger Fabric platform seems suitable for many applications as it creates separate Blockchain for different applications.

A Low Coupling and Lightweight Algorithm for Ship Detection in Optical Remote Sensing Images

In recent years, many ship detection algorithms based on convolutional neural networks (CNNs) have been proposed to improve the performance of ship detection. However, with the increase in model complexity and size, it is challenging to deploy these models to resource-constrained edge platforms. In this letter, a low coupling algorithm that belongs to anchor-free methods is proposed for ship detection to reduce the model complexity and still obtain a competitive performance. The proposed low coupling network (LCNet) is easy to deploy and contributes to speeding up the inference and improving memory utilization. In addition, we propose a model compression process consisting of the quantization-aware training (QAT) method and a structural pruning method based on Taylor expansion, which can effectively reduce the model size according to hardware resource constraints. Comparative experimental results demonstrate that LCNet outperforms the state-of-the-art ship detection and natural object detection algorithms, with a 95.27&#x0025; mAP and 88.91&#x0025; F1 score on the HRSC2016 dataset. Our proposed model compression method also achieves a compression ratio of at least 80&#x0025; with a negligible loss of performance.

Memory Optimization System for SGXv2 Trusted Execution Environment

PDF HTML XML Export Cite reminder Memory Optimization System for SGXv2 Trusted Execution Environment DOI: 10.21655/ijsi.1673-7288.00287 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Trusted Execution Environment (TEE) is an architectural solution for secure computing that requires confidentiality and integrity for private data and code. In recent years, TEE has become the research hotspot for machine learning privacy protection, encrypted database, blockchain security, etc. This paper addresses the performance problem of the system under this new trusted hardware. We analyze the performance of the new trusted hardware, i.e., Intel SGXv2. We find that the paging overhead in SGXv1 is no longer the main issue in SGXv2 under the premise of configuring large secure memory. However, the setup of large secure memory leads to two new problems. First, the available range of normal memory is narrowed down, which increases the memory pressure of normal applications, especially big data applications. Second, secure memory is usually underutilized, resulting in low overall physical memory utilization. To solve the above problems, this paper proposes a new lightweight code migration approach, which dynamically migrates the code of normal applications into secure memory, while leaving the data in place. The migrated code can use secure memory and avoid the drastic performance degradation caused by disk swapping. Experimental results show that the proposed approach can reduce the runtime overhead of normal applications by 73.2\% to 98.7\% without affecting the isolation and the use of secure applications. Reference Related Cited by