Additional Execution Time Research Articles

Evaluation of the Implementation of Contract Change Order as A Solution to the Problem of Contract Scope Changes

Construction industry activities are increasing. Construction industry players need to be prepared to face the possibility of changes of design planed that are not in accordance with field conditions. The purpose of this research is to identify, analyze the causes and impacts of Contract Change Orders (CCO) at the pre-construction, construction implementation, and post-construction stages with case study is Proyek Simpang Susun Balaraja Timur at Tol Jakarta – Merak, Kabupaten Tangerang. The research method used is descriptive and qualitative methods by conducting Earned Value Analysis and Fishbone Analysis to identify the causal factors and impacts of CCO so that efforts can be made to mitigate the performance of the construction contract from the aspect of quality, cost, and time. The results of the analysis show 3 factors causing CCO namely consultant factors, such as lack of planning preparation and non-compliance with field conditions, project owner factors, and contractor factors. The results of Earned Value Analysis with Estimate at Completion (EAC) calculations can be used to identify the need for additional contract value and additional execution time. On initial contract identification: Cost Performance Index (CPI) is 0.77 (insufficient costs with cost overrun – Rp. 52.502,111,631.83) and Schedule Performance Index (SPI) 1.21, means that Addendum 1 needs to be done with the results: CPI is 1.17 (the costs used are sufficient from the available fees), EAC is Rp. 189,848,751,106.02 with changes in the contract value is Rp. 222,017,999,900.65, SPI is 1.21 from the initial contract value Rp. 174,437,684,167.96

GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System.

Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment.

A New Approach in Expanding the Hash Size of MD5

The enhanced MD5 algorithm has been developed by expanding its hash value up to 1280 bits from the original size of 128 bit using XOR and AND operators. Findings revealed that the hash value of the modified algorithm was not cracked or hacked during the experiment and testing using powerful bruteforce, dictionary, cracking tools and rainbow table such as CrackingStation, Hash Cracker, Cain and Abel and Rainbow Crack which are available online thus improved its security level compared to the original MD5. Furthermore, the proposed method could output a hash value with 1280 bits with only 10.9 ms additional execution time from MD5. Keywords: MD5 algorithm, hashing, client-server communication, modified MD5, hacking, bruteforce, rainbow table.

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.

FreqCounter: Efficient Cacheability of Encryption and Integrity Tree Counters in Secure Processors

The data in the off-chip main memory can be potentially extracted or tampered by an adversary having physical access to the device and thus it becomes inevitable to secure the data present in the off-chip memory. The modern designs consider storing the counters on-chip to prevent replay attacks. However, these designs have significant overheads in terms of the on-chip storage used to store counters, and the additional execution time. In this paper, we propose a new mechanism FreqCounter that trims the on-chip storage by storing the counters blockwise in a tag array on-chip cache rather than in a page-wise manner as is conventionally done. The counters for only those blocks are maintained that are expected to miss frequently in the last level cache (LLC). We show that the FreqCounter reduces the space overheads compared to prior schemes by 53.02% keeping performance almost similar if not better. We further show that our design reduces the ED2 by 14.5% on an average compared to the recent competing scheme Morphable Counters without any compromise in the security.

Security Design Pattern for Login System through Authentication using Modified Sha 384 Algorithm

The research has been carried out to develop secure login system by authenticating the login using modified SHA-384 algorithm. It derives 896-bit hash value for the password entered by the user in the user registration form and saves the credentials entered by the user in system’s database. Results obtained are evaluated by resolving the general attacks confirmed that the modified SHA-384 algorithm was more secured compared to the original SHA-384 algorithm as it was not broken using generic attacks such as brute force, rainbow table and other cracking tools available online such as Cain and Abel. The performance of the modified algorithm was measured with only 2 ms additional execution time from SHA-384.

EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection

Automated pig monitoring is important for smart pig farms; thus, several deep-learning-based pig monitoring techniques have been proposed recently. In applying automated pig monitoring techniques to real pig farms, however, practical issues such as detecting pigs from overexposed regions, caused by strong sunlight through a window, should be considered. Another practical issue in applying deep-learning-based techniques to a specific pig monitoring application is the annotation cost for pig data. In this study, we propose a method for managing these two practical issues. Using annotated data obtained from training images without overexposed regions, we first generated augmented data to reduce the effect of overexposure. Then, we trained YOLOv4 with both the annotated and augmented data and combined the test results from two YOLOv4 models in a bounding box level to further improve the detection accuracy. We propose accuracy metrics for pig detection in a closed pig pen to evaluate the accuracy of the detection without box-level annotation. Our experimental results with 216,000 “unseen” test data from overexposed regions in the same pig pen show that the proposed ensemble method can significantly improve the detection accuracy of the baseline YOLOv4, from 79.93% to 94.33%, with additional execution time.

A Fault-Tolerant Workflow Scheduling Algorithm for Grid with Near-Optimal Redundancy

In scheduling workflows in grid environment, concerns such as minimizing the makespan and cost, meeting the time and budget constraints and the possibility of resource failures and so on have motivated researchers to propose numerous scheduling algorithms. Several heuristics and meta-heuristic algorithms have been proposed to address these issues, each of which often only considers one or a few of these criteria. However, less attention has been paid to fault-tolerant scheduling of workflows. Adding fault-tolerance to a workflow scheduling algorithm leads to an inevitable increase in the makespan and cost. Using the resubmission technique may result to an unacceptable increase in the execution time and possible violation of deadline while the replication method increases the execution cost. In this paper, we propose a fault-tolerant workflow scheduling algorithm with near-optimal time and cost overhead. The proposed approach brings a two-fold novelty. First, we assume a stochastic model of workflow with nondeterministic task parameters and use interval arithmetic to model task execution times and propose a new scheduling algorithm in which the task assignment decisions are taken according to the performability fluctuations of the computational resources. Second, we employ an Efficient combination of resubmission and replication techniques to achieve the benefits of both and propose an algorithm for reliable scheduling of scientific workflows with near-optimal additional time and cost. The proposed method, achieves a significant increase in the reliability while the additional execution time and cost is almost negligible.

Ant colony algorithm for text classification in multicore-multithread environment

This paper presents about Ant Colony Algorithm (ACO) for Text Classification in Multicore-Multithread Environment in Artificial Intelligent domain. We had develop a software which assimilate concurrency concept to multiple artificial ants. Pheromone in ACO is the main concept used to solve the text classification problem. In regards to its role, pheromone value is changed depending on the solution finding that has been discovered at the pseudo random heuristic attempt in selecting path from text words. However, ACO can take up longer time to process larger training document. Based on the cooperative concept of ants living in colony, the ACO part is examined to work in multicore-multithread environment as to cater additional execution time benefit. In running multicore-multithread environment, the modification aims to make artificial ants actively communicate between multiple physical cores of processor. The execution time reduction is expected to show an improvement without compromising the original classification accuracy by the investment of trading on more processing power. The single and multicore-multithreaded version of ACO was compared statistically by conduction relevant test. It was found that the result shows a positive time reduction improvement.

GPU implementation of Kampmann-Wagner numerical precipitation models

A Kampmann-Wagner type numerical precipitation model (KWN) has been implemented using NVIDIA's CUDA framework for numerical programming of the graphics processing unit (GPU). Different implementation strategies are discussed and subjected to performance When the KWN model is used in combination with other calculations that are processed by the CPU, the performance improvements can be such that the KWN model incurs only emph{negligible} additional execution time. Also if the KWN model is used standalone for a large case, the GPU implementation achieves good scalability and performance.measurements. We study two representative cases corresponding to a large and a small workload. The model is found to be well suited for a GPU implementation, provided that there is enough work to keep the device busy and the right parallelization strategy is chosen. For our hardware, we recommend a minimum work load of more than $2^{16}$ histogram bins (as the total of multiple histograms) which corresponds to 146 histogram bins per GPU core.

Fast and accurate simulation of the turbulent phase screen using fast Fourier transform

This work describes an accurate method for simulating turbulent phase screens. The phase screen is divided into a fast Fourier transform (FFT)-based screen and a tilt screen. The simulation of the FFT-based screen is different from that of the standard method. In the simulation, the discrete power spectrum of the turbulence is obtained from the discrete Fourier transform of the phase autocorrelation matrix, not from the theoretical power spectrum. This method avoids the drawbacks of the undersampling of the low frequency and high frequency components which occurs in the standard FFT-based method. The maximum error in the phase structure function can be reduced to <0.13% , and the additional execution time increases by only several percents. This method is only suitable for square screens.

Stopping Criterion in Iterative Solution Methods for Reynolds Equations

Iterative solution methods are usually used for solving a variety of Reynolds equations in lubrication analysis due to their simplicity and effectiveness. The objective of this study is to present a robust stopping criterion for iterative methods, by which the iterative process of the methods can be terminated for high execution efficiency without sacrificing the solution accuracy. In this study, the compressible and incompressible fluid Reynolds equations are solved by popular relaxation methods. A very efficient preconditioned conjugate gradient method is also applied in a case for verification. The proposed stopping criterion for iterative methods is based on a coarse-grid truncation error analysis. Three different gridwork groups are required for estimating the truncation errors, which involves only a small amount of additional execution time. In the numerical models examined, the amount of truncation error in a model is insensitive to the gridwork used. It is also found that in a calculation the best prediction of truncation error for terminating the iteration is obtained by using the average fluid film pressure. It is shown that for all the cases tested the proposed stopping criterion can meet the objective stated. The stopping criterion can also be applied when the efficiency of iterative methods is to be compared in solving Reynolds equations.

Implementation of an implicit shear Alfvén operator in the M3D code

A new pair of linear operators is introduced into the partially implicit but largely explicit time advance algorithm for the MHD equations in the M3D code. These operators jointly render the algorithm implicit with respect to propagation of the shear Alfvén wave to leading order. As a result, the maximum stable time step is increased by up to a factor of three for many practical applications. The additional computational cost is trivial for the linear mode of operation and adds only approximately 30% additional execution time per step for nonlinear calculations.

Functional and embedded dependency inference: a data mining point of view

The issue of discovering functional dependencies from populated databases has received a great deal of attention because it is a key concern in database analysis. Such a capability is strongly required in database administration and design while being of great interest in other application fields such as query folding. Investigated for long years, the issue has been recently addressed in a novel and more efficient way by applying principles of data mining algorithms. The two algorithms fitting in such a trend are T ANE and Dep-Miner. They strongly improve previous proposals. In this paper, we propose a new approach adopting a data mining point of view. We define a novel characterization of minimal functional dependencies. This formal framework is sound and simpler than related work. We introduce the new concept of free set for capturing source of functional dependencies. By using the concepts of closure and quasi-closure of attribute sets, targets of such dependencies are characterized. Our approach is enforced through the algorithm F UN which is particularly efficient since it is comparable or improves the two best operational solutions (according to our knowledge): T ANE and Dep-Miner. It makes use of various optimization techniques and it can work on very large databases. Applying on real life or synthetic data more or less correlated, comparative experiments are performed in order to assess performance of F UN against T ANE and Dep-Miner. Moreover, our approach also exhibits (without significant additional execution time) embedded functional dependencies, i.e. dependencies captured in any subset of the attribute set originally considered. Embedded dependencies capture a knowledge specially relevant in all fields where materialized data sets are managed (e.g. materialized views widely used in data warehouses).

A floating point multiplier performing IEEE rounding and addition in parallel

In the conventional floating point multipliers, the rounding stage is usually constructed by using a high speed adder for the increment operation, increasing the overall execution time and occupying a large amount of chip area. Furthermore, it may accompany additional execution time and hardware components for renormalization which may occur by an overflow from the rounding operation. A floating-point multiplier performing addition and IEEE rounding in parallel is designed by optimizing the operational flow based on the characteristics of floating point multiplication operation. A hardware model for the floating point multiplier is proposed and its operational model is algebraically analyzed in this research. The floating point multiplier proposed does not require any additional execution time nor any high speed adder for rounding operation. In addition, the renormalization step is not required because the rounding step is performed prior to the normalization operation. Thus, performance improvement and cost-effective design can be achieved by this approach.

Performing geometric transformations by program transformation

Problems in geometry often possess symmetry properties that may be exploited in order to develop solutions. Algorithms based on these symmetry properties will frequently use geometric transformations to transform one case into another (symmetric) case. One advantage of this approach is that the algorithm avoids enumeration of cases and thus is shorter and generally easier to read. One disadvantage is that some additional execution time is required to perform these transformations. We describe how simple program equivalences may be used as program transformations to eliminate this additional execution time from programs that use geometric transformations. This approach has been used to develop an efficient implementation of a new algorithm for the two-dimensional line-clipping problem.

A Statistical Method for Software Quality Control

This paper proposes a statistical method that can be used to monitor, control, and predict the quality (measured in terms of the failure intensity) of a software system being tested. The method consists of three steps: estimation of the failure intensity (failures per unit of execution time) based on groups of failures, fitting the logarithmic Poisson model to the estimated failure intensity data, and constructing confidence limits for the failure intensity process. The proposed estimation method is validated through a simulation study. A method for predicting the additional execution time required to achieve a failure intensity objective is also discussed. A set of failure data collected from a real-time command and control system is used to demonstrate the proposed method.