scholarly journals A Heuristic Approach for Distributing Pilgrims over Mina Tents

2019 ◽  
Vol 30 (2) ◽  
pp. 11-23
Author(s):  
Mohd K. Shambour Mohd K. Shambour
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Heuristic Approach ◽  
Experimental Results ◽  
Search Process ◽  
Accommodation Space ◽  
Best Fit ◽  
Holy City ◽  
Available Resources

t. Every year, more than two million Muslims come to the holy city, Makkah, to perform Hajj (the Islamic pilgrimage). One of Hajj rituals is to spend two to three nights in Mina, one of the holy sites. Distributing Muslim pilgrims in Mina, respecting different kinds of constraints, over limited number of tents is a real-world optimization problem. In this paper, a heuristic based algorithm is proposed, called Mina Tent Distribution Algorithm (MTDA), attempting to better utilize the available capacity of Mina’s area in the best possible way with an efficient use of the available resources. MTDA employs seven functions during the search process to find the best fit accommodation for pilgrims on the available tents of Mina. Experimental results revealed that MTDA achieves better performance compared with eight algorithm schemes in all experimental cases. The best result of MTDA was obtained through allocating 80% of the total number of pilgrims over 76.2% of the total available accommodation space of Mina area.

scholarly journals Budgeted Optimization with Constrained Experiments

2016 ◽  
Vol 56 ◽  
pp. 119-152 ◽  
Author(s):  
Javad Azimi ◽  
Xiaoli Fern ◽  
Alan Fern
Keyword(s):  
Real World ◽  
Unknown Function ◽  
Optimization Problem ◽  
Experimental Results ◽  
Trade Off ◽  
Input Space ◽  
Real World Problem ◽  
Real Functions ◽  
The Cost

Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(.) given a budget by requesting a sequence of samples from the function. In our setting, however, evaluating the function at precisely specified points is not practically possible due to prohibitive costs. Instead, we can only request constrained experiments. A constrained experiment, denoted by Q, specifies a subset of the input space for the experimenter to sample the function from. The outcome of Q includes a sampled experiment x, and its function output f(x). Importantly, as the constraints of Q become looser, the cost of fulfilling the request decreases, but the uncertainty about the location x increases. Our goal is to manage this trade-off by selecting a set of constrained experiments that best optimize f(.) within the budget. We study this problem in two different settings, the non-sequential (or batch) setting where a set of constrained experiments is selected at once, and the sequential setting where experiments are selected one at a time. We evaluate our proposed methods for both settings using synthetic and real functions. The experimental results demonstrate the efficacy of the proposed methods.

EFFICIENT COLLABORATIVE FILTERING IN CONTENT-ADDRESSABLE SPACES

2007 ◽  
Vol 21 (02) ◽  
pp. 265-289 ◽  
Author(s):  
SHLOMO BERKOVSKY ◽  
YANIV EYTANI ◽  
LARRY MANEVITZ
Keyword(s):  
Collaborative Filtering ◽  
Computational Effort ◽  
Heuristic Approach ◽  
Point Of View ◽  
Experimental Results ◽  
Search Process ◽  
Computational Point

Collaborative Filtering (CF) is currently one of the most popular and most widely used personalization techniques. It generates personalized predictions based on the assumption that users with similar tastes prefer similar items. One of the major drawbacks of the CF from the computational point of view is its limited scalability since the computational effort required by the CF grows linearly both with the number of available users and items. This work proposes a novel efficient variant of the CF employed over a multidimensional content-addressable space. The proposed approach heuristically decreases the computational effort required by the CF algorithm by limiting the search process only to potentially similar users. Experimental results demonstrate that the proposed heuristic approach is capable of generating predictions with high levels of accuracy, while significantly improving the performance in comparison with the traditional implementations of the CF.

scholarly journals Adaptively Multi-Objective Adversarial Training for Dialogue Generation

2020 ◽  
Author(s):  
Xuemiao Zhang ◽  
Zhouxing Tan ◽  
Xiaoning Zhang ◽  
Yang Cao ◽  
Rui Yan
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Experimental Results ◽  
Sampling Distribution ◽  
Multi Objective Optimization ◽  
Generation Task ◽  
Multi Objective ◽  
Adversarial Models ◽  
Adversarial Training ◽  
Real World Datasets

Naive neural dialogue generation models tend to produce repetitive and dull utterances. The promising adversarial models train the generator against a well-designed discriminator to push it to improve towards the expected direction. However, assessing dialogues requires consideration of many aspects of linguistics, which are difficult to be fully covered by a single discriminator. To address it, we reframe the dialogue generation task as a multi-objective optimization problem and propose a novel adversarial dialogue generation framework with multiple discriminators that excel in different objectives for multiple linguistic aspects, called AMPGAN, whose feasibility is proved by theoretical derivations. Moreover, we design an adaptively adjusted sampling distribution to balance the discriminators and promote the overall improvement of the generator by continuing to focus on these objectives that the generator is not performing well relatively. Experimental results on two real-world datasets show a significant improvement over the baselines.

scholarly journals Selecting Compliant Agents for Opt-in Micro-Tolling

2019 ◽  
Vol 33 ◽  
pp. 565-572
Author(s):  
Josiah P. Hanna ◽  
Guni Sharon ◽  
Stephen D. Boyles ◽  
Peter Stone
Keyword(s):  
Social Welfare ◽  
Real World ◽  
Marginal Cost ◽  
Transportation Network ◽  
Heuristic Approach ◽  
Experimental Results ◽  
Np Hard ◽  
Marginal Impact ◽  
System Benefit ◽  
The Impact

This paper examines the impact of tolls on social welfare in the context of a transportation network in which only a portion of the agents are subject to tolls. More specifically, this paper addresses the question: which subset of agents provides the most system benefit if they are compliant with an approximate marginal cost tolling scheme? Since previous work suggests this problem is NP-hard, we examine a heuristic approach. Our experimental results on three real-world traffic scenarios suggest that evaluating the marginal impact of a given agent serves as a particularly strong heuristic for selecting an agent to be compliant. Results from using this heuristic for selecting 7.6% of the agents to be compliant achieved an increase of up to 10.9% in social welfare over not tolling at all. The presented heuristic approach and conclusions can help practitioners target specific agents to participate in an opt-in tolling scheme.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals A New No Equilibrium Fractional Order Chaotic System, Dynamical Investigation, Synchronization, and Its Digital Implementation

Inventions ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 49
Author(s):  
Zain-Aldeen S. A. Rahman ◽  
Basil H. Jasim ◽  
Yasir I. A. Al-Yasir ◽  
Raed A. Abd-Alhameed ◽  
Bilal Naji Alhasnawi
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Chaotic System ◽  
Real World ◽  
Experimental Results ◽  
Chaotic Attractors ◽  
State Variables ◽  
Digital Implementation ◽  
Dynamical Behaviors ◽  
Real World Applications

In this paper, a new fractional order chaotic system without equilibrium is proposed, analytically and numerically investigated, and numerically and experimentally tested. The analytical and numerical investigations were used to describe the system’s dynamical behaviors including the system equilibria, the chaotic attractors, the bifurcation diagrams, and the Lyapunov exponents. Based on the obtained dynamical behaviors, the system can excite hidden chaotic attractors since it has no equilibrium. Then, a synchronization mechanism based on the adaptive control theory was developed between two identical new systems (master and slave). The adaptive control laws are derived based on synchronization error dynamics of the state variables for the master and slave. Consequently, the update laws of the slave parameters are obtained, where the slave parameters are assumed to be uncertain and are estimated corresponding to the master parameters by the synchronization process. Furthermore, Arduino Due boards were used to implement the proposed system in order to demonstrate its practicality in real-world applications. The simulation experimental results were obtained by MATLAB and the Arduino Due boards, respectively, with a good consistency between the simulation results and the experimental results, indicating that the new fractional order chaotic system is capable of being employed in real-world applications.

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

Local Graph Edge Partitioning

2021 ◽  
Vol 12 (5) ◽  
pp. 1-25
Author(s):  
Shengwei Ji ◽  
Chenyang Bu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Real World ◽  
Graph Partitioning ◽  
Large Scale ◽  
Complete Information ◽  
Local Information ◽  
Experimental Results ◽  
Two Stage ◽  
Graph Computation ◽  
Local Graph ◽  
Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

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