Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human-machine collaboration. In this paper, we analyze the eff...
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Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human-machine collaboration. In this paper, we analyze the effects of algorithm transparency on the use of advice from algorithms with different degrees of complexity. We conduct a set of laboratory experiments in which participants receive identical advice from algorithms with different levels of transparency and complexity. Our results indicate that not the algorithm itself, but the individually perceived appropriateness of algorithmic complexity moderates the effects of transparency on the use of advice. We summarize this effect as a plateau curve: While perceiving an algorithm as too simple severely harms the use of its advice, the perception of an algorithm as being too complex has no significant effect. Our insights suggest that managers do not have to be concerned about revealing algorithms that are perceived to be appropriately complex or too complex to decision-makers, even if the decision-makers do not fully comprehend them. However, providing transparency on algorithms that are perceived to be simpler than appropriate could disappoint people's expectations and thereby reduce the use of their advice.
A neighborhood total dominating set, abbreviated for NTD-set D, is a vertex set of G such that D is a dominating set with an extra property: the subgraph induced by the open neighborhood of D has no isolated vertex. T...
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A neighborhood total dominating set, abbreviated for NTD-set D, is a vertex set of G such that D is a dominating set with an extra property: the subgraph induced by the open neighborhood of D has no isolated vertex. The neighborhood total domination number, denoted by , is the minimum cardinality of a NTD-set in G. In this paper, we prove that NTD problem is NP-complete for bipartite graphs and split graphs. Then we give a linear-time algorithm to determine for a given tree T. Finally, we characterize a constructive property of -trees and provide a constructive characterization for -graphs, where and are domination number and packing number for the given graph, respectively.
The main goal of this study is to present technique realized with the numeric experiments, that can come to the aid in algorithm practical characterization. Input data of both varied size and varied values are conside...
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ISBN:
(纸本)9780735412873
The main goal of this study is to present technique realized with the numeric experiments, that can come to the aid in algorithm practical characterization. Input data of both varied size and varied values are considered. Informational sensitivity and confidence complexity are calculated.
This paper presents a concise, efficient, and adaptive step detection algorithm based on foot-mounted inertial measurement unit sensors. The proposed method maps the temporal values of pedestrian motion and gait diver...
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This paper presents a concise, efficient, and adaptive step detection algorithm based on foot-mounted inertial measurement unit sensors. The proposed method maps the temporal values of pedestrian motion and gait diversity into two variables: the distance between peaks and valleys, and the slope. Compared to traditional sliding window methods, this approach amplifies the differences between normal and abnormal steps, allowing it to adapt to various indoor activities such as fast walking, slow walking, running, jogging, standing still, and turning. By incorporating adaptive factors, it addresses the challenge of detecting steps while going up and down stairs. The proposed algorithm overcomes the limitations of traditional adaptive threshold methods that require different temporal and peak thresholds for various gait conditions. By utilizing the significant differences in distance and slope, it effectively resolves the issue of detecting steps during stationary periods. Unlike neural network-based gait classifiers, this algorithm does not need to account for multiple gait conditions, thereby simplifying the training process. Experimental results demonstrate that the algorithm achieves an average accuracy of over 99% under mixed indoor walking conditions and over 98% accuracy in long-term outdoor walking conditions.
As a typical combinatorial optimization problem, the 3-path vertex cover problem has wide applications in practice. To solve the 3-path vertex cover problem from the perspective of distributed optimization, we treat e...
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As a typical combinatorial optimization problem, the 3-path vertex cover problem has wide applications in practice. To solve the 3-path vertex cover problem from the perspective of distributed optimization, we treat each vertex as an agent (i.e., player) with computation, and decision-making capabilities. First, we establish a 3-player symmetric game model to describe the 3-path vertex cover problem, and design the corresponding cost function for each player. Then, we prove that under the established game model, strict Nash equilibriums (SNEs) act as the basis of the connection between 3-path vertex cover states and minimum 3-path vertex cover states. Next, we propose a novel memory-based synchronous learning (MSL) algorithm, where the initial profile strategy generation of players relies on the designed degree preference rule, and each player has a memory length for recording strategies and independently update their strategies concurrently based on the accessed local information. After that, we prove that our proposed MSL algorithm can guarantee that any strategy profile converges to an SNE, and provide a theoretical analysis of the algorithm's complexity. Finally, we present numerous numerical simulations to demonstrate the performance of our proposed algorithm on various networks. Moreover, we find that increasing the memory length and adopting the degree preference initialization can yield a better SNE.
The most important challenges in the optimization of real-time charging scheduling (CS) problems are (i) the need to model CS problems with a large number of decision variables for precise control, (ii) the increase i...
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The most important challenges in the optimization of real-time charging scheduling (CS) problems are (i) the need to model CS problems with a large number of decision variables for precise control, (ii) the increase in computational complexity with the high penetration of electric vehicles, and (iii) the lack of research on the stability and computation time of optimization algorithms on CS problems. In this paper, we design a real-time model and introduce the CS Benchmark Problems (CSBP) suite of twelve problems of four different types. Furthermore, a driver satisfaction model is introduced for the first time to analyse the impact of the results on user satisfaction. Best known solutions for all problems in CSBP are presented for the first time in this study. According to the statistical analysis results, the three competitive algorithms among 66 competitors in the optimization of CSs are LSHADE-CnEpSin, LSHADE-SPACMA and LRFDB-COA. Stability and computational complexity analyses revealed that LSHADE-SPACMA is the most successful algorithm for problems where consumers outnumber prosumer and LRFDB-COA is the most successful algorithm for problems where consumers equal or exceed prosumer. When the performance of the algorithms is evaluated regardless of the problem type, LSHADE-Spacma is the most stable algorithm with an overall success rate of 100 % on CSs. In addition, the average peak load shaving for the best known solutions of the algorithms with the highest success rate for each problem is calculated to be 94.84 %, and the average satisfaction score for all drivers is calculated to be 0.81.
The water flow optimizer (WFO) is the latest swarm intelligence algorithm inspired by the shape of water flow. Its advantages of simplicity, efficiency, and robust performance have motivated us to further enhance it. ...
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The water flow optimizer (WFO) is the latest swarm intelligence algorithm inspired by the shape of water flow. Its advantages of simplicity, efficiency, and robust performance have motivated us to further enhance it. In this paper, we introduce fractional-order (FO) technology with memory properties into the WFO, called fractional-order water flow optimizer (FOWFO). To verify the superior performance and practicality of FOWFO, we conducted comparisons with nine state-of-the-art algorithms on benchmark functions from the IEEE Congress on Evolutionary Computation 2017 (CEC2017) and four real-world optimization problems with large dimensions. Additionally, tuning adjustments were made for two crucial parameters within the fractional-order framework. Finally, an analysis was performed on the balance between exploration and exploitation within FOWFO and its algorithm complexity.
In the last half of the twentieth century, psychology and neuroscience have experienced a renewed interest in intraindividual variation. To date, there are few quantitative methods to evaluate whether a population (be...
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In the last half of the twentieth century, psychology and neuroscience have experienced a renewed interest in intraindividual variation. To date, there are few quantitative methods to evaluate whether a population (between-person) structure is likely to hold for individual people, often referred to as ergodicity. We introduce a new network information theoretic metric, the ergodicity information index (EII), that quantifies the amount of information lost by representing all individuals with a between-person structure. A Monte Carlo simulation demonstrated that EII can effectively delineate between ergodic and nonergodic systems. A bootstrap test is derived to statistically determine whether the empirical data is likely generated from an ergodic process. When a process is identified as nonergodic, then it's possible that a mixture of groups exist. To evaluate whether groups exist, we develop an information theoretic clustering method to detect groups. Finally, two empirical examples are presented using intensive longitudinal data from personality and neuroscience domains. Both datasets were found to be nonergodic, and meaningful groupings were identified in each dataset. Subsequent analysis showed that some of these groups are ergodic, meaning that the individuals can be represented with a single population structure without significant loss of information. Notably, in the neuroscience data, we could correctly identify two clusters of individuals (young vs. older adults) measured by a pattern separation task that were related to hippocampal connectivity to the default mode network.
This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-conn...
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This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm algorithm (SSA), Genetic algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.
A complex-valued recurrent neural network equalizer (RNNE) is proposed for optical IMDD communication systems. complexity comparisons to the real-valued RNNE is performed.
ISBN:
(纸本)9781665481557
A complex-valued recurrent neural network equalizer (RNNE) is proposed for optical IMDD communication systems. complexity comparisons to the real-valued RNNE is performed.
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