WILLINGNESS AND ABILITY TO PERFORM INFORMATION SECURITY COMPLIANCE BEHAVIOR: PSYCHOLOGICAL OWNERSHIP AND SELF-EFFICACY PERSPECTIVE

Information security policy effectiveness relies on how well an individual employee can follow the specified instruction described in security policies. The actual taking place of such compliance behavior is determined by individuals’ willingness and capability of performing such behavior. In this study, we used psychological ownership to represent the driver of willingness and self-efficacy to represent individuals’ capability belief. In addition to understanding the impacts of these two variables on compliance behavior, we also explore their antecedents. Data collected from 234 employees in organizations with specific security policies were used to examine the proposed hypotheses. We confirmed the positive impact of selfefficacy but, surprisingly, found the negative impact of psychological ownership. Such a result generates some interesting implications for researchers and practitioners

Economic order quantity under retailer partial trade credit in two-echelon supply chain

In this paper, we want to investigate the retailer’s inventory policy when the retailer maintains a powerful position in two-echelon supply chain. That is, we assumed that the retailer can obtain the full trade credit offered by the supplier yet the retailer just offers the partial trade credit to their customers under two-level trade credit situation. Then, we investigate the retailer’s inventory system as a cost minimization problem to determine the retailer’s optimal inventory policy in two-echelon supply chain. Finally, numerical examples are given to illustrate the results and to obtain managerial insights

Impacts of Light Rail Transit Tram on the Voltage and Unbalance of the Distribution System

This paper presents the three-phase voltage and unbalance analysis for the distribution system with the loading of a light rail transit (LRT) tram. To investigate the dynamic responses of the system voltage and current, this paper adopts the Alternative Transients Program (ATP) software to model and simulate a multigrounded four-wire distribution system with an LRT loading. Two different definitions about unbalance are used to evaluate the problem. In this paper, the traction supply substation (TSS) with a single-phase transformer configuration is designed first for providing the electric power to the trams of LRT. However, it may result in the significant neutral line current and unbalance phenomenon to deteriorate the power quality of the distribution system. A Le-Blanc connection transformer in the TSS is therefore proposed to solve the problems

Why We Should Report the Details in Subjective Evaluation of TTS More Rigorously

This paper emphasizes the importance of reporting experiment details in subjective evaluations and demonstrates how such details can significantly impact evaluation results in the field of speech synthesis. Through an analysis of 80 papers presented at INTERSPEECH 2022, we find a lack of thorough reporting on critical details such as evaluator recruitment and filtering, instructions and payments, and the geographic and linguistic backgrounds of evaluators. To illustrate the effect of these details on evaluation outcomes, we conducted mean opinion score (MOS) tests on three well-known TTS systems under different evaluation settings and we obtain at least three distinct rankings of TTS models. We urge the community to report experiment details in subjective evaluations to improve the reliability and interpretability of experimental results.Comment: Interspeech 2023 camera-ready versio

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively