Less acoustic features means more statistical relevance: Disclosing the clustering behavior in music stimuli

Managing a tourism supply chain is predominantly focused on managing a tourism-specific product that can be perceived as all kinds of goods and services utilized by tourists during their trips. The predominant goal of this article is to empirically identify the level of engagement of entities operating in the tourism-oriented branch of industry concerning the satisfaction of end-customers with the offered tourism-related services and products. Within the scope of this study, the statistical relevance of elements of active cooperation within a tourism-specific supply chain was analyzed. Empirical examinations covered the assessment of the cooperation within the framework of the tourism-oriented supply chain and its impact on consumer satisfaction. A research questionnaire was utilized to meet examination-specific goals. Theoretical considerations and the analysis of branches of industry in relation to the available statistical data showed that tourist-oriented supply chain covers various entities, the engagement of which may have a factual impact on the efficiency of managing the entire chain, as well as on the overall client satisfaction, improving tourism sustainability. The obtained results clearly showed that the examined entities considered the analyzed cooperation aspects to be very important with regard to the supply chain management. Said aspects included the total length of cooperation within the framework of a particular supply chain, which, according to the examined entities, directly translated into the quality of cooperation—to either significant or very significant extent, as well as making it much easier to solve certain problems that were strictly connected with the provision of tourism-oriented services. Yet another aspect of cooperation that was touched upon was the transfer of the so-called know-how between the entities engaged in a given supply chain. As proven by the examination, 70% of the surveyed entities claimed that it was of significant or very significant importance. The last analyzed aspect of cooperation were relations between the supply chain-specific partners and their impact on the satisfaction of end customers. According to over half of the sample (61.54%), decent relations between supply chain participants affected the satisfaction of end customers to a notable extent.

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Categorizing Touch-Input Locations from Touchscreen Device Interfaces via On-Board Mechano-Acoustic Transducers

Applied Sciences ◽

10.3390/app11114834 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4834

Author(s):

Kai Ren Teo ◽

Balamurali B T ◽

Jianying Zhou ◽

Jer-Ming Chen

Keyword(s):

User Interaction ◽

Data Access ◽

Machine Learning Techniques ◽

Acoustic Features ◽

Touch Input ◽

Acoustic Transducers ◽

Learning Techniques ◽

Access Controls ◽

Input Location ◽

Mitigation Methods

Many mobile electronics devices, including smartphones and tablets, require the user to interact physically with the device via tapping the touchscreen. Conveniently, these compact devices are also equipped with high-precision transducers such as accelerometers and microphones, integrated mechanically and designed on-board to support a range of user functionalities. However, unintended access to these transducer signals (bypassing normal on-board data access controls) may allow sensitive user interaction information to be detected and thereby exploited. In this study, we show that acoustic features extracted from the on-board microphone signals, supported with accelerometer and gyroscope signals, may be used together with machine learning techniques to successfully determine the user’s touch input location on a touchscreen: our ensemble model, namely the random forest model, predicts touch input location with up to 86% accuracy in a realistic scenario. Accordingly, we present the approach and techniques used, the performance of the model developed, and also discuss limitations and possible mitigation methods to thwart possible exploitation of such unintended signal channels.

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