title: IoT data stream analytics

authors: Bifet, Albert

abstract: The volume of IoT data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. Traditional one shot memory-based learning methods trained offline from a static historic data cannot cope with evolving data streams. This is because firstly, it is not feasible to store all incoming data over time and secondly the generated models become quickly obsolete due to data distribution changes, also known as “concept drift.” The basic assumption of offline learning is that data is generated by a stationary process and the learning models are consistent with future data. However, in multiple applications like IoT, web mining, social networks, network monitoring, sensor networks, telecommunications, financial forecasting, etc., data samples arrive continuously as unlimited streams often at high speed. Moreover, the phenomena generating these data streams may evolve over time. In this case, the environment in which the system or the phenomenon generated the data is considered to be dynamic, evolving, or non-stationary.

text: IoT data stream analytics The volume of IoT data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. Traditional one shot memory-based learning methods trained offline from a static historic data cannot cope with evolving data streams. This is because firstly, it is not feasible to store all incoming data over time and secondly the generated models become quickly obsolete due to data distribution changes, also known as “concept drift.” The basic assumption of offline learning is that data is generated by a stationary process and the learning models are consistent with future data. However, in multiple applications like IoT, web mining, social networks, network monitoring, sensor networks, telecommunications, financial forecasting, etc., data samples arrive continuously as unlimited streams often at high speed. Moreover, the phenomena generating these data streams may evolve over time. In this case, the environment in which the system or the phenomenon generated the data is considered to be dynamic, evolving, or non-stationary.

year: 2020

title: Determinants of risky decision-making: What is peer influence?

authors: Andrews, Tegan Emily Anne-Marie

subjects: Peer influence

abstract: Impulsive and risky decision-making have been linked to dangerous driving, substance use, gambling, overspending, and general delinquency. The aim of the series of studies presented in this thesis was to gain an understanding into contextual factors that influence decision-making, focusing on the effects of peers, age, and gender. Article 1 (Chapter 2) reports on a single study where participants completed a delay-discounting task either alone, or in the presence of a same-aged peer of the same gender. Those in the peer condition exhibited significantly greater rates of discounting compared to those alone when comparing the area under the curve; however, there was no significant difference between the discount rates (k) between those in the peer condition and those in the alone condition. Due to difficulty with recruitment, we had a small sample, therefore, these results should be interpreted with caution. Article 2 (Chapter 3) presents two studies which examine the difference between peer presence, and peer influence (when peers provide an opinion) on risk-taking. In both studies, we used three hypothetical probabilistic-discounting scenarios to examine risk-taking. The first study investigated risk-taking under three conditions: peer absence, peer presence, and negative peer influence (risk-promoting peer). The second study added a fourth condition, positive peer influence (risk-averse peer), to examine whether positive peer influence was more like peer absence, or peer presence. Both studies showed that participants exhibited the greatest amount of risk-taking in the negative peer-influence condition, compared to both the peer-present and peer-absent conditions. Risk-taking was also greater in the peer-present condition compared to in the peer-absent condition. Therefore, both the presence and negative influence of peers increased risk-taking. The results of the second study in Article 2 extended our findings, showing that positive peer influence resulted in the lowest rates of risk-taking behaviour, lower than when decision-making occurred in the absence of peers.
Article 3 (Chapter 4) outlines two studies which examined whether peer influence extends to other social relationships. The first study in Article 3 investigated whether risk-taking differs under the influence of parents, partners, and friends. We found that risk-taking was higher for parents and partners, compared to friends. We hypothesised that people were more influenced by their partners and parents as they had a closer relationship with them, compared to friends. Thus, the second study in Article 3 examined whether risk-taking differed based on the closeness of the relationship. Using a combination of probability and social discounting methods, we asked participants how likely they would be to risk receiving a fine for five social contacts of varied degrees of closeness. We found that risk-taking systematically differed based on the closeness of the relationship. Participants exhibited greater risk-taking for a closer social contact, compared to a more distant social contact. We also examined the effects of age and gender on decision-making across the five studies presented in this thesis. Our findings provided little evidence that risk-taking was related to age, however, there was some evidence that risk-taking of younger participants may be more influenced by peers than risk-taking of older participants. Risk-taking between men and women was similar and susceptibility to the presence and influence of peers was also similar between men and women. Collectively, these studies illustrate that both the presence and influence of peers affect decision-making, however, providing a direct influence has a greater effect. The greatest risk-taking occurs when peers provided a risk-promoting opinion, and the least when peers provided a risk-averse opinion. The influence of peers is not just limited to friends, but peer influence extended to others, such as romantic partners and parents. People were most influenced by those with whom they had a closer relationship. Thus, the social context wherein decision-making occurs is fundamental to the likelihood of risky choices.

text: Determinants of risky decision-making: What is peer influence? Impulsive and risky decision-making have been linked to dangerous driving, substance use, gambling, overspending, and general delinquency. The aim of the series of studies presented in this thesis was to gain an understanding into contextual factors that influence decision-making, focusing on the effects of peers, age, and gender. Article 1 (Chapter 2) reports on a single study where participants completed a delay-discounting task either alone, or in the presence of a same-aged peer of the same gender. Those in the peer condition exhibited significantly greater rates of discounting compared to those alone when comparing the area under the curve; however, there was no significant difference between the discount rates (k) between those in the peer condition and those in the alone condition. Due to difficulty with recruitment, we had a small sample, therefore, these results should be interpreted with caution. Article 2 (Chapter 3) presents two studies which examine the difference between peer presence, and peer influence (when peers provide an opinion) on risk-taking. In both studies, we used three hypothetical probabilistic-discounting scenarios to examine risk-taking. The first study investigated risk-taking under three conditions: peer absence, peer presence, and negative peer influence (risk-promoting peer). The second study added a fourth condition, positive peer influence (risk-averse peer), to examine whether positive peer influence was more like peer absence, or peer presence. Both studies showed that participants exhibited the greatest amount of risk-taking in the negative peer-influence condition, compared to both the peer-present and peer-absent conditions. Risk-taking was also greater in the peer-present condition compared to in the peer-absent condition. Therefore, both the presence and negative influence of peers increased risk-taking. The results of the second study in Article 2 extended our findings, showing that positive peer influence resulted in the lowest rates of risk-taking behaviour, lower than when decision-making occurred in the absence of peers.
Article 3 (Chapter 4) outlines two studies which examined whether peer influence extends to other social relationships. The first study in Article 3 investigated whether risk-taking differs under the influence of parents, partners, and friends. We found that risk-taking was higher for parents and partners, compared to friends. We hypothesised that people were more influenced by their partners and parents as they had a closer relationship with them, compared to friends. Thus, the second study in Article 3 examined whether risk-taking differed based on the closeness of the relationship. Using a combination of probability and social discounting methods, we asked participants how likely they would be to risk receiving a fine for five social contacts of varied degrees of closeness. We found that risk-taking systematically differed based on the closeness of the relationship. Participants exhibited greater risk-taking for a closer social contact, compared to a more distant social contact. We also examined the effects of age and gender on decision-making across the five studies presented in this thesis. Our findings provided little evidence that risk-taking was related to age, however, there was some evidence that risk-taking of younger participants may be more influenced by peers than risk-taking of older participants. Risk-taking between men and women was similar and susceptibility to the presence and influence of peers was also similar between men and women. Collectively, these studies illustrate that both the presence and influence of peers affect decision-making, however, providing a direct influence has a greater effect. The greatest risk-taking occurs when peers provided a risk-promoting opinion, and the least when peers provided a risk-averse opinion. The influence of peers is not just limited to friends, but peer influence extended to others, such as romantic partners and parents. People were most influenced by those with whom they had a closer relationship. Thus, the social context wherein decision-making occurs is fundamental to the likelihood of risky choices.

year: 2022

title: Aroha

authors: Maxwell, Te Kahautu

abstract: Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

text: Aroha Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

year: 2023-02-22