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The AI and Society discourse has previously drawn attention to the ways that digital systems embody the values of the technology development community from which they emerge through the development and deployment process. Research shows how this effect leads to a particular treatment of gender in computer systems development, a treatment which lags far behind the rich understanding of gender that social studies scholarship reveals and people across society experience. Many people do not relate to the narrow binary gender options of male or female, and many people express their gender identity in much richer ways than the sex/gender binary female/woman and male/man Boolean terms will allow. We ask: are "born-digital" gendered datasets in digital systems experienced as marginalising by those who express their identity beyond the male/female binary? Case Study: Ireland. To answer this universal question, this paper presents the findings of an empirical case study of people in Ireland with diverse gender identities and expressions, and their experiences with public data systems and new technologies. In spite of great social changes in Ireland which have led to constitutional change in favour of LGBTQI + people, born-digital systems were experienced by respondents as embodying socio-cultural values which were no longer accepted in society at large. For many of the respondents, digital technologies routinely marginalise them in all kinds of ways. These systems keep alive violence and oppression long after civil rights have been enshrined in constitutional law. This study is just one example of the way assumptions about digital are disengaged from society-at-large. It is a call to arms to all who are passionate about socially-responsible technology. [ABSTRACT FROM AUTHOR]

This paper proposes a framework using a community maturity level metric to determine the integration readiness of interface elements in a particular network cluster. As a proof of concept this methodology is applied to an aircraft seating system to assess the readiness of complex interfaces before proceeding to full‐scale production and systems integration. A multi‐objective genetic algorithm, MOGA‐Net, is coupled with the Newman‐Girvan modularity metric as a clustering algorithm. This algorithm identifies system elements grouped by common interfaces, referred to as community clusters. The TRL and IRL values for these elements is then used to calculate an overall community maturity level. The achieved performance in these clusters is then compared to the target performance to determine overall maturity of the interfaces. This is compared to other system readiness metrics and interface readiness metrics as applied to the aircraft seating system and was found to be more consistent with subject matter expert evaluations during the critical design review. This gives a better representation of the true readiness of system interfaces before entering design reviews to reduce overall integration risks. [ABSTRACT FROM AUTHOR]

Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions). This paper considers IRL when noisy estimates of the gradient of a reward function generated by multiple stochastic gradient agents are observed. We present a generalized Langevin dynamics algorithm to estimate the reward function R(θ); specifically, the resulting Langevin algorithm asymptotically generates samples from the distribution proportional to exp(R(θ)). The proposed adaptive IRL algorithms use kernel-based passive learning schemes. We also construct multi-kernel passive Langevin algorithms for IRL which are suitable for high dimensional data and achieve variance reduction. The performance of the proposed IRL algorithms are illustrated on examples in adaptive Bayesian learning, logistic regression (high dimensional problem) and constrained Markov decision processes. We prove weak convergence of the proposed IRL algorithms using martingale averaging methods. We also analyze the tracking performance of the IRL algorithms in non-stationary environments where the utility function R(θ) has a hyper-parameter that jump changes over time as a slow Markov chain which is not known to the inverse learner. In this case, martingale averaging yields a Markov switched diffusion limit as the asymptotic behavior of the IRL algorithm. [ABSTRACT FROM AUTHOR]

Cows’health is the foundation of large scale dairy farm’development. How to monitor cows accurately and management efficiently is important to the development of scale dairy farm. In the spring of 2018, 17 high-yielding Holstein lactation cows were selected from the same group of Anxing dairy farm in Hulin city for experimental study. The sample cows’average weight (500±50) kg and lactation age (203±83) days. The average daily milk yield of each cow was (30±2) kg. Materials selected for this experiment include lactating cows, computers, cow collars (MooMonitor+, Dairymaster, Ireland), data base stations, and amazon cloud storage terminal. The cows and computer were provided by Anxing dairy farm. The cow collar and data base station were provided by Dairy Master company in Ireland, and the cow collar is equipped with the MEMS (micro electro-mechanical system) accelerometer, according to the principle of accelerometer sensor technology, big data clustering analysis was carried out for the time of acceleration changes in different directions, and the position of cows was tracked through RFID tags to monitor the activity behavior of cows. Refer to the number of samples selected by domestic and foreign scholars related to cow feeding behavior, ruminant behavior, reclining and resting behavior and estrus behavior. From April 1, 2018 to May 31, 2018, Dairymaster’s Moonmonitor + information collection system was used to monitor and collect test data of 17 cows’behavior time every day (24h) in Anxing dairy farm, and physical health conditions of the cows were recorded the worker at the same time, such as mastitis, lameness, diarrhea and trauma. During the test period, test data were downloaded from the cloud data storage server through the monitor system at 08:00 am every day for 61 days. The data collection interval was 24 h, the downloaded data includes the rest time (min/24 h), rumination time (min/24 h) and feeding time (min/24 h) of the cows, and 1 037 data records were obtained. And 937 data were randomly selected as training data set, one hundred data were randomly selected as validation data to verify the model prediction. Then, binary logistic regression analysis method and statistical analysis software SPSS23.0 were used to study the collected data. In order to meet the binary logistic regression analysis conditions, the different behaviors time were converted into classification variables. The independent variables of binomial Logistic regression model were entered by force method. The entry criteria of variables was α<0.05, and the exclusion standard was α>0.1. The results showed that: 1) The behavior time changed differently in different cows, and the average rest time changed greatly. The average rest time during abnormal behavior period increased by 25.7% compared with that during normal behavior period. Compared with the normal behavior period, the ruminant time and feeding time during abnormal behavior period decreased by 12.7% and 2.3%, respectively. 2) The behavior time of healthy Holstein lactation cows was normally distributed, with an average rest time of 300-600 min, ruminant time of 400-700 min and feeding time of 200-400 min per day (24 h). The behavior time of abnormal cows was distributed discreetly without obvious distribution rule. 3) Rest time and feeding time are the main influencing factors of the prediction model, among which feeding time had a greater influence on the prediction probability of the model than rest time. When other conditions remain unchanged, the prediction probability change of abnormal behavior of cows increased by 4.2 times for one additional unit of feeding time. Compared with the results of human visual observation, the prediction accuracy of the model was 91%. Therefore, the paper can provide a reference for scientific and accurate management of modern large-scale dairy farms. [ABSTRACT FROM AUTHOR]

Highlights • Co-simulation and building energy models provide a framework to test control algorithms. • Machine learning algorithms are effective to develop predictive optimisation models. • Predictive controls for residential can reduce heating systems costs up to 40%. • Smart predictive control in buildings results in lower carbon footprint up to 39%. • The smart grid dynamically controls dwellings to balance electricity supply/demand. Abstract This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm). [ABSTRACT FROM AUTHOR]