A. Trans-Saharan trade network
Answer:
b. Try not to argue with her or be defensive.
c. When talking with her, use clarifying statements to show her you understand the problem.
d. Don't dismiss what she says the problem might be.
Explanation:
In facing or relating with a difficult client as a customer relation officer, one of the basic things to do is to avoid arguing with her or to be defensive. You simply avoid anything that will lead to argument, politely and calmly make your point and pass it across.
The next thing needed to be done is to ensure you clarify her properly, leave no room for doubt from her concerning your knowledge on the product.
Finally, dont overlook her complaint on what the problem is, show empathy, listen through and offer your best solution.
Answer:
reinforcement; increase
Explanation:
B.F Skinner's operant conditioning explains how the rewards and punishments increase or decrease the likelihood of repetition of a particular behavior. According to this theory, learning occurs through an association between a behavior and its consequence. A reward given for a particular behavior would act as a reinforcement for that behavior in the future. As per the question when the kid Gets a candy for tantrums at the counter it will increase the chances of repetition of that behavior in the future.
Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-occlusion, especially for the monocular setting. In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
About SportCaps :
SportsCap proposes a challenging sports dataset called Sports Motion and Recognition Tasks (SMART) dataset, which contains per-frame action labels, manually annotated pose, and action assessment of various challenging sports video clips from professional referees.
Their approaches :
This is especially prevalent in non-daily action classes like fitness and sports domains. This can be mitigated, for example, by annotating domain-specific datasets , but that requires a considerable amount of manual annotation efforts, financial resources, and 3D annotations can only be obtained in controlled conditions. Therefore, we propose to learn domain-specific pose-sensitive representations from unlabeled videos, which can be fine tuned using only a small labeled dataset. ...
Learn more about Monocular 3 D Human :
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