Smartphones. Service terminations before the end of the Minimum Service Term for an Advanced Device are subject to an early termination fee of up to $350 per device, in addition to any other Service charges that apply to the terminated Advanced Device.
Smartphones. Advanced Devices are subject to an ETF of up to $350 per device, in addition to any other Service charges that apply to the terminated Advanced Device.
Smartphones. Each Resident will be offered a University-issued phone capable of running all applications needed to perform their jobs at no charge in accordance with the UCLA Health Policy on Personal Use of Electronic Communication Devices. Residents shall return the phone upon separation from employment with the University or when otherwise directed. If the phone is lost or damaged, the Resident is responsible for any associated costs for repair and/or replacement; the resident shall not be responsible for costs associated with normal wear and tear. In the event that the Resident opts to use his/her own phone, all University-required security measures must be installed. There is no reimbursement for a personal phone.
Smartphones. TRANSPORTATION
Smartphones. Supply new handsets required at start of contract. Approximate number of devices at contract start is 30, likely to grow to 40 in year one. The option to port existing numbers to new service provider. The option for replacement and upgrade of handsets through the life of the contract. The ability to add and remove users as required. The ability to add and remove handsets as required. International roaming is required for most handsets All smartphone devices have to be BlackBerry Enterprise able. The handset needs to support CESG recommended security policies for interoperability with ICO’s IL3 environment. Smartphone Services are to commence on 1 July 2013.
Smartphones. In smartphone based sensing realm the majority of the work has been focused only on proximity detection. In these approaches authors focused on estimating only which users are in proximity and did not take into consideration the directionality of the participants. The existing trend for detecting social interactions through proximity on mobile phone based sensing platforms has been based on data collection (simple discovery) achieves resolution proportional to the utilised technology mean, via Bluetooth, GSM signals or Wi-Fi access points such as in [7], CenceMe [8], PeopleTones [9], Serendipity [10] and SoundSense [11]. As a result, there would be numerous occasions when being in proximity does not guarantee the existence of a F2F interaction, leading to a considerable amount of false positive error in social interaction detection. In order to improve the simple discovery approach, fine-grain distance estimation was proposed in multiple works to improve the granularity. Among them is Comm2Sense [12], a client-server implementation performing proximity detection by analysing WiFi RSSI. For the detection of nearby devices, each device must operate as a hotspot and then switch to normal WiFi. However, in order to be able to enable and disable the hotspot mode and set the transmission power to 0dBm, rooting of the Android device is required. By applying machine learning techniques on 20-sample RSSI window for distance estimation, they claim of achieving a median error of 0.5m in indoor and outdoor environments. PhoneMonitor [13] is a monitoring application that logs various sensors of a mobile phone for the detection of F2F interactions based on proximity estimation. A number of experiments are performed in order to increase the accuracy of estimating the distance among the devices. A probabilistic model for the Bluetooth RSSI is proposed to classify if the users' interpersonal distance is suitable to perform F2F interaction. Although PhoneMonitor, estimates if the users are in proximity or not while they are required to hold the device in their hands, authors introduce privacy issues by performing off-line analysis. MAUC [14] is a contact detection method for mobile phones that based on user's movements, detects start and ending point of a contact. The authors infer about the existence of social interaction through simple threshold on Bluetooth RSSI measurements, which is erroneous due to large fluctuation in RSSI values. However, MAUC is not able to de...
