SDN - Architectural Enabler for Reliable Communication over Millimeter-Wave 5G Networks

Millimeter-wave (mmWave) frequency bands offer a new frontier for next-generation wireless networks, popularly known as 5G, to enable multi-gigabit communication; however, the availability and reliability of mmWave signals are significantly limited due to its unfavorable propagation characteristics. Thus, mmWave networks rely on directional narrow-beam transmissions to overcome severe path-loss. To mitigate the impact of transmission-reception directionality and provide uninterrupted network services, ensuring the availability of mmWave transmission links is important. In this paper, we proposed a new flexible network architecture to provide efficient resource coordination among serving basestations during user mobility. The key idea of this holistic architecture is to exploit the software-defined networking (SDN) technology with mmWave communication to provide a flexible and resilient network architecture. Besides, this paper presents an efficient and seamless uncoordinated network operation to support reliable communication in highly-dynamic environments characterized by high density and mobility of wireless devices. To warrant high-reliability and guard against the potential radio link failure, we introduce a new transmission framework to ensure that there is at least one basestation is connected to the UE at all times. We validate the proposed transmission scheme through simulations.Comment: This article has been accepted for publication at the IEEE GLOBECOM 2018 Workshops, Abu Dhabi, UAE, 9-13 December 201

Stratigraphic succession and depositional framework of the Sandakan Formation, Sabah

The Sandakan Formation of the Segama Group is exposed across the Sandakan Peninsular in eastern Sabah. This Upper Miocene part of the Segama Group unconformably overlies the Garinono Formation and is conformably overlain by the Bongaya Formation. This formation was investigated with detailed logging of outcrops and microfossils analysis in order to map the depositional facies and sedimentary environment. This study showed the presence of seven lithofacies: Thick amalgamated sandstone; thin, lenticular interbedded HCS sandstones and mudstone; laminated mudstone with Rhizophora; trough cross-bedded sandstone; laminated mudstone; strip mudstone with thin sandstone and siltstone; and interbedded HCS sandstone and mudstone. Based on the presence of Rhizophora, Brownlowia, Florchuetia sp., Polypodium, Stenochleana palustris, Ascidian spicule low angle cross bedding, very fine grained sandstone, thin alternations of very fine sandstone, silt and clay layers showing cyclicity (muddy rhythemites), rocks in the Sandakan Formation are interpreted as mangal estuary and open marine facies. Three facies associations could be deduced from the seven lithofacies: Gradual coarsening upwards shoreface; abrupt change facies and prograding estuary facies association

Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the tradeoff between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this trade-off can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself, minimizing the download cost. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on both the MNIST and CIFAR-10 datasets. For the MNIST dataset, the data-driven approach significantly outperforms a non-learning based scheme which combines source coding with multiple file download, while the CIFAR-10 performance is notably better.Comment: Submitted to IEEE for possible publication. This paper was presented in part at the NeurIPS 2020 Workshop on Privacy Preserving Machine Learning - PRIML and PPML Joint Editio

Web-based computer adaptive assessment of individual perceptions of job satisfaction for hospital workplace employees

<p>Abstract</p> <p>Background</p> <p>To develop a web-based computer adaptive testing (CAT) application for efficiently collecting data regarding workers' perceptions of job satisfaction, we examined whether a 37-item Job Content Questionnaire (JCQ-37) could evaluate the job satisfaction of individual employees as a single construct.</p> <p>Methods</p> <p>The JCQ-37 makes data collection via CAT on the internet easy, viable and fast. A Rasch rating scale model was applied to analyze data from 300 randomly selected hospital employees who participated in job-satisfaction surveys in 2008 and 2009 via non-adaptive and computer-adaptive testing, respectively.</p> <p>Results</p> <p>Of the 37 items on the questionnaire, 24 items fit the model fairly well. Person-separation reliability for the 2008 surveys was 0.88. Measures from both years and item-8 job satisfaction for groups were successfully evaluated through item-by-item analyses by using <it>t</it>-test. Workers aged 26 - 35 felt that job satisfaction was significantly worse in 2009 than in 2008.</p> <p>Conclusions</p> <p>A Web-CAT developed in the present paper was shown to be more efficient than traditional computer-based or pen-and-paper assessments at collecting data regarding workers' perceptions of job content.</p