SEMI-SUPERVISED DEVICE TAG PREDICTION FOR AUTOMATIC NETWORK PROVISIONING

Device tagging is an important element in the world of network administration, offering an efficient way to organize network and computation resources (such as, for example, network devices, virtual machines, instances, etc.) and support efficient device provisioning and network segmentation (e.g., firewall rules, routing rules, etc.). The manual selection of tags and labelling of individual devices may be error prone and quite time consuming, particularly as the scale of a network grows. To address such challenges, techniques are presented herein that leverage aspects of Graph Convolutional Network (GCN) theory to offer a GCN-based approach for the accurate and automatic tagging of network devices employing a semi-supervised deep learning approach and requiring only minimal human expert knowledge (e.g., for training)

MEASURING AND VISUALIZING USER SENTIMENT CHANGES OVER MULTIPLE CHANNELS

Techniques are provided to simultaneously infer approximately where a speaker is looking and the speaker\u27s emotion during a conversation. Due to privacy concerns, only the speaker\u27s approximate facial features may be estimated. The inferred face may be converted into a cartoon face that retains the main facial features. This may enhance user interaction experience even when a speaker does not turn on video in teleconference

DETERMINING LIKE PEERS AND DOMINANT FEATURES USING MACHINE LEARNING

Peer comparison is one of the most desirable features for network customers. One of the most frequently asked questions is, How does my company\u27s network performance compare with that of my peers? To provide effective peer comparison results there are two fundamental questions that must be resolved – the first question concerns finding the most similar peers and the second question addresses understanding why the peers are similar. To address these types of challenges, techniques are presented herein that leverage machine learning (ML) models to resolve the two fundamental questions that were described above. Aspects of the presented techniques encompass an end-to-end system, which for convenience may be referred to herein as DeepSense, which resolves the entire lifecycle mystery of peer comparison. Additionally, aspects of the presented techniques employ a singular value decomposition (SVD) algorithm to define similarity among customers in a way that is able to overcome the limitations that are caused by latent information. Further, aspects of the presented techniques leverage non-negative matrix factorization (NMF) to capture the dominant features which can influence the similarity among peers. Still further, aspects of the presented techniques support a user-friendly customer interface in real working production systems

Key concerns on petroleum proved reserves evaluation in different development stage for international cooperated assets under SEC rules

The petroleum proved reserves estimation of companies listed in American stock market must follow the SEC rules. However, there are no specified guidelines on the reserves evaluation besides the issued general regulations. This paper presents the procedures and key concerns for sub-classification of proved reserves in different development stages which are summarized from many practical cases. The important concerns include the well test data, initial well productivity, recoverable volumes assessment, five-year development workload and investment, reservoir connectivity, evaluation unit classification, historical performance identification, infill wells recognition, operating cost split, abandonment cost, etc. This study can provide a meaningful reference for SEC reserves evaluation and assets transaction in petroleum industry

An Automatic Implementation of Oropharyngeal Swab Sampling for Diagnosing Respiratory Infectious Diseases via Soft Robotic End-Effectors

Abstract The most widely adopted method for diagnosing respiratory infectious diseases is to conduct polymerase chain reaction (PCR) assays on patients’ respiratory specimens, which are collected through either nasal or oropharyngeal swabs. The manual swab sampling process poses a high risk to the examiner and may cause false-negative results owing to improper sampling. In this paper, we propose a pneumatically actuated soft end-effector specifically designed to achieve all of the tasks involved in swab sampling. The soft end-effector utilizes circumferential instability to ensure grasping stability, and exhibits several key properties, including high load-to-weight ratio, error tolerance, and variable swab-tip stiffness, leading to successful automatic robotic oropharyngeal swab sampling, from loosening and tightening the transport medium tube cap, holding the swab, and conducting sampling, to snapping off the swab tail and sterilizing itself. Using an industrial collaborative robotic arm, we integrated the soft end-effector, force sensor, camera, lights, and remote-control stick, and developed a robotic oropharyngeal swab sampling system. Using this swab sampling system, we conducted oropharyngeal swab-sampling tests on 20 volunteers. Our Digital PCR assay results (RNase P RNA gene absolute copy numbers for the samples) revealed that our system successfully collected sufficient numbers of cells from the pharyngeal wall for respiratory disease diagnosis. In summary, we have developed a pharyngeal swab-sampling system based on an “enveloping” soft actuator, studied the sampling process, and implemented whole-process robotic oropharyngeal swab-sampling