Towards Distributed and Adaptive Detection and Localisation of Network Faults

We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of network faults. A detected fault is isolated to a node or link by collaborative fault-localisation. From local measurements obtained through probing between nodes, probe response delay and packet drop are modelled via parameter estimation for each link. Estimated model parameters are used for autonomous configuration of algorithm parameters, related to probe intervals and detection mechanisms. Expected fault-detection performance is formulated as a cost instead of specific parameter values, significantly reducing configuration efforts in a distributed system. The benefit offered by using our algorithm is fault-detection with increased certainty based on local measurements, compared to other methods not taking observed network conditions into account. We investigate the algorithm performance for varying user parameters and failure conditions. The simulation results indicate that more than 95 % of the generated faults can be detected with few false alarms. At least 80 % of the link faults and 65 % of the node faults are correctly localised. The performance can be improved by parameter adjustments and by using alternative paths for communication of algorithm control messages

Exploiting Sentence Embedding for Medical Question Answering

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.Comment: 8 page

In-band network telemetry in industrial wireless sensor networks

With the emergence of the Internet of Things (IoT) and Industry 4.0 concepts, industrial applications are going through a tremendous change that is imposing increasingly diverse and demanding network dynamics and requirements with a wider and more fine-grained scale. Therefore, there is a growing need for more flexible and reconfigurable industrial networking solutions complemented with powerful monitoring and management functionalities. In this sense, this paper presents a novel efficient network monitoring and telemetry solution for Industrial Wireless Sensor Networks mainly focusing on the 6TiSCH Network stack, a complete protocol stack for ultra-reliable ultra-low-power wireless mesh networks. The proposed monitoring solution creates a flexible and powerful in-band network telemetry design with minimized resource consumption and communication overhead while supporting a wide range of monitoring operations and strategies for dealing with various network scenarios and use cases. Besides, the technical capabilities and characteristics of the proposed solution are evaluated via a real-life implementation, practical and theoretical analysis. These experiments demonstrate that in-band telemetry can provide ultra-efficient network monitoring operations without any effect on the network behavior and performance, validating its suitability for Industrial Wireless Sensor Networks

DYSWIS: Collaborative Network Fault Diagnosis - Of End-users, By End-users, For End-users

With increase in application complexity, the need for network faults diagnosis for end-users has increased. However, existing failure diagnosis techniques fail to assist the endusers in accessing the applications and services. We present DYSWIS, an automatic network fault detection and diagnosis system for end-users. The key idea is collaboration of end-users; a node requests multiple nodes to diagnose a network fault in real time to collect diverse information from different parts of the networks and infer the cause of failure. DYSWIS leverages DHT network to search the collaborating nodes with appropriate network properties required to diagnose a failure. The framework allows dynamic updating of rules and probes into a running system. Another key aspect is contribution of expert knowledge (rules and probes) by application developers, vendors and network administrators; thereby enabling crowdsourcing of diagnosis strategy for growing set of applications. We have implemented the framework and the software and tested them using our test bed and PlanetLab to show that several complex commonly occurring failures can be detected and diagnosed successfully using DYSWIS, while single-user probe with traditional tools fails to pinpoint the cause of such failures. We validate that our base modules and rules are sufficient to detect infrastructural failures causing majority of application failures

Principles of Periodontology

Periodontal diseases are among the most common diseases affecting humans. Dental biofilm is a contributor to the etiology of most periodontal diseases. It is also widely accepted that immunological and inflammatory responses to biofilm components are manifested by signs and symptoms of periodontal disease. The outcome of such interaction is modulated by risk factors (modifiers), either inherent (genetic) or acquired (environmental), significantly affecting the initiation and progression of different periodontal disease phenotypes. While definitive genetic determinants responsible for either susceptibility or resistance to periodontal disease have yet to be identified, many factors affecting the pathogenesis have been described, including smoking, diabetes, obesity, medications, and nutrition. Currently, periodontal diseases are classified based upon clinical disease traits using radiographs and clinical examination. Advances in genomics, molecular biology, and personalized medicine may result in new guidelines for unambiguous disease definition and diagnosis in the future. Recent studies have implied relationships between periodontal diseases and systemic conditions. Answering critical questions regarding host‐parasite interactions in periodontal diseases may provide new insight in the pathogenesis of other biomedical disorders. Therapeutic efforts have focused on the microbial nature of the infection, as active treatment centers on biofilm disruption by non‐surgical mechanical debridement with antimicrobial and sometimes anti‐inflammatory adjuncts. The surgical treatment aims at gaining access to periodontal lesions and correcting unfavorable gingival/osseous contours to achieve a periodontal architecture that will provide for more effective oral hygiene and periodontal maintenance. In addition, advances in tissue engineering have provided innovative means to regenerate/repair periodontal defects, based upon principles of guided tissue regeneration and utilization of growth factors/biologic mediators. To maintain periodontal stability, these treatments need to be supplemented with long‐term maintenance (supportive periodontal therapy) programs

A framework for modelling mobile radio access networks for intelligent fault management

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