Fluctuation Analysis of Decoy State QKD with Finite Data-Set Size

Decoy state method quantum key distribution (QKD) is one of the promising practical solutions for BB84QKD with coherent light pulses.The number of data-set size in practical QKD protocol is always finite,which will causestatistical fluctuations.In this paper,we apply absolutely statistical fluctuation to amend the yield and error rate of thequantum state.The relationship between exchanged number of quantum signals and key generation rate is analyzed inour simulation,which offers a useful reference for experiment.

A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark.In view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault classifiers.The data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation current.In addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor current.The package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification algorithms.The database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are provided.The developed code is available online,and is free to use.

A Heuristic Reputation Based System to Detect Spam Activities in a Social Networking Platform, HRSSSNP

The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [1]. Almost 33% of the crimes on the internet are initiated through a social networking website [1]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data set are used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.

A cross-analysis framework formulti-source volunteered, crowdsourced, and authoritative geographic information: The case study of volunteered personal traces analysis against transport network data

The paper discusses the need of a high-level query language to allow analysts,geographers and,in general,non-programmers to easily cross-analyze multi-source VGI created by means of apps,crowd-sourced data from social networks and authoritative geo-referenced data,usually represented as JSON data sets(nowadays,the de facto standard for data exported by social networks).Since an easy to use high-level language for querying and manipulating collections of possibly geo-tagged JSON objects is still unavailable,we propose a truly declarative language,named J-CO-QL,that is based on a well-defined execution model.A plug-in for a GIS permits to visualize geo-tagged data sets stored in a NoSQL database such as MongoDB;furthermore,the same plug-in can be used to write and execute J-CO-QL queries on those databases.The paper introduces the language by exemplifying its operators within a real study case,the aim of which is to understand the mobility of people in the neighborhood of Bergamo city.Cross-analysis of data about transportation networks and VGI from travelers is performed,by means of J-CO-QL language,capable to manipulate and transform,combine and join possibly geo-tagged JSON objects,in order to produce new possibly geo-tagged JSON objects satisfying users’needs.

Enrichment of topographic road database for the purpose of routing and navigation

With growing demand on multi-purpose or multi-modal navigation,the route calculation needs to traverse semantically enriched road networks for different transportation modes.Currently,operational route planning algorithms reveal rather limited performances or their potential for comprehensive applications are constrained by the unavailable or insufficient interoperation among the under-lying geo-data that are separately maintained in different spatial databases.To overcome this limitation,a novel approach has been proposed to integrate the routing-relevant information from different data sources,which involves three processes:(1)automatic matching to identify the corresponding road objects between different datasets;(2)interaction to refine the automatic matching result;and(3)transferring the routing-relevant information from one data-set to another.In process(1),the Delimited Stroke Oriented algorithm is employed to achieve the automatic data matching between different datasets,which has revealed a high matching rate and certainty.However uncertain matching problems occur in areas where topological conditions are too complicated or inconsistent.The remaining unmatched or wrongly matched objects are treated in process(2),with the help of a series of interaction tools.On the basis of refined matching results after the interaction,process(3)is dedicated to automatic integration of the routing-relevant information from different data sources.