Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

/pdf/community-detection-in-graphs-34rogm3r6p.pdf

Community detection in graphs

Statistics for Spatial Data.

The risk of urban inundation has been increasing worldwide. The integrated green-gray-blue (IGGB) drainage system is considered to have great potential in urban areas, as it has not only the resilience and sustainability of green and blue infrastructure, but also the reliability of gray infrastructure on stormwater drainage. This study aims to identify whether there is synergistic effect in IGGB drainage system and how to optimize it. An automatic optimization tool is needed for scenario generation and optimal decision-making because of the diversity, complicacy and decentrality of IGGB drainage system. So far, existing optimization frameworks focus on green infrastructure which cannot be applied to explore synergistic effect of IGGB drainage system. This paper proposed an integrated framework to combine hydrological model and optimization algorithm. Based on this framework, seven optimization strategies including green, gray, blue, green-gray, green–blue, gray-blue and green-gray-blue, were established with the optimization objectives of flood risk reduction rate (FRRR), life cycle cost (LCC) and land occupied (LO). A case study was conducted in Dongying, Shandong province, China. Based on the Pareto fronts of the seven optimization strategies, the average FRRRs of green, gray, blue, green-gray, green–blue, gray-blue and green-gray-blue strategy under the 24-hour rainfall with 30-year return period are 56.0%, 7.7%, 14.5%, 66.1%, 78.3%, 18.4% and 85.5% respectively. The green-gray-blue strategy shows the best performance. Then the existence of synergistic effect in IGGB drainage system is confirmed. The synergistic effect of green-gray, green–blue and green-gray-blue optimization strategies was 5.7%, 9.7% and 10.6%, respectively. However, synergistic effect was not generated in the gray-blue optimization strategy in this study. The synergistic effect is influenced by the hydrological mechanism of synergistic effect and the law of diminishing marginal utility. The results provide more evidence for the outstanding performance of the green-gray-blue integrated urban drainage system and provide technical support and greater clarity to decision-makers on how to integrate the green, gray and blue infrastructure. 

A multi-objective optimization model for synergistic effect analysis of integrated green-gray-blue drainage system in urban inundation control

Method, system and computer-readable medium to select monitors that increase the likelihood of detecting prefix hijacking events of a destination prefix are disclosed. The method includes assigning each of the candidate prefix hijack monitors to a respective cluster of a plurality of clusters. Each of the candidate prefix hijack monitors is associated with an autonomous system (AS) that indicates an AS path of autonomous systems (ASes) from the AS to a destination prefix associated with a destination AS. The method further includes iteratively merging a pair of clusters with a highest similarity score amongst cluster pairs of the plurality of clusters into a single cluster until a processed number of clusters is less than or equal to a predetermined number of clusters. The method also includes ranking each candidate prefix hijack monitor of each of the processed number of clusters according to a route type from an AS associated with the candidate prefix hijack monitor and an AS distance from the AS associated with the candidate prefix hijack monitor to the destination AS. Yet further, the method includes determining a highest ranked candidate prefix hijack monitor of each of the processed number of clusters.

System and method to select monitors that detect prefix hijacking events

Peer-to-Peer (P2P) applications [4], [6], [2], [3] have increased traffic significantly. We propose application specific peering that allows searches for a resource (using existing or modified P2P protocols) to be directed to a copy of resource on an ISP’s network, using a novel technique that automatica lly classifies traffic (shown in Fig 1) as belonging to an ISP, its c ustomers, peers, or their customers. We examined a large amoun t of traffic belonging to three popular P2P protocols and used a graph transformation to detect connected components. We pa rtition traffic in each component into signaling and data sect ions based on request/response sizes of P2P protocols and extrac t high traffic volume (by bytes) entities by examining large co nnected components in the graph. We discuss the important com ponents of our traffic classification system here. We analyzed router-level data from multiple Internet Gatew y Routers (IGRs) across AT&T IP backbone using Cisco’s NetFlow services. Note that statistics are from a subset of IGRs at some AT&T’s peering links. They are not representative of ov erall traffic pattern on AT&T IP backbone. We examined DirectConnect[2], Gnutella[3], and FastTrack[4], extracting re cords that matched the default application ports (source or desti nation, 411/412, 6346/6347, and 1214 (FastTrack) respective ly), involving TCP traffic. Data was gathered over three separate months in 2001, each lasting 5-7 days. The number of netflow records varies from a low of 0.5M (DirectConnect) to a high

Traffic classification for application specific peering

Today, most large companies maintain virtual private networks (VPNs) to connect their remote locations into a single secure network. VPNs can be quite large covering more than 1000 locations and in most cases use standard Internet protocols and services. Such VPNs are implemented using a diverse set of technologies such as Frame Relay, MPLS, or IPSEC to achieve the goal of privacy and performance isolation from the public Internet.Using VPNs to distribute live content has recently received tremendous interest. For example, a VPN could be used to broadcast a CEO-employee town hall meeting. To distribute this type of content economically without overloading the network, the deployment of streaming caches or splitters is most likely required.In this paper, we address the problem of optimally placing such streaming splitters or caches to broadcast to a given set of VPN endpoints under the constraints typically found within a VPN. In particular, we introduce an efficient algorithm with complexity O(V), V being the number of routers in the VPN. This guarantees the optimal cache placement if interception is used for redirection. We prove that the general problem is NP-hard and introduce multiple heuristics for efficient and robust cache placement suitable under different constraints. At the expense of increased implementation complexity, each heuristic solution provides additional saving in the number of caches required. We evaluate proposed solutions using extensive simulations. In particular, we show our flow-based solution is very close to the optimal.

/pdf/efficient-and-robust-streaming-provisioning-in-vpns-3cnf3ob4si.pdf

Efficient and robust streaming provisioning in VPNs

Systems and methods to model user activity information associated with a network system are provided. A particular method includes receiving, at a computing device, a request for user activity information associated with selected channels of a television access network that provides multimedia content to users. The method includes executing a model of user activity associated with the television access network at the computing device. The model estimates the user activity information as user multimedia access demands during particular time periods within a day. The method also includes storing the user activity information at a computer-readable non-transitory storage medium.

Jia Wang

Papers

A multi-objective optimization model for synergistic effect analysis of integrated green-gray-blue drainage system in urban inundation control

System and method to select monitors that detect prefix hijacking events

Traffic classification for application specific peering

Efficient and robust streaming provisioning in VPNs

Modeling User Activity Information Associated with a Network System