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.

As the Internet steadily increases in importance, it is still based on a quite fragile routing design. From network operators perspective it is therefore crucial to detect end-to- end path performance due to routing outages early to either mitigate them directly or contact other entities to mitigate them. In this work we demonstrate the feasibility of a real-time tool for detecting degraded forwarding performance due to routing problems. Our tool passively monitors the traffic within the network and actively probes paths for which the TCP traffic characteristics indicate a possible routing problem. More importantly, our tool focuses on detecting routing events that actually affect network traffic, which from the network operators' perspective is most relevant. The experimental results based on large-scale measurement in the Internet indicate that our tool effectively detects a significant number of routing outages and forwarding loops.

STRID: Scalable Trigger-Based Route Incidence Diagnosis

Disclosed is a method and apparatus for compensating for a performance degradation of an application session in a plurality of application sessions associated with a network link. The performance of each application session in the plurality of application sessions associated with the network link is determined. The performance of each application session in the plurality is then compared. From this comparison, a lowest performance application session in the plurality of application sessions is identified. Corrective action is performed on packets scheduled to be transmitted over the lowest performance application session.

Method and apparatus for compensating for performance degradation of an application session

An essential step in the customer care routine of cellular service carriers is determining whether an individual user is impacted by on-going service issues. This is traditionally done by monitoring the network and the services. However, user feedback data, generated when users call customer care agents with problems, is a complementary source of data for this purpose. User feedback data is particularly valuable as it provides the user perspective of the service issues. However, this data is extremely noisy, due to range of issues that users have and the diversity of the language used by care agents. In this paper, we present LOTUS, a system that identifies users impacted by a common root cause (such as a network outage) from user feedback. LOTUS is based on novel algorithmic framework that tightly couples co-training and spatial scan statistics. To model the text in the user feedback, LOTUS also incorporates custom-built language models using deep sequence learning. Through experimental analysis on synthetic and live data, we demonstrate the accuracy of LOTUS. LOTUS has been deployed for several months, and has identified the impact over 200 events.

Towards Identifying Impacted Users in Cellular Services

Regarding the Choquet integral as a multi-input single-output system, one can use a set of input-output data to determine the belief measure or plausibility measure for the Choquet integral concerned. The least square method and a genetic algorithm are applied for this purpose.

http://ieeexplore.ieee.org/iel3/4920/13572/00624036.pdf

Genetic algorithms used for determining belief measures and plausibility measures

Recent advances in residential broadband access technologies have led to a wave of commercial IPTV deployments. As IPTV services are rolled out at scale, it is essential for IPTV systems to maintain ultra-high reliability and performance. A major issue that disrupts IPTV service is the crash of the set-top box (STB) software. The STB directly resides inside the consumer's home network and provides the essential interface to both the user and the network to deliver rich content that goes well beyond traditional TV. To understand the potential causes of STB crashes, we perform an indepth statistical analysis focused on the relationships between STB crashes, video stream content, and user activities. Our initial results suggest that (i) impaired video streams may cause STB crashes, and (ii) continuous STB usage may gradually degrade the STB health over time.

/pdf/analyzing-iptv-set-top-box-crashes-582321vmep.pdf

Jia Wang

Papers

STRID: Scalable Trigger-Based Route Incidence Diagnosis

Method and apparatus for compensating for performance degradation of an application session

Towards Identifying Impacted Users in Cellular Services

Genetic algorithms used for determining belief measures and plausibility measures

Analyzing IPTV set-top box crashes