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

The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Bowling alone: The collapse and revival of American community

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

/pdf/context-aware-computing-for-the-internet-of-things-a-survey-3nibvvc921.pdf

Context Aware Computing for The Internet of Things: A Survey

Organizational learning II: Theory, method and practice

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

/pdf/situations-and-attitudes-57f065zg2d.pdf

Situations and Attitudes.

A survey of context modelling and reasoning techniques

This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.

/pdf/learning-situation-models-in-a-smart-home-4ye2ye5cat.pdf

Learning Situation Models in a Smart Home

This paper addresses learning and recognition of human behavior models from multimodal observation in a smart home environment. The proposed approach is part of a framework for acquiring a high-level contextual model for human behavior in an augmented environment. A 3-D video tracking system creates and tracks entities (persons) in the scene. Further, a speech activity detector analyzes audio streams coming from head set microphones and determines for each entity, whether the entity speaks or not. An ambient sound detector detects noises in the environment. An individual role detector derives basic activity like ldquowalkingrdquo or ldquointeracting with tablerdquo from the extracted entity properties of the 3-D tracker. From the derived multimodal observations, different situations like ldquoaperitifrdquo or ldquopresentationrdquo are learned and detected using statistical models (HMMs). The objective of the proposed general framework is two-fold: the automatic offline analysis of human behavior recordings and the online detection of learned human behavior models. To evaluate the proposed approach, several multimodal recordings showing different situations have been conducted. The obtained results, in particular for offline analysis, are very good, showing that multimodality as well as multiperson observation generation are beneficial for situation recognition.

Detecting Human Behavior Models From Multimodal Observation in a Smart Home

The annual incidence of insider attacks continues to grow, and there are indications this trend will continue. While there are a number of existing tools that can accurately identify known attacks, these are reactive (as opposed to proactive) in their enforcement, and may be eluded by previously unseen, adversarial behaviors. This paper proposes an approach that combines Structural Anomaly Detection (SA) from social and information networks and Psychological Profiling (PP) of individuals. SA uses technologies including graph analysis, dynamic tracking, and machine learning to detect structural anomalies in large-scale information network data, while PP constructs dynamic psychological profiles from behavioral patterns. Threats are finally identified through a fusion and ranking of outcomes from SA and PP. The proposed approach is illustrated by applying it to a large data set from a massively multi-player online game, World of War craft (WoW). The data set contains behavior traces from over 350,000 characters observed over a period of 6 months. SA is used to predict if and when characters quit their guild (a player association with similarities to a club or workgroup in non-gaming contexts), possibly causing damage to these social groups. PP serves to estimate the five-factor personality model for all characters. Both threads show good results on the gaming data set and thus validate the proposed approach.

/pdf/proactive-insider-threat-detection-through-graph-learning-23hqqti0su.pdf

Proactive Insider Threat Detection through Graph Learning and Psychological Context

This paper addresses the problem of detecting interaction groups in an intelligent environment. To understand human activity, we need to identify human actors and their interpersonal links. An interaction group can be seen as basic entity, within which individuals collaborate in order to achieve a common goal. In this regard, the dynamic change of interaction group configuration, i.e. the split and merge of interaction groups, can be seen as indicator of new activities. Our approach takes speech activity detection of individuals forming interaction groups as input. A classical HMM-based approach learning different HMM for the different group configurations did not produce promising results. We propose an approach for detecting interaction group configurations based on the assumption that conversational turn taking is synchronized inside groups. The proposed detector is based on one HMM constructed upon conversational hypotheses. The approach shows good results and thus confirms our conversational hypotheses.

/pdf/automatic-detection-of-interaction-groups-34h5gv8yon.pdf

Oliver Brdiczka

Papers

A survey of context modelling and reasoning techniques

Learning Situation Models in a Smart Home

Detecting Human Behavior Models From Multimodal Observation in a Smart Home

Proactive Insider Threat Detection through Graph Learning and Psychological Context

Automatic detection of interaction groups