There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Data Mining - Concepts and Techniques.

Changes and advancements in technology have the potential to benefit older adults by promoting independence and increasing the ability to age in place. However, older adults are less likely to adopt new technology unless they see benefits to themselves. This study assessed the perceptions of 30 older adults in the Midwest concerning technology via three separate focus groups (i.e., independent apartment complex, a rural community, exercise program participants), which addressed a need in the literature (i.e., inclusion of oldest-old and rural individuals). The focus group questions included items such as what technology older adults currently used, desired improvements in technology, and the greatest challenges participants were facing or would face in the future. Overall, older adults were enthusiastic about learning new forms of technology that could help them maintain their independence and quality of life. Five themes emerged from all three focus groups: (a) Frustrations, Limitations, and Usability Concerns; (b) Transportation; (c) Help and Assistance; (d) Self-Monitoring; and (e) Gaming. The themes have important implications for future technology developed for older adults; in particular, older adults were willing and eager to adopt new technology when usefulness and usability outweighed feelings of inadequacy.

Perceptions of technology among older adults.

Recent advances in the field of intrusion detection brought new requirements to intrusion prevention and response. Traditionally, the response to an attack is manually triggered by an administrator. However, increased complexity and speed of the attack-spread during recent years show acute necessity for complex dynamic response mechanisms. Although intrusion detection systems are being actively developed, research efforts in intrusion response are still isolated. In this work we present a taxonomy of intrusion response systems, together with a review of current trends in intrusion response research. We also provide a set of essential features as a requirement for an ideal intrusion response system.

/pdf/a-taxonomy-of-intrusion-response-systems-593ob4z799.pdf

A taxonomy of intrusion response systems

The paper focuses on intrusion detection and countermeasures with respect to widely-used operating systems and networks. The design and architecture of an intrusion detection system built from distributed agents is proposed to implement an intelligent system on which data mining can be performed to provide global, temporal views of an entire networked system. A starting point for agent intelligence in the system is the research into the use of machine learning over system call traces from the privileged sendmail program on UNIX. The authors use a rule learning algorithm to classify the system call traces for intrusion detection purposes and show the results.

/pdf/intelligent-agents-for-intrusion-detection-vf31xaphc0.pdf

Intelligent agents for intrusion detection

Early detection of polyps and cancers is one of the most important goals of colonoscopy. Computer-based analysis of video files using texture features, as has been proposed for polyps of the stomach and colon, has two major limitations: this method uses a fixed size analysis window and relies heavily on a training set of images for accuracy. To overcome these limitations, we propose a new technique focusing on shape instead of texture in this paper. The proposed polyp region detection method is based on the elliptical shape that is common for nearly all small colon polyps.

Johnny Wong

Papers

Perceptions of technology among older adults.

A taxonomy of intrusion response systems

Intelligent agents for intrusion detection

Polyp Detection in Colonoscopy Video using Elliptical Shape Feature

Lightweight agents for intrusion detection