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

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

The PASCAL Visual Object Classes Challenge

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

In this paper a robust regulation problem for infinite-dimensional systems with infinite-dimensional exosystems is discussed. The input and output spaces are also allowed to be infinite-dimensional. A new definition of internal model in terms of the controller parameters is given. It is shown that there exists a feedback controller containing an internal model of the exosystem, which robustly regulates the class of signals generated by the exosystem and strongly or weakly stabilizes the closed-loop system. As far as the authors know, the results are new even for finite-dimensional systems with infinite-dimensional exosystems.

Robust regulation of distributed parameter systems with infinite-dimensional exosystems

As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additionally, the expected number of connected devices as well as the fast-paced development caused by the Internet of Things, raises huge risks in cyber security that must be dealt with accordingly. When considering all above-mentioned reasons, there is no doubt that there is plenty of room for more advanced methods in network anomaly detection hence Deep Learning based techniques have been proposed recently in detecting anomalies.

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State of the Art Literature Review on Network Anomaly Detection with Deep Learning

This paper presents a new performance modeling approach for the design of embedded real-time systems using UML 2.0. The approach responds to the lack of specific semantics for the performance modeling. The existing UML metamodel is extended by defining stereotypes to include the message latency and execution time in UML statecharts. The information may contain both the real-time constraints and measured values that are back-annotated to the UML model. Further, fully automated model transformation is used to visualize this information with sequence diagrams. The modeling approach has been prototyped with the UML implementation of a WLAN medium access control protocol. The experiences proved the approach to be practical and intuitive.

Performance Modeling and Reporting for the UML 2.0 Design of Embedded Systems

Advances in wireless communication technologies are driving the evolution toward ever faster networks in terms of throughput and latency. At the same time, machine-to machine (M2M) communications is expected to have considerable potential with millions of devices connected within the following years. M2M communications is one of the key capabilities for implementation of the Internet of Things (IoT). Due to this potential, several standard organizations are now focusing on developing enhancements into their standards to support M2M communications. As there is no consensus for the architecture of a general scenario for M2M communications, the heterogeneous mobile ad hoc network (HetMANET) can generally be considered suitable for the M2M challenges. Hence, this calls for the IP mobility management, allowing a device to connect to services and access technologies that are best suited for that need. To examine how-to improve the HetMANET concept using existing standards such as IEEE 802.11 and 3GPP Evolved Packed Core (EPC) this paper introduces a straightforward and energy efficient algorithm for vertical handovers. A reference implementation of the handover concept in Android based vehicular-to-infrastructure is introduced. This reference implementation examines energy and performance efficiency in heterogeneous network environment. In addition, real-life use cases with real life services on top of WLAN and cellular network environments are presented to back up the selected approach.

Efficient Handovers for Machine-to-Machine Communications between IEEE 802.11 and 3GPP Evolved Packed Core Networks

Frequency optimization is required to maximize the WLAN throughput in environments where several networks coexist. This paper evaluates four throughput estimation models and two optimization algorithms. Throughput is selected as the optimization criteria for channel assignment. Thus, the result of a throughput estimation model is used as an input for an optimization algorithm. The target is a frequency plan that maximizes multi-cell WLAN throughput. The throughput estimation models are based on radio spectrum usage, practical throughput measurements, WLAN protocol behavior, and theoretical coverage estimations. The models use separate functions for defining the minimum channel distance. In the evaluation, Genetic Algorithm (GA) and a distributed optimization algorithm produce the final frequency plan. A dedicated simulator has been implemented for the evaluation. As overall results, the evaluated throughput estimation models did not produce significant WLAN throughput improvements compared to each other. Still, the selection of the throughput estimation model and optimization algorithm pair is significant for results, since certain combinations cause poor results.

Timo Hämäläinen

Papers

Robust regulation of distributed parameter systems with infinite-dimensional exosystems

State of the Art Literature Review on Network Anomaly Detection with Deep Learning

Performance Modeling and Reporting for the UML 2.0 Design of Embedded Systems

Efficient Handovers for Machine-to-Machine Communications between IEEE 802.11 and 3GPP Evolved Packed Core Networks

Evaluation of throughput estimation models and algorithms for WLAN frequency planning