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

This paper presents an implementation of Multicore Communications API (MCAPI), with focus on portability, stability, and simplicity of the design. The main motivation for the implementation is instability of other publicly available implementations. The developed implementation utilizes POSIX message queues, that is an easily portable interface and readily compatible with MCAPI. The performance was measured as latency and transfer rate of the API. The measurement platforms were a x86-64 PC and a development board featuring an ARM processor. A MCAPI implementation was used as reference for comparison. PMQ-MCAPI is much more stable and easily usable than other MCAPI implementations publicly available for PC. When transfer size was between 1–8 KiB, latency of transfers between cores was between 9–15 µs and transfer rate 500–5000 MBps. This translates to 27 000–45 000 cycles and 0.16–1.67 bytes per cycle. CPU and especially performance of its cache were concluded as the most important factors contributing to the performance. In comparison to the reference, latency of the implementation was 1/8 at best, while transfer rate was up to 35x.

Implementation of Multicore communications API

This paper presents an add-on Class of Service (CoS) layer for wireless mesh networks. The proposed protocol is applicable for contention-based MACs and is therefore compatible with most of the Wireless Local Area Network (WLAN) and Wireless Sensor Network (WSN) protocols. The protocol has a locally centralized control for managing data flows, which either reserve a fixed bandwidth or are weighted by fair scheduling. The protocol reduces transmission collisions, thus improving the overall throughput. IEEE 802.11 adhoc WLAN has been taken as a platform for simulations and prototyping for evaluating the protocol performance. Network Simulator Version 2 (NS2) simulations show that the CoS protocol efficiently differentiates bandwidth, supports bandwidth reservations, and reaches less than 10 ms transfer delay on IEEE 802.11b WLAN. Testing with a full prototype implementation verified the performance of the protocol.

/pdf/class-of-service-support-layer-for-wireless-mesh-networks-3t48rehoea.pdf

Class of Service Support Layer for Wireless Mesh Networks

Low power nodes, such as small cells and relays, have been a hot topic in research, standardization, and industry communities, and are typically considered under an umbrella term called heterogeneous networking. In this paper, we look at the problem of finding the optimal ratio between resources allocated for the access and relay links for in-band relays. We present an algorithm that can find the optimal ratio for given input parameters, such as number of relay and mobile stations and their SINR values. We run extensive system level simulations for several deployment scenarios, including a static and mobile case, to show that the proposed algorithm results in better performance when compared to the static resource allocation between the access and relay links.

On adaptive access and relay link resource allocation for in-band deployments

Smart buffer management in ATM switching

In recent times, major cybersecurity breaches and cyber fraud within the public and private sectors are making international headlines. Majority of organisations are facing cybersecurity adversity and advanced threats. On the one hand, we have asynchronous cybersecurity practices, many standards and frameworks to consider while on the other hand, we have to deal and secure our organisations against cyber-criminals, organised hacktivists, insider threats, hackers and nation-states with malafide intentions. The Center for Cyber Safety and Education's Global Information Security Workforce Study (GISWS) confirms that globally we are not only loosing but also backpedalling against threats and risks at cyberspace. How do national-protection actors and organisations conduct and practice their cybersecurity to protect against dramatic attack surfaces? Most importantly, how do they allocate limited cybersecurity resources in defence? Most organisations advice to adopt systematic approaches using standards, framework, audits and best practices. However, the current security technologies, policies and processes are lacking robust cybersecurity capabilities and a mechanism to solve advanced cyber threats and risks. In this paper, we are proposing a novel solution to detect and protect against advanced cybersecurity challenges by leveraging the benefits of big data security intelligence with fast data technologies. The paper is presenting a technology-independent reference model utilising a robust optimisation approach for the cybersecurity analytic systems. This study is utilising state-of-the-art and cutting-edge reference model and solution that enables cyber secure internet and digital technologies usage along with underlying data network and information systems in the multiorganisational environment. The underlying solution enables interoperability and flawless message and information exchanges within national protection actors. The study concludes with the proof-of-concept in the cyber secure decentralised multipurpose communications network.

Timo Hämäläinen

Papers

Implementation of Multicore communications API

Class of Service Support Layer for Wireless Mesh Networks

On adaptive access and relay link resource allocation for in-band deployments

Smart buffer management in ATM switching

Leveraging the Benefits of Big Data with Fast Data for Effective and Efficient Cybersecurity Analytics Systems: A Robust Optimisation Approach