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

The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing behavior of the attackers. The focus of this study is on android based mobile malwares due to its popularity among users. We have used ensemble methods to combine output of 5 supervised ML algorithms such as RF, PART, JRIP, J.48 and Ridor. Based on the evaluation results, the proposed model was found efficient at detecting known and unknown threats with the accuracy of 98.2%.

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Evaluation of ensemble machine learning methods in mobile threat detection

This paper introduces the architecture of a base station designed for a new Wireless Local Area Network (WLAN). The emphasis is on the software but also backgrounds for the design choices made are discussed. The main design objective has been to develop a simple and low-cost system while maintaining suitability for a large variety of applications, ranging from wireless sensors to multimedia workstations. A base station is implemented by attaching a custom WLAN adapter card into a PC. The network adapter consists of a custom wireless Medium Access Control (MAC) protocol and a standard low-power radio subsystem. The PC hosts other wireless network related functions, such as management of a wireless cell and bridging of data between the wireless network and a wired LAN. Currently, a demonstrator network is being implemented. Components implemented for a base station contain the network adapter card, a Windows NT device driver, and a management application that is a user mode program needed for configuring and managing the network interface hardware and the MAC protocol. In addition, a Windows NT protocol driver has been developed for implementing the data exchange between the device driver and the management application.

Windows NT software design and implementation for a wireless LAN base station

Key challenge for industrial embedded system companies is product data management (PMD) of rapidly changing requirements, new platforms and own legacy intellectual property This can be alleviated with IP component reuse methods, platform based design, and Model Driven Development (MDD) methodologies We propose an open source product integration environment suitable for small and mid-size enterprises (SME) utilizing FPGAs We extend the use of IP-XACT standard from HW integration at IP-level to board and chip level designs, describe SW as IP-XACT metadata objects, and add reusability status to objects for PDM We do not modify or extend IP-XACT metadata format to maintain compatibility with standard compliant tools Instead, we propose naming conventions and usage profiles An exemplar FPGA-product shows feasibility in practice in our Kactus2 tool The benefits are covering complete products including SW objects and HW/SW mappings while strictly keeping the standard XML format

Applying IP-XACT in product data management

Disclosed are additives which are useful as dispersants in lubricating oils, gasolines, marine crankcase oils and hydraulic oils. In particular, disclosed are multiply adducted alkenyl or alkyl succinimides which contain carbamate functionalities.

Modeling Embedded Software Platforms with a UML Profile.

With the recent progress in development of low-budget sensors and machine-to-machine communication, the Internet of Things (IoT) has attracted considerable attention. Unfortunately, many of today’s IoT devices are rushed to market with little consideration for basic security and privacy protection making them easy targets for various attacks. As a result, number of malware and their variants designed for IoT devices has been constantly increasing. Traditional intrusion detection approaches are unsuitable for IoT networks due to limited computational capacity of smart devices and diversity in their technology. In this paper, we propose a defense system for IoT networks based on software-defined networking and network function virtualization. The defense system core component is a reinforcement machine learning agent that evaluates risks of potential attack and takes the most optimal action in order to mitigate it.

Timo Hämäläinen

Papers

Evaluation of ensemble machine learning methods in mobile threat detection

Windows NT software design and implementation for a wireless LAN base station

Applying IP-XACT in product data management

Modeling Embedded Software Platforms with a UML Profile.

On Artificial Intelligent Malware Tolerant Networking for IoT