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

Mobile systems have limited resources, such as battery life, network bandwidth, storage capacity, and processor performance. These restrictions may be alleviated by computation offloading: sending heavy computation to resourceful servers and receiving the results from these servers. Many issues related to offloading have been investigated in the past decade. This survey paper provides an overview of the background, techniques, systems, and research areas for offloading computation. We also describe directions for future research.

/pdf/a-survey-of-computation-offloading-for-mobile-systems-24sqh42qcl.pdf

A Survey of Computation Offloading for Mobile Systems

In embodiments of the present invention improved capabilities are described for displaying mobile content in association with a website on a mobile communication facility based at least in part on receiving a website request from a mobile carrier gateway, receiving contextual information relating to the requested website, associating the received contextual information with a mobile content, and, finally, displaying the mobile content with the website on a mobile communication facility.

Contextual Mobile Content Placement on a Mobile Communication Facility

Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has significantly increased due to the different form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research field is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach.

/pdf/a-survey-on-security-for-mobile-devices-29lf2mr79f.pdf

A Survey on Security for Mobile Devices

A scaleable graphical user interface system utilizes a device platform independent model to provide dynamic layout of graphical user interface widgets migrated between any of a plurality of heterogeneous device platforms. The device platform independent model includes at least one platform independent graphical user interface widget. Based on the device platform independent model, platform specific graphical user interface widgets that correspond to each of the platform independent graphical user interface widgets may be laid out on a page and compared to the size of a display screen of a heterogeneous device platform. Where the page does not fit within the display screen, the platform independent graphical user interface widgets may be dynamically rearranged within the page to fit the display screen. In addition, where designated as splitable, the platform independent graphical user interface widgets may be split among multiple pages of a presentation to fit within the display screen.

Layout of platform specific graphical user interface widgets migrated between heterogeneous device platforms

A browser session mobility (BSM) system allows a user to save and restore the runtime state of active sessions of multi-platform network applications established with a browser. A platform specific runtime state of an active session with a browser that includes a current browser state and a current server state may be captured from a platform specific version of a multi-platform network application. The platform specific runtime state may be transformed to a platform independent runtime state and stored. The stored platform independent runtime state may be retrieved and transformed to another platform specific version of the multi-platform network application and instantiated as the same active session.

Browser session mobility system for multi-platform applications

One of the biggest challenges in future application development is device heterogeneity. In the future, we expect to see a rich variety of computing devices that can run applications. These devices have different capabilities in processors, memory, networking, screen sizes, input methods, and software libraries. We also expect that future users are likely to own many types of devices. Depending on users' changing situations and environments, they may choose to switch from one type of device to another that brings the best combination of application functionality and device mobility (size, weight, etc.). Based on this scenario, we have designed and implemented a seamless application framework called the Roam system that can both assist developers to build multiplatform applications that can run on heterogeneous devices and allow a user to move/migrate a running application among heterogeneous devices in an effortless manner. The Roam system is based on partitioning of an application into components and it automatically selects the most appropriate adaptation strategy at the component level for a target platform. To evaluate our system, we have created several multi-platform Roam applications including a Chess game, a Connect4 game, and a shopping aid application. We also provide measurements on application performance and describe our experience with application development in the Roam system. Our experience shows that it is relatively easy to port existing applications to the Roam system and runtime application migration latency is within a few seconds and acceptable to most non-real-time applications.

Roam, a seamless application framework

A context-aware service recommender system receives current user context and recommends a list of browser-based services to a user on a mobile devices. A user's mobile device receives context events from smart environments in which the mobile device is operating. Data about the context events is relayed to a service recommendation server. The server develops recommendations based on the context and other factors, and relays information about the recommended services to the mobile device. As each recommended service is selected or ignored by the user of the mobile device, the device sends implicit feedback with this information to the service recommendation server for use in subsequent recommendations.

Method and system for pushing services to mobile devices in smart environments using a context-aware recommender

A data processing method includes, at a client device, retrieving client state information from a remote location associated with the client device and using the client state information for access of network locations The data processing method further includes storing updated client state information at the remote location upon completion of the access of the network locations The remote location forms a personal cookie repository where cookies may stored for subsequent use by any device of a user

Masaji Katagiri

Papers

Layout of platform specific graphical user interface widgets migrated between heterogeneous device platforms

Browser session mobility system for multi-platform applications

Roam, a seamless application framework

Method and system for pushing services to mobile devices in smart environments using a context-aware recommender

Apparatus and method for a personal cookie repository service for cookie management among multiple devices