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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

/pdf/directed-diffusion-a-scalable-and-robust-communication-4tsx5vxsas.pdf

Directed diffusion: a scalable and robust communication paradigm for sensor networks

https://www.science.smith.edu/~jcardell/Courses/EGR328/Readings/WSNSurvey2.pdf

Wireless sensor network survey

On Important Aspects of Modeling User Associations in Wireless LAN Traces

Epileptic seizures and dangerous falls affect over 100 million people worldwide, including patients and seniors. There is currently no affordable solution or product for the detection of seizures and falls to assist in improving the lives of millions. Such detection problem poses several major challenges, including continuous sensing with minimal user disruption, accurate real-time emergency classification and alert notification, energy-efficient sensing and detection, and scalability and affordability of the devised solution. This paper presents Seizario; the first mobile app for continuous, energy-efficient, detection of seizures and falls to provide ambient assisted living for people with Epilepsy and fall risk. Seizario uses (only) smartphones to offer an affordable scalable solution with two main features; automatic detection of seizure convulsions and fall emergency scenarios, and immediate communication of critical information to emergency contacts. Seizario introduces novel accelerometer-based learning algorithms with elaborate finite-state-machines to automatically detect grand mal seizures and harmful falls. Seizario also offers a suite of useful features to assist in providing help for the affected population. Seizario's detection algorithms are designed to efficiently and effectively work on mobile phones, and have shown great promise with over 95% detection rate for both seizures and falls, with minimal number of false alarms.

Seizario: Novel Mobile Algorithms for Seizure and Fall Detection

Modeling and Characterization of Urban Streets' Vehicular Mobility using Web Cameras

Realistic modeling of user mobility is one of the most critical research areas in wireless networks. Currently, several mobility models are proposed based on the analysis of real WLAN traces [1,2,7]. However, the large collection of WLAN usage traces, seem to capture little mobility from the users. In this paper, we focus on a subset of the wireless users, who use lightweight ultra mobile devices (i.e. VoIP devices). These users leave their devices on most of the time and the devices are light enough to walk-and-talk. Hence, these users show a more mobile (ultra mobile) characteristic than laptop users while connected to the network. We expect many of the users to become ultra mobile in the future with the introduction of the next generation handheld devices. By analyzing these traces we aim to compare behavior of ultra mobile device users to the general WLAN users. This sheds light on the realism of WLAN trace-based models. We also aim to examine the effect of any differences on protocol performance, e.g., prediction protocols. Particularly, we compare the mobility of VoIP device user traces with whole WLAN traces (as used in previous studies) and also with some samples we have generated based on criteria that distinguish these samples as ultra mobile compared to others, such as prevalence, the number of APs visited and activity range. We compare these different sets of traces using several different predictors such as the Markov O(1), O(2), O(3) and also the LZ predictor. There has been work done with Wi-Fi mobility data using predictors [3] and work done on VoIP users [4] along with related work in the field such as analyzing different characteristics in various WLAN data [5,6,9], most of these works directly based on WLAN traces which can be found under the Mobilib project [10] or the CRAWDAD project [11]. There is even work done on prediction algorithms targeting cellular networks [8] but to the best of our knowledge there has not been any work done using predictors to compare the mobility of users. Our experiments indicate that the number of access points (AP) visited better represent mobility than the actual area range the user has covered and also that the Markov O(2) is the predictor with the highest accuracy among the four predictors and the LZ has the lowest. Surprisingly, all predictors perform quite poorly with VoIP device users compared to general WLAN users, prompting re-visiting of such algorithms for ultra mobile users.

Poster abstract: the challenges of accurate mobility prediction for ultra mobile users

Two major factors affecting mobile network performance are mobility and traffic patterns. Simulations and analytical-based performance evaluations rely on models to approximate factors affecting the network. Hence, the understanding of mobility and traffic is imperative to the effective evaluation and efficient design of future mobile networks. Current models target either mobility or traffic, but do not capture their interplay. Many trace-based mobility models have largely used pre-smartphone datasets (e.g., AP-logs), or much coarser granularity (e.g., cell-towers) traces. This raises questions regarding the relevance of existing models, and motivates our study to revisit this area. In this study, we conduct a multidimensional analysis, to quantitatively characterize mobility and traffic spatio-temporal patterns, for laptops and smartphones, leading to a detailed integrated mobility-traffic analysis. Our study is data-driven, as we collect and mine capacious datasets (with 30TB, 300k devices) that capture all of these dimensions. The investigation is performed using our systematic (FLAMeS) framework. Overall, dozens of mobility and traffic features have been analyzed. The insights and lessons learnt serve as guidelines and a first step towards future integrated mobility-traffic models. In addition, our work acts as a stepping-stone towards a richer, morerealistic suite of mobile test scenarios and benchmarks.

Ahmed Helmy

Papers

On Important Aspects of Modeling User Associations in Wireless LAN Traces

Seizario: Novel Mobile Algorithms for Seizure and Fall Detection

Modeling and Characterization of Urban Streets' Vehicular Mobility using Web Cameras

Poster abstract: the challenges of accurate mobility prediction for ultra mobile users

Flutes vs. Cellos: Analyzing Mobility-Traffic Correlations in Large WLAN Traces