Nonintrusive orientation detection is an important yet largely unaddressed area. It can be used in many important applications, e.g., interactive games, medical care, and various industrial scenarios. Moreover, our novel techniques can be readily implemented in other critical areas, such as indoor localization, objective tracking, movement detection, etc. Many factors can limit the performance of detection algorithms in real-word applications, an important one of which is the negligence of subcarrier correlations. Therefore, we propose to build our system based on existing WiFi infrastructure and its channel state information (CSI). To explore the correlations of adjacent subcarriers, we apply the visibility graph (VG)-based network analysis method to process the CSI data. Specifically, in this article we make the following contributions: 1) we design a CSI-based orientation detection system; 2) we model the correlations between subcarriers using complex network and propose a VG-based feature extraction technique; and 3) we demonstrate the performance and effectiveness of our system with commercial products in real-world deployments. The experimental results show that our technique can achieve more than 98% accuracy and at least 26% better than the baseline approaches.

Open source software (OSS) projects and communities are becoming increasingly popular and influential recently. Communications and collaborations are essential for the success of projects. Usually, the most active and productive programmers are awarded with promotion to developers. To more effectively manage and progress the projects, it is important and beneficial to rank the programmers and thus, predict the developer candidates. In this work, we propose to combine machine learning techniques with existing complex network node ranking algorithms to improve the prediction results. Specifically, we have made the following contributions: (1), we have designed a novel machine learning-based classifier with significantly improved prediction performance; (2), we have constructed and tested various networks built based on the programmer email communication information; and (3), we have used real-world project data to compare different techniques and validate our methods. Experimental results demonstrate that our technique reduces the error rate by 25% compared with the second best. Moreover, we discover that the [Formula: see text] nearest neighbor (KNN)-based machine learning algorithm and non-directional temporal network with a time window of 1–3 months give the best prediction results.

Animals search for food based on certain optimal principles and over time form foraging patterns effective for survival in changing environments. Due to the many choices available in modern society, we also face a decision on where to get their food. We call this “modern human food foraging,” since the Internet makes foraging much more convenient than before. People search online for food venues, or restaurants, through websites such as Yelp, and write reviews for the food they tasted, which in turn, facilitate others' searches in the future. These activities make the whole community of restaurant patrons wiser over time. Moreover, the archives of all these choices and evaluations are publicly available, and can help researchers better understand human foraging patterns in modern society. In this paper, we use a Yelp data set to study modern human food foraging patterns, with respect to both geography and cuisine. To understand spatial patterns, we cluster reviewed restaurants geographically and construct a taste similarity network, representing the topology of restaurant cuisine space. We find that people steadily expand their foraging domains from the nearest to them to the distant in geography and from the most familiar to the novel in cuisine. Using longitudinal data of restaurant reviews, we build a geographical foraging network and a taste foraging network for each patron based on which, we propose three kinds of entropies to characterize foraging patterns. We show that the modern foraging patterns of restaurant patrons in both geography and cuisine are of high regularity, indicating that their behaviors are rather predictable. The foraging patterns are also associated with individual social status in the community. Namely, people having a higher variety in the restaurant cuisines they have visited, but fewer actual locations they visited, tend to attract more followers.

Real-world networks feature weights of interactions, where link weights often represent some physical attributes. In many situations, to recover the missing data or predict the network evolution, we need to predict link weights in a network. In this paper, we first proposed a series of new centrality indices for links in line graph. Then, utilizing these line graph indices, as well as a number of original graph indices, we designed three supervised learning methods to realize link weight prediction both in the networks of single layer and multiple layers, which perform much better than several recently proposed baseline methods. We found that the resource allocation index (RA) plays a more important role in the weight prediction than other topological properties, and the line graph indices are at least as important as the original graph indices in link weight prediction. In particular, the success application of our methods on Yelp layered network suggests that we can indeed predict the offline co-foraging behaviors of users just based on their online social interactions, which may open a new direction for link weight prediction algorithms, and meanwhile provide insights to design better restaurant recommendation systems.

Passive indoor localization is important. Unlike active localization techniques, it does not require for users to carry measuring devices, e.g., smart phones. Thus, it is widely used in applications such as security, smart housing, object tracking, etc. However, in real-world applications, the passive localization accuracy is limited due to the environment noises, multipath effect, etc. To address those problems, in this paper, we propose to use channel state information (CSI) instead. Specifically, we make the following contributions: 1) we design a CSI-based passive indoor localization system; 2) we develop a Naive Bayes classifier enhanced with confidence level information; and 3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve more than 86% accuracy on average and at least 15% better than the baseline Naive Bayes classifier.