Abstract-This paper addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. A challenge in social sensing applications lies in the noisy nature of data. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown trustworthiness together with their sensory measurements, this paper poses the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. The paper focuses on binary measurements. While some previous work approached the answer in a heuristic manner, we offer the first optimal solution to the above truth discovery problem. Optimality, in the sense of maximum likelihood estimation, is attained by solving an expectation maximization problem that returns the best guess regarding the correctness of each measurement. The approach is shown to outperform the state of the art fact-finding heuristics, as well as simple baselines such as majority voting.

Galanin-immunoreactive profiles were localized within the monkey and human central nervous system. In the monkey telencephalon, galanin-immunoreactive perikarya were seen within the anterior olfactory nucleus, basal forebrain, endopiriform nucleus, hippocampus, and bed nucleus of the stria terminalis. The caudate nucleus and putamen contained galanin-immunoreactive perikarya whereas the nucleus accumbens displayed only galanin-immunoreactive fibers. In the diencephalon, galanin-immunoreactive profiles were seen within the medial preoptic area, periventricular, suprachiasmatic, paraventricular, and arcuate nuclei as well as the lateral hypothalamic area. Within the thalamus, only galanin-immunoreactive fibers were seen within the midline paraventricular, reuniens, and rhomboid nuclei. In the mesencephalon, scattered galanin-immunoreactive fibers were seen in the periaquaductal gray, ventral tegmental area, and midbrain reticular formation. In the metencephalon, galanin-immunoreactive neurons were observed in the medial vestibular nucleus and nucleus prepositus. In the myelencephalon, galanin-immunoreactive perikarya were seen within the nucleus of the tractus solitarius and hypoglossal nucleus. Dense collections of galanin-immunoreactive fibers were found in the spinal descending tract of V, nucleus of the tractus solitarius, and dorsal motor nucleus of X. Galanin immunoreactivity was also observed within all circumventricular organs. Spinal anterior horn neurons expressed galanin immunoreactivity, and immunopositive fibers were seen within the tract of Lissauer and the substantia gelatinosa. Although the distribution of galanin immunoreactivity was generally similar between monkeys and humans, there were a few striking exceptions. The human supraoptic nucleus contained galanin-immunoreactive neurons, whereas the monkey supraoptic nucleus displayed only immunopositive fibers. Similarly, galanin-immunoreactive perikarya and fibers were seen in the human locus coeruleus and subcoeruleus, whereas in monkeys these regions contained only fibers. These data demonstrate a widespread distribution of galanin-containing profiles in primates, suggesting that galanin may modulate cognitive, sensory, motor, and autonomic processes.

Abstract-The explosive growth in social network content suggests that the largest "sensor network" yet might be human.Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a "sensor network" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.

Despite availability of multiple orthogonal communication channels on common sensor network platforms, such as MicaZ motes, and despite multiple simulation-supported designs of multi-channel MAC protocols, most existing sensor networks use only one channel for communication, which is a source of bandwidth inefficiency. In this work, we design, implement, and experimentally evaluate a practical MAC protocol which utilizes multiple channels efficiently for WSNs. A control theory approach is used to dynamically allocate channels for each mote in a distributed manner transparently to the application and routing layers. The protocol assumes that sensor nodes are equipped with one half-duplex radio interface which is most common in current hardware platforms. The protocol does not require time synchronization among nodes and takes the channel switching cost of current hardware into account. Evaluation results on a real testbed show that it achieves a non-trivial bandwidth improvement using 802.15.4 radios in topologies which are typical in WSNs. The MAC protocol was implemented in TinyOS-2.x and packaged as a software component to enable seamless use with existing applications.

In this paper, we present a large-scale measurement study of the smart TV advertising and tracking ecosystem. First, we illuminate the network behavior of smart TVs as used in the wild by analyzing network traffic collected from residential gateways. We find that smart TVs connect to well-known and platform-specific advertising and tracking services (ATSes). Second, we design and implement software tools that systematically explore and collect traffic from the top-1000 apps on two popular smart TV platforms, Roku and Amazon Fire TV. We discover that a subset of apps communicate with a large number of ATSes, and that some ATS organizations only appear on certain platforms, showing a possible segmentation of the smart TV ATS ecosystem across platforms. Third, we evaluate the (in)effectiveness of DNS-based blocklists in preventing smart TVs from accessing ATSes. We highlight that even smart TV-specific blocklists suffer from missed ads and incur functionality breakage. Finally, we examine our Roku and Fire TV datasets for exposure of personally identifiable information (PII) and find that hundreds of apps exfiltrate PII to third parties and platform domains. We also find evidence that some apps send the advertising ID alongside static PII values, effectively eliminating the user’s ability to opt out of ad personalization.