Texts posted on the web by users from diverse cultures provide a nearly endless source of data that researchers can use to study human thoughts and language patterns. However, unless care is taken to avoid it, models may be developed in one cultural setting and deployed in another, leading to unforeseen consequences. We explore the effects of using models built from a corpus of texts from multiple cultures in order to learn about each represented people group separately. To do this, we employ a topic modeling approach to quantify open-ended writing responses describing personal values and everyday behaviors in two distinct cultures. We show that some topics are more prominent in one culture compared to the other, while other topics are mentioned to similar degrees. Furthermore, our results indicate that culture influences how value-behavior relationships are exhibited. While some relationships exist in both cultural groups, in most cases we see that the observed relations are dependent on the cultural background of the data set under examination.

Social media is increasingly populated with bots. To protect the authenticity of the user, experience machine learning algorithms are used to detect these bots. Ethical dimensions of these methods have not been thoroughly considered yet. Taking histogram analysis of Twitter users' profile images as example, the paper demonstrates the trade-offs of accuracy, transparency, and robustness. Because there is no general optimum in ethical considerations, these dimensions form a "devil's triangle".

Dialog between drivers and speech-based robot vehicle interfaces can be used as an instrument to find out what drivers might be concerned, confused or curious about in driving simulator studies. Eliciting ongoing conversation with drivers about topics that go beyond navigation, control of entertainment systems, or other traditional driving related tasks is important to getting drivers to engage with the activity in an open-ended fashion. In a structured improvisational Wizard of Oz study that took place in a highly immersive driving simulator, we engaged participant drivers (N=6) in an autonomous driving course where the vehicle spoke to drivers using computer-generated natural language speech. First, using microanalyses of drivers’ responses to the car’s utterances, we identify a set of topics that are expected and treated as appropriate by the participants in our study. Second, we identify a set of topics and conversational strategies that are treated as inappropriate. Third, we show that it is just these unexpected, inappropriate utterances that eventually increase users’ trust into the system, make them more at ease, and raise the system’s acceptability as a communication partner.

The increasing mere presence of robots in everyday life does not automatically result in gradual acceptance of these systems by human users. Over the past years, we have conducted several studies with the goal to provide insight into the long-term process of social robots in domestic environments. This paper presents our overall conclusions from the combined findings of our multiple studies on social robot acceptance. We will provide insights from a user’s perspective of what makes robots social, describe a phased framework of the long-term process of robot acceptance, present some key factors for social robot acceptance, offer guidelines to build better sociable robots, and provide some recommendations for conducting research in domestic environments. With sharing our experiences with conducting (long-term) user studies in domestic environments, we aim to serve to push this sub-field of HRI in real-world contexts forward and thereby the community at large.

We present a new algorithm called PIEM to approximately solve for the policy of an infinite-horizon decentralized partially observable Markov decision process (DEC-POMDP). The algorithm uses expectation maximization (EM) only in the step of policy improvement, with policy evaluation achieved by solving the Bellman's equation in terms of finite state controllers (FSCs). This marks a key distinction of PIEM from the previous EM algorithm of (Kumar and Zilberstein, 2010), i.e., PIEM directly operates on a DEC-POMDP without transforming it into a mixture of dynamic Bayes nets. Thus, PIEM precisely maximizes the value function, avoiding complicated forward/backward message passing and the corresponding computational and memory cost. To overcome local optima, we follow (Pajarinen and Peltonen, 2011) to solve the DEC-POMDP for a finite length horizon and use the resulting policy graph to initialize the FSCs. We solve the finite-horizon problem using a modified point-based policy generation (PBPG) algorithm, in which a closed-form solution is provided which was previously found by linear programming in the original PBPG. Experimental results on benchmark problems show that the proposed algorithms compare favorably to state-of-the-art methods.

In a time of pervasive and increasingly transparent computing, humans will interact with information objects and less and less with the computing devices that define them. Artificial Intelligence (AI) will be the proxy for humans’ interaction with information. Because interaction creates opportunities for error, the trend towards AI-augmented human information interaction (HII) will mandate an increased emphasis on cognition-oriented information science research and new ways of thinking about errors and error handling. A review of HII and its relationship to AI is presented, with a focus on errors in this context.

Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on kernel methods. Motivated by the powerful framework of Hilbert space embeddings of distributions, in this paper we apply this methodology for the kernel embedding of an autoregressive process of order $p$. By doing so, we provide a non-linear version of an autoregressive process, that shows increased performance over the linear model in highly complex time series. We use the method proposed for one-step ahead forecasting of different time-series, and compare its performance against other non-linear methods.

Pathological conditions of the retina examined through regular screening [1], [2] can heavily assist prevention of visual blindness. Fundus imaging is the most widely used modality for early screening and detection of such blindness causing diseases like diabetic retinopathy, glucoma, agerelated macular degeneration [3], hypertension and stroke induced changes [4]. Imaging of fundus has largely improved with progress from the film based photography camera to use of electronic imaging sensors; as well as red free imaging, stereo photography, hyperspectral imaging, angiography, etc. [5], thereby reducing inter-and intra-observer reporting variability. Retinal image analysis has also significantly contributed to this technological development [5], [6]. Since fundus imaging is predominantly used for first level of abnormality screening, research focus includes: (i) detection and segmentation of retinal structures (vessels, fovea, optic disc), (ii) segmentation of abnormalities, and (iii) quality quantification of images acquired to assess reporting fitness [5]. Related Work: The process of clinical reporting of retinal abnormalities is systematic and lesions are reported with respect to their location from vessels or optic disc. Computer assisted diagnosis systems are accordingly being developed to improve the clinical workflow [5].

Sparse coding (Sc) has been studied very well as a powerful data representation method. It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a $L_2$ norm distance function is usually used as the loss function for the reconstruction error. In this paper, we investigate using Sc as the representation method within multi-instance learning framework, where a sample is given as a bag of instances, and further represented as a histogram of the quantized instances. We argue that for the data type of histogram, using $L_2$ norm distance is not suitable, and propose to use the earth mover's distance (EMD) instead of $L_2$ norm distance as a measure of the reconstruction error. By minimizing the EMD between the histogram of a sample and the its reconstruction from some basic histograms, a novel sparse coding method is developed, which is refereed as SC-EMD. We evaluate its performances as a histogram representation method in tow multi-instance learning problems --- abnormal image detection in wireless capsule endoscopy videos, and protein binding site retrieval. The encouraging results demonstrate the advantages of the new method over the traditional method using $L_2$ norm distance.

Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph setting can inform dictionary design for graph signals, lead to algorithms for reconstructing missing information from graph signals via sparse representations, and yield new graph analysis tools. While previous work has focused on generalizing notions of spreads of a graph signal in the vertex and graph spectral domains, our approach is to generalize the methods of Lieb in order to develop uncertainty principles that provide limits on the concentration of the analysis coefficients of any graph signal under a dictionary transform whose atoms are jointly localized in the vertex and graph spectral domains. One challenge we highlight is that due to the inhomogeneity of the underlying graph data domain, the local structure in a single small region of the graph can drastically affect the uncertainty bounds for signals concentrated in different regions of the graph, limiting the information provided by global uncertainty principles. Accordingly, we suggest a new way to incorporate a notion of locality, and develop local uncertainty principles that bound the concentration of the analysis coefficients of each atom of a localized graph spectral filter frame in terms of quantities that depend on the local structure of the graph around the center vertex of the given atom. Finally, we demonstrate how our proposed local uncertainty measures can improve the random sampling of graph signals.