May I Interrupt? Diverging Opinions on Proactive Smart Speakers

Although smart speakers support increasingly complex multi-turn dialogues, they still play a mostly reactive role, responding to user’s questions or requests. With rapid technological advances, they are becoming more capable of initiating conversations by themselves. However, before developing such proactive features, it is important to understand how people perceive different types of agent-initiated interactions. We conducted an online survey in which participants () rated 8 scenarios around proactive smart speakers on different aspects. Despite some controversy around proactive systems, we found that participants’ ratings were surprisingly positive. However, they also commented on potential issues around user privacy and agency as well as undesirable interference with ongoing (social) activities. We discuss these findings and their implications for future avenues of research on proactive smart speakers

Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma

The next major evolutionary stage for voice assistants will be their capability to initiate interactions by themselves. However, to design proactive interactions, it is crucial to understand whether and when this behaviour is considered useful and how desirable it is perceived for different social contexts or ongoing activities. To investigate people's perspectives on proactivity and appropriate circumstances for it, we designed a set of storyboards depicting a variety of proactive actions in everyday situations and social settings and presented them to 15 participants in interactive interviews. Our findings suggest that, although many participants see benefits in agent proactivity, such as for urgent or critical issues, there are concerns about interference with social activities in multi-party settings, potential loss of agency, and intrusiveness. We discuss our implications for designing voice assistants with desirable proactive features

From C-3PO to HAL: Opening The Discourse About The Dark Side of Multi-Modal Social Agents

The increasing prevalence of communicative agents raises questions about human-agent communication and the impact of such interaction on people's behavior in society and human-human communication. This workshop aims to address three of those questions: (i) How can we identify malicious design strategies - known as dark patterns - in social agents?; (ii) What is the necessity for and the effects of present and future design features, across different modalities and social contexts, in social agents?; (iii) How can we incorporate the findings of the first two questions into the design of social agents? This workshop seeks to conjoin ongoing discourses of the CUI and wider HCI communities, including recent trends focusing on ethical designs. Out of the collaborative discussion, the workshop will produce a document distilling possible research lines and topics encouraging future collaborations

A Decision Tree-Based Classification Approach to Rule Extraction for Security Analysis

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain

INVESTIGATING THE CAPABILITY OF IRS-P6-LISS IV SATELLITE IMAGE FOR PISTACHIO FORESTS DENSITY MAPPING (CASE STUDY: NORTHEAST OF IRAN)

In order to investigate the capability of satellite images for Pistachio forests density mapping, IRS-P6-LISS IV data were analyzed in an area of 500 ha in Iran. After geometric correction, suitable training areas were determined based on fieldwork. Suitable spectral transformations like NDVI, PVI and PCA were performed. A ground truth map included of 34 plots (each plot 1 ha) were prepared. Hard and soft supervised classifications were performed with 5 density classes (0–5%, 5–10%, 10–15%, 15–20% and > 20%). Because of low separability of classes, some classes were merged and classifications were repeated with 3 classes. Finally, the highest overall accuracy and kappa coefficient of 70% and 0.44, respectively, were obtained with three classes (0–5%, 5–20%, and > 20%) by fuzzy classifier. Considering the low kappa value obtained, it could be concluded that the result of the classification was not desirable. Therefore, this approach is not appropriate for operational mapping of these valuable Pistachio forests

A DECISION TREE-BASED CLASSIFICATION APPROACH TO RULE EXTRACTION FOR SECURITY ANALYSIS

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.Stock selection rules, stock prediction model, decision tree, data mining, C4.5 decision tree algorithm

Map-based linear estimation of drive cycle for hybrid electric vehicles

Applications of hybrid electric vehicles (HEVs) and plug-in electric vehicles (PEVs) in modern power grids are increasing due to the growing concerns about environmental issues and unpredictable fuel prices. However, detailed information on drivers' behaviors which is required for vehicle control and management is not widely available. This paper presents a map-based linear estimation approach to estimate the drive cycles of hybrid electric vehicles (HEVs). It is shown that knowing geological data of the vehicle, a linear estimation of drive cycle is possible. Detailed simulations are presented to investigate the accuracy of the linear estimation compared with the real drive cycles. Simulation results are presented and analyzed for the linear estimations of two typical drive cycles including the highway fuel economy test (HWFET) cycle and the New York City cycle (NYCC)

SHIFTS OF START AND END OF SEASON IN RESPONSE TO AIR TEMPERATURE VARIATION BASED ON GIMMS DATASET IN HYRCANIAN FORESTS

Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes. The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region. The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012. In order to find response of vegetation activity to local temperature variations, we used air temperature provided from I.R. Iran Meteorological Organization (IRIMO). At the first step in order to remove the existing gap from the original time series, the iterative Interpolation for Data Reconstruction (IDR) model was applied to GIMMS and temperature dataset. Then we applied significant Mann Kendall test to determine significant trend for each pixel of GIMMS and temperature datasets over the Hyrcanian forests. The results demonstrated that start and end of season (SOS & EOS respectively) derived from GIMMS3g NDVI time series increased by -0.16 and +0.41 days per year respectively. The trends derived from temperature time series indicated increasing trend in the whole of this region. Results of this study showed that global warming and its effect on growth and photosynthetic activity can increased the vegetation activity in our study area. Otherwise extension of the growing season, including an earlier start of the growing season, later autumn and higher rate of production increased NDVI value during the study period