From people to entities : typed search in the enterprise and the web

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Search behaviour before and after search success

Why do users continue searching after reviewing all relevant documents with which they could have completed a work task? If we knew the answer, then a search system may be able to help users learn about their current search processes, which in turn may enable them to make the whole search process more efficient, leading to greater effectiveness and user satisfaction. This paper is a first step towards solving this problem. Using a previously collected data set, we identified the point of success and hence task completion, and investigated the search behaviour before and after users had accessed all relevant documents for answering assigned tasks. We used a set of search behaviour actions derived from Marchionini's (1995) Information Seeking Process model, and modeled the distribution of these actions throughout the entire search process, comparing actions before and after success could have been attained. Our results suggest that six defined actions, namely user-submitted query, system-suggested query, forward to items, evaluate relevant items, reflect, and answer appeared to change according to the stage of the entire search process. Also, users have notably distinct patterns before and after search success was obtained, but not realised by the user. Not all action were affected; user-submitted query and system-suggested query appeared to be unaffected by time in post-success case and presuccess case, respectively

Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

Human Beyond the Machine: Challenges and Opportunities of Microtask Crowdsourcing

In the 21st century, where automated systems and artificial intelligence are replacing arduous manual labor by supporting data-intensive tasks, many problems still require human intelligence. Over the last decade, by tapping into human intelligence through microtasks, crowdsourcing has found remarkable applications in a wide range of domains. In this article, the authors discuss the growth of crowdsourcing systems since the term was coined by columnist Jeff Howe in 2006. They shed light on the evolution of crowdsourced microtasks in recent times. Next, they discuss a main challenge that hinders the quality of crowdsourced results: the prevalence of malicious behavior. They reflect on crowdsourcing's advantages and disadvantages. Finally, they leave the reader with interesting avenues for future research

Evaluating the Quality of Repurposed Data – The Role of Metadata

Existing approaches for evaluating data quality were established for settings where user requirements regarding data use can be explicitly gathered. However, users are often faced with new, unfamiliar, and repurposed datasets, where they have not been involved in the data collection and data creation processes. Furthermore, there is evidence that there is typically a lack of supporting information, such as metadata, for such datasets. Yet, users need to evaluate the quality of such data and determine if the data can be used for intended purposes. In this paper, we aim to gain an empirical understanding of the role of metadata in evaluating the quality of repurposed data. Using an interview approach, we collected rich qualitative data that reveals current practices, key challenges, preferences, and approaches for improvement regarding evaluating the quality of repurposed data

Health Cards to Assist Decision Making in Consumer Health Search

We investigate the effectiveness of health cards to assist decision making in Consumer Health Search (CHS). A health card is a concise presentation of a health concept shown along side search results to specific queries. We specifically focus on the decision making tasks of determining the health condition presented by a person and determining which action should be taken next with respect to the health condition. We explore two avenues for presenting health cards: a traditional single health card interface, and a novel multiple health cards interface. To validate the utility of health cards and their presentation interfaces, we conduct a laboratory user study where users are asked to solve the two decision making tasks for eight simulated scenarios. Our study makes the following contributions: (1) it proposes the novel multiple health card interface, which allows users to perform differential diagnoses, (2) it quantifies the impact of using health cards for assisting decision making in CHS, and (3) it determines the health card appraisal accuracy in the context of multiple health cards

Investigating User Perception of Gender Bias in Image Search

There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results

A Neural Model to Jointly Predict and Explain Truthfulness of Statements

Automated fact-checking (AFC) systems exist to combat disinformation, however their complexity usually makes them opaque to the end user, making it difficult to foster trust in the system. In this paper, we introduce the E-BART model with the hope of making progress on this front. E-BART is able to provide a veracity prediction for a claim, and jointly generate a human-readable explanation for this decision. We show that E-BART is competitive with the state-of-the-art on the e-FEVER and e-SNLI tasks. In addition, we validate the joint-prediction architecture by showing 1) that generating explanations does not significantly impede the model from performing well in its main task of veracity prediction, and 2) that predicted veracity and explanations are more internally coherent when generated jointly than separately. We also calibrate the E-BART model, allowing the output of the final model be correctly interpreted as the confidence of correctness. Finally, we also conduct and extensive human evaluation on the impact of generated explanations and observe that: explanations increase human ability to spot misinformation and make people more skeptical about claims, and explanations generated by E-BART are competitive with ground truth explanations