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Built-in pervasive cameras have become an integral part of mobile/wearable devices and enabled a wide range of ubiquitous applications with their ability to be "always-on". In particular, life-logging has been identified as a means to enhance the quality of life of older adults by allowing them to reminisce about their own life experiences. However, the sensitive images captured by the cameras threaten individuals' right to have private social lives and raise concerns about privacy and security in the physical world. This threat gets worse when image recognition technologies can link images to people, scenes, and objects, hence, implicitly and unexpectedly reveal more sensitive information such as social connections. In this paper, we first examine life-log images obtained from 54 older adults to extract (a) the artifacts or visual cues, and (b) the context of the image that influences an older life-logger's ability to recall the life events associated with a life-log image. We call these artifacts and contextual cues "stimuli". Using the set of stimuli extracted, we then propose a set of obfuscation strategies that naturally balances the trade-off between reminiscability and privacy (revealing social ties) while selectively obfuscating parts of the images. More specifically, our platform yields privacy-utility tradeoff by compromising, on average, modest 13.4% reminiscability scores while significantly improving privacy guarantees -- around 40% error in cloud estimation.

Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel semantic relevance mapping ( SRM ) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method.

Contains fulltext : 219674.pdf (Publisher’s version ) (Open Access) Lifelog photo review is considered to enhance the recall of personal events. While a sizable body of research has explored the neural basis of autobiographical memory (AM), there is limited neural evidence on the retrieval-based enhancement effect on event memory among older adults in the real-world environment. This study examined the neural processes of AM as was modulated by retrieval practice through lifelog photo review in older adults. In the experiment, blood-oxygen-level dependent response during subjects' recall of recent events was recorded, where events were cued by photos that may or may not have been exposed to a priori retrieval practice (training). Subjects remembered more episodic details under the trained relative to non-trained condition. Importantly, the neural correlates of AM was exhibited by (1) dissociable cortical areas related to recollection and familiarity, and (2) a positive correlation between the amount of recollected episodic details and cortical activation within several lateral temporal and parietal regions. Further analysis of the brain activation pattern at a few regions of interest within the core remember network showed a training_condition x event_detail interaction effect, suggesting that the boosting effect of retrieval practice depended on the level of recollected event details.

Active vision is a desirable perceptual feature for robots. Existing approaches usually make strong assumptions about the task and environment, thus are less robust and efficient. This study proposes an adaptive view planning approach to boost the efficiency and robustness of active object detection. We formulate the multi-object detection task as an active multiview object detection problem given the initial location of the objects. Next, we propose a novel adaptive action prediction (A2P) method built on a deep Q-learning network with a dueling architecture. The A2P method is able to perform view planning based on visual information of multiple objects; and adjust action ranges according to the task status. Evaluated on the AVD dataset, A2P leads to 21.9% increase in detection accuracy in unfamiliar environments, while improving efficiency by 22.7%. On the T-LESS dataset, multi-object detection boosts efficiency by more than 30% while achieving equivalent detection accuracy.

Object shape is a subtle but important factor for object detection. It has been observed that the object-region-percentage (ORP) can be utilized to improve detection accuracy for elongated objects, which have much lower ORPs than other types of objects. In this paper, we propose an approach to improve the detection performance for objects with high ORPs. Our method consists of three steps. First, we adjust the ground truth bounding boxes of high-ORP objects to an optimal range. Second, we train an object detector, Faster R-CNN, based on adjusted bounding boxes to achieve high recall. Finally, we train a DCNN to learn the adjustment ratios towards four directions and adjust detected bounding boxes of objects to get better localization for higher precision. We evaluate the effectiveness of our method on 12 high-ORP objects in COCO and 8 objects in a proprietary gearbox dataset. The experimental results show that our method can achieve state-of-the-art performance on these objects while costing less resources in training and inference stages.

Although industrial robots are successfully deployed in many assembly processes, high-mix, low-volume applications are still difficult to automate, as they involve small batches of frequently changing parts. Setting up a robotic system for these tasks requires repeated re-programming by expert users, incurring extra time and costs. In this paper, we present a solution which enables a robot to learn new objects and new tasks from non-expert users without the need for programming. The use case presented here is the assembly of a gearbox mechanism. In the proposed solution, first, the robot can autonomously register new objects using a visual exploration routine, and train a deep learning model for object detection accordingly. Secondly, the user can teach new tasks to the system via visual demonstration in a natural manner. Finally, using multimodal perception from RGB-D (color and depth) cameras and a tactile sensor, the robot can execute the taught tasks with adaptation to changing configurations. Depending on the task requirements, it can also activate human-robot collaboration capabilities. In summary, these three main modules enable any non-expert user to configure a robot for new applications in a fast and intuitive way.

This work demonstrates the feasibility and benefits of using pointing gestures, a naturally-generated additional input modality, to improve the multi-modal comprehension accuracy of human instructions to robotic agents for collaborative tasks.We present M2Gestic, a system that combines neural-based text parsing with a novel knowledge-graph traversal mechanism, over a multi-modal input of vision, natural language text and pointing. Via multiple studies related to a benchmark table top manipulation task, we show that (a) M2Gestic can achieve close-to-human performance in reasoning over unambiguous verbal instructions, and (b) incorporating pointing input (even with its inherent location uncertainty) in M2Gestic results in a significant (30%) accuracy improvement when verbal instructions are ambiguous.

Cobots that can work in human workspaces and adapt to human need to understand and respond to human’s inquiry and instruction. In this paper, we propose new question answering (QA) task and dataset for human-robot collaboration on task-oriented operation, i.e., task-oriented collaborative QA (TCQA). Differing from conventional video QA for answering questions about what happened in video clips constrained by scripts and subtitles, TC-QA aims to share common ground for task-oriented operation through question answering. We propose an open-end (OE) format of answer with text reply, image with annotated related objects, and video with operation duration to guide operation execution. Designed for grounding, the TC-QA dataset comprises query videos and questions to seek acknowledgement, correction, attention to task-related objects, and information on objects or operation. Due to the flexibility of real-world task with limited training sample, we propose and evaluate a baseline method based on a hybrid approach. The hybrid approach employs deep learning methods for object detection, hand detection and gesture recognition, and symbolic reasoning to ground question on observation for providing the answer. Our experiments show that the hybrid method is effective for the TC-QA task.

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.