Analyzing Midair Object Pointing Mappings for Smart Display Input

One common task when controlling smart displays is the manipulation of menu items. Given current examples of smart displays that support distant bare hand control, in this paper we explore menu item selection tasks with three different mappings of barehand movement to target selection. Through a series of experiments, we demonstrate that Positional mapping is faster than other mappings when the target is visible but requires many clutches in large targeting spaces. Rate-based mapping is, in contrast, preferred by participants due to its perceived lower effort, despite being slightly harder to learn initially. Tradeoffs in the design of target selection in smart tv displays are discussed.

Theoretical Rationale for Design of Tasks in a Virtual Reality-Based Exergame for Rehabilitation Purposes

Virtual reality games are playing a greater role in rehabilitation settings. Previously, commercial games have dominated, but increasingly, bespoke games for specific rehabilitation contexts are emerging. Choice and design of tasks for VR-games are still not always clear, however; some games are designed to motivate and engage players, not necessarily with the facilitation of specific movements as a goal. Other games are designed specifically for the facilitation of specific movements. A theoretical background for the choice of tasks seems warranted. As an example, we use a game that was designed in our lab: VR Walk. Here, the player walks on a treadmill while wearing a head-mounted display showing a custom-made virtual environment. Tasks include walking on a glass bridge across a drop, obstacle avoidance, narrowing path, walking in virtual footsteps, memory, and selection tasks, and throwing and catching objects. Each task is designed according to research and theory from movement science, exercise science, and cognitive science. In this article, we discuss how for example walking across a glass bridge gives perceptual challenges that may be suitable for certain medical conditions, such as hearing loss, when perceptual abilities are strained to compensate for the hearing loss. In another example, walking in virtual footsteps may be seen as a motor and biomechanical constraint, where the double support phase and base of support can be manipulated, making the task beneficial for falls prevention. In a third example, memory and selection tasks may challenge individuals that have cognitive impairments. We posit that these theoretical considerations may be helpful for the choice of tasks and for the design of virtual reality games.

Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach

The networked opinion diffusion in online social networks is often governed by the two genres of opinions— endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.

Decline in Emotional Face Recognition Among Elderly People May Reflect Mild Cognitive Impairment

BackgroundAs with cognitive function, the ability to recognize emotions changes with age. In the literature regarding the relationship between recognition of emotion and cognitive function during aging, the effects of predictors such as aging, emotional state, and cognitive domains on emotion recognition are unclear. This study was performed to clarify the cognitive functions underlying recognition of emotional facial expressions, and to evaluate the effects of depressive mood on recognition of emotion in elderly subjects, as well as to reproduce the effects of aging on the recognition of emotional facial expressions.Materials and MethodsA total of 26 young (mean age = 20.9 years) and 30 elderly subjects (71.6 years) participated in the study. All subjects participated in face perception, face matching, emotion matching, and emotion selection tasks. In addition, elderly subjects were administered a multicomponent cognitive test: the Neurobehavioral Cognitive Status Examination (Cognistat) and the Geriatric Depression Scale-Short Version. We analyzed these factors using multiple linear regression.ResultsThere were no significant differences between the two groups in the face perception task, but in the face matching, emotion matching, and emotion selection tasks, elderly subjects showed significantly poorer performance. Among elderly subjects, multiple regression analyses showed that performance on the emotion matching task was predicted by age, emotional status, and cognitive function, but paradoxical relationships were observed between recognition of emotional faces and some verbal functions. In addition, 47% of elderly participants showed cognitive decline in one or more domains, although all of them had total Cognistat scores above the cutoff.ConclusionIt might be crucial to consider preclinical pathological changes such as mild cognitive impairment when testing for age effects in elderly populations.

Development of a Simplified Portion Size Selection Task

Portion size is an important determinant of energy intake and the development of easy to use and valid tools for measuring portion size are required. Standard measures, such as ad libitum designs and currently available computerized portion selection tasks (PSTs), have several limitations including only being able to capture responses to a limited number of foods, requiring participants’ physical presence and logistical/technical demands. The objective of the current study was to develop and test robust and valid measures of portion size that can be readily prepared by researchers and be reliably utilized for remote online data collection. We developed and tested two simplified PSTs that could be utilized online: (1) portion size images presented simultaneously along a horizontal continuum slider and (2) multiple-choice images presented vertically. One hundred and fifty participants (M = 21.35 years old) completed both simplified PSTs, a standard computerized PST and a series of questionnaires of variables associated with portion size (e.g., hunger, food item characteristics, Three Factor Eating Questionnaire). We found average liking of foods was a significant predictor of all three tasks and cognitive restraint also predicted the two simplified PSTs. We also found significant agreement between the standard PST and estimated portion sizes derived from the simplified PSTs when accounting for average liking. Overall, we show that simplified versions of the standard PST can be used online as an analogue of estimating ideal portion size.

Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT

Retrieval-based dialogue systems select the best response from many candidates. Although many state-of-the-art models have shown promising performance in dialogue response selection tasks, there is still quite a gap between R@1 and R@10 performance. To address this, we propose to leverage linguistic coordination (a phenomenon that individuals tend to develop similar linguistic behaviors in conversation) to rerank the N-best candidates produced by BERT, a state-of-the-art pre-trained language model. Our results show an improvement in R@1 compared to BERT baselines, demonstrating the utility of repairing machine-generated outputs by leveraging a linguistic theory.

Using Information Content to Select Keypoints for UAV Image Matching

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.

Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.