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Through a user study in the field of livestock farming, we verify the effectiveness of an XAI framework for video surveillance systems. The systems can be made interpretable by incorporating experts' decision-making processes. AI systems are becoming increasingly common in real-world applications, especially in fields related to human decision-making, and its interpretability is necessary. However, there are still relatively few standard methods for assessing and addressing the interpretability of machine learning-based systems in real-world applications. In this study, we examine the framework of a video surveillance AI system that presents the reasoning behind predictions by incorporating experts' decision-making processes with rich domain knowledge of the notification target. While general black-box AI systems can only present final probability values, the proposed framework can present information relevant to experts' decisions, which is expected to be more helpful for their decision-making. In our case study, we designed a system for detecting signs of calving in cattle based on the proposed framework and evaluated the system through a user study (N=6) with people involved in livestock farming. A comparison with the black-box AI system revealed that many participants referred to the presented reasons for the prediction results, and five out of six participants selected the proposed system as the system they would like to use in the future. It became clear that we need to design a user interface that considers the reasons for the prediction results.

Qualification tests in crowdsourcing are often used to pre-filter workers by measuring their ability in executing microtasks.While creating qualification tests for each task type is considered as a common and reasonable way, this study investigates into its worker-filtering performance when the same qualification test is used across multiple types of tasks.On Amazon Mechanical Turk, we tested the annotation accuracy in six different cases where tasks consisted of two different difficulty levels, arising from the identical real-world domain: four combinatory cases in which the qualification test and the actual task were the same or different from each other, as well as two other cases where workers with Masters Qualification were asked to perform the actual task only.The experimental results demonstrated the two following findings: i) Workers that were assigned to a difficult qualification test scored better annotation accuracy regardless of the difficulty of the actual task; ii) Workers with Masters Qualification scored better annotation accuracy on the low-difficulty task, but were not as accurate as those who passed a qualification test on the high-difficulty task.

Crowd markets have traditionally limited workers by not providing transparency information concerning which tasks pay fairly or which requesters are unreliable. Researchers believe that a key reason why crowd workers earn low wages is due to this lack of transparency. As a result, tools have been developed to provide more transparency within crowd markets to help workers. However, while most workers use these tools, they still earn less than minimum wage. We argue that the missing element is guidance on how to use transparency information. In this paper, we explore how novice workers can improve their earnings by following the transparency criteria of Super Turkers, i.e., crowd workers who earn higher salaries on Amazon Mechanical Turk (MTurk). We believe that Super Turkers have developed effective processes for using transparency information. Therefore, by having novices follow a Super Turker criteria (one that is simple and popular among Super Turkers), we can help novices increase their wages. For this purpose, we: (i) conducted a survey and data analysis to computationally identify a simple yet common criteria that Super Turkers use for handling transparency tools; (ii) deployed a two-week field experiment with novices who followed this Super Turker criteria to find better work on MTurk. Novices in our study viewed over 25,000 tasks by 1,394 requesters. We found that novices who utilized this Super Turkers' criteria earned better wages than other novices. Our results highlight that tool development to support crowd workers should be paired with educational opportunities that teach workers how to effectively use the tools and their related metrics (e.g., transparency values). We finish with design recommendations for empowering crowd workers to earn higher salaries., Comment: 12.pages, 8 figures, The Web Conference 202, ACM WWW 2020

Workers in crowd markets struggle to earn a living. One reason for this is that it is difficult for workers to accurately gauge the hourly wages of microtasks, and they consequently end up performing labor with little pay. In general, workers are provided with little information about tasks, and are left to rely on noisy signals, such as textual description of the task or rating of the requester. This study explores various computational methods for predicting the working times (and thus hourly wages) required for tasks based on data collected from other workers completing crowd work. We provide the following contributions. (i) A data collection method for gathering real-world training data on crowd-work tasks and the times required for workers to complete them; (ii) TurkScanner: a machine learning approach that predicts the necessary working time to complete a task (and can thus implicitly provide the expected hourly wage). We collected 9,155 data records using a web browser extension installed by 84 Amazon Mechanical Turk workers, and explored the challenge of accurately recording working times both automatically and by asking workers. TurkScanner was created using ~150 derived features, and was able to predict the hourly wages of 69.6% of all the tested microtasks within a 75% error. Directions for future research include observing the effects of tools on people's working practices, adapting this approach to a requester tool for better price setting, and predicting other elements of work (e.g., the acceptance likelihood and worker task preferences.), Comment: Proceedings of the 28th International Conference on World Wide Web (WWW '19), San Francisco, CA, USA, May 13-17, 2019

Layered electrides, as typified by Ca$_2$N, are a new class of quasi-two-dimensional materials with low work functions. Using first-principles calculations, we have shown that a graphene layer deposited on Ca$_2$N is doped to $n=5\times 10^{14}$ cm$^{-2}$ with its Fermi level aligned with the logarithmically divergent van Hove singularity in the graphene $\pi^*$ band. For bilayer graphene on Ca$_2$N, the inner graphene layer is doped to the same level, while the doping of the outer graphene layer is much more modest. This finding opens an interesting possibility of using layered electrides for the exploration of van Hove physics. The work function changes nonmonotonically with the number of graphene layers, which we explain in terms of the peculiar electronic structures of the constituent materials and their bonding.

We propose a systematic approach to obtain various forms of insulating titanium oxynitrides Ti$_{n}$N$_{2}$O$_{2n-3}$ and we conduct a detailed study on its $n=2$ case, Ti$_{2}$N$_{2}$O. We study the energetics and the electronic structures of Ti$_{2}$N$_{2}$O and compare these results with those of pristine and nitrogen-doped TiO$_{2}$ within the framework of the density-functional theory (DFT) and the GW approximation. We find that Ti$_{2}$N$_{2}$O is semiconducting with the calculated band-gap of 1.81 eV, which is significantly smaller than those of pristine TiO$_{2}$ rutile (3.14 eV) or anatase (3.55 eV). Furthermore, the reduction of the band-gap of Ti$_{2}$N$_{2}$O is realized not by lowering of the conduction-band minimum but by rising the valence-band maximum. Therefore the proposed Ti$_{2}$N$_{2}$O has suitable band-edge alignment for water-splitting photocatalysis. Finally, total energy calculations indicate that Ti$_{2}$N$_{2}$O is potentially easier to synthesize than nitrogen-doped TiO$_{2}$. Based on these results, we propose Ti$_{2}$N$_{2}$O as a promising visible-light photocatalytic material., Comment: 10 pages, 7 figures

We propose an alternative structure of TiO2 anatase that has a higher energy oxygen p-like valence band maximum than the pristine TiO2 anatase and thus has a much better alignment with the water splitting levels. This alternative structure is unique when considering a large subspace of possible structural distortions of TiO2 anatase. We propose two routes towards this state and argue that one of them might have been realized in the recently discovered so-called black TiO2., Comment: 7 pages, 9 figures

We theoretically show that an interlayer bias voltage in the AB-stacked bilayer graphene nanoribbons with armchair edges induces an electric polarization along the ribbon. Both tight-binding and ab initio calculations consistently indicate that when the bias voltage is weak, the polarization shows opposite signs depending on the ribbon width modulo three. This nontrivial dependence is explained using a two-band effective model. A strong limit of the bias voltage in the tight-binding model shows either one-third or zero polarization, which agrees with topological argument., Comment: 11 pages, 9 figures, accepted by PRL