Using graphical representation of user interfaces as visual references

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-133).My thesis investigates using a graphical representation of user interfaces - screenshots - as a direct visual reference to support various kinds of applications. We have built several systems to demonstrate and validate this idea in domains like searching documentation, GUI automation and testing, and cross-device information migration. In particular, Sikuli Search enables users to search documentation using screenshots of GUI elements instead of keywords. Sikuli Script enables users to programmatically control GUIs without support from the underlying applications. Sikuli Test lets GUI developers and testers create test scripts without coding. Deep Shot introduces a framework and interaction techniques to migrate work states across heterogeneous devices in one action, taking a picture. We also discuss challenges inherent in screenshot-based interactions and propose potential solutions and directions of future research.by Tsung-Hsiang Chang.Ph.D

A Hierarchical Attention-based Contrastive Learning Method for Micro Video Popularity Prediction

Micro videos popularity prediction (MVPP) has recently attracted widespread research interests given the increasing prevalence of video-based social platforms. However, previous studies have overlooked the unique patterns between popular and unpopular videos and the interactions between asynchronous features different data dimensions. To address this, we propose a novel hierarchical attention contrastive learning method named HACL, which extracts explainable representation features, learns their asynchronous interactions from both temporal and spatial levels, and separates the positive and negative embeddings identities. This reveals video popularity in a contrastive and interrelated view, and thus can be responsible for a better MVPP. Dual neural networks account for separate positive and negative patterns via contrastive learning. To obtain the temporal-wise interaction coefficients, we propose a Hadamard-product based attention approach to optimize the trainable attention-map matrices. Results from our experiments on a TikTok micro video dataset show that HACL outperforms benchmarks and provides insightful managerial implications

The effect of transformational leadership on innovative work behavior with moderating role of internal locus of control and psychological empowerment

The current study examined the effect of the style of transformational leadership on innovative work behavior as well as the internal locus of control’s moderating role and psychological empowerment between the relationships of them. We collected data from 422 respondents who are related to family business in Malaysia. Path coefficient analysis was employed to test the hypotheses and SPSS software was used for analyzing descriptive data. The results showed that transformational leadership style, psychological empowerment and internal locus of control have affirmative relationship and significant impact on innovative work behavior. Finally, psychological empowerment and internal locus of control were not found to have moderating effect between innovative work behavior and the style of transformational leadership

Developing New Oligo Probes to Distinguish Specific Chromosomal Segments and the A, B, D Genomes of Wheat (Triticum aestivum L.) Using ND-FISH

Non-denaturing FISH (ND-FISH) technology has been widely used to study the chromosomes of Triticeae species because of its convenience. The oligo probes for ND-FISH analysis of wheat (Triticum aestivum L.) chromosomes are still limited. In this study, the whole genome shotgun assembly sequences (IWGSC WGA v0.4) and the first version of the reference sequences (IWGSC RefSeq v1.0) of Chinese Spring (T. aestivum L.) were used to find new tandem repeats. One hundred and twenty oligo probes were designed according to the new tandem repeats and used for ND-FISH analysis of chromosomes of wheat Chinese Spring. Twenty nine of the 120 oligo probes produce clear or strong signals on wheat chromosomes. Two of the 29 oligo probes can be used to conveniently distinguish wheat A-, B-, and D-genome chromosomes. Sixteen of the 29 oligo probes only produce clear or strong signals on the subtelomeric regions of 1AS, 5AS, 7AL, 4BS, 5BS, and 3DS arms, on the telomeric regions of 1AL, 5AL, 2BS, 3BL, 6DS, and 7DL arms, on the intercalary regions of 4AL and 2DL arms, and on the pericentromeric regions of 3DL and 6DS arms. Eleven of the 29 oligo probes generate distinct signal bands on several chromosomes and they are different from those previously reported. In addition, the short and long arms of 6D chromosome have been confirmed. The new oligo probes developed in this study are useful and convenient for distinguishing wheat chromosomes or specific segments of wheat chromosomes

Re-ID done right: towards good practices for person re-identification

Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism

The Influence of Self-Control and Social Status on Self-Deception

The purpose of this study was to explore the effects of self-control and social status on self-deception. The present study adopted a forward-looking paradigm to investigate how self-control and social status influence self-deception. In Experiment 1, participants were asked to complete 10 questions, after they predicted and completed 40 questions (commonsense judgment materials) either with or without answer hints. The results indicated that the participants had higher predicted scores under conditions with answer hints compared with conditions without answer hints and that the predicted scores were much higher than the actual scores under conditions with answer hints. In Experiment 2, individuals with different self-control traits were chosen to perform the operation and induction of the perception of social status and then complete tests such as Experiment 1. The results showed that differences in the predicted scores between conditions with answer hints and those without answer hints were observed to be greater in individuals with low self-control traits than in individuals with higher self-control traits, however, such differences between individuals with higher and low self-control traits were only observed in conditions with low social status perception, not in the conditions with high social status perception. The findings indicated that compared with individuals with high self-control, low self-control individuals tended to produce more self-deception. In addition, high social status in the individuals’ perception could restrain the influence of low self-control on self-deception, while low social status in the individuals’ perception could increase the self-control’s influence on self-deception

Power System Transition with Multiple Flexibility Resources: A Data-Driven Approach

Power systems are transitioning toward having high shares of variable renewable energy (VRE) with the help of flexibility resources. However, multiple flexibility resources on the generation, storage and demand sides introduce multiple technical and economic uncertainties, making the transition hard to predict. Moreover, the benefit of these resources in the transition is unclear. To fill these gaps, this paper proposes a data-driven approach to explore the transition to a high VRE share-oriented power system with multiple flexibility resources. This approach generates a wealth of possible transition paths under multiple uncertainties and then uses them to quantitatively analyze the transition. Specifically, the proposed method includes principal component analysis-based path visualization, multiple index-based transition milestone identification, cluster and distance calculation-based key influential factor identification, marginal index-based flexibility resource benefit comparison and Pareto frontier-based path recommendation. Case studies based on the Northwest China power system, which involves wind, photovoltaics and concentrated solar plants, validate the effectiveness of the proposed approach and further indicate that flexibility resources increase rapidly with the growth of the VRE share. Of the multiple flexibility resources, storage contributes the most. Key influential factors include the capital cost of VRE and storage along with coal price. These factors should be the focus in a low-cost and low-carbon transition

Power System Transition with Multiple Flexibility Resources: A Data-Driven Approach

Power systems are transitioning toward having high shares of variable renewable energy (VRE) with the help of flexibility resources. However, multiple flexibility resources on the generation, storage and demand sides introduce multiple technical and economic uncertainties, making the transition hard to predict. Moreover, the benefit of these resources in the transition is unclear. To fill these gaps, this paper proposes a data-driven approach to explore the transition to a high VRE share-oriented power system with multiple flexibility resources. This approach generates a wealth of possible transition paths under multiple uncertainties and then uses them to quantitatively analyze the transition. Specifically, the proposed method includes principal component analysis-based path visualization, multiple index-based transition milestone identification, cluster and distance calculation-based key influential factor identification, marginal index-based flexibility resource benefit comparison and Pareto frontier-based path recommendation. Case studies based on the Northwest China power system, which involves wind, photovoltaics and concentrated solar plants, validate the effectiveness of the proposed approach and further indicate that flexibility resources increase rapidly with the growth of the VRE share. Of the multiple flexibility resources, storage contributes the most. Key influential factors include the capital cost of VRE and storage along with coal price. These factors should be the focus in a low-cost and low-carbon transition