Reviewerly/exHarmony huggingface dataset:
Think Harder! Investigating the Effect of Password Strength on Cognitive Load during Password Creation

Strict password policies can frustrate users, reduce their productivity, and lead them to write their passwords down. This paper investigates the relation between password creation and cognitive load inferred from eye pupil diameter. We use a wearable eye tracker to monitor the user’s pupil size while creating passwords with different strengths. To assess how creating passwords of different strength (namely weak and strong) influences users’ cognitive load, we conducted a lab study (N = 15). We asked the participants to create and enter 6 weak and 6 strong passwords. The results showed that passwords with different strengths affect the pupil diameter, thereby giving an indication of the user’s cognitive state. Our initial investigation shows the potential for new applications in the field of cognition-aware user interfaces. For example, future systems can use our results to determine whether the user created a strong password based on their gaze behavior, without the need to reveal the characteristics of the password.
LFIEM: Lightweight Filter-based Image Enhancement Model

Photo retouching features are being integrated into a growing number of mobile applications. Current learning-based approaches enhance images using large convolutional neural network-based models, where the result is received directly from the neural network outputs. This method can lead to artifacts in the resulting images, models that are complicated to interpret, and can be computationally expensive. In this paper, we explore the application of a filter-based approach in order to overcome the problems outlined above. We focus on creating a lightweight solution suitable for use on mobile devices when designing our model. A significant performance increase was achieved through implementing consistency regularization used in semi-supervised learning. The proposed model can be used on mobile devices and achieves competitive results compared to known models. In this paper, we explore the application of a filter-based approach in order to overcome the problems outlined above. We focus on creating a lightweight solution suitable for use on mobile devices when designing our model. A significant performance increase was achieved through implementing consistency regularization used in semi-supervised learning. The proposed model can be used on mobile devices and achieves competitive results compared to known models.
On The Power of Deep But Naive Partial Label Learning

Partial label learning (PLL) is a class of weakly supervised learning where each training instance consists of a data and a set of candidate labels containing a unique ground truth label. To tackle this problem, a majority of current state-of-the-art methods employs either label disambiguation or averaging strategies. So far, PLL methods without such techniques have been considered impractical. In this paper, we challenge this view by revealing the hidden power of the oldest and naivest PLL method when it is instantiated with deep neural networks. Specifically, we show that, with deep neural networks, the naive model can achieve competitive performances against the other state-of-the-art methods, suggesting it as a strong baseline for PLL. We also address the question of how and why such a naive model works well with deep neural networks. Our empirical results indicate that deep neural networks trained on partially labeled examples generalize very well even in the over-parametrized regime and without label disambiguations or regularizations. We point out that existing learning theories on PLL are vacuous in the over-parametrized regime. Hence they cannot explain why the deep naive method works. We propose an alternative theory on how deep learning generalize in PLL problems.
Research on News Recommendation Algorithm Based on User Interest and Timeliness Modeling

Aiming at personalized news recommendation, a news recommendation model based on user interest and timeliness is proposed. Firstly, tF-IDF text classification is applied to analyze the characteristics of user behavior and news text, and a user interest model based on the interest degree of user behavior characteristics and news text characteristics is constructed. Then, the ebbinghaus forgetting curve is used to construct the time-based function and integrate it into the user's interest model for time weighting. On this basis, the adaptive updating strategy of the user's interest model is designed to solve the problem of user's interest variability. Finally, the weighted Jaccard similarity is used to analyze the similarity between users' interest preferences and news text features and generate personalized recommendations. Experiments show that the news recommendation model based on user interest and timeliness is superior to the traditional news recommendation algorithm in terms of accuracy and recall rate, and has higher recommendation performance.
The Effect of Sound in VR Exergame to Adult Player: A Primary Investigation

A serious game is a new concept of game development that is a digital game created to entertain and achieve an objective or learning at least one other goal. Ascribable, the digital game has affected of player motivation, and users gain emotional level. Like the reward system, sound is typically embedded as a part of game mechanics and affecting player`s motivation. The research aims to explore how the game components affect the motivation in virtual reality (VR) exergames. We evaluated the impact of the immersive rehabilitation system on motivation and affect. We conducted a study with 5 participants to compare the motivation in VR exergame with enabling and disable the sound system because We preliminary employ the formative evaluation to evaluate the in-depth detail of sounds effect to the player experience. Participants reported significant improvements in motivation and engagement in the experiment to enable system conditions than questionnaires and performance players. In conclusion, the sound system is an inclination key feature to increase motivation in exergame. We derive a design experiment for future research: (1) Increasing participants in virtual reality exergame experiments. (2) Addition decibel sound chat to compare with a sound system. (3) Addition a new method player performance with motivation.
Pretty good state transfer in discrete-time quantum walks

Abstract We establish the theory for pretty good state transfer in discrete-time quantum walks. For a class of walks, we show that pretty good state transfer is characterized by the spectrum of certain Hermitian adjacency matrix of the graph; more specifically, the vertices involved in pretty good state transfer must be strongly cospectral relative to this matrix, and the arccosines of its eigenvalues must satisfy some number theoretic conditions. Using normalized adjacency matrices, cyclic covers, and the theory on linear relations between geodetic angles, we construct several infinite families of walks that exhibits this phenomenon.
An automated method with anchor-free detection and U-shaped segmentation for nuclei instance segmentation

Nuclei segmentation plays an important role in cancer diagnosis. Automated methods for digital pathology become popular due to the developments of deep learning and neural networks. However, this task still faces challenges. Most of current techniques cannot be applied directly because of the clustered state and the large number of nuclei in images. Moreover, anchor-based methods for object detection lead a huge amount of calculation, which is even worse on pathological images with a large target density. To address these issues, we propose a novel network with an anchor-free detection and a U-shaped segmentation. An altered feature enhancement module is attached to improve the performance in dense target detection. Meanwhile, the U-Shaped structure in segmentation block ensures the aggregation of features in different dimensions generated from the backbone network. We evaluate our work on a Multi-Organ Nuclei Segmentation dataset from MICCAI 2018 challenge. In comparisons with others, our proposed method achieves state-of-the-art performance.
From The Art of Reflection to The Art of Noticing: A Shifting View of Self-Tracking Technologies’ Role in Supporting Sustainable Food Practices

This paper explores the possibility of using self-tracking technologies to promote sustainable food habits. To explore socio-cultural aspects in food practices and to understand the potential role of self-tracking tools in participants' everyday lives, we designed and deployed a probe kit with seven participants. We found that (1) participants had differing, and at times conflicting, conceptions of what made their diet sustainable; (2) they viewed self-tracking tools as an unobtrusive means to help them achieve a goal–but didn't report that those goals were ever met; and (3) they expressed strong associations between particular eating behaviors and rituals and/or holidays. Reflecting on the results and the concept of design (in)action, we suggest an alternative way of designing self-tracking tools for self-reflection (i.e., the art of noticing) that might help people draw attention to the impact of their food practices on the environment. We conclude by recommending a shift in focus from traditional and "always-on" self-tracking solutions to everyday practices, holidays, or rituals in which a technological tool could bring more attention to a particular facet of food sustainability.
Pixel-based Facial Expression Synthesis

Facial expression synthesis has achieved remarkable advances with the advent of Generative Adversarial Networks (GANs). However, GAN-based approaches mostly generate photo-realistic results as long as the testing data distribution is close to the training data distribution. The quality of GAN results significantly degrades when testing images are from a slightly different distribution. Moreover, recent work has shown that facial expressions can be synthesized by changing localized face regions. In this work, we propose a pixel-based facial expression synthesis method in which each output pixel observes only one input pixel. The proposed method achieves good generalization capability by leveraging only a few hundred training images. Experimental results demonstrate that the proposed method performs comparably well against state-of-the-art GANs on in-dataset images and significantly better on out-of-dataset images. In addition, the proposed model is two orders of magnitude smaller which makes it suitable for deployment on resource-constrained devices.
ADIO: An Interactive Artifact Physically Representing the Intangible Digital Audiobook Listening Experience in Everyday Living Spaces

Although audiobooks are increasingly being used, people tend to perceive audiobook experiences as 'not real reading' due to its intangibility and ephemerality. In this paper, we developed ADIO, a device augmenting audiobook experience through representing personal listening state in the form of an interactive physical bookshelf. ADIO displays a user's listening progress through a pendant's changing length and the user's digital audiobook archive titles. The result of our four-week in-field study with six participants revealed that ADIO provided proof of the user's listening-to, which brought a sense of reading and gave a trigger for recalling the listened-to audiobook content. Additionally, audiobooks' improved visibility reminded participants to listen to them, and ADIO's physical interaction allowed participants to form personal patterns for listening to audiobooks. Our findings proposed new methods for augmenting the audiobook listening experience at three stages and further implications for designing physical curation on users’ digital archives.
Part-Aligned Network with Background for Misaligned Person Search

Person search is a significant computer vision task that requires addressing person detection and re-identification simultaneously. Body parts are frequently misaligned due to variation poses, occlusions, and partial missing, leading to the unsatisfied results of person search. Existing methods usually extract local features from the human body by the key point information, that cannot tackle the recognition task between a pair of persons with different body parts due to misalignment. Moreover, these methods overlook background information (e.g. the carries and the background reference object) which can also supplement effective features for representing the person. In this paper, we propose a part-aligned network with background (PANB) to address this misalignment issue. To learn local fine-grained features of different body parts, we fine-tune a parsing network to divide the body region into seven parts. In particular, our proposed method considers extracting the background features as the eighth part features to extract more robust representations, which is more rational and efficient. Furthermore, we design a reconstruction method to align the parts existing in both the query image and the cropped gallery image. Extensive experiments show that our proposed method achieves competitive performance on CUHK-SYSU and PRW datasets.
Energy-Quality Scalable Monocular Depth Estimation on Low-Power CPUs

The recent advancements in deep learning have demonstrated that inferring high-quality depth maps from a single image has become feasible and accurate, thanks to convolutional neural networks (CNNs), but how to process such compute- and memory-intensive models on portable and low-power devices remains a concern. Dynamic energy-quality scaling is an interesting yet less explored option in this field. It can improve efficiency through opportunistic computing policies where performances are boosted only when needed, achieving on average substantial energy savings. Implementing such a computing paradigm encompasses the availability of a scalable inference model, which is the target of this work. Specifically, we describe and characterize the design of an energy-quality scalable pyramidal network (EQPyD-Net), a lightweight CNN capable of modulating at runtime the computational effort with minimal memory resources. We describe the architecture of the network and the optimization flow, covering the important aspects that enable the dynamic scaling, namely, the optimized training procedures, the compression stage via fixed-point quantization, and the code optimization for the deployment on commercial low-power CPUs adopted in the edge segment. To assess the effect of the proposed design knobs, we evaluated the prediction quality on the standard KITTI data set and the energy and memory resources on the ARM Cortex-A53 CPU. The collected results demonstrate the flexibility of the proposed network and its energy efficiency. EQPyD-Net can be shifted across five operating points, ranging from a maximum accuracy of 82.2% with 0.4 Frame/J and up to 92.6% of energy savings with 6.1% of accuracy loss, still keeping a compact memory footprint of 5.2 MB for the weights and 38.3 MB (in the worst case) for the processing.
Integration of Concept Maps into the Mixed Reality Learning Space: Quasi-Experimental Design and Preliminary Results

It is difficult to establish the relationship among complex concepts learned in virtual learning activities in the absence of appropriate instructional scaffolds, which may lead to students' confusion and frustration. To address such an issue, a mixed reality (MR) friction experiment system integrated with interactive hierarchical concept maps with the name of MMRCM is proposed as a scaffolding tool to guide students in learning friction concepts. Results of a quasi-experiment assessing the effectiveness of MMRCM showed that students' academic performance improved significantly. Interviews with students and teachers also showed that the MMRCM system could help learners navigate educational concepts and material efficiently.
Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

Multi-domain knowledge organization is an effective way of correlating cross-domain knowledge or intercommunicating between cross-domain knowledge systems. As a knowledge organization model, ontology is widely used in information and management systems. To organize multi-domain knowledge, ontologies in different domains correlate to each other directly or indirectly. Generally, matching and integrating ontologies of different domain into a large scale ontology is the common way of directly correlating, while building a top level ontology is the main method for indirectly correlating. As the scale of domain ontologies get larger and larger, both direct and indirect methods become more difficult and time-consuming. In order to improve the organization of multi-domain knowledge, this paper presents a novel ontology organization method to build real-time ontology by adaptive filter while user presenting requirements. Only the entities related to user requirements are integrated, while building a real-time ontology. Firstly, the method searches domain ontologies that are related to user requirements. Then sub-ontologies are extracted from search results by filter, and they are integrated into a new ontology under direction of filter, i.e. real-time ontology. Finally, four criteria are introduced to evaluate real-time ontology. The experiment results illuminate that real-time ontology perform excellently in accuracy, recall, correctness and especially time-consuming.
Analysis of College English Teaching Model Based on Network Automation Transformation

English education in colleges and universities has always been limited to the conventional teaching mode by multiple restrictions such as lecture system, teaching objectives and students’ quality for years. Based on the hot topic of automation transformation, this paper will analyze the problems faced by English teaching in colleges and universities from the perspective of the most popular Internet+ education, collect and organize relevant data, and then put forward relevant suggestions for the development of English teaching in colleges and universities, and put forward the prospect of its development in the future, with the aim of promoting the transformation of college English teaching mode in order to improve the teaching quality of English classroom.
Personal Identifiability of User Tracking Data During VR Training

Recent research indicates that user tracking data from virtual reality (VR) experiences can be used to personally identify users at accuracies as high as 95 percent. However, these results indicating that non-verbal data should be understood as personally identifying data were based on observing 360-degree videos. In this paper, we present participant identification results based on a session of user tracking data from a VR training application, which show accuracies above 90 percent. While still highly accurate, this decrease indicates that the personal identifiability of user tracking data is likely dependent upon the nature of the underlying VR experience.
Conceptual Modeling Versus User Story Mapping: Which is the Best Approach to Agile Requirements Engineering?

User stories are primary requirements artifacts within agile methods. They are comprised of short sentences written in natural language expressing units of functionality for the to-be system. Despite their simple format, when modelers are faced with a set of user stories they might be having difficulty in sorting them, evaluating their redundancy, and assessing their relevancy in the effort to prioritize them. The present paper tests the ability of modelers to understand the requirements problem through a visual representation (named the Rationale Tree) which is a conceptual model and is built out of a user stories’ set. The paper is built upon and extends previous work relating to the feasibility of generating such a representation out of a user stories’ set by comparing the performance of the Rationale Tree with the User Story Mapping approach. This is achieved by performing a two-group quantitative comparative study. The identified comparative variables for each method were understandability, recognition of missing requirements/epics/themes, and adaptability. The Rational Tree was not easy to understand and did not perform as anticipated in assisting with the recognition of missing requirements/epics/themes. However, its employment allowed modelers to offer qualitative representations of a specific software problem. Overall, the present experiment evaluates whether a conceptual model could be a consistent solution towards the holistic comprehension of a software development problem within an agile setting, compared to more ‘conventional’ techniques used so far.
Sibyl: Explaining Machine Learning Models for High-Stakes Decision Making

As machine learning is applied to an increasingly large number of domains, the need for an effective way to explain its predictions grows apace. In the domain of child welfare screening, machine learning offers a promising method of consolidating the large amount of data that screeners must look at, potentially improving the outcomes for children reported to child welfare departments. Interviews and case-studies suggest that adding an explanation alongside the model prediction may result in better outcomes, but it is not obvious what kind of explanation would be most useful in this context. Through a series of interviews and user studies, we developed Sibyl, a machine learning explanation dashboard specifically designed to aid child welfare screeners’ decision making. When testing Sibyl, we evaluated four different explanation types, and based on this evaluation, decided a local feature contribution approach was most useful to screeners.
Summarizing Relevant Parts from Technical Videos

Software developers frequently watch technical videos and tutorials online as solutions to their problems. However, the audiovisual explanations of the videos might also claim more time from the developers than the text-only materials (e.g., programming Q&A threads). Thus, pinpointing and summarizing the relevant fragments from these videos could save the developers valuable time and effort. In this paper, we propose a novel technique - TechTube - that can be used to find video segments that are relevant to a given technical task. TechTube allows a developer to express the task as a natural language query. To account for missing vocabularies in the query, TechTube automatically reformulates the query using techniques based on information retrieval. The reformulated query is matched against a repository of online technical videos. The output from TechTube is a sequence of relevant video segments that can be useful to implement the task at hand. Unlike previous researches, our approach splits the video by detecting silence in video audio tracks. Experiments using 98 programming related search queries show that our approach delivers the relevant videos within the Top-5 results 93% of the time with a mean average precision of 76%. We also find that TechTube can deliver the most relevant section of a technical video with 67% precision and 53% recall that outperforms the closely related existing approach from the literature. Our developer study involving 16 participants reports that they found the video summaries generated by TechTube very accurate, precise, concise, and very useful for their programming tasks rather than the original complete videos.