Reviewerly/exHarmony huggingface dataset:
Adapting Grad-CAM for Embedding Networks

The gradient-weighted class activation mapping (Grad-CAM) method can faithfully highlight important regions in images for deep model prediction in image classification, image captioning and many other tasks. It uses the gradients in back-propagation as weights (grad-weights) to explain network decisions. However, applying Grad-CAM to embedding networks raises significant challenges because embedding networks are trained by millions of dynamically paired examples (e.g. triplets). To overcome these challenges, we propose an adaptation of the Grad-CAM method for embedding networks. First, we aggregate grad-weights from multiple training examples to improve the stability of Grad-CAM. Then, we develop an efficient weight-transfer method to explain decisions for any image without back-propagation. We extensively validate the method on the standard CUB200 dataset in which our method produces more accurate visual attention than the original Grad-CAM method. We also apply the method to a house price estimation application using images. The method produces convincing qualitative results, showcasing the practicality of our approach.
Supervised deep hashing with a joint deep network

Hashing has gained great attention in large-scale image retrieval due to efficient storage and fast search. Recently, many deep hashing approaches have achieved good results since deep neural network owns powerful learning capability. However, these deep hashing approaches can perform deep features learning and binary-like codes learning synchronously, the information loss between binary-like codes and binary codes will increase due to the binarization operation. A further deficiency is that binary-like codes learning based on deep feature representations is a shallow learning procedure, which cannot fully exploit deep feature representations to generate hash codes. To solve the above problems, we propose a Deep Learning Supervised Hashing (DLSH) method which adopts deep structure to learn binary codes based on deep feature representations for large-scale image retrieval. Specifically, we integrate deep features learning module, deep mapping module and binary codes learning module in one unified architecture. The network is trained in an end-to-end way. In addition, a new objective function is designed to preserve the balancing property and semantic similarity of binary codes by incorporating the semantic similarity term and the balanceable property term. Experimental results on four benchmarks demonstrate that the proposed approach outperforms several state-of-the-art hashing methods.
A Blended Learning System to Improve Motivation, Mood State, and Satisfaction in Undergraduate Students: Randomized Controlled Trial

Smartphone-based learning, or mobile learning (m-learning), has become a popular learning-and-teaching strategy in educational environments. Blended learning combines strategies such as m-learning with conventional learning to offer continuous training, anytime and anywhere, via innovative learning activities.The main aim of this work was to examine the short-term (ie, 2-week) effects of a blended learning method using traditional materials plus a mobile app-the iPOT mobile learning app-on knowledge, motivation, mood state, and satisfaction among undergraduate students enrolled in a health science first-degree program.The study was designed as a two-armed, prospective, single-blind, randomized controlled trial. Subjects who met the inclusion criteria were randomly assigned to either the intervention group (ie, blended learning involving traditional lectures plus m-learning via the use of the iPOT app) or the control group (ie, traditional on-site learning). For both groups, the educational program involved 13 lessons on basic health science. The iPOT app is a hybrid, multiplatform (ie, iOS and Android) smartphone app with an interactive teacher-student interface. Outcomes were measured via multiple-choice questions (ie, knowledge), the Instructional Materials Motivation Survey (ie, motivation), the Profile of Mood States scale (ie, mood state), and Likert-type questionnaires (ie, satisfaction and linguistic competence).A total of 99 students were enrolled, with 49 (49%) in the intervention group and 50 (51%) in the control group. No difference was seen between the two groups in terms of theoretical knowledge gain (P=.92). However, the intervention group subjects returned significantly higher scores than the control group subjects for all postintervention assessed items via the motivation questionnaire (all P<.001). Analysis of covariance (ANCOVA) revealed a significant difference in the confusion and bewilderment component in favor of the intervention group (P=.01), but only a trend toward significance in anger and hostility as well as total score. The intervention group subjects were more satisfied than the members of the control group with respect to five out of the six items evaluated: general satisfaction (P<.001), clarity of the instructions (P<.01), clarity with the use of the learning method (P<.001), enough time to complete the proposed exercises (P<.01), and improvement in the capacity to learn content (P<.001). Finally, the intervention group subjects who were frequent users of the app showed stronger motivation, as well as increased perception of greater gains in their English-language competence, than did infrequent users.The blended learning method led to significant improvements in motivation, mood state, and satisfaction compared to traditional teaching, and elicited statements of subjective improvement in terms of competence in English.ClinicalTrials.gov NCT03335397; https://clinicaltrials.gov/ct2/show/NCT03335397.
Segmenting the Future

Predicting the future is an important aspect for decision-making in robotics or autonomous driving systems, which heavily rely upon visual scene understanding. While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semantic segmentation solely from the previous frame RGB data in a single end-to-end trainable model do not exist. In this letter, we propose a temporal encoder-decoder network architecture that encodes RGB frames from the past and decodes the future semantic segmentation. The network is coupled with a new knowledge distillation training framework specific for the forecasting task. Our method, only seeing preceding video frames, implicitly models the scene segments while simultaneously accounting for the object dynamics to infer the future scene semantic segments. Our results on Cityscapes and Apolloscape outperform the baseline and current state-of-the-art methods. Code will be available soon.
Analyzing Sentiment of Movie Reviews in Bangla by Applying Machine Learning Techniques

This paper proposes a process of sentiment analysis of movie reviews written in Bangla language.This process can automate the analysis of audience's reaction towards a specific movie or TV show.With more and more people expressing their opinions openly in the social networking sites, analyzing the sentiment of comments made about a specific movie can indicate how well the movie is being accepted by the general public.The dataset used in this experiment was collected and labeled manually from publicly available comments and posts from social media websites.Using Support Vector Machine algorithm, this model achieves 88.90% accuracy on the test set and by using Long Short Term Memory network [1] the model manages to achieve 82.42% accuracy.Furthermore, a comparison with some other machine learning approaches is presented in this paper.
Recurrent neural network with pooling operation and attention mechanism for sentiment analysis: A multi-task learning approach

Sentiment analysis is designed to classify documents into a fixed number of pre-defined categories that represent different sentiments. Focusing on the limitation of insufficient training data, multi-task learning models based on deep learning have recently achieved significant progress in this field. In general, these models leverage multiple datasets annotated for different tasks to improve the performance on each individual dataset. The improvement is particularly evident on tasks with limited training data. However, most of these models suffer from two limitations. First, they use the final output of the hidden layer as the overall representation of the text, which initially loses a certain amount of semantic information. Second, although some of them utilize a certain gate mechanism to select shared features, some irrelevant shared features are erroneously used owing to polysemy. To address these two limitations, we integrate a pooling layer into a Bi-directional Recurrent Neural Network (BRNN) to extract semantic information comprehensively. We then apply the attention mechanism between shared layers and task-specific layers to identify the effective shared features, and propose an Attention-based Separate Pooling BRNN (ASP-BRNN) model. We conduct experiments to show the effectiveness of our models on four datasets (SST1, SST2, SUBJ, and IMDB), and the accuracy of our models increases steadily by approximately 0.5% for each model. It proves the effectiveness of every newly added component in solving the two problems. A further evaluation on eight datasets shows our proposed ASP-BRNN model outperforms current state-of-the-art models, such as ASP-MTL model (at least +0.2% on Electronics and at most +6.9% on IMDB), MT-ARC-II model (at least +0.2% on SST2 and at most +3.8% on DVDs).
Complementarity-Aware Attention Network for Salient Object Detection

In this article, we tackle the saliency detection task from an interesting perspective: we focus both on salient regions (or foreground) detection and nonsalient regions (or background) detection instead of only the foreground and propose a novel complementarity-aware attention network. It is a unified framework with two branches, namely, positive attention module (PAM) and negative attention module (NAM), for the foreground and background detection, respectively. More specifically, the PAM exploits a position self-attention mechanism to enhance the discriminant ability of feature representation, which can detect most of the salient object regions. Meanwhile, the NAM is designed to detect the background regions, aiming to pop out the missing object parts and details in the prediction map produced by the PAM. By fusing these two attention modules together, NAM can provide complementary cues to assist PAM for precise object detection. Furthermore, in order to capture more multiscale contextual information, we introduce a bidirectional structure with multisupervision to the proposed complementarity-aware attention module for performance improvement. Experiments on five benchmark datasets show that the proposed framework achieves comparable results compared with the state-of-the-art saliency detection methods.
Mobile phones and women's empowerment in Maasai communities: How men shape women's social relations and access to phones

Mobile phones have been heralded by many as promising new tools to empower women throughout the Global South. However, some have asserted that new information and communication technologies (ICTs) may serve to amplify disparities between more powerful and less powerful people. Few studies have examined which women stand to benefit and under what conditions. This study seeks to better understand the relationships between mobile phones and women’s empowerment by examining diverse women’s experiences within Maasai agro-pastoralist communities in northern Tanzania. Specifically, we ask three guiding questions: (1) How do Maasai women access and use phones? (2) What processes of empowerment do phones support or undermine? and (3) How are these processes embedded in diverse social relations? To address these questions, we use a framework that integrates a Social Relations Approach with a modified version of the Sustainable Livelihoods Approach. Our team conducted semi-structured and individual-stakeholder interviews with Maasai women in June-July, 2018 to learn their perspectives on phones, social-relations and multiple aspects of empowerment. We analyzed the content of these interviews using deductive and inductive qualitative strategies. These efforts yield multiple findings: (1) women’s access to phones is fluid; (2) multiple pathways to empowerment and disempowerment exist; (3) phones reinforce inequalities; (4) women’s identities are intersectional; and (5) women’s networks remain homogenous. Taken together, this approach and these insights provide a more conservative account of the benefits of mobile phones than many studies and also an important technology-empowerment narrative for development scholars and practitioners.
CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings

Window settings to rescale and contrast stretch raw data from radiographic images such as Computed Tomography (CT), X-ray and Magnetic Resonance images is a crucial step as data pre-processing to examine abnormalities and diagnose diseases. We propose a distant-supervised method for determining automatically the best window settings by attaching a window estimator module (WEM) to a deep convolutional neural network (DCNN)-based lesion classifier and training them in conjunction. Aside from predicting a flexible window setting for each raw image, we statistically identify the top four window settings by calculating the mean and standard deviations for the entire dataset. Images are scaled on each of the top settings estimated by WEM and following lesion classifiers are subsequently trained. We study the effects of only using the flexible window, the single fixed window as either a known default window used by radiologists or an estimated mean value, and two different approaches to combine results from the top window settings to improve the detection of intracranial hemorrhage (ICH) from brain CT images. Experimental results showed that using the top four window settings identified from the window estimator module and combining the results had the best performance.
Burst-Aware Predictive Autoscaling for Containerized Microservices

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this article, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. We evaluated our approach through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math></inline-formula> 1.09 in total processed requests, a reduction of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math></inline-formula> 5.17 for SLO violations, and an increase of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math></inline-formula> 0.767 cost as compared to the baseline method.
"Nobody Speaks that Fast!" An Empirical Study of Speech Rate in Conversational Agents for People with Vision Impairments

The number of people with vision impairments using Conversational Agents (CAs) has increased because of the potential of this technology to support them. As many visually impaired people are accustomed to understanding fast speech, most screen readers or voice assistant systems offer speech rate settings. However, current CAs are designed to interact at a human-like speech rate without considering their accessibility. In this study, we tried to understand how people with vision impairments use CA at a fast speech rate. We conducted a 20-day in-home study that examined the CA use of 10 visually impaired people at default and fast speech rates. We investigated the difference in visually impaired people's CA use with different speech rates and their perception toward CA at each rate. Based on these findings, we suggest considerations for the future design of CA speech rate for those with visual impairments.
Reality aware VR headsets

We present a model of a reality aware VR headset, a VR headset capable of identifying, interpreting and responding to elements of a user's reality. We report on an evaluation of 2 prototypes which investigated potential behaviours such a headset might have. Our first prototype compared 4 notifications to automatically notify a VR user to a bystander's presence. We compared notifying with and without information about the bystander's position. Our results highlight a subset of VR users who when notified of copresence without position were made uncomfortable. Our second prototype investigated how dynamic audio adjustment could react to audio events in the user's surroundings. We compared on-board headset audio and headphones with a dynamic audio version of each which decreased the application volume to direct attention to an audio source and increased application volume to block out an audio source. Our results found positive results when decreasing application volume but increasing volume to block out a nearby audio source was ineffective.
Teachers’ digital competence in using and analytically managing information in flipped learning (<i>Competencia digital docente para el uso y gestión analítica informacional del aprendizaje invertido</i>)

The use of flipped learning as a methodological approach entails the constant use of content management platforms. The interactions generated on these platforms yield big data, and managing and analysing these data facilitates the teachers’ decision-making to improve their educational actions. The objective of this study is to ascertain the influence of the teachers’ level of digital competence on the use of flipped learning and the management and analysis of big data. To do so, a descriptive, correlational quantitative method was used. A sample of 744 Spanish teachers was chosen to fill out a questionnaire. The results reveal that teachers have a medium level of digital competence, with Communication-collaboration and Information being the strongest areas. The use of flipped learning is rare, and the data management and analysis levels are deficient. Each area in digital competence bears a significant influence — albeit at different levels — on the use of flipped learning and the analytical management of educational data.
Characterizing gaze position signals and synthesizing noise during fixations in eye-tracking data

Abstract The magnitude of variation in the gaze position signals recorded by an eye tracker, also known as its precision, is an important aspect of an eye tracker’s data quality. However, data quality of eye-tracking signals is still poorly understood. In this paper, we therefore investigate the following: (1) How do the various available measures characterizing eye-tracking data during fixation relate to each other? (2) How are they influenced by signal type? (3) What type of noise should be used to augment eye-tracking data when evaluating eye-movement analysis methods? To support our analysis, this paper presents new measures to characterize signal type and signal magnitude based on RMS-S2S and STD, two established measures of precision. Simulations are performed to investigate how each of these measures depends on the number of gaze position samples over which they are calculated, and to reveal how RMS-S2S and STD relate to each other and to measures characterizing the temporal spectrum composition of the recorded gaze position signal. Further empirical investigations were performed using gaze position data recorded with five eye trackers from human and artificial eyes. We found that although the examined eye trackers produce gaze position signals with different characteristics, the relations between precision measures derived from simulations are borne out by the data. We furthermore conclude that data with a range of signal type values should be used to assess the robustness of eye-movement analysis methods. We present a method for generating artificial eye-tracker noise of any signal type and magnitude.
Making Design Memoirs: Understanding and Honoring Difficult Experiences

Design is commonly understood as a storytelling practice, yet we have few narratives with which to describe the felt experiences of struggle, pain, and difficulty, beyond treating them as subjects to resolve. This work uses the praxis of embodied design as a way to bring more complex narratives to the community for contemplation---to engage and entangle personal and difficult stories within a public context. We propose the term Design Memoirs for these first-person practices and reflections. Design Memoirs are subjective and corporeal in nature, and provide a direct and observable way to reckon with felt experiences through, and for, design. We demonstrate Design Memoirs by drawing on our own experiences as mothers, caregivers, and corporeal subjects. Following Barad, we propose a practice of diffractive reading to locate resonances between Design Memoirs which render difficult autobiographical material addressable, shareable, and open for new interpretations. We present this strategy as a method for arriving at deeper understandings of difficult experiences.
Monsters, Metaphors, and Machine Learning

Machine learning (ML) poses complex challenges for user experience (UX) designers. Typically unpredictable and opaque, it may produce unforeseen outcomes detrimental to particular groups or individuals, yet simultaneously promise amazing breakthroughs in areas as diverse as medical diagnosis and universal translation. This results in a polarized view of ML, which is often manifested through a technology-as-monster metaphor. In this paper, we acknowledge the power and potential of this metaphor by resurfacing historic complexities in human-monster relations. We (re)introduce these liminal and ambiguous creatures, and discuss their relation to ML. We offer a background to designers' use of metaphor, and show how the technology-as-monster metaphor can generatively probe and (re)frame the questions ML poses. We illustrate the effectiveness of this approach through a detailed discussion of an early-stage generative design workshop inquiring into ML approaches to supporting student mental health and well-being.
Bee Colony Optimization metaheuristic for fuzzy membership functions tuning

The successfulness of the application of fuzzy logic to real-life problems depends on a number of parameters, such as the number and shapes of fuzzy membership functions, which are usually defined upon intuition or the subjective knowledge of relevant experts. One way to improve the performance of the fuzzy reasoning model is to use heuristic or metaheuristic techniques in order to optimize the shapes of membership functions. In this paper, the Bee Colony Optimization (BCO) algorithm is applied as a tool suitable for the specified optimization problem. The BCO belongs to the group of nature-inspired metaheuristics. The purpose of this paper is to present and discuss a strategy for the adjustment of fuzzy logic membership functions using the variant of the BCO algorithm based on the improvement of complete solutions and show its real-life application to the problem of the estimation of freight train energy consumption. According to the obtained results, it can be concluded that the precision of the developed fuzzy reasoning model is significantly increased after tuning membership functions by the BCO.