Reviewerly/exHarmony huggingface dataset:
Learning discriminative features via weights-biased softmax loss

Loss functions play a key role in training superior deep neural networks. In convolutional neural networks (CNNs), the popular cross entropy loss together with softmax does not explicitly guarantee minimization of intra-class variance or maximization of inter-class variance. In the early studies, there is no theoretical analysis and experiments explicitly indicating how to choose the number of units in fully connected layer. To help CNNs learn features more fast and discriminative, there are two contributions in this paper. First, we determine the minimum number of units in FC layer by rigorous theoretical analysis and extensive experiment, which reduces CNNs’ parameter memory and training time. Second, we propose a negative-focused weights-biased softmax (W-Softmax) loss to help CNNs learn more discriminative features. The proposed W-Softmax loss not only theoretically formulates the intra-class compactness and inter-class separability, but also can avoid overfitting by enlarging decision margins. Moreover, the size of decision margins can be flexibly controlled by adjusting a hyperparameter α. Extensive experimental results on several benchmark datasets show the superiority of W-Softmax in image classification tasks.
MagTouch: Robust Finger Identification for a Smartwatch Using a Magnet Ring and a Built-in Magnetometer

Completing tasks on smartwatches often requires multiple gestures due to the small size of the touchscreens and the lack of sufficient number of touch controls that are easily accessible with a finger. We propose to increase the number of functions that can be triggered with the touch gesture by enabling a smartwatch to identify which finger is being used. We developed MagTouch, a method that uses a magnetometer embedded in an off-the-shelf smartwatch. It measures the magnetic field of a magnet fixed to a ring worn on the middle finger. By combining the measured magnetic field and the touch location on the screen, MagTouch recognizes which finger is being used. The tests demonstrated that MagTouch can differentiate among the three fingers used to make contacts at a success rate of 95.03%.
CAdViSE

Attempting to cope with fluctuations of network conditions in terms of available bandwidth, latency and packet loss, and to deliver the highest quality of video (and audio) content to users, research on adaptive video streaming has attracted intense efforts from the research community and huge investments from technology giants. How successful these efforts and investments are, is a question that needs precise measurements of the results of those technological advancements. HTTP-based Adaptive Streaming (HAS) algorithms, which seek to improve video streaming over the Internet, introduce video bitrate adaptivity in a way that is scalable and efficient. However, how each HAS implementation takes into account the wide spectrum of variables and configuration options, brings a high complexity to the task of measuring the results and visualizing the statistics of the performance and quality of experience. In this paper, we introduce CAdViSE, our Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players. The paper aims to demonstrate a test environment which can be instantiated in a cloud infrastructure, examines multiple media players with different network attributes at defined points of the experiment time, and finally concludes the evaluation with visualized statistics and insights into the results.
Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination

In applications ranging from image search to recommendation systems, the problem of identifying a set of "similar" real-valued vectors to a query vector plays a critical role. However, retrieving these vectors and computing the corresponding similarity scores from a large database is computationally challenging. Approximate nearest neighbor (ANN) search relaxes the guarantee of exactness for efficiency by vector compression and/or by only searching a subset of database vectors for each query. Searching a larger subset increases both accuracy and latency. State-of-the-art ANN approaches use fixed configurations that apply the same termination condition (the size of subset to search) for all queries, which leads to undesirably high latency when trying to achieve the last few percents of accuracy. We find that due to the index structures and the vector distributions, the number of database vectors that must be searched to find the ground-truth nearest neighbor varies widely among queries. Critically, we further identify that the intermediate search result after a certain amount of search is an important runtime feature that indicates how much more search should be performed. To achieve a better tradeoff between latency and accuracy, we propose a novel approach that adaptively determines search termination conditions for individual queries. To do so, we build and train gradient boosting decision tree models to learn and predict when to stop searching for a certain query. These models enable us to achieve the same accuracy with less total amount of search compared to the fixed configurations. We apply the learned adaptive early termination to state-of-the-art ANN approaches, and evaluate the end-to-end performance on three million to billion-scale datasets. Compared with fixed configurations, our approach consistently improves the average end-to-end latency by up to 7.1 times faster under the same high accuracy targets. Our approach is open source at github.com/efficient/faiss-learned-termination.
Toward Immersive Self-Driving Simulations: Reports from a User Study across Six Platforms

As self-driving car technology matures, autonomous vehicle research is moving toward building more human-centric interfaces and accountable experiences. Driving simulators avoid many ethical and regulatory concerns about self-driving cars and play a key role in testing new interfaces or autonomous driving scenarios. However, apart from validity studies for manual driving simulation, the capabilities of driving simulators in replicating the experience of self-driving cars have not been widely investigated. In this paper, we build six self-driving simulation platforms with varying levels of visual and motion fidelities ranging from a screen-based in-lab simulator to the mixed-reality on-road simulator we propose. We compare the sense of presence and simulator sickness for each simulator composition, as well as its visual and motion fidelities with a user study. Our novel mixed-reality automotive driving simulator, named MAXIM, showed highest fidelity and presence. Our findings suggest how visual and motion configurations affect experience in autonomous driving simulators.
A Conversation Analysis of Non-Progress and Coping Strategies with a Banking Task-Oriented Chatbot

Task-oriented chatbots are becoming popular alternatives for fulfilling users' needs, but few studies have investigated how users cope with conversational 'non-progress' (NP) in their daily lives. Accordingly, we analyzed a three-month conversation log between 1,685 users and a task-oriented banking chatbot. In this data, we observed 12 types of conversational NP; five types of content that was unexpected and challenging for the chatbot to recognize; and 10 types of coping strategies. Moreover, we identified specific relationships between NP types and strategies, as well as signs that users were about to abandon the chatbot, including 1) three consecutive incidences of NP, 2) consecutive use of message reformulation or switching subjects, and 3) using message reformulation as the final strategy. Based on these findings, we provide design recommendations for task-oriented chatbots, aimed at reducing NP, guiding users through such NP, and improving user experiences to reduce the cessation of chatbot use.
Dimensions of UX Practice that Shape Ethical Awareness

HCI researchers are increasingly interested in describing the complexity of design practice, including ethical, organizational, and societal concerns. Recent studies have identified individual practitioners as key actors in driving the design process and culture within their respective organizations, and we build upon these efforts to reveal practitioner concerns regarding ethics on their own terms. In this paper, we report on the results of an interview study with eleven UX practitioners, capturing their experiences that highlight dimensions of design practice that impact ethical awareness and action. Using a bottom-up thematic analysis, we identified five dimensions of design complexity that influence ethical outcomes and span individual, collaborative, and methodological framing of UX activity. Based on these findings, we propose a set of implications for the creation of ethically-centered design methods that resonate with this complexity and inform the education of future UX practitioners.
RA-GCN

Knowledge graphs display various entities and their relationships in the real world based on knowledge representation and can analyze and predict the intrinsic relationship between knowledge embeddings through data mining and information processing, which are widely used in search engines, web analytics and smart recommendation areas. As existing knowledge graph information is continuously created and grown, it is a curial task to determine whether the information in knowledge graph is correct and to complete the missing information. In response to this challenge, the researchers proposed a number of graph convolutional network (GCN)-based models to characterize knowledge graphs. The state-of-the-art model is R-GCN, which can effectively extract features. This paper deeply studies the algorithm ideas and results of the R-GCN model, explores whether the model can be further optimized in entity classification and link prediction, and finally, improves the original model and proposes a relational aggregation graph convolutional network. Specifically, this paper finds that a subset of the set of entities may be directly connected to a central entity. All the entities in this subset possess partially identical attributes. At the same time, the relationships between these entities and the central entity may be similar. These similar attributes and relationships can be abstractly aggregated into virtual entities and virtual relationships, respectively, to better extract the topological relationship features. This paper uses the FB15k dataset to evaluate the performance of the proposed model on knowledge graph completion tasks. The experimental results show that the proposed RA-GCN model achieves a certain level improvement compared with the original model and that it can extract knowledge graph topological relationship characteristics more effectively.
SleepBandits: Guided Flexible Self-Experiments for Sleep

Self-experiments allow people to explore what behavioral changes lead to improved health and wellness. However, it is challenging to run such experiments in a scientifically valid way that is also flexible and able to accommodate the realities of daily life. We present a set of design principles for guided self-experiments that aim to lower this barrier to self-experimentation. We demonstrate the value of the principles by implementing them in SleepBandits, an integrated system that includes a smartphone application for sleep experiments. SleepBandits guides users through the steps of a single-case experiment, automatically collecting data from the built-in sensors and user input and calculating and presenting results in real-time. We released SleepBandits to the Google Play Store and people voluntarily downloaded and used it. Based on the data from 365 active users from this in-the-wild study, we discuss opportunities and challenges with the design principles and the SleepBandits system.
Development framework for context-aware augmented reality applications

Augmented Reality (AR) is a technology on the rise. Due to its growing popularity and application in various domains, ways of increasing user friendliness and usability of AR are now becoming more important. Context-awareness is one of these ways, as it can make an AR application adjust to the user, their situation and their needs, making the application more ergonomic and easy to work with. Especially in mobile AR, context changes happen often, due to the user moving around and using their devices in different environments and situations. In order to ease the development of context-aware applications for mobile AR, the modular development framework AARCon is proposed. AARCon consists of context monitoring features to keep track of the context of use and adaptation features to react to context changes. To show the potential of AARCon, a case study is presented, where AARCon is used to create a context-aware AR printer maintenance application.
Collaborative Writing Across Multiple Artifact Ecologies

Research focusing on how collaborative writing takes place across multiple applications and devices and over longer projects is sparse. We respond to this gap by presenting the results of a qualitative study of longer-term academic writing projects, showing how co-writers employ multiple tools when working on a common text. We identify three patterns of multi-application collaboration as well as four common types of motivations for transitions between applications. We also extend existing taxonomies of collaborative writing by proposing a categorization of the functions served by the text as object and backbone of the collaboration. Together, these contributions offer a framing for understanding transitions within and across artifact ecologies in work around a common object. Our findings highlight ways in which features like concurrent editing may in fact challenge the collaborative writing process, and we point to opportunities for alternative application models.
Designers in White Coats: Deploying Ovum, a Fertility Tracking Device

As self-tracking practices continue to proliferate, there has been a call for a consideration of how the design of these devices influence the users experience of themselves and their bodies beyond utility, efficacy and accuracy. The research product Ovum was designed to facilitate a DIY, shared, domestic experience, rather than an expert-led, individual, clinical experience of fertility tracking. Ovum uses the method of saliva sampling to determine ovulation. This paper unpacks the findings from a three-month long deployment of Ovum with seven couples trying to conceive. Besides an evaluation of the device in terms of the three experiential qualities aimed for in the design process, we report on the consequences of executing a design deployment that resembles a clinical trial. We contribute our experience in order to develop an understanding of how designing for the body places interaction designers in novel and complex situations.
SAD: An Unsupervised System for Subsequence Anomaly Detection

Subsequence anomaly (or outlier) detection in long sequences is an important problem with applications in a wide range of domains. However, current approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. We recently proposed NorM, a novel approach suitable for domain-agnostic anomaly detection, which addresses the aforementioned problems by detecting anomalies based on their (dis)similarity to a model that represents normal behavior. The experimental results on several real datasets demonstrate that the proposed approach outperforms the current state-of-the art in terms of both accuracy and execution time. In this demonstration, we present a system for unsupervised Subsequence Anomaly Detection (SAD) that uses the NorM method. Through various scenarios with real datasets, we showcase the challenges of the problem, and we demonstrate the advantages of the proposed system.
"We can learn. Why not?"

HCI researchers have increasingly studied how technology might improve the lives of marginalized workers. We explored this question through a qualitative study with home health aides in New York City, a vulnerable group of frontline caregivers whose work with patients is poorly paid and highly stressful, often involving life-or-death situations. To elicit the perspectives of aides and their supervisors on how technology interventions might contribute to moving aides towards a better future, we created a design provocation that centers aides' needs and suggests more equitable roles for them within the home care ecosystem. Findings from design sessions with 16 aides, nurses, and aide coordinators illuminate the ethical and pragmatic dilemmas inherent in this complex ecosystem, and show that designing technology for equity requires attention to structural problems in addition to workers' stated needs. We analyze our findings through the lens of social justice-oriented interaction design, and discuss how our work extends key strategies within this framework.
Simulating Ising Spins in External Magnetic Fields with a Network of Degenerate Optical Parametric Oscillators

We report a coherent Ising machine (CIM) that can simulate the Ising model with external magnetic fields. To represent Ising spins, the CIM employs degenerate optical parametric oscillator (DOPO) pulses, which are coupled by a measurement-feedback scheme. By adding to a feedback signal a term that is proportional to a given magnetic field and an absolute value of the measured amplitude of a DOPO pulse, the networked DOPO pulses behave like Ising spins in a magnetic field. Using the CIM, we implement antiferromagnetically coupled one- and two-dimensional networks of the Ising spins in uniform magnetic fields. At the field amplitudes that agree with a theoretical prediction, we experimentally observe a transition from an antiferromagnetic state governed by spin-spin interactions to a ferromagnetic state induced by the external magnetic field. In addition, staggered magnetization measurement reveals that the CIM behaves differently depending on the network dimensionality, which is consistent with the results obtained with the analytical solution and Monte Carlo simulations, but cannot be reproduced with a simple mean-field theory. These results suggest that the CIM clearly simulates the behavior of Ising spin networks better than a mere mean-field solver and will thus become a useful tool for simulating various statistical physics problems based on the Ising model.
Gaze Tracking for Eye-Hand Coordination Training Systems in Virtual Reality

Eye-hand coordination training systems are used to improve user performance during fast movements in sports training. In this work, we explored gaze tracking in a Virtual Reality (VR) sports training system with a VR headset. Twelve subjects performed a pointing study with or without passive haptic feedback. Results showed that subjects spent an average of 0.55 s to visually find and another 0.25 s before their finger selected a target. We also identified that, passive haptic feedback did not increase the performance of the user. Moreover, gaze tracker accuracy significantly deteriorated when subjects looked below their eye level. Our results also point out that practitioners/trainers should focus on reducing the time spent on searching for the next target to improve their performance through VR eye-hand coordination training systems. We believe that current VR eye-hand coordination training systems are ready to be evaluated with athletes.
Visualizing and Interpreting RNN Models in URL-based Phishing Detection

Existing studies have demonstrated that using traditional machine learning techniques, phishing detection simply based on the features of URLs can be very effective. In this paper, we explore the deep learning approach and build four RNN (Recurrent Neural Network) models that only use lexical features of URLs for detecting phishing attacks. We collect 1.5 million URLs as the dataset and show that our RNN models can achieve a higher than 99% detection accuracy without the need of any expert knowledge to manually identify the features. However, it is well known that RNNs and other deep learning techniques are still largely in black boxes. Understanding the internals of deep learning models is important and highly desirable to the improvement and proper application of the models. Therefore, in this work, we further develop several unique visualization techniques to intensively interpret how RNN models work internally in achieving the outstanding phishing detection performance. Especially, we identify and answer six important research questions, showing that our four RNN models (1) are complementary to each other and can be combined into an ensemble model with even better accuracy, (2) can well capture the relevant features that were manually extracted and used in the traditional machine learning approach for phishing detection, and (3) can help identify useful new features to enhance the accuracy of the traditional machine learning approach. Our techniques and experience in this work could be helpful for researchers to effectively apply deep learning techniques in addressing other real-world security or privacy problems.
UAV image analysis for leakage detection in district heating systems using machine learning

In this paper, we propose automatic energy leakage detection in underground pipes of district heating systems based on Infrared (IR) images, captured by an Unmanned Aerial Vehicle (UAV). Hot water or steam is distributed to homes and industries through underground pipes from a central power plan. Leakages in underground pipes pose a very common problem, which can occur for many reasons, e.g. unprofessional installation and end of service life. Potentially, a leakage remains undiscovered for a very long period of time. Therefore, it is of great interest for power supply companies to monitor district heating networks to identify leakages. In this paper, the original IR images are captured in a 16 bit format by a UAV. On ground, potential leakages are extracted using a region extraction algorithm. Thereafter a Convolutional Neural Network (CNN) as well as eight conventional Machine Learning (ML) classifiers are applied on these regions to classify whether or not it is a leakage. In total, twelve UAV sequences are captured at different cities in Denmark. Based on these, around 13.4 million samples of image patches of district heating systems are extracted. Eleven sequences are used for training and the remaining one for testing. This was performed on all splits in the leave-one-out testing. The deep learning CNN achieved an average weighted accuracy of 0.872 with a false positive and negative rate of 12.7 % and 10.4 %, respectively. This CNN model detected around 98.6 % of the true leakages. In comparison, conventional ML classifiers, i.e. Adaboost (AB), Random Forest (RF), etc. provide lower average weighted accuracy, but on the other hand they require less computational resources. We have compared our method with a state-of-art method and the result shows that the proposed method is very competitive.
AirTouch: 3D-printed Touch-Sensitive Objects Using Pneumatic Sensing

3D printing technology can be used to rapidly prototype the look and feel of 3D objects. However, the objects produced are passive. There has been increasing interest in making these objects interactive, yet they often require assembling components or complex calibration. In this paper, we contribute AirTouch, a technique that enables designers to fabricate touch-sensitive objects with minimal assembly and calibration using pneumatic sensing. AirTouch-enabled objects are 3D printed as a single structure using a consumer-level 3D printer. AirTouch uses pre-trained machine learning models to identify interactions with fabricated objects, meaning that there is no calibration required once the object has completed printing. We evaluate our technique using fabricated objects with various geometries and touch sensitive locations, obtaining accuracies of at least 90% with 12 interactive locations.