Reviewerly/exHarmony huggingface dataset:
The Fundamental Uncertainties of Mothering

Present day ideals of good parenting are socio-technical constructs formed at the intersection of medical best practices, cultural norms, and technical innovation. These ideals take shape in relation to the fundamental uncertainty that parents/mothers face, an uncertainty that comes from not knowing how to do what is best for one's children, families, and selves. The growing body of parent-focused smart devices and data-tracking platforms emerging from this intersection frame the responsible parent as one who evaluates, analyzes, and mitigates data-defined risks for their children and family. As these devices and platforms proliferate, whether from respected medical institutions or commercial interests, they place new demands on families and add an implicit emphasis on how humans (often mothers) can be augmented and improved by data-rich technology. This is expressed both in the actions they support (e.g., breastfeeding, monitoring food intake), as well as in the emotions they render marginal (e.g., rage, struggle, loss, and regret). In this article, we turn away from optimization and self-improvement narratives to attend to our own felt experiences as mothers and designers. Through an embodied practice of creating Design Memoirs, we speak directly to the HCI community from our position as both users and subjects of optimized parenting tools. Our goal in this work is to bring nuance to a domain that is often rendered in simplistic terms or frames mothers as figures who could endlessly do more for the sake of their families. Our Design Memoirs emphasize the conflicting and often negative emotions we experienced while navigating these tools and medical systems. They depict our feelings of being at once powerful and powerless, expressing rage and love simultaneously, and struggling between expressing pride and humility. The Design Memoirs serve us in advocating that designers should use caution when considering a problem/solution focus to the experiences of parents. We conclude by reflecting on how our shared practice of making memoirs, as well as other approaches within feminist and queer theory, suggest strategies that trouble these optimization and improvement narratives. Overall, we present a case for designing for mothers who feel like they are just making do or falling short, in order to provide relief from the anxiety of constantly seeking improvement.
Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008-2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.
On Edge Computing for Remote Pathology Consultations and Computations

Telepathology aims to replace the pathology operations performed on-site, but current systems are limited by their prohibitive cost, or by the adopted underlying technologies. In this work, we contribute to overcoming these limitations by bringing the recent advances of edge computing to reduce latency and increase local computation abilities to the pathology ecosystem. In particular, this paper presents LiveMicro, a system whose benefit is twofold: on one hand, it enables edge computing driven digital pathology computations, such as data-driven image processing on a live capture of the microscope. On the other hand, our system allows remote pathologists to diagnosis in collaboration in a single virtual microscope session, facilitating continuous medical education and remote consultation, crucial for under-served and remote hospital or private practice. Our results show the benefits and the principles underpinning our solution, with particular emphasis on how the pathologists interact with our application. Additionally, we developed simple yet effective diagnosis-aided algorithms to demonstrate the practicality of our approach.
Enriching Social Media Personas with Personality Traits: A Deep Learning Approach Using the Big Five Classes

To predict personality traits of data-driven personas, we apply an automatic persona generation methodology to generate 15 personas from the social media data of an online news organization. After generating the personas, we aggregate each personas’ YouTube comments and predict the “Big Five” personality traits of each persona from the comments pertaining to that persona. For this, we develop a deep learning classifier using three publicly available datasets. Results indicate an average performance increase of 4.84% in F1 scores relative to the baseline. We then analyze how the personas differ by their detected personality traits and discuss how personality traits could be implemented in data-driven persona profiles, as either scores or narratives.
Tweedie gradient boosting for extremely unbalanced zero-inflated data

Tweedie's compound Poisson model is a popular method to model insurance claims with probability mass at zero and nonnegative, highly right-skewed distribution. In particular, it is not uncommon to have extremely unbalanced data with excessively large proportion of zero claims, and even traditional Tweedie model may not be satisfactory for fitting the data. In this paper, we propose a boosting-assisted zero-inflated Tweedie model, called EMTboost, that allows zero probability mass to exceed a traditional model. We makes a nonparametric assumption on its Tweedie model component, that unlike a linear model, is able to capture nonlinearities, discontinuities, and complex higher order interactions among predictors. A specialized Expectation-Maximization algorithm is developed that integrates a blockwise coordinate descent strategy and a gradient tree-boosting algorithm to estimate key model parameters. We use extensive simulation and data analysis on synthetic zero-inflated auto-insurance claim data to illustrate our method's prediction performance.
Library for evolutionary algorithms in Python (LEAP)

There are generally three types of scientific software users: users that solve problems using existing science software tools, researchers that explore new approaches by extending existing code, and educators that teach students scientific concepts. Python is a general-purpose programming language that is accessible to beginners, such as students, but also as a language that has a rich scientific programming ecosystem that facilitates writing research software. Additionally, as high-performance computing (HPC) resources become more readily available, software support for parallel processing becomes more relevant to scientific software.
Understanding end-users' perspectives: Towards developing usability guidelines for disaster apps

Several mobile applications (apps) targeted for use by the public during disasters already exist. A large amount of research has been conducted to investigate the functionalities of these apps in aiding the public during disasters; however, only a few studies have investigated the apps' usability in the context of crises. In acute situations, seemingly minor issues can become critical concerns. This study develops usability guidelines for app designers and researchers using an iterative design process. The proposed usability guidelines consider concerns raised by end-users and provide three main recommendations: make critical information salient; account for cognitive load; and build trust.
SAR Image Speckle Reduction Based on Nonconvex Hybrid Total Variation Model

Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the visual effect and brings great difficulties to the postprocessing of the SAR image. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized to reduce the speckle. However, the strong scatters in SAR image with radiometry several orders of magnitude larger than their surrounding regions limit the effectiveness of TV regularization. Meanwhile, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm first-order TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant, and it usually underestimates high-amplitude components of image gradient as the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm uniformly penalizes the amplitude. To overcome these shortcomings, a new hybrid variation model, called Fisher-Tippett (FT) distribution-ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm first-and second-order hybrid TVs (HTpVs), is proposed to reduce the speckle after removing the strong scatters. Especially, the FT-HTpV inherits the advantages of the distribution based data fidelity term, the nonconvex regularization, and the higher order TV regularization. Therefore, it can effectively remove the speckle while preserving point scatters and edges and reducing staircase artifacts well. To efficiently solve the nonconvex minimization problem, an iterative framework with a nonmonotone-accelerated proximal gradient (nmAPG) method and a matrix-vector acceleration strategy are used. Extensive experiments on both the simulated and real SAR images demonstrate the effectiveness of the proposed method.
A lightweight anonymous two‐factor authentication protocol for wireless sensor networks in Internet of Vehicles

Summary Internet of Vehicles (IoV), as the next generation of transportation systems, tries to make highway and public transportation more secure than used to be. In this system, users use public channels for their communication so they can be the victims of passive or active attacks. Therefore, a secure authentication protocol is essential for IoV; consequently, many protocols are presented to provide secure authentication for IoV. In 2018, Yu et al proposed a secure authentication protocol for WSNs in vehicular communications and claimed that their protocol could satisfy all crucial security features of a secure authentication protocol. Unfortunately, we found that their protocol is susceptible to sensor capture attack, user traceability attack, user impersonation attack, and offline sink node's secret key guessing attack. In this paper, we propose a new authentication protocol for IoV which can solve the weaknesses of Yu et al's protocol. Our protocol not only provides anonymous user registration phase and revocation smart card phase but also uses the biometric template in place of the password. We use both Burrow‐Abadi‐Needham (BAN) logic and real‐or‐random (ROR) model to present the formal analysis of our protocol. Finally, we compare our protocol with other existing related protocols in terms of security features and computation overhead. The results prove that our protocol can provide more security features and it is usable for IoV system.
Multi-dimensional Bayesian network classifiers: A survey

Multi-dimensional classification is a cutting-edge problem, in which the values of multiple class variables have to be simultaneously assigned to a given example. It is an extension of the well known multi-label subproblem, in which the class variables are all binary. In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers. We focus on multi-dimensional Bayesian network classifiers, which directly cope with multi-dimensional classification and consider dependencies among class variables. A comprehensive survey of this state-of-the-art classification model is offered by covering aspects related to their learning and inference process complexities. We also describe algorithms for structural learning, provide real-world applications where they have been used, and compile a collection of related software.
On opportunistic software reuse

Abstract The availability of open source assets for almost all imaginable domains has led the software industry to opportunistic design —an approach in which people develop new software systems in an ad hoc fashion by reusing and combining components that were not designed to be used together. In this paper we investigate this emerging approach. We demonstrate the approach with an industrial example in which Node.js modules and various subsystems are used in an opportunistic way. Furthermore, to study opportunistic reuse as a phenomenon, we present the results of three contextual interviews and a survey with reuse practitioners to understand to what extent opportunistic reuse offers improvements over traditional systematic reuse approaches.
Distilling Ordinal Relation and Dark Knowledge for Facial Age Estimation

In this article, we propose a knowledge distillation approach with two teachers for facial age estimation. Due to the nonstationary patterns of the facial-aging process, the relative order of age labels provides more reliable information than exact age values for facial age estimation. Thus, the first teacher is a novel ranking method capturing the ordinal relation among age labels. Especially, it formulates the ordinal relation learning as a task of recovering the original ordered sequences from shuffled ones. The second teacher adopts the same model as the student that treats facial age estimation as a multiclass classification task. The proposed method leverages the intermediate representations learned by the first teacher and the softened outputs of the second teacher as supervisory signals to improve the training procedure and final performance of the compact student for facial age estimation. Hence, the proposed knowledge distillation approach is capable of distilling the ordinal knowledge from the ranking model and the dark knowledge from the multiclass classification model into a compact student, which facilitates the implementation of facial age estimation on platforms with limited memory and computation resources, such as mobile and embedded devices. Extensive experiments involving several famous data sets for age estimation have demonstrated the superior performance of our proposed method over several existing state-of-the-art methods.
Harnessing Deep Learning via a Single Building Block

Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL libraries with highly-specialized kernels for each workload/architecture, leading to numerous, complex code-bases that strive for performance, yet they are hard to maintain and do not generalize. In this work, we introduce the batch-reduce GEMM kernel and show how the most popular DL algorithms can be formulated with this kernel as the basic building-block. Consequently, the DL library-development degenerates to mere (potentially automatic) tuning of loops around this sole optimized kernel. By exploiting our new kernel we implement Recurrent Neural Networks, Convolution Neural Networks and Multilayer Perceptron training and inference primitives in just 3K lines of high-level code. Our primitives outperform vendor-optimized libraries on multi-node CPU clusters, and we also provide proof-of-concept CNN kernels targeting GPUs. Finally, we demonstrate that the batch-reduce GEMM kernel within a tensor compiler yields high-performance CNN primitives, further amplifying the viability of our approach.
On Addressing the Impact of ISO Speed Upon PRNU and Forgery Detection

Photo Response Non-Uniformity (PRNU) has been used as a powerful device fingerprint for image forgery detection because image forgeries can be revealed by finding the absence of the PRNU in the manipulated areas. The correlation between an image's noise residual with the device's reference PRNU is often compared with a decision threshold to check the existence of the PRNU. A PRNU correlation predictor is usually used to determine this decision threshold assuming the correlation is content-dependent. However, we found that not only the correlation is content-dependent, but it also depends on the camera sensitivity setting. Camera sensitivity, commonly known by the name of ISO speed, is an important attribute in digital photography. In this work, we will show the PRNU correlation's dependency on ISO speed. Due to such dependency, we postulate that a correlation predictor is ISO speed-specific, i.e. reliable correlation predictions can only be made when a correlation predictor is trained with images of similar ISO speeds to the image in question. We report the experiments we conducted to validate the postulate. It is realized that in the real-world, information about the ISO speed may not be available in the metadata to facilitate the implementation of our postulate in the correlation prediction process. We hence propose a method called Content-based Inference of ISO Speeds (CINFISOS) to infer the ISO speed from the image content.
What Can I Say?

Discoverability, the ability for users to find and execute features through a user interface, is a recurrent problem with Voice User Interface (VUI) design that makes it difficult for users to understand what commands are supported by a newly encountered system. We studied the effects of two different discoverability strategies proposed in literature, one which provides informational prompts automatically and one which provides help only when the user requests it by asking 'What Can I Say?'. Our study adopted a Wizard of Oz approach that allowed users to order food delivery by voice. Through statistical analysis, we confirmed the beneficial nature of both strategies, with significantly better task performance and higher usability scores in comparison to a baseline. This suggests designers should consider the use of a discoverability strategy in the design of VUIs. While no significant differences were found between the strategies, a majority of the participants highlighted their preference for the 'What Can I Say?' strategy if they were to use the VUI more frequently. Finally, we reflect on the implications for the design of VUIs, highlighting the need to distinguish between initial use and longer-term use in the selection of a strategy.
HEAR: Fog-Enabled Energy-Aware Online Human Eating Activity Recognition

Eating activity recognition (EAR) plays an important role in ensuring healthy eating habits. Recent advancements of the Internet of Things (IoT) have bolstered automated EAR through various wearable edge devices. State-of-the-art work uses some offline trained classifiers at the fog device to recognize eating activities. However, the eating habits of a person change quite frequently and vary from person to person. Therefore, the classifiers should be updated continuously with new data to adapt to these changes and be personalized over time through online learning. To the best of our knowledge, no state-of-the-art work has addressed this issue so far. In this article, we propose an online learning methodology called human eating activity recognition (HEAR) by introducing an online update phase. We also design an algorithm to be used in the online update phase that provides approximate true labels for the new data. Moreover, we also design a wearable neckband as the edge device to capture eating activity data (Chewing, Swallowing, Talking, and Idle) in a lab environment. Through a detailed experimental evaluation on 12 users, we show that an online learned neural network (OLNN) classifier using our HEAR methodology performs better than any state-of-the-art offline trained classifier. We also demonstrate that our OLNN classifier is energy efficient compared to the competitive offline trained classifiers.
Trustworthy AI Development Guidelines for Human System Interaction

Artificial Intelligence (AI) is influencing almost all areas of human life. Even though these AI-based systems frequently provide state-of-the-art performance, humans still hesitate to develop, deploy, and use AI systems. The main reason for this is the lack of trust in AI systems caused by the deficiency of transparency of existing AI systems. As a solution, “Trustworthy AI” research area merged with the goal of defining guidelines and frameworks for improving user trust in AI systems, allowing humans to use them without fear. While trust in AI is an active area of research, very little work exists where the focus is to build human trust to improve the interactions between human and AI systems. In this paper, we provide a concise survey on concepts of trustworthy AI. Further, we present trustworthy AI development guidelines for improving the user trust to enhance the interactions between AI systems and humans, that happen during the AI system life cycle.
CycLedger: A Scalable and Secure Parallel Protocol for Distributed Ledger via Sharding

Traditional public distributed ledgers have not been able to scale-out well and work efficiently. Sharding is deemed as a promising way to solve this problem. By partitioning all nodes into small committees and letting them work in parallel, we can significantly lower the amount of communication and computation, reduce the overhead on each node’s storage, as well as enhance the throughput of the distributed ledger. Existing sharding-based protocols still suffer from several serious drawbacks. The first thing is that all non-faulty nodes must connect well with each other, which demands a huge number of communication channels in the network. Moreover, previous protocols have faced great loss in efficiency in the case where the honesty of each committee’s leader is in question. At the same time, no explicit incentive is provided for nodes to actively participate in the protocol.We present CycLedger, a scalable and secure parallel protocol for distributed ledger via sharding. Our protocol selects a leader and a partial set for each committee, who are in charge of maintaining intra-shard consensus and communicating with other committees, to reduce the amortized complexity of communication, computation, and storage on all nodes. We introduce a novel semi-commitment scheme between committees and a recovery procedure to prevent the system from crashing even when leaders of committees are malicious. To add incentive for the network, we use the concept of reputation, which measures each node’s trusty computing power. As nodes with a higher reputation receive more rewards, there is an encouragement for nodes with strong computing ability to work honestly to gain reputation. In this way, we strike out a new path to establish scalability, security, and incentive for the sharding-based distributed ledger.