Reviewerly/exHarmony huggingface dataset:
Super-aged society: Constructing an integrated information platform of self-recording lifelogs and medical records to support health care in Japan

As the super-aged society, Japan is facing challenges in health care system. As one of measures to cope with challenges, the Ministry of Health, Labor, and Welfare started to construct an open medical information platform, named PeOPLe, in 2016 for personalized medical care, improvement of medical services, and the redistribution of medical resources. The Ministry plans to build the platform infrastructure by 2020 and put the platform into full-scale operation by 2025. PeOPLe collects only medical records, but it should collect lifelogs as well in order to better improve the health, especially for elderly. A lifelog is a record of a person's activity and it has potential to predict the probability a person will suffer a lifestyle-related disease as a result of the person's lifestyle. This prediction could help to maintain the health of the elderly. In addition, constructing a self-recording platform integrated with the medical platform is the best way to collect lifelogs since collecting a large amount of lifelogs for a long time from various people at public or medical agencies is difficult. A self-recording platform is a place where people can post and manage their lifelogs. In return for posting lifelogs, people will receive personalized health advice, which will attract more people.
Data Fine-Tuning

In real-world applications, commercial off-the-shelf systems are utilized for performing automated facial analysis including face recognition, emotion recognition, and attribute prediction. However, a majority of these commercial systems act as black boxes due to the inaccessibility of the model parameters which makes it challenging to fine-tune the models for specific applications. Stimulated by the advances in adversarial perturbations, this research proposes the concept of Data Fine-tuning to improve the classification accuracy of a given model without changing the parameters of the model. This is accomplished by modeling it as data (image) perturbation problem. A small amount of “noise” is added to the input with the objective of minimizing the classification loss without affecting the (visual) appearance. Experiments performed on three publicly available datasets LFW, CelebA, and MUCT, demonstrate the effectiveness of the proposed concept.
Sign-Full Random Projections

The method of 1-bit (“sign-sign”) random projections has been a popular tool for efficient search and machine learning on large datasets. Given two D-dim data vectors u, v ∈ ℝD, one can generate x = ∑i=1D uiri, and y = ∑i=1D viri, where ri ∼ N(0, 1) iid. Then one can estimate the cosine similarity ρ from sgn(x) and sgn(y). In this paper, we study a series of estimators for “sign-full” random projections. First we prove E(sgn(x)y) = √2/πρ, which provides an estimator for ρ. Interestingly this estimator can be substantially improved by normalizing y. Then we study estimators based on E (y−1x≥0 + y+1x&lt;0) and its normalized version. We analyze the theoretical limit (using the MLE) and conclude that, among the proposed estimators, no single estimator can achieve (close to) the theoretical optimal asymptotic variance, for the entire range of ρ. On the other hand, the estimators can be combined to achieve the variance close to that of the MLE. In applications such as near neighbor search, duplicate detection, knn-classification, etc, the training data are first transformed via random projections and then only the signs of the projected data points are stored (i.e., the sgn(x)). The original training data are discarded. When a new data point arrives, we apply random projections but we do not necessarily need to quantize the projected data (i.e., the y) to 1-bit. Therefore, sign-full random projections can be practically useful. This gain essentially comes at no additional cost.
EARS 2019

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also interpretability of the models or explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations regarding AI interpretability, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond.
Neural Text Style Transfer via Denoising and Reranking

We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style. To control for aspects such as preserving meaning while modifying style, we propose a reranking approach in the data synthesis phase. We evaluate our method on three novel style transfer tasks: transferring between British and American varieties, text genres (formal vs. casual), and lyrics from different musical genres. By measuring style transfer quality, meaning preservation, and the fluency of generated outputs, we demonstrate that our method is able both to produce high-quality output while maintaining the flexibility to suggest syntactically rich stylistic edits.
Traceability Establishment and Visualization of Software Artefacts in DevOps Practice: A Survey

DevOps based software process has become popular with the vision of an effective collaboration between the development and operations teams that continuously integrates the frequent changes.Traceability manages the artefact consistency during a software process.This paper explores the trace-link creation and visualization between software artefacts, existing tool support, quality aspects and the applicability in a DevOps environment.As the novelty of this study, we identify the challenges that limit the traceability considerations in DevOps and suggest research directions.Our methodology consists of concept identification, state-of-practice exploration and analytical review.Despite the existing related work, there is a lack of tool support for the traceability management between heterogeneous artefacts in software development with DevOps practices.Although many existing studies have low industrial relevance, a few proprietary traceability tools have shown a high relevance.The lack of evidence of the related applications indicates the need for a generalized traceability approach.Accordingly, we conclude that the software artefact traceability is maturing and applying collaboratively in the software process.This can be extended to explore features such as artefact change impact analysis, change propagation, continuous integration to improve software development in DevOps environments.
Adaptive BRDF‐Oriented Multiple Importance Sampling of Many Lights

Abstract Many‐light rendering is becoming more common and important as rendering goes into the next level of complexity. However, to calculate the illumination under many lights, state of the art algorithms are still far from efficient, due to the separate consideration of light sampling and BRDF sampling. To deal with the inefficiency of many‐light rendering, we present a novel light sampling method named BRDF‐oriented light sampling, which selects lights based on importance values estimated using the BRDF's contributions. Our BRDF‐oriented light sampling method works naturally with MIS, and allows us to dynamically determine the number of samples allocated for different sampling techniques. With our method, we can achieve a significantly faster convergence to the ground truth results, both perceptually and numerically, as compared to previous many‐light rendering algorithms.
Discrete Binary Coding based Label Distribution Learning

Label Distribution Learning (LDL) is a general learning paradigm in machine learning, which includes both single-label learning (SLL) and multi-label learning (MLL) as its special cases. Recently, many LDL algorithms have been proposed to handle different application tasks such as facial age estimation, head pose estimation and visual sentiment distributions prediction. However, the training time complexity of most existing LDL algorithms is too high, which makes them unapplicable to large-scale LDL. In this paper, we propose a novel LDL method to address this issue, termed Discrete Binary Coding based Label Distribution Learning (DBC-LDL). Specifically, we design an efficiently discrete coding framework to learn binary codes for instances. Furthermore, both the pair-wise semantic similarities and the original label distributions are integrated into this framework to learn highly discriminative binary codes. In addition, a fast approximate nearest neighbor (ANN) search strategy is utilized to predict label distributions for testing instances. Experimental results on five real-world datasets demonstrate its superior performance over several state-of-the-art LDL methods with the lower time cost.
Combining ADMM and the Augmented Lagrangian Method for Efficiently Handling Many Constraints

Many machine learning methods entail minimizing a loss-function that is the sum of the losses for each data point. The form of the loss function is exploited algorithmically, for instance in stochastic gradient descent (SGD) and in the alternating direction method of multipliers (ADMM). However, there are also machine learning methods where the entailed optimization problem features the data points not in the objective function but in the form of constraints, typically one constraint per data point. Here, we address the problem of solving convex optimization problems with many convex constraints. Our approach is an extension of ADMM. The straightforward implementation of ADMM for solving constrained optimization problems in a distributed fashion solves constrained subproblems on different compute nodes that are aggregated until a consensus solution is reached. Hence, the straightforward approach has three nested loops: one for reaching consensus, one for the constraints, and one for the unconstrained problems. Here, we show that solving the costly constrained subproblems can be avoided. In our approach, we combine the ability of ADMM to solve convex optimization problems in a distributed setting with the ability of the augmented Lagrangian method to solve constrained optimization problems. Consequently, our algorithm only needs two nested loops. We prove that it inherits the convergence guarantees of both ADMM and the augmented Lagrangian method. Experimental results corroborate our theoretical findings.
An Efficient Algorithm for Skeptical Preferred Acceptance in Dynamic Argumentation Frameworks

Though there has been an extensive body of work on efficiently solving computational problems for static Dung's argumentation frameworks (AFs), little work has been done for handling dynamic AFs and in particular for deciding the skeptical acceptance of a given argument. In this paper we devise an efficient algorithm for computing the skeptical preferred acceptance in dynamic AFs. More specifically, we investigate how the skeptical acceptance of an argument (goal) evolves when the given AF is updated and propose an efficient algorithm for solving this problem. Our algorithm, called SPA, relies on two main ideas: i) computing a small portion of the input AF, called "context-based" AF, which is sufficient to determine the status of the goal in the updated AF, and ii) incrementally computing the ideal extension to further restrict the context-based AF. We experimentally show that SPA significantly outperforms the computation from scratch, and that the overhead of incrementally maintaining the ideal extension pays off as it speeds up the computation.
One-Shot Texture Retrieval with Global Context Metric

In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image. To address this problem, we present an OS-TR network to encoding both reference patch and query image, leading to achieve texture segmentation towards the reference category. Unlike the existing texture encoding methods that integrate CNN with orderless pooling, we propose a directionality-aware network to capture the texture variations at each direction, resulting in spatially invariant representation. To segment new categories given only few examples, we incorporate a self-gating mechanism into relation network to exploit global context information for adjusting per-channel modulation weights of local relation features. Extensive experiments on benchmark texture datasets and real scenarios demonstrate the above-par segmentation performance and robust generalization across domains of our proposed method.
“How Do I Do That?” A Literature Review of Research Data Management Skill Gaps of Canadian Health Sciences Information Professionals

Abstract: There is a recognized need to provide research data management (RDM) services in health sciences libraries. A review of the literature reveals numerous strategies to provide training for health sciences librarians as they provide RDM services to health sciences researchers, faculty, and students. However, no consensus emerges through this literature review with respect to RDM training initiatives. With training initiatives being developed and documented, more in-depth research will emerge that verifies which initiatives have the greatest success for upskilling information professionals in managing research data. This is an area where future library and information studies research can be conducted. It is the hope that with this literature review, I can conduct my own survey to gain more perspective on RDM in a Canadian health sciences library context.
Capturing Spatial and Temporal Patterns for Facial Landmark Tracking through Adversarial Learning

The spatial and temporal patterns inherent in facial feature points are crucial for facial landmark tracking, but have not been thoroughly explored yet. In this paper, we propose a novel deep adversarial framework to explore the shape and temporal dependencies from both appearance level and target label level. The proposed deep adversarial framework consists of a deep landmark tracker and a discriminator. The deep landmark tracker is composed of a stacked Hourglass network as well as a convolutional neural network and a long short-term memory network, and thus implicitly capture spatial and temporal patterns from facial appearance for facial landmark tracking. The discriminator is adopted to distinguish the tracked facial landmarks from ground truth ones. It explicitly models shape and temporal dependencies existing in ground truth facial landmarks through another convolutional neural network and another long short-term memory network. The deep landmark tracker and the discriminator compete with each other. Through adversarial learning, the proposed deep adversarial landmark tracking approach leverages inherent spatial and temporal patterns to facilitate facial landmark tracking from both appearance level and target label level. Experimental results on two benchmark databases demonstrate the superiority of the proposed approach to state-of-the-art work.
Fixation Detection with Ray-casting in Immersive Virtual Reality

This paper demonstrates the application of a proposed eye fixation detection algorithm to eye movement recorded during eye gaze input within immersive Virtual Reality and compares it with the standard frame-by-frame analysis for validation.Pearson correlations and a sample paired t-test indicated strong correlations between the two analysis methods in terms of fixation duration.The results showed that the principle of eye movement event detection in 2D can be applied successfully in a 3D environment and ensures efficient detection when combined with ray-casting and event time.
Provable Indefinite-Horizon Real-Time Planning for Repetitive Tasks

In many robotic manipulation scenarios, robots often have to perform highly-repetitive tasks in structured environments e.g. sorting mail in a mailroom or pick and place objects on a conveyor belt. In this work we are interested in settings where the tasks are similar, yet not identical (e.g., due to uncertain orientation of objects) and motion planning needs to be extremely fast. Preprocessing-based approaches prove to be very beneficial in these settings—they analyze the configuration-space offline to generate some auxiliary information which can then be used in the query phase to speedup planning times. Typically, the tighter the requirement is on query times the larger the memory footprint will be. In particular, for high-dimensional spaces, providing real-time planning capabilities is extremely challenging. While there are planners that guarantee real-time performance by limiting the planning horizon, we are not aware of general-purpose planners capable of doing it for indefinite horizon (i.e., planning to the goal). To this end, we propose a preprocessingbased method that provides provable bounds on the query time while incurring only a small amount of memory overhead in the query phase. We evaluate our method on a 7-DOF robot arm and show a speedup of over tenfold in query time when compared to the PRM algorithm.
Surfing In Sound: Sonification of Hidden Web Tracking

Web tracking is found on 90% of common websites. It allows online behavioral analysis which can reveal insights to sensitive personal data of an individual. Most users are not aware of the amount of web tracking happening in the background. This paper contributes a sonification-based approach to raise user awareness by conveying information on web tracking through sound while the user is browsing the web. We present a framework for live web tracking analysis, conversion to Open Sound Control events and sonification. The amount of web tracking is disclosed by sound each time data is exchanged with a web tracking host. When a connection to one of the most prevalent tracking companies is established, this is additionally indicated by a voice whispering the company name. Compared to existing approaches on web tracking sonification, we add the capability to monitor any network connection, including all browsers, applications and devices. An initial user study with 12 participants showed empirical support for our main hypothesis: exposure to our sonification significantly raises web tracking awareness.
Media and information literacy: a measurement instrument for adolescents

Media and information literacy is the process of empowering people to seek, evaluate, use and create information effectively in order to achieve their personal, social, occupational and educational goals. This paper presents the dimensions and categories of an instrument (the MIL questionnaire) developed within the University of the Basque Country's UNESCO Chair in Communication and Educational Values, which aims to analyse media and information literacy levels amongst adolescents. The instrument is based on four dimensions that encompass basic aspects of the field of educommunication. These dimensions are: (1) Media access and use; (2) Media language and critical comprehension; (3) Production and programming processes; and (4) Transforming one's situation through communication. The aim of this piece of research was to test the reliability and validity of this instrument in a pilot study conducted with 167 adolescents aged between 13 and 15, from schools in Colombia, Ecuador and Spain. With the exception of the first dimension, the results indicate that the questionnaire has an acceptable reliability level and is an adequate instrument for evaluating media and information literacy among adolescents.
Utilizing Class Information for Deep Network Representation Shaping

Statistical characteristics of deep network representations, such as sparsity and correlation, are known to be relevant to the performance and interpretability of deep learning. When a statistical characteristic is desired, often an adequate regularizer can be designed and applied during the training phase. Typically, such a regularizer aims to manipulate a statistical characteristic over all classes together. For classification tasks, however, it might be advantageous to enforce the desired characteristic per class such that different classes can be better distinguished. Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and classwise Variance Regularizer (cw-VR). cw-CR targets to reduce the covariance of representations calculated from the same class samples for encouraging feature independence. cw-VR is similar, but variance instead of covariance is targeted to improve feature compactness. For the sake of completeness, their counterparts without using class information, Covariance Regularizer (CR) and Variance Regularizer (VR), are considered together. The four regularizers are conceptually simple and computationally very efficient, and the visualization shows that the regularizers indeed perform distinct representation shaping. In terms of classification performance, significant improvements over the baseline and L1/L2 weight regularization methods were found for 21 out of 22 tasks over popular benchmark datasets. In particular, cw-VR achieved the best performance for 13 tasks including ResNet-32/110.
Attentiondrop for Convolutional Neural Networks

Dropout has been widely used in fully connected networks but becomes less effective for convolutional neural networks (CNNs), since the spatially correlated features still allow dropped information to flow through the network. To make dropout more practical for CNNs, structured dropout methods have been recently proposed by dropping regions with fixed shapes and random positions, which nonetheless may lead to unexpected discarding of information. To address this problem, in this paper, we propose a novel dropout variant based on attention information named AttentionDrop that drops features adaptively. Specifically, it precisely localizes masks that have irregular shapes according to the values of activation units. In addition, the use of soft values in adaptive masks lowers the risk of a complete loss of indispensable information. Experimental results demonstrate the effectiveness of our AttentionDrop on public datasets for image classification.
Financial market reaction to cyberattacks

Drawing upon an extensive dataset comprising 3,680 cyberattacks on firms listed in 5 stock markets, our main objective is to ascertain the financial market reaction based on a hybrid valuation inspired by the event study methodology and a counterfactual analysis. Analyses concern three dates that are specific to cyberattacks: 1) the accident date; 2) the first notice date; and 3) the original loss start date. Results indicate that there is a negative abnormal return for the NASDAQ after the accident date. The reactions of the NASDAQ and NYSE are similar, and negative for the first notice date but positive after the original loss start date. In the European context, cumulative abnormal returns are negative for French and German companies after the first notice date.