Reviewerly/exHarmony huggingface dataset:
Bidirectional recurrent autoencoder for 3D skeleton motion data refinement

In this paper, we propose a novel 3D skeleton human motion data refinement method that is based on a bidirectional recurrent autoencoder (BRA). The BRA has two main characteristics: (1) the motion manifold is extracted by a bidirectional long short-term memory recurrent neural network (B-LSTM-RNN) and (2) apart from statistical information of motion data, kinematic information including smoothness and bone length constrain, are also simultaneously exploited with noisy-clean motion pairs. Using a bidirectional LSTM unit, which is more suitable for time series and can infer information from the data in both time directions, our autoencoder extracts a manifold that can exploit the spatial and temporal relationships between previous and subsequent motion data. As a result, the refined data that are projected by the decoder from the motion manifold have much lower reproduction error. Furthermore, owing to the consideration of kinematic information, our reproduced motion data are of higher visual quality, while preserving positional precision. The proposed method is not action-specific and can handle a wide variety of noise types. The proposed method does not require the noise amplitude, which may be unknown in many scenarios, as a priori knowledge. Extensive experimental results demonstrate that our method outperforms several state-of-the-art methods.
Post-editing neural machine translation versus translation memory segments

The use of neural machine translation (NMT) in a professional scenario implies a number of challenges despite growing evidence that, in language combinations such as English to Spanish, NMT output quality has already outperformed statistical machine translation in terms of automatic metric scores. This article presents the result of an empirical test that aims to shed light on the differences between NMT post-editing and translation with the aid of a translation memory (TM). The results show that NMT post-editing involves less editing than TM segments, but this editing appears to take more time, with the consequence that NMT post-editing does not seem to improve productivity as may have been expected. This might be due to the fact that NMT segments show a higher variability in terms of quality and time invested in post-editing than TM segments that are 'more similar' on average. Finally, results show that translators who perceive that NMT boosts their productivity actually performed faster than those who perceive that NMT slows them down.
Self-testing quantum random-number generator based on an energy bound

We present a scheme for a self-testing quantum random number generator. Compared to the fully device-independent model, our scheme requires an extra natural assumption, namely that the mean energy per signal is bounded. The scheme is self-testing, as it allows the user to verify in real-time the correct functioning of the setup, hence guaranteeing the continuous generation of certified random bits. Based on a prepare-and-measure setup, our scheme is practical, and we implement it using only off-the-shelf optical components. The randomness generation rate is 1.25 Mbits/s, comparable to commercial solutions. Overall, we believe that this scheme achieves a promising trade-off between the required assumptions, ease-of-implementation and performance.
FTPN: Scene Text Detection With Feature Pyramid Based Text Proposal Network

Scene text detection is to detect the position of a text in the natural scene, the quality of which will directly affect the subsequent text recognition. It plays an important role in fields such as image retrieval and autopilot. How to perform multi-scale and multi-oriented text detection in the scene still remains as a problem. This paper proposes an effective scene text detection method that combines the convolutional neural network (CNN) and recurrent neural network (RNN). In order to better adapt to texts in different scales, feature pyramid networks (FPN) have been applied in the CNN part to extract multi-scale features of the image. We then utilize bidirectional long-short-term memory (Bi-LSTM) to encode these features to make full use of the text sequence characteristics with the outputs as a series of text proposals. The generated proposals are finally linked into a text line through a well-designed text connector, which can be flexibly adapted to any oriented texts. The proposed method is evaluated on three public datasets: ICDAR2013, ICDAR2015, and USTB-SV1K. For ICDAR2013 and USTB-1K, we have reached 92.5% and 62.6% F-measure, respectively. Our method has reached 72.8% F-measure on the more challenging ICDAR2015 which demonstrates the effectiveness of our method.
Photo-realistic image bit-depth enhancement via residual transposed convolutional neural network

Nowadays, with the rapid development of high bit-depth (HBD) monitors, the demands for high quality image visualization have been raised. However, a prominent problem is the inconsistency between existing low bit-depth (LBD) images and HBD monitors. When LBD images are simply de-quantized to HBD ones, there will be severe false contour artifacts in smooth gradient areas, degrading image visual quality. Therefore, bit-depth enhancement plays a key role in viewing LBD images on HBD monitors. In this paper, motivated by the promising results of deep Convolutional Neural Network (CNN) in generating realistic high-quality images, we proposed a novel algorithm to recover photo-realistic HBD images. To the best of our knowledge, CNN is introduced to bit-depth enhancement task for the first time. A novel neural network is proposed with summation and concatenation skip connections among transposed convolutional layers to cope with the gradient vanishing problem. Besides, different from traditional pixel-wise loss functions, perceptual loss is adopted to reconstruct images with higher visual quality and structural similarity to original HBD sources. Experiments performed on three datasets demonstrate that the proposed method outperforms state-of-the-art algorithms objectively and subjectively with suppressed false contour artifacts and preserved textures.
Research Trends in Digital Technologies and Modeling in Mathematics Education

This document presents a literature review that analyzes the articulation of modeling and digital technologies in the field of Mathematics Education. The review aims to find evidence of the use of digital technologies in modeling processes and how these practices can change some ways of working with students in the classroom. The results show, on the one hand, different roles that technology plays when it is articulated to a modeling process (as a resource in the process or as a means that reorganizes the process) and the uses given to diverse technological tools in the empirical studies analyzed. The findings present a new category that extends the classification of technologies and suggest the need to expand both theoretical and empirical research to get a better understanding of the impact of digital tools in modeling processes. In addition, the findings draw attention to the inclusion of mobile devices in future studies.
Impact of ICT on students with high abilities. Bibliographic review (2008–2018)

The students with high abilities process information differently, as a high creativity and involvement in their tasks are found among their personal characteristics. This implies the need for conducting specific educational interventions, within which some theoretical models have shown the effectiveness of the ICT. The aim of this work was to analyze the scientific production in the shape of articles on the ICT as a support for the learning of students with high abilities, during the 2008–2018 period in five databases: Web of Science (WoS), Scopus, Google Scholar, Education Resource Information Center (ERIC) and PsycINFO. A descriptive and quantitative methodology was used to present the most significant bibliometric data (citation index and impact), as well as the analysis of co-words, and the visualization of the relationships between words or terms through bi-dimensional bibliometric maps. A total of 725 documents were analyzed, from which 92 KW+ were obtained. Among the conclusions, a medium-low impact index is underlined, as referring to the number of articles published in the five databases, as well as the citations received. With respect to the thematic network of interrelations of studies on ICT and high abilities, these are being thematically linked with the importance of the Technologies in the classroom, the curriculum, the family's participation and the training of teachers.
Density-Based Outlier Detection for Safeguarding Electronic Patient Record Systems

This paper concerns the detection of abnormal data usage and unauthorized access in large-scale critical networks, specifically healthcare infrastructures. Hospitals in the U.K. are now connecting their traditionally isolated equipment on a large scale to Internet-enabled networks to enable remote data access. This step-change makes sensitive data accessible to a broader spectrum of users. The focus of this paper is on the safeguarding of electronic patient record (EPR) systems in particular. With over 83% of hospitals adopting EPRs, access to this healthcare data needs to be proactively monitored for malicious activity. Hospitals must maintain patient trust and ensure that the information security principles of integrity, availability, and confidentiality are applied to EPR data. Access to EPR is often heavily audited within healthcare infrastructures. However, this data is regularly left untouched in a data silo and only ever accessed on an ad hoc basis. Without proactive monitoring of audit records, data breaches may go undetected. In addition, external threats, such as phishing or social engineering techniques to acquire a clinician's logon credentials, need to be identified. Data behavior within healthcare infrastructures, therefore, needs to be proactively monitored for malicious, erratic, or unusual activity. This paper presents a system that employs a density-based local outlier detection model. The system is intended to add to the defense-in-depth of healthcare infrastructures. Patterns in EPR data are extracted to profile user behavior and device interactions in order to detect and visualize anomalous activities. The system is able to detect 144 anomalous behaviors in an unlabeled dataset of 1,007,727 audit logs. This includes 0.66% of the users on the system, 0.17% of patient record accesses, 0.74% of routine accesses, and 0.53% of the devices used in a specialist Liverpool (U.K.) hospital.
Cross-Lingual Sentiment Quantification

\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of unlabelled documents) is an important topic in sentiment analysis, as the study of sentiment-related quantities and trends across a population is often of higher interest than the analysis of individual instances. In this work we propose a method for \emph{Cross-Lingual Sentiment Quantification}, the task of performing sentiment quantification when training documents are available for a source language $\mathcal{S}$ but not for the target language $\mathcal{T}$ for which sentiment quantification needs to be performed. Cross-lingual sentiment quantification (and cross-lingual \emph{text} quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved. We present experimental results obtained on publicly available datasets for cross-lingual sentiment classification; the results show that the presented methods can perform cross-lingual sentiment quantification with a surprising level of accuracy.
Convolutional Edge Constraint-Based U-Net for Salient Object Detection

The salient object detection is receiving more and more attention from researchers. An accurate saliency map will be useful for subsequent tasks. However, in most saliency maps predicted by existing models, the objects regions are very blurred and the edges of objects are irregular. The reason is that the hand-crafted features are the main basis for existing traditional methods to predict salient objects, which results in different pixels belonging to the same object often being predicted different saliency scores. Besides, the convolutional neural network (CNN)-based models predict saliency maps at patch scale, which causes the objects edges of the output to be fuzzy. In this paper, we attempt to add an edge convolution constraint to a modified U-Net to predict the saliency map of the image. The network structure we adopt can fuse the features of different layers to reduce the loss of information. Our SalNet predicts the saliency map pixel-by-pixel, rather than at the patch scale as the CNN-based models do. Moreover, in order to better guide the network mining the information of objects edges, we design a new loss function based on image convolution, which adds an L1 constraint to the edge information of saliency map and ground-truth. Finally, experimental results reveal that our SalNet is effective in salient object detection task and is also competitive when compared with 11 state-of-the-art models.
Local Standards for Anonymization Practices in Health, Wellness, Accessibility, and Aging Research at CHI

When studying technologies pertaining to health, wellness, accessibility, and aging, researchers are often required to perform a balancing act between controlling and sharing sensitive data of the people in their studies and protecting the privacy of these participants. If the data can be anonymized and shared, it can boost the impact of the research by facilitating replication and extension. Despite anonymization, data reporting and sharing may lead to re-identification of participants, which can be particularly problematic when the research deals with sensitive topics, such as health. We analyzed 509 CHI papers in the domains of health, wellness, accessibility, and aging to examine data reporting and sharing practices. Our analysis revealed notable patterns and trends regarding the reporting of age, gender, participant types, sample sizes, methodology, ethical considerations, anonymization techniques, and data sharing. Based on our findings, we propose several suggestions for community standards and practices that could facilitate data reporting and sharing while limiting the privacy risks for study participants.
Communicating Uncertainty in Fertility Prognosis

Communicating uncertainty has been shown to provide positive effects on user understanding and decision-making. Surprisingly however, most personal health tracking applications fail to disclose the accuracy of their measurements and predictions. In the case of fertility tracking applications (FTAs), inaccurate predictions have already led to numerous unwanted pregnancies and law suits. However, integrating uncertainty into FTAs is challenging: Prediction accuracy is hard to understand and communicate, and its effect on users' trust and behavior is not well understood. We created a prototype for uncertainty visualizations for FTAs and evaluated it in a four-week field study with real users and their own data (N=9). Our results uncover far-reaching effects of communicating uncertainty: For example, users interpreted prediction accuracy as a proxy for their cycle health and as a security indicator for contraception. Displaying predicted and detected fertile phases next to each other helped users to understand uncertainty without negative emotional effects.
A User Identification Algorithm Based on User Behavior Analysis in Social Networks

The precision of the conventional user identification algorithm is not satisfactory because it ignores the role of user-generated data in identity matching. In this paper, we propose a frequent pattern mining-based cross-social network user identification algorithm that analyzes user-generated data in a personalized manner. We adopt the posterior probability-based information entropy weight allocation method that improves the precision rate and recall rate compared to the empirical weight allocation method. The extensive simulations are provided to demonstrate that the proposed algorithm can enhance the precision rate, recall rate, as well as the F-Measure (F1).
Scene Graph Prediction With Limited Labels

Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete relationships. All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each. Hiring human annotators is expensive, and using textual knowledge base completion methods are incompatible with visual data. In this paper, we introduce a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few' labeled examples. We analyze visual relationships to suggest two types of image-agnostic features that are used to generate noisy heuristics, whose outputs are aggregated using a factor graph-based generative model. With as few as 10 labeled examples per relationship, the generative model creates enough training data to train any existing state-of-the-art scene graph model. We demonstrate that our method outperforms all baseline approaches on scene graph prediction by 5.16 recall@ 100 for PREDCLS. In our limited label setting, we define a complexity metric for relationships that serves as an indicator (R2 = 0.778) for conditions under which our method succeeds over transfer learning, the de-facto approach for training with limited labels.
An Explanation of Fitts' Law-like Performance in Gaze-Based Selection Tasks Using a Psychophysics Approach

Eye gaze as an input method has been studied since the 1990s, to varied results: some studies found gaze to be more efficient than traditional input methods like a mouse, others far behind. Comparisons are often backed up by Fitts' Law without explicitly acknowledging the ballistic nature of saccadic eye movements. Using a vision science-inspired model, we here show that a Fitts'-like distribution of movement times can arise due to the execution of secondary saccades, especially when targets are small. Study participants selected circular targets using gaze. Seven different target sizes and two saccade distances were used. We then determined performance across target sizes for different sampling windows ("dwell times") and predicted an optimal dwell time range. Best performance was achieved for large targets reachable by a single saccade. Our findings highlight that Fitts' Law, while a suitable approximation in some cases, is an incomplete description of gaze interaction dynamics.
Efficient and secure multi-dimensional geometric range query over encrypted data in cloud

Secure geometric range query, which aims to retrieve data points within a given geometric range from an encrypted dataset in the cloud, attracts more and more attention due to its wide applications. Up to now, several secure geometric range query schemes have been put forward. However, the existing schemes still suffer from various disadvantages, such as they are of low efficiency, cannot support multi-dimensional data and general range query, or even have security flaws. In this paper, we study secure geometric range query on encrypted dataset in cloud. First, we show the security problem of the state-of-the-art scheme by proposing an efficient attack method. Then, we propose a new secure solution for general multi-dimensional range query, which is secure under known-background model, and leverage R-tree index to achieve sub-linear search efficiency. Finally, through theoretical analysis and extensive experiments, we demonstrate the effectiveness and efficiency of our proposed approaches.
No-Reference Quality Evaluator of Transparently Encrypted Images

In past years, various encrypted algorithms have been proposed to fully or partially protect the multimedia content in view of practical applications. In the context of digital TV broadcasting, transparent encryption only protects partial content and fulfills both security and quality requirements. To date, only a few reference-based works have been reported to evaluate the quality of transparently encrypted images. However, these works are incapable of reference-unavailable conditions. In this paper, we conduct the first attempt that proposes a novel quality evaluator in the absence of reference images. The key strategy of the proposed metric lies in extracting features by considering the motivation of transparently encrypted images. Specifically, given that encrypted images prevent content from being easily recognized, several features, including correlation coefficient, information entropy, and intensity statistic, are preliminarily extracted to estimate visual recognizability. Meanwhile, considering that encrypted images are avoided since they are of extremely low quality, we also capture many features to measure the distortions on multiple quality-sensitive image attributes, such as naturalness, structure, and texture. Finally, the quality evaluator is built by bridging all extracted features and corresponding quality scores via a regression module. Experimental results demonstrate that the proposed method is superior to the mainstream no-reference quality evaluation methods designed for synthetically distorted images and possesses a close approximation to state-of-the-art reference-based methods designed for encrypted images.
HandSee

We present HandSee, a novel sensing technique that can capture the state and movement of the user's hands touching or gripping a smartphone. We place a right angle prism mirror on the front camera to achieve a stereo vision of the scene above the touchscreen surface. We develop a pipeline to extract the depth image of hands from a monocular RGB image, which consists of three components: a stereo matching algorithm to estimate the pixel-wise depth of the scene, a CNN-based online calibration algorithm to detect hand skin, and a merging algorithm that outputs the depth image of the hands. Building on the output, a substantial set of valuable interaction information, such as fingers' 3D location, gripping posture, and finger identity can be recognized concurrently. Due to this unique sensing ability, HandSee enables a variety of novel interaction techniques and expands the design space for full hand interaction on smartphones.
Does It Feel Real?

Professionals in domains like film, theater, or architecture often rely on physical models to visualize spaces. With virtual reality (VR) new tools are available providing immersive experiences with correct perceptions of depth and scale. However, these lack the tangibility of physical models. Using tangible objects in VR can close this gap but creates the challenges of producing suitable objects and interacting with them with only the virtual objects visible. This work addresses these challenges by evaluating tangibles with three haptic fidelities: equal disc-shaped tangibles for all virtual objects, Lego-built tangibles, and 3D-printed tangibles resembling the virtual shapes. We present results from a comparative study on immersion, performance, and intuitive interaction and interviews with domain experts. The results show that 3D-printed objects perform best, but Lego offers a good trade-off between fast creation of tangibles and sufficient fidelity. The experts rate our approach as useful and would use all three versions.