Reviewerly/exHarmony huggingface dataset:
Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition

We propose a novel model, called stroke sequence-dependent deep convolutional neural network (SSDCNN), which uses the stroke sequence information and eight-directional features of Chinese characters for online handwritten Chinese character recognition (OLHCCR). SSDCNN learns the representation of OLHCCs by incorporating the natural sequence information of the strokes. Furthermore, it naturally incorporates the eight-directional features. First, SSDCNN inputs the stroke sequence and transforms it into stacks of feature maps following the writing order of the strokes. Second, the fixed-length, stroke sequence-dependent representations of OLHCC are derived through convolutional, residual, and max-pooling operations. Third, the stroke sequence-dependent representation is combined with the eight-directional features via a number of fully connected neural network layers. Finally, the Chinese characters are recognized using a softmax classifier. The SSDCNN is trained in two stages: 1) the whole architecture is pretrained using the training data until the performance converges to an acceptable degree. 2) The stroke sequence-dependent representation is combined with the eight-directional features by a fully connected neural network and a softmax layer for further training. The model was experimentally evaluated on the OLHCCR competition tasks of International Conference on Document Analysis and Recognition (ICDAR) 2013. The recognition error was a maximum 58.28% lower in SSDCNN than in a model using the eight-directional features alone (5.13% versus 2.14%). Owing to its high accuracy (97.86%), the proposed SSDCNN reduced the recognition error by approximately 18.0% as compared with that of the winning system in the ICDAR 2013 competition. SSDCNN integrated with an adaptation mechanism, called the SSDCNN+Adapt model, and reached a new state-of-the-art (SOTA) standard with an accuracy of 97.94%. The SSDCNN exploits the stroke sequence information to learn high-quality OLHCC representations. Moreover, the learned representation and the classical eight-directional features complement each other within the SSDCNN architecture.
Cascade multi-head attention networks for action recognition

Long-term temporal information yields crucial cues for video action understanding. Previous researches always rely on sequential models such as recurrent networks, memory units, segmental models, self-attention mechanism to integrate the local temporal features for long-term temporal modeling. Recurrent or memory networks record temporal patterns (or relations) by memory units, which are proved to be difficult to capture long-term information in machine translation. Self-attention mechanisms directly aggregate all local information with attention weights which is more straightforward and efficient than the former. However, the attention weights from self-attention ignore the relations between local information and global information which may lead to unreliable attention. To this end, we propose a new attention network architecture, termed as Cascade multi-head ATtention Network (CATNet), which constructs video representations with two-level attentions, namely multi-head local self-attentions and relation based global attentions. Starting from the segment features generated by backbone networks, CATNet first learns multiple attention weights for each segment to capture the importance of local features in a self-attention manner. With the local attention weights, CATNet integrates local features into several global representations, and then learns the second level attention for the global information by a relation manner. Extensive experiments on Kinetics, HMDB51, and UCF101 show that our CATNet boosts the baseline network with a large margin. With only RGB information, we respectively achieve 75.8%, 75.2%, and 96.0% on these three datasets, which are comparable or superior to the state of the arts.
Multimodal Learning in Health Sciences and Medicine: Merging Technologies to Enhance Student Learning and Communication

Advances in consumer-level educational technologies show great promise for enhancing the learning experiences of students in health and medicine. There are particular benefits to using a combination of various devices and technologies when teaching challenging concepts. These include augmented reality-enabled devices enriched with accompanying 3D printed models, or virtual reality sessions coupled with online quizzes or revision activities. Tablet applications can also be integrated while students engage concurrently in desktop-based online learning. This mixing and merging of different technologies can allow educators to focus on the strengths of each device, while mitigating limitations arising from a single mode's stand-alone use. This chapter describes a series of options to integrate multiple digital modes when educating health science and medical students using technology. It also presents the opportunity for health professional program graduates to be trained in teaching using technology, as their future careers can be enhanced by an ability to educate effectively, or from the skills developed when incorporating innovations such as serious games into a health curriculum. With the dynamic and ever-changing nature of health and medical education, educators can find great benefits when introducing multimodal digital learning into their respective courses.
On Security Enhancement of Steganography via Generative Adversarial Image

Steganography plays an important role in information hiding. With the development of steganalysis, traditional steganography faces more detection threat. It is necessary to improve security of current steganographic methods. One effective way is to generate suitable covers for steganography, which can be achieved by adversarial learning. In this letter, we propose a new approach for quickly constructing high-quality adversarial images. Compared with original images, the generative adversarial images are more suitable for carrying secret information. According to the characteristics of steganography, we design a new loss function in adversarial attacks, which makes the adversarial images obtain the similar classification results before and after steganography. In addition, to further improve security of the adversarial images, we also make use of the zero-sum idea of generative adversarial networks. Experimental results show that the proposed method can significantly enhance security of steganography.
Identifying geochemical anomaly through spatially anisotropic singularity mapping: A case study from silver-gold deposit in Pangxidong district, SE China

The anisotropic singularity analysis, also referred to as Spatially Weighted Singularity Mapping (SWSM), was developed by means of multifractal modeling and spatial weighting. The SWSM extends the application of the Ordinary Singularity Mapping (OSM) technique, which is spatially isotropic, to the anisotropic case. In this paper, the SWSM method was demonstrated in a case study where the geochemical anomaly associated with the silver-gold mineralization was identified through mapping the spatially anisotropic singularity of the pathfinder elements including Ag, Au, Cu, Pb, and Zn, to in Pangxidong district, south of the Qinzhou-Hangzhou Metallogenic Belt, SE China. Firstly, using the stream sediment geochemical data and spatial weighting factor linked to NE-trending ore-controlling fault system, the local anisotropic singularity index values (i.e. ϵ-values) were estimated for each of the five elements. Then, Principal Component Analysis (PCA) was applied to the ϵ-values to obtain comprehensive geochemical anomaly of the multiple element (i.e. PC1 scores). It appeared that the comprehensive geochemical anomaly has a better prediction efficiency with the discovered silver-gold occurrences than the one derived from the OSM-PCA approach (PCA applied to singularity index values estimated by the OSM technique). The performance tests were assessed by using the receiver operating characteristics as well as prediction-area plot and concentration-area fractal analysis methods. Finally, the student's t-static method was employed to determine the reasonable threshold values separating the geochemical anomaly and background. The student's t-values indicate that spatial correlation between the locations of the known silver-gold mineral deposits and the geochemical anomaly derived by the SWSM-PCA approach was much stronger than that of the geochemical anomaly derived by the OSM-PCA method. The geochemical anomalies delineated by the SWSM-PCA approach were considered to be the favorable target areas for exploration of similar undiscovered silver-gold polymetallic deposits. This study has demonstrated that the SWSM approach can incorporate the spatial anisotropic information (e.g. direction and range of spatial variability), which exhibits geological controlling features, into the singularity analysis of geo-variables in various geo-spatial datasets such as geochemical, geophysical and remote sensing data.
Long Short-Term Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records

Individuals suffer from chronic diseases without being identified in time, which brings lots of burden of disease to the society. This paper presents a multiple disease risk prediction method to systematically assess future disease risks for patients based on their longitudinal medical records. In this study, medical diagnoses based on International Classification of Diseases (ICD) are aggregated into different levels for prediction to meet the needs of different stakeholders. The proposed approach gets validated using two independent hospital medical datasets, which includes 7105 patients with 18, 893 patients and 4170 patients with 13, 124 visits, respectively. The initial analysis reveals a high variation in patients' characteristics. The study demonstrates that recurrent neural network with long-short time memory units performs well in different levels of diagnosis aggregation. Especially, the results show that the developed model can be well applied to predicting future disease risks for patients, with the exact-match score of 98.90% and 95.12% using 3-digit ICD code aggregation, while 96.60% and 96.83% using 4-digit ICD code aggregation for these two datasets, respectively. Moreover, the approach can be developed as a reference tool for hospital information systems, enhancing patients' healthcare management over time.
PAC-GAN: An effective pose augmentation scheme for unsupervised cross-view person re-identification

Person re-identification (person Re-Id) aims to retrieve the pedestrian images of the same person that captured by disjoint and non-overlapping cameras. Lots of researchers recently focused on this hot issue and proposed deep learning based methods to enhance the recognition rate in a supervised or unsupervised manner. However,there are two limitations that cannot be ignored: firstly, compared with other image retrieval benchmarks, the size of existing person Re-Id datasets is far from meeting the requirement, which cannot provide sufficient pedestrian samples for the training of deep model; secondly, the samples in existing datasets do not have sufficient human motions or postures coverage to provide more priori knowledges for learning. In this paper, we introduce a novel unsupervised pose augmentation cross-view person Re-Id scheme called PAC-GAN to overcome these limitations. We firstly present the formal definition of cross-view pose augmentation and then propose the framework of PAC-GAN that is a novel conditional generative adversarial network (CGAN) based approach to improve the performance of unsupervised corss-view person Re-Id. Specifically, the pose generation model in PAC-GAN called CPG-Net is to generate enough quantity of pose-rich samples from original image and skeleton samples. The pose augmentation dataset is produced by combining the synthesized pose-rich samples with the original samples, which is fed into the corss-view person Re-Id model named Cross-GAN. Besides, we use weight-sharing strategy in the CPG-Net to improve the quality of new generated samples. To the best of our knowledge, we are the first to enhance the unsupervised cross-view person Re-Id by pose augmentation, and the results of extensive experiments show that the proposed scheme can combat the state-of-the-arts with recognition rate.
Lancer: Your Code Tell Me What You Need

Programming is typically a difficult and repetitive task. Programmers encounter endless problems during programming, and they often need to write similar code over and over again. To prevent programmers from reinventing wheels thus increase their productivity, we propose a context-aware code-to-code recommendation tool named Lancer. With the support of a Library-Sensitive Language Model (LSLM) and the BERT model, Lancer is able to automatically analyze the intention of the incomplete code and recommend relevant and reusable code samples in real-time. A video demonstration of Lancer can be found at https://youtu.be/tO9nhqZY35g. Lancer is open source and the code is available at https://github.com/sfzhou5678/Lancer.
Normalizing misogyny: hate speech and verbal abuse of female politicians on Japanese Twitter

Social media platforms such as Twitter have gained tremendous political importance in recent years. Moreover, being considered as platforms for organizing grass root political movements or political participation in general, this positive view has given way to more critical perspectives on the negative sides of social media, such as attempts of algorithmically manipulating public opinion or the outcome of elections and racist or sexist hate speech. For the case of Japan, despite particularly xenophobic hate speech on bulletin boards such as “2channel” (ni-channeru) or Twitter has been extensively studied from various angles, misogynic forms of verbal abuse towards females on social media, female politicians in particular, have received much lesser attention in existing research. In this article we present results from an explorative analysis of instances of misogynist or sexist hate speech and abusive language against female politicians on Twitter, applying computational corpus-linguistic tools and methods, supplemented by a qualitative in-depth study of verbal abuse of four prominent female politicians, namely Renhō, Tsujimoto Kiyomi, Yamao Shiori, and Koike Yuriko, thereby fruitfully combining quantitative-statistical and qualitative-hermeneutic approaches.
Gendered by Design

Using fitness trackers to generate and collect quantifiable data is a widespread practice aimed at better understanding one’s health and body. The intentional design of fitness trackers as genderless or universal is predicated on masculinist design values and assumptions that do not result in “neutral” devices and systems. Instead, ignoring gender in the design of fitness tracking devices marks a dangerous ongoing inattention to the needs, desires, and experiences of women, as well as transgender and gender non-conforming persons. We utilize duoethnography, a methodology emphasizing personal narrative and dialogue, as a tool that promotes feminist reflexivity in the design and study of fitness tracking technologies. Using the Jawbone UP3 as our object of study, we present findings that illustrate the gendered physical and interface design features and discuss how these features reproduce narrow understandings of gender, health, and lived experiences.
On the 2-Adic Complexity of the Ding-Helleseth-Martinsen Binary Sequences

We determine the 2-adic complexity of the Ding-Helleseth-Martinsen (DHM) binary sequences by using cyclotomic numbers of order four, “Gauss periods” and “quadratic Gauss sums” on finite field Fq and valued in Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2N-1</sub> , where q ≡ 5 (mod 8) is a prime number and N = 2q is the period of the DHM sequences.
Measuring Cognitive Load and Insight: A Methodology Exemplified in a Virtual Reality Learning Context

Recent improvements of Virtual Reality (VR) technology have enabled researchers to investigate the benefits VR may provide for various domains such as health, entertainment, training, and education. A significant proportion of VR system evaluations rely on perception-based measures such as user pre-and post-questionnaires and interviews. While these self-reports provide valuable insights into users' perceptions of VR environments, recent developments in digital sensors and data collection techniques afford researchers access to measures of physiological response. This work explores the merits of physiological measures in the evaluation of emotional responses in virtual environments (ERVE). We include and place at the center of our ERVE methodology emotional response data by way of electrodermal activity and heart-rate detection which are analyzed in conjunction with event-driven data to derive further measures. In this paper, we present our ERVE methodology together with a case study within the context of VR-based learning in which we derive measures of cognitive load and moments of insight. We discuss our methodology, and its potential for use in many other application and research domains to provide more in-depth and objective analyses of experiences within VR.
Detection of Specific Language Impairment in Children Using Glottal Source Features

Developmental dysphasia, also known as specific language impairment (SLI), is a language disorder in children that involves difficulty in speaking and understanding spoken words.Detecting SLI at an early stage is very important for successful speech therapy in children.In this paper, we propose a novel approach based on glottal source features for detecting children with SLI using the speech signal.The proposed method utilizes time-and frequency-domain glottal parameters, which are extracted from the voice source signal obtained using glottal inverse filtering (GIF).In addition, Mel-frequency cepstral coefficient (MFCC) and openSMILE based acoustic features are also extracted from speech utterances.Two machine learning algorithms, namely, support vector machine (SVM) and feed-forward neural network (FFNN), are trained separately for the MFCC, openSMILE and glottal features.A leave-fourteen-speakers-out cross-validation strategy is used for evaluating the classifiers.The experiments are conducted using the SLI speech corpus launched by the LANNA research group.Experimental results show that the glottal parameters contain significant discriminative information required for identifying children with SLI.Furthermore, the complementary nature of glottal parameters is investigated by independently combining these features with the MFCC and openSMILE acoustic features.The overall results indicate that the glottal features when used in combination with MFCC feature set provides the best performance with the FFNN classifier in the speaker-independent scenario.
Deep Reinforcement Learning for Instruction Following Visual Navigation in 3D Maze-Like Environments

In this work, we address the problem of visual navigation by following instructions. In this task, the robot must interpret a natural language instruction in order to follow a predefined path in a possibly unknown environment. Despite different approaches have been proposed in the last years, they are all based on the assumption that the environment contains objects or other elements that can be used to formulate instructions, such as houses or offices. On the contrary, we focus on situations where the environment objects cannot be used to specify a navigation path. In particular, we consider 3D maze-like environments as our test bench because they can be very large and offer very intricate structures. We show that without reference points, visual navigation and instruction following can be rather challenging, and that standard approaches can not be applied successfully. For this reason, we propose a new architecture that explicitly learns both visual navigation and instruction understanding. We demonstrate with simulated experiments that our method can effectively follow instructions and navigate in previously unseen mazes of various sizes.
RefBot: Intelligent Software Refactoring Bot

The adoption of refactoring techniques for continuous integration received much less attention from the research community comparing to root-canal refactoring to fix the quality issues in the whole system. Several recent empirical studies show that developers, in practice, are applying refactoring incrementally when they are fixing bugs or adding new features. There is an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases. Furthermore, several studies show that manual refactoring is expensive and existing automated refactoring tools are challenging to configure and integrate into the development pipelines with significant disruption cost. In this paper, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Integrated into the version control system (e.g. GitHub), our bot continuously monitors the software repository, and it is triggered by any "open" or "merge" action on pull requests. The bot analyzes the files changed during that pull request to identify refactoring opportunities using a set of quality attributes then it will find the best sequence of refactorings to fix the quality issues if any. The bot recommends all these refactorings through an automatically generated pull-request. The developer can review the recommendations and their impacts in a detailed report and select the code changes that he wants to keep or ignore. After this review, the developer can close and approve the merge of the bot's pull request. We quantitatively and qualitatively evaluated the performance and effectiveness of RefBot by a survey conducted with experienced developers who used the bot on both open source and industry projects.
Data-driven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs

As increasingly more semantic real-world data is stored in knowledge graphs, providing intuitive and effective query methods for end-users is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF question/answering (Q/A) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel data-driven graph similarity framework for RDF Q/A to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ, based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for same-distance graph elements and equip the process with an edge association-aware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms state-of-the-art methods significantly.