Reviewerly/exHarmony huggingface dataset:
Hierarchical Concept-Driven Language Model

For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.
Joint Modeling Dynamic Preferences of Users and Items Using Reviews for Sequential Recommendation

The emerging of sequential recommender (SR) has attracted increasing attention in recent years, which focuses on understanding and modeling the temporal dynamic of user behaviors hidden in the sequence of user-item interactions. However, with the tremendous increase of users and items, SR still faces several challenges: (1) the hardness of modeling user interests from spare explicit feedback; (2) the time and semantic irregularities hidden in the user’s successive actions. In this study, we present a neural network-based sequential recommender model to learn the temporal-aware user preferences and item popularity jointly from reviews. The proposed model consists of the semantic extracting layer and the dynamic feature learning layer, besides the embedding layer and the output layer. To alleviate the data sparse issue, the semantic extracting layer focuses on exploiting the enriched semantic information hidden in reviews. To address the time and semantic irregularities hidden in user behaviors, the dynamic feature learning layer leverages convolutional fitters with varying size, integrating with a time-ware controller to capture the temporal dynamic of user and item features from multiple temporal dimensions. The experimental results demonstrate that our proposed model outperforms several state-of-art methods consistently.
Facilitating Text Entry on Smartphones with QWERTY Keyboard for Users with Parkinson’s Disease

QWERTY is the primary smartphone text input keyboard configuration. However, insertion and substitution errors caused by hand tremors, often experienced by users with Parkinson's disease, can severely affect typing efficiency and user experience. In this paper, we investigated Parkinson's users' typing behavior on smartphones. In particular, we identified and compared the typing characteristics generated by users with and without Parkinson's symptoms. We then proposed an elastic probabilistic model for input prediction. By incorporating both spatial and temporal features, this model generalized the classical statistical decoding algorithm to correct insertion, substitution and omission errors, while maintaining direct physical interpretation. User study results confirmed that the proposed algorithm outperformed baseline techniques: users reached 22.8 WPM typing speed with a significantly lower error rate and higher user-perceived performance and preference. We concluded that our method could effectively improve the text entry experience on smartphones for users with Parkinson's disease.
Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation

Unpaired image-to-image (I2I) translation has received considerable attention in pattern recognition and computer vision because of recent advancements in generative adversarial networks (GANs). However, due to the lack of explicit supervision, unpaired I2I models often fail to generate realistic images, especially in challenging datasets with different backgrounds and poses. Hence, stabilization is indispensable for GANs and applications of I2I translation. Herein, we propose Augmented Cyclic Consistency Regularization (ACCR), a novel regularization method for unpaired I2I translation. Our main idea is to enforce consistency regularization originating from semi-supervised learning on the discriminators leveraging real, fake, reconstructed, and augmented samples. We regularize the discriminators to output similar predictions when fed pairs of original and perturbed images. We qualitatively clarify why consistency regularization on fake and reconstructed samples works well. Quantitatively, our method outperforms the consistency regularized GAN (CR-GAN) in real-world translations and demonstrates efficacy against several data augmentation variants and cycle-consistent constraints.
An Appraisal Transition System for Event-Driven Emotions in Agent-Based Player Experience Testing

Player experience (PX) evaluation has become a field of interest in the game industry. Several manual PX techniques have been introduced to assist developers to understand and evaluate the experience of players in computer games. However, automated testing of player experience still needs to be addressed. An automated player experience testing framework would allow designers to evaluate the PX requirements in the early development stages without the necessity of participating human players. In this paper, we propose an automated player experience testing approach by suggesting a formal model of event-based emotions. In particular, we discuss an event-based transition system to formalize relevant emotions using Ortony, Clore, & Collins (OCC) theory of emotions. A working prototype of the model is integrated on top of Aplib, a tactical agent programming library, to create intelligent PX test agents, capable of appraising emotions in a 3D game case study. The results are graphically shown e.g. as heat maps. Visualization of the test agent's emotions would ultimately help game designers to produce contents that evoke a certain experience in players.
Fact Investigation and Proof Standards in Legal Argumentation

A logical framework is presented to model legal interpretation and bring an appropriate construction of legal arguments. Reasoning with assumptions allows the construction of hypotheses. This is proposed in the context of legal procedures’ dynamics, where the framework evolves as part of the investigation prior to each trial instance. Two gender-biased legal cases from the Argentinian jurisprudence are analyzed in the light of the proposed theory. Afterward, some proof standards for civil law systems are discussed as a way to define a specialized dialectical argumentation semantics. We claim that the application of the proposed theory would bring software assistance for controlling the influence of external factors of the law in the investigation of a legal case.
Ltaf-Net: Learning Task-Aware Adaptive Features and Refining Mask for Few-Shot Semantic Segmentation

Few shot segmentation is a newly-developing and challenging computer vision task which is only provided with few labeled samples of the novel class. Some recent works on this problem focus more on how to design an effective comparison module but ignore how to extract the features passed to compare. In this paper we propose a novel model named LTAF-Net for few-shot segmentation. This model aims to adaptively recalibrate the extracted features which could boost the accuracy of dense comparison between support features and query features. Besides an additional prediction refinement module is designed to refine the initial mask. Meanwhile this method can apply to k-shot setting without developing a new specialized architecture and achieve competitive performance. Experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and FSS-1000 strongly prove the effectiveness of the proposed model. Our model outperforms the second-best method 1.4% in 1-shot and 0.76% in 5-shot respectively in PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> .
An X-Ray Vision System for Situation Awareness in Action Space

Usable x-ray vision has long been a goal in augmented reality research and development. X-ray vision, or the ability to view and understand information presented through an opaque barrier, would be imminently useful across a variety of domains. Unfortunately, however, the effect of x-ray vision on situation awareness, an operator's understanding of a task or environment, has not been significantly studied. This is an important question; if x-ray vision does not increase situation awareness, of what use is it? Thus, we have developed an x-ray vision system, in order to investigate situation awareness in the context of action space distances.
Person’s multiple intelligence classification based on tweet post using SentiStrength and processed on the Apache Spark framework

Abstract A person’s self-development is influenced by his ability in solving problems and adapting to his environment. This ability is commonly known as intelligence. Every person has a different dominant intelligence. Honed intelligence can result in managed self-development and self-mastery. A person can discover his talent and expertise to be more advanced in his field. However, discovering one’s dominant intelligence requires several variables to assess his behaviours. This research makes use of a person’s activities on the social media Twitter. Twitter is a platform on which its users share thoughts. Posted tweets are used as objects to determine the corresponding user’s dominant intelligence. The user’s tweet will go through a sentiment analysis and intelligence type classification by applying the SentiStrength method to the Apache Spark framework. This research was conducted on 20 Twitter user accounts with the types of intelligence consisting of musical-rhythmic, visual-spatial, verbal-linguistic, logical-mathematical, bodily-kinesthetics, interpersonal, intrapersonal, naturalistic, and spiritual intelligence. The 72% average accuracy is obtained by calculating the correlation between manual testing and the system using the Spearman’s rank correlation coefficient ( α = 0.05 and the Spearman’s value of 0.700).
Surrogate-Aloud: A Human Surrogate Method for Remote Usability Evaluation and Ideation in Virtual Reality

Virtual and augmented reality offer comparative performance in terms of remote usability testing to lab-based co-located settings. However, direct contact with a researcher is still required to provide setup, troubleshoot, and training. In this paper, we present Surrogate-Aloud as a remote ideation and usability method that establishes a surrogate relationship between participants and a facilitating researcher through video conferencing. The researcher wears a VR headset and shares their viewpoint through video conferencing to a remote participant, who applies think-aloud protocol to express movement and interaction commands to be executed by the researcher, alongside their thought process as they interact with virtual prototypes or scenarios. We conducted a preliminary study to evaluate the Surrogate-Aloud method for remote usability evaluation and ideation of a new instructional technique with volumetric recordings. Results show that Surrogate-Aloud leverages the surrogate’s technical expertise and enables sufficient capability to conduct truly remote usability evaluation and ideation.
A Better Approach to Track the Evolution of Static Code Warnings

Static bug detection tools help developers detect code problems. However, it is known that they remain underutilized due to various reasons. Recent advances to incorporate static bug detectors in modern software development workflows can better motivate developers to fix the reported warnings on the fly. In this paper, we study the effectiveness of the state-of-the-art (SOA) solution in tracking warnings by static bug detectors and propose a better solution based on our analysis of the insufficiencies of the SOA solution. In particular, we examined four large-scale open-source systems and crafted a data set of 3,452 static code warnings by two static bug detectors. We manually uncover the ground-truth evolution status of the selected warnings: persistent, resolved, or newly-introduced. Moreover, upon manual analysis, we identified the critical reasons behind the insufficiencies of the SOA matching algorithm. Finally, we propose a better approach to improve the tracking of static warnings over software development history. Our evaluation shows that our proposed approach provides a significant improvement in the precision of the tracking, i.e., from 66.9% to 90.0%.
A Study of Vocabulary Learning Using Annotated 360° Pictures

Second language (L2) learning research suggests that virtual reality (VR) has the potential to enhance the development of language skills due to its immersive nature and the situated learning opportunities it provides. This quantitative, between-subjects study compared the effectiveness of three learning conditions. University students (N = 63) studied vocabulary annotated on: (1) 360° pictures viewed using a VR headset, (2) 360° pictures viewed on a desktop monitor, and (3) standard two-dimensional (2D) pictures viewed on a desktop monitor (control condition). After the experiment, the students completed productive and receptive post-tests measuring vocabulary recall. Through analyses of variance, the study revealed that learning new vocabulary with annotated 360° pictures is associated with significantly higher post-tests scores, compared with learning with standard 2D pictures, as long as viewing is on a desktop monitor and not with a VR headset.
Scale‐adaptive deep network for deformable image registration

Purpose Multiresolution hierarchical strategy is typically used in conventional optimization‐based image registration to capture varying magnitudes of deformations while avoiding undesirable local minima. A rough concept of the scale is captured in deep networks by the reception field of kernels, and it has been realized to be both desirable and challenging to capture convolutions of different scales simultaneously in registration networks. In this study, we propose a registration network that is conscious of and self‐adaptive to deformation of various scales to improve registration performance. Methods Dilated inception modules (DIMs) are proposed to incorporate receptive fields of different sizes in a computationally efficient way. Scale adaptive modules (SAMs) are proposed to guide and adjust shallow features using convolutional kernels with spatially adaptive dilation rate learned from deep features. DIMs and SAMs are integrated into a registration network which takes a U‐net structure. The network is trained in an unsupervised setting and completes registration with a single evaluation run. Results Experiment with two‐dimensional (2D) cardiac MRIs showed that the adaptive dilation rate in SAM corresponded well to the deformation scale. Evaluated with left ventricle segmentation, our method achieved a Dice coefficient of (0.93 ± 0.02), significantly better than SimpleElastix and networks without DIM or SAM. The average surface distance was less than 2 mm, comparable to SimpleElastix without statistical significance. Experiment with synthetic data demonstrated the effectiveness of DIMs and SAMs, which led to a significant reduction in target registration error (TRE) based on dense deformation field. The three‐dimensional (3D) version of the network achieved a 2.52 mm mean TRE on anatomical landmarks in DIR‐Lab thoracic 4DCTs, lower than SimpleElastix and networks without DIM or SAM with statistical significance. The average registration times were 0.002 s for 2D images with size 256 × 256 and 0.42 s for 3D images with size 256 × 256 × 96. Conclusions The introduction and integration of DIMs and SAMs addressed the heterogeneous scale problem in an efficient and self‐adaptive way. The proposed method provides an alternative to the inefficient multiresolution registration setups.
Scalability and Robustness Testing for Open Source Web Crawlers

This paper implemented the proposed framework. It focuses on evaluating the crawlers based on scalability and robustness on e-commerce websites. The scalability is a feature that the system can adapt to the amount of data continuing to increase, and the performance does not decrease. The robustness is an ability that can handle exceptions when web crawlers are crawling. Multiple testing environments were set up on e-commerce websites. Scalability testing and robustness testing were used to measure the scalability and robustness of web crawlers. The scalability attributes and robustness failure rate were used to quantify the scalability and robustness. Statistical methods such as the Friedman test and the Nemenyi test were used to analyze the significant differences among crawlers. The experimental results show Heritrix, Scrapy, and Nutch have the best overall scalability. In the non-interference test, Scrapy has the best robustness. However, Webmagic, Webcolletor, and Gecco have the best robustness in the interference test based on general test and database test.Testing, Rrιhrιrtneτc Testirrg, E-comrnerce websites.
Virtual Reality Esports - Understanding Competitive Players’ Perceptions of Location Based VR Esports

Competitive VR gaming has emerged as a new trend in recent years, due to the availability of consumer grade VR technologies and the rise of esports as a billion dollar industry. Despite the considerable attention to competitive VR gaming, there is a lack of research on attitudes and experiences that players have with these games. In this qualitative study with a pre-post interview design, we recruited eight competitive Counter-Strike: Global Offensive players from a university esports club. We aimed to understand their attitudes towards VR esports and their experiences playing a representative location based VR esports game. Findings showed that players had visceral and positive affective experiences in the game, such as how players map physical movements to the game. These findings can help design future competitive VR esports, while also further contributing to HCI as the first exploration on player experiences with VR esports, laying groundwork for future studies.
Efficient Sentence Embedding via Semantic Subspace Analysis

A novel sentence embedding method built upon semantic subspace analysis, called semantic subspace sentence embedding (S3E), is proposed in this work. Given the fact that word embeddings can capture semantic relationship while semantically similar words tend to form semantic groups in a high-dimensional embedding space, we develop a sentence representation scheme by analyzing semantic subspaces of its constituent words. Specifically, we construct a sentence model from two aspects. First, we represent words that lie in the same semantic group using the intra-group descriptor. Second, we characterize the interaction between multiple semantic groups with the inter-group descriptor. The proposed S3E method is evaluated on both textual similarity tasks and supervised tasks. Experimental results show that it offers comparable or better performance than the state-of-the-art. The complexity of our S3E method is also much lower than other parameterized models.
Beyond Security and Efficiency: On-Demand Ratcheting with Security Awareness

Secure asynchronous two-party communication applies ratcheting to strengthen privacy, in the presence of internal state exposures. Security with ratcheting is provided in two forms: forward security and post-compromise security. There have been several such secure protocols proposed in the last few years. However, they come with a high cost.
Collaborative Innovation of Online Ideological Education Platform with Data Mining and Text Recognition Algorithms

With modern rapid and emerging development trend of Internet technology, great number of data are accumulated in the society, which leads to a larger data scale. With the target to discover the potential value of general data, it has been usually necessary to flexibly apply various data mining algorithms based on the actual situation and scenarios. The implementation of data mining algorithm is faced with great challenges in terms of execution efficiency, algorithm parallelization and platform usability. Different from horizontal text, multi-directional text and curved text, the surface text of surface object has some geometric features. In the detection phase, the text box label of surface text needs more vertex coordinates to express its spatial information, which makes it difficult to label large-scale samples. In the recognition stage, it is more difficult to recognize the surface text of the surface object than the regular text on the plane. At present, several universities ignore the importance to the construction of political education micro platform, and fail to develop a scientific micro platform according to the actual needs and interests of students. Based on this situation, this paper applies the data mining models and text recognition algorithm to study innovative smart political education platform.
Digital Music Resource Copyright Management Mechanism Based on Blockchain

The popularity of the Internet has accelerated the circulation of digital resources, and the problem of copyright management of digital resources has become prominent. The introduction of block chain technology has a breakthrough meaning for the solution of digital resource copyright management. Based on the development of block chain technology, this article combines digital music resource copyright management with block chain technology by establishing a research model. In this way, the issues of copyright protection and transaction management of digital music resources are explored in depth. This article aims at the decentralization of blockchain technology and other features requiring the intervention of relevant authorities, in order to enhance the credibility of digital music resource copyright transactions by using blockchain technology.
Convolutional Neural Network Powered Identification of the Location and Orientation of Human Body via Human Form Point Cloud

This paper proposes a Convolutional Neural Network (CNN) based scheme using the point cloud of human body to identify its location and posture. The point cloud is randomly generated but confined within a human form. The CNN-based model is fed with point cloud for predicting mass center location and orientation of the body with help of high end graphical processing units. We propose to project the point cloud in two vertical planes to exploit the image recognition capability of CNN. The proposed method is tested with a single person for three primary postures: standing, sitting and lying, to evaluate the prediction capability. Effects of the number of points indicating point cloud density and the distance between the observation station and the target are investigated. Simulation results show a body part dependent localization accuracy smaller than 8 cm, and posture dependent success rate above 93%, validating the functionality of proposed scheme.