Reviewerly/exHarmony huggingface dataset:
Blowing your mind: a conceptual framework of augmented reality and virtual reality enhanced cultural visitor experiences using EEG experience measures

Cultural tourism is regarded as a key contributor for economic growth in various destinations. While increasing awareness and interest of different cultures play a key role, technology has made information and interaction at these sites more accessible and engaging. An increasing amount of research is being conducted around the potential and implementation of AR and VR technology to enhance the visitor experience. However, limited theoretical knowledge has been developed on how these need to be designed to facilitate forming memorable experiences at cultural tourism sites. This study discusses elements affecting the visitor experience and discusses how AR and VR should be designed to contribute to enhancing the experience and making it memorable from a theoretical perspective. Further research recommendations are outlined that suggest the use of complementing research methodologies to better understand the nature of experiences in order to design AR and VR application more purpose-specifically.
A Students’ Action Recognition Database In Smart Classroom

With the development of human action recognition, it is possible to automatically recognize students' actions in classroom, providing a new direction for classroom observation in teaching research. Training effective students' action recognition algorithms depends significantly on the quality of the action database. However, only a few existing action databases focus on learning environment. In this paper, we contribute to this topic from two aspects. First, a novel students' action recognition database is introduced. The spontaneous action database consists 15 action categories, 817 video clips of 73 students, which are collected in real smart classroom environment. Second, a benchmark experiment was conducted on the database using two kinds of recognition algorithms. The best result is achieved by Inception V3 with 0.9310 accuracy. Such a spontaneous database will help in the development and validation of algorithms for action recognition in learning environment.
Skeleton Filter: A Self-Symmetric Filter for Skeletonization in Noisy Text Images

Robustly computing the skeletons of objects in natural images is difficult due to the large variations in shape boundaries and the large amount of noise in the images.Inspired by recent findings in neuroscience, we propose the Skeleton Filter, which is a novel model for skeleton extraction from natural images.The Skeleton Filter consists of a pair of oppositely oriented Gabor-like filters; by applying the Skeleton Filter in various orientations to an image at multiple resolutions and fusing the results, our system can robustly extract the skeleton even under highly noisy conditions.We evaluate the performance of our approach using challenging noisy text datasets and demonstrate that our pipeline realizes state-of-the-art performance for extracting the text skeleton.Moreover, the presence of Gabor filters in the human visual system and the simple architecture of the Skeleton Filter can help explain the strong capabilities of humans in perceiving skeletons of objects, even under dramatically noisy conditions.
Emotion Recognition in Conversations with Transfer Learning from Generative Conversation Modeling.

Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annotated data, which is lacking in most of the available corpora in this task. To tackle this challenge, we look at transfer learning approaches as a viable alternative. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for detecting emotions in context. We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target). In addition to the popular practice of using pre-trained sentence encoders, our approach also incorporates recurrent parameters that model inter-sentential context across the whole conversation. Based on this idea, we perform several experiments across multiple datasets and find improvement in performance and robustness against limited training data. TL-ERC also achieves better validation performances in significantly fewer epochs. Overall, we infer that knowledge acquired from dialogue generators can indeed help recognize emotions in conversations.
Effects of Shared Gaze Parameters on Visual Target Identification Task Performance in Augmented Reality

Augmented reality (AR) technologies provide a shared platform for users to collaborate in a physical context involving both real and virtual content. To enhance the quality of interaction between AR users, researchers have proposed augmenting users' interpersonal space with embodied cues such as their gaze direction. While beneficial in achieving improved interpersonal spatial communication, such shared gaze environments suffer from multiple types of errors related to eye tracking and networking, that can reduce objective performance and subjective experience.
Light Repository Blockchain System with Multisecret Sharing for Industrial Big Data

Blockchain is an emerging technology that promises many exciting applications in various fields, including financial, medical, energy, and logistics management. However, there are still some limitations in the existing blockchain framework that prevents its widespread adoption in the commercial world. One important limitation is the storage requirement, wherein each blockchain node has to store a copy of the distributed ledger. Thus, as the number of transactions increases, this storage requirement grows quadratically, eventually limiting the scalability of a blockchain system. Moreover, the public ledger in a blockchain framework allows anyone in the network to audit the transaction, which may not be favourable in some privacy-sensitive applications. In this paper, a secret-sharing scheme is proposed to reduce the size of the blockchain transactions. Each transaction block is divided into t parts, and the size of each part is 1/ t size of transaction block. We use the secret-sharing mechanism to share t parts into n shares. Hence, each node stores not one transaction but one share in the blockchain system. The proposed scheme can eventually reduce the storage cost of a blockchain transaction by 1/ t without introducing an additional recovery communication cost; however, robustness is reduced in node failure as a tradeoff. Meanwhile, the proposed scheme was more efficient and secure compared to other state-of-the-art schemes that aim to reduce blockchain storage for industrial big data.
Bayesian Double Feature Allocation for Phenotyping With Electronic Health Records

Abstract Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes—in our case, latent disease—based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases from EHR data. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously allocates features to the rows and the columns of a categorical matrix. Using a Bayesian approach, available prior information on known diseases (e.g., hypertension and diabetes) greatly improves identifiability and interpretability of the latent diseases. We assess the proposed approach by simulation studies including mis-specified models and comparison with sparse latent factor models. In the application to a Chinese EHR dataset, we identify 10 latent diseases, each of which is shared by groups of subjects with specific health traits related to lipid disorder, thrombocytopenia, polycythemia, anemia, bacterial and viral infections, allergy, and malnutrition. The identification of the latent diseases can help healthcare officials better monitor the subjects’ ongoing health conditions and look into potential risk factors and approaches for disease prevention. We cross-check the reported latent diseases with medical literature and find agreement between our discovery and reported findings elsewhere. We provide an R package “dfa” implementing our method and an R shiny web application reporting the findings. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
The college students perspective on what makes an educator well-qualified

The educators of the 21st century have a great task ahead. In today’s world, the educators are expected to have a great deal of professional and personal qualities, and extraordinary skills. This descriptive-correlational study aimed to determine perceptions of college students on a well-qualified educator. The study included 123 selected college students from different colleges in Central Luzon, Philippines using a convenience sampling technique. As data collection tool, a questionnaire prepared by the researcher team was used. Some of the findings of the study include: (1) the respondents answered agree on the context of professional and personal qualities that a teacher should have; (2) while in terms of the skills exhibited by an educator, it got an answer to Very Satisfactory. We also found significant differences in professional qualities, learning and innovation skills and life and career skills when grouped according to the respondent’s profile. There was low-to-moderate evidence of the relationship that existed between the qualities and skills of an educator. The results further confirmed the relationship through regression analysis and found that communication skills affect the qualities of an educator. Based on the results, we discussed and recommended practical suggestions on professional, personal and skills improvements of an educator.
A Real-Time Ball Detection Approach Using Convolutional Neural Networks

Ball detection is one of the most important tasks in the context of soccer-playing robots. The ball is a small moving object which can be blurred and occluded in many situations. Several neural network based methods with different architectures are proposed to deal with the ball detection. However, they are either neglecting to consider the computationally low resources of humanoid robots or highly depend on manually-tuned heuristic methods to extract the ball candidates. In this paper, we propose a new ball detection method for low-cost humanoid robots that can detect most soccer balls with a high accuracy rate of up to 97.17%. The proposed method is divided into two steps. First, some coarse regions that may contain a full ball are extracted using an iterative method employing an efficient integral image based feature. Then they are fed to a light-weight convolutional neural network to finalize the bounding box of a ball. We have evaluated the proposed approach using a comprehensive dataset and the experimental results show the efficiency of our method.
State Alignment-based Imitation Learning

Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of existing imitation learning methods fail because they focus on the imitation of actions. We propose a novel state alignment-based imitation learning method to train the imitator by following the state sequences in the expert demonstrations as much as possible. The alignment of states comes from both local and global perspectives. We combine them into a reinforcement learning framework by a regularized policy update objective. We show the superiority of our method on standard imitation learning settings as well as the challenging settings in which the expert and the imitator have different dynamics models.
Improving Surface-syntactic Universal Dependencies (SUD): MWEs and deep syntactic features

SUD is an annotation scheme for syntactic dependency treebanks, near isomorphic to UD (Universal Dependencies).Contrary to UD, it is based on syntactic criteria (favoring functional heads) and the relations are defined on distributional and functional bases.In this paper, we will recall and specify the general principles underlying SUD, present the updated set of SUD relations, discuss the central question of MWEs, and introduce an orthogonal layer of deep-syntactic features converted from the deep-syntactic part of the UD scheme.
Documentation in a community informatics project

Purpose The purpose of this paper is to discuss the creation and sharing of information by Bangladeshi women participants in a community informatics project and to assess to what extent the information provided to them meets their short and longer-term needs. Design/methodology/approach The analysis is based on data collected during a workshop with village women in Dhaka and focus group discussions in rural Bangladesh in March and April 2019. The information continuum model is used as a framework to analyse the data. Findings The study shows that the women document their learning and share it with their families and communities and that they are very conscious of the importance of keeping analogue back-ups of the information provided to them in digital format. They use notebooks to write down information that they find useful and they copy information provided to them on brown paper sheets hung in the village community houses. Practical implications This paper raises questions about how information is communicated to village women, organised and integrated in a community informatics project, and more generally about the suitability and sustainability of providing information in digital formats in a developing country. Originality/value The paper shows how village women participants in a community informatics project in Bangladesh took the initiative to create and preserve the information that was useful to them in analogue formats to remedy the limitations of the digital formats and to keep the information accessible in the longer term.
Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Existing approaches of cyber attack-defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual cyber attack-defense, it is difficult for both sides of attacker and defender to meet the high requirement of complete rationality. For this aim, the influence of bounded rationality on attack-defense stochastic game is analyzed. We construct a stochastic game model. Aiming at the problem of state explosion when the number of network nodes increases, we design the attack-defense graph to compress the state space and extract network states and defense strategies. On this basis, the intelligent learning algorithm WoLF-PHC is introduced to carry out strategy learning and improvement. Then, the defense decision-making algorithm with online learning ability is designed, which helps to select the optimal defense strategy with the maximum payoff from the candidate strategy set. The obtained strategy is superior to previous evolutionary equilibrium strategy because it does not rely on prior data. By introducing eligibility trace to improve WoLF-PHC, the learning speed is further improved and the defense timeliness is significantly promoted.
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition

In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due to the missing of informative details during resolution degradation. However, these images are still recognizable for subjects who are familiar with the corresponding high-resolution ones. Inspired by that, we propose a teacher-student learning approach to facilitate low-resolution image recognition via hybrid order relational knowledge distillation. The approach refers to three streams: the teacher stream is pretrained to recognize high-resolution images in high accuracy, the student stream is learned to identify low-resolution images by mimicking the teacher's behaviors, and the extra assistant stream is introduced as bridge to help knowledge transfer across the teacher to the student. To extract sufficient knowledge for reducing the loss in accuracy, the learning of student is supervised with multiple losses, which preserves the similarities in various order relational structures. In this way, the capability of recovering missing details of familiar low-resolution images can be effectively enhanced, leading to a better knowledge transfer. Extensive experiments on metric learning, low-resolution image classification and low-resolution face recognition tasks show the effectiveness of our approach, while taking reduced models.
Toward a Cloud Computing Learning Community

Cloud computing continues to be an important technology in higher education. This domain is a rapidly evolving space, and continues to gain momentum as a primary infrastructure topology for technological advances across emergent industries. The on-the-cloud paradigm provides numerous affordances and new methods of working in industry, and also for end users within recent fields of study, such as machine learning and artificial intelligence. As we move into the Industry 4.0 era with technology pillars such as the internet of things and cybersecurity, the importance of skillsets for cloud-based services will be an essential attribute for the majority of technology-related professions. Many higher education institutions have focused on offering training opportunities and programs for cloud computing, however, a lack of high-quality, contextualized to industry, curricula materials continues to be a challenge for educators. The purpose of this paper is to report on analyses conducted to categorize cloud computing courses currently taught in the higher education sector and to determine the possibility of moving towards the goal of a model curriculum. Additionally, the paper aims to provide guidance to educators about cloud computing skill-sets sought in the job market, and to report on a community platform designed to host cloud learning resources.
Specific Emitter Identification via Bispectrum-Radon Transform and Hybrid Deep Model

Specific emitter identification is a technique that distinguishes different emitters using radio fingerprints. Feature extraction and classifier selection are critical factors affecting SEI performance. In this paper, we propose an SEI method using the Bispectrum-Radon transform (BRT) and a hybrid deep model. We propose BRT to characterize the unintentional modulation of pulses due to the superiority of bispectrum distributions in characterizing nonlinear features of signals. We then apply a hybrid deep model based on denoising autoencoders and a deep belief network to perform further deep feature extraction and discriminative identification. We design an automatic dependent surveillance-broadcast signal acquisition system to capture signals and to build dataset for validating our proposed SEI method. Theoretical analysis and experimental results show that the BRT feature outperformed traditional features in characterizing UMOP, and our proposed SEI method outperformed other feature and classifier combination methods.
Facial Expression Recognition Using Supervised Learning

Facial Expression Recognition (FER) draws much attention in present research discussions. The paper presents a relative analysis of recognition systems for facial expression. Facial expression recognition is generally carried out in three stages such as detection of face, extraction of features and expressions’ classification. The proposed work focuses on a face detection and extraction method is presented based on the Haar cascade features. The classifier is trained by using many positive and negative images. The features are extracted from it. For standard Haar feature images like convolutional kernel are used. Next, Fisher face classifier a supervised learning method is applied on COHN-KANADE database, to have a facial expression classifying system with eight possible classes (seven basic emotions along with neutral). A recognition rate of 65$$\%$$ in COHN-KANADE database is achieved.
Facebook’s Libra: Why Does US Government Fear Price Stable Cryptocurrency?

US President Donald J. Trump says he is not “a fan of Bitcoin and other cryptocurrencies”, and he does not have to be, but using this premature reason (like a bully) to rage a war against Bitcoin and Libra is ludicrous. Will Trump (or the United States government) try to destroy everything that he dislikes or is not a fan of? Satoshi Nakamoto (pseudonym) designed Bitcoin as public good in mind, but the US dollar serves totally the opposite as it has been increasingly used as a weapon of mass economic destruction. The real issue is, President Trump feels agitated and concerned because Bitcoin and Libra create an undesired situation of diminishing US power. The anonymity aspect of Bitcoin limits Trump’s (the US government’s) role as the global policeman (i.e. succeeded the UK in 1945). Just to curb Bitcoin’s popularity, politicians produce lies, make short-sighted assertions, and publicly share ill-advised thoughts; regardless, Bitcoin mania is nothing like the tulip mania or the dot.com mania, it is with us now and it will continue to forge ahead unabated in spite of doubters, pessimists, doomsayers, skeptics, and disbelievers. At the backdrop of US-China trade war and the regulatory backlash to force Facebook to halt its Libra project, one is compelled to wonder till when the United States will exploit the world’s scarce resources and how many more lives will be perished for petrodollar so that the U.S. can continue enjoying the “exorbitant privilege” of dollar hegemony.
Region-Merging Method With Texture Pattern Attention for SAR Image Segmentation

This letter proposes a region-merging-based method for synthetic aperture radar (SAR) image segmentation, where the merging cost is a fusion of texture pattern similarity measure (TPSM), statistical similarity measure (SSM), and the relative common boundary length penalty (RCBLP). The segmentation is implemented in three steps. First, an image is oversegmented based on the multiscale Bhattacharyya distance to generate an initial partition of considerable regions. Second, regions with sizes under a given threshold are mandatorily merged to yield a middle segmentation. Third, a region-merging process using the new merging cost is iteratively conducted to achieve final segmentation. Due to the existence of the TPSM in the merging cost, the new method avoids the false merging of adjacent regions with different textures. Experimental results of the real SAR images show that the proposed method outperforms existing region-merging-based methods.