Reviewerly/exHarmony huggingface dataset:
Design and Deployment of Online PBL Model for High School Students Promoting Collaborative Learning

Problem (Project) Based Learning has been applied successfully in many fields in many higher educational institutions worldwide to develop learning efficiency though idea exchanging, debating and interacting. Students learn how to analyze the given problems, develop thinking s and self-study skills from learning activities in the classroom. Recently, COVID-19 is a major factor contributing to the digital transformation for learning. From offline to online learning, all students are required to adapt their preference of normal learning including high school students. This paper presents the design and the deployment of online Problem (Project) Based Learning for high school students so that enhance their learning efficiency and develop collaborative learning skill required for the future employment.
Classification of papillary thyroid carcinoma histological images based on deep learning

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.
Crypto-Hotwire

Blockchains and cryptocurrencies disrupted the conversion of energy into a medium of exchange. Numerous applications for blockchains and cryptocurrencies are now envisioned for purposes ranging from inventory control to banking applications. Naturally, in order to mine in an economically viable way, regions where energy is plentiful and cheap, e.g., close to hydroelectric plants, are sought. The possibility of converting energy into cash, however, also opens up opportunities for a new kind of cyber attack aimed at illegally mining cryptocurrencies by stealing energy. In this work, we indicate, using data from January and February of 2018 from our university, that such a threat is real, and present a projection of the gains derived from these attacks.
Experiencing Presence in a Virtual Reality Music Video

As some music artists begin to explore virtual reality (VR) as a medium for their music videos, it was found challenging to direct people's attention using traditional film techniques. These must, therefore, be innovative or alternative methods established. To evaluate the effectiveness of attention cues and their correlation of eliciting a sense of presence, a music video was created in a virtual environment (VE). Two design iterations were completed. The first investigated the size, colour, and movement pattern of the visual cues and the second examined if the cue was perceived as a part of the virtual world. It was found that the cue should have a vibrant yet not necessarily contrasting colour, depending on the background (here pink on purple), and have animate motion, giving a more coherent perception the world. This study investigated the user's level of presence experiencing the music video in VR, compared to a smartphone. The results demonstrate that the feeling of presence in the VR is significantly higher.
Anticipating Activity from Multimodal Signals

Images, videos, audio signals, sensor data, can be easily collected in huge quantity by different devices and processed in order to emulate the human capability of elaborating a variety of different stimuli. Are multimodal signals useful to understand and anticipate human actions if acquired from the user viewpoint? This paper proposes to build an embedding space where inputs of different nature, but semantically correlated, are projected in a new representation space and properly exploited to anticipate the future user activity. To this purpose, we built a new multimodal dataset comprising video, audio, tri-axial acceleration, angular velocity, tri-axial magnetic field, pressure and temperature. To benchmark the proposed multimodal anticipation challenge, we consider classic classifiers on top of deep learning methods used to build the embedding space representing multimodal signals. The achieved results show that the exploitation of different modalities is useful to improve the anticipation of the future activity.
Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks

This paper introduces a new approach to build the rule-base on Neo-Fuzzy-Neuron (NFN) Networks. The NFN is a Neuro-Fuzzy network composed by a set of n decoupled zero-order Takagi-Sugeno models, one for each input variable, each one containing m rules. Employing Multi-Gene Genetic Programming (MG-GP) to create and adjust Gaussian membership functions and a Gradient-based method to update the network parameters, the proposed model is dubbed NFN-MG-GP. In the proposed model, each individual of MG-GP represents a complete rule-base of NFN. The rule-base is adjusted by genetic operators (Crossover, Reproduction, Mutation), and the consequent parameters are updated by a predetermined number of Gradient method epochs, every generation. The algorithm uses Elitism to ensure that the best rule-base is not lost between generations. The performance of the NFN-MG-GP is evaluated using instances of time series forecasting and non-linear system identification problems. Computational experiments and comparisons against state-of-the-art alternative models show that the proposed algorithms are efficient and competitive. Furthermore, experimental results show that it is possible to obtain models with good accuracy applying Multi-Gene Genetic Programming to construct the rule-base on NFN Networks.
Attention Based Multi-Instance Thyroid Cytopathological Diagnosis with Multi-Scale Feature Fusion

In recent years, deep learning has been popular in combining with cytopathology diagnosis. Using the whole slide images (WSI) scanned by electronic scanners at clinics, researchers have developed many algorithms to classify the slide (benign or malignant). However, the key area that support the diagnosis result can be relatively small in a thyroid WSI, and only the global label can be acquired, which make the direct use of the strongly supervised learning framework infeasible. What's more, because the clinical diagnosis of the thyroid cells requires the use of visual features in different scales, a generic feature extraction way may not achieve good performance. In this paper, we propose a weakly supervised multi-instance learning framework based on attention mechanism with multi-scale feature fusion (MSF) using convolutional neural network (CNN) for thyroid cytopathological diagnosis. We take each WSI as a bag, each bag contains multiple instances which are the different regions of the WSI, our framework is trained to learn the key area automatically and make the classification. We also propose a feature fusion structure, merge the low-level features into the final feature map and add an instance-level attention module in it, which improves the classification accuracy. Our model is trained and tested on the collected clinical data, reaches the accuracy of 93.2 %, which outperforms the other existing methods. We also tested our model on a public histopathology dataset and achieves better result than the state-of-the-art deep multi-instance method.
Exploring Opportunities to Aid Generation of Input Action Ideas for Tangible User Interfaces

Novice tangible interaction design students often find it challenging to generate input action ideas for tangible interfaces. To identify opportunities to aid input action idea generation, we built and evaluated a tool consisting of interactive physical artifacts coupled with digital examples of tangible systems and technical implementation guidance. Through video recorded design sessions and interviews with twelve students, we investigated how they used the tool to generate input action ideas, how it supported them, and what challenges they faced. We found that the tool helped in generating input action ideas by enabling to experience input actions, supporting hands-on explorations, and introducing possibilities. However, introducing examples at times caused design fixation. The tool fell short in supporting the planning of technical implementation of the generated ideas. This research is useful for tangible interaction design students, instructors, and researchers to apply in education, design similar tools, or conduct further research.
Understanding the Fake Removal Information Advertisement Sites

Fake antivirus (AV) software is a type of malware that disguises as legitimate antivirus software and causes harm to users and their devices. Fake removal information advertisement (FRAD) sites, which introduce fake removal information for cyber threats, have emerged as platforms for distributing fake AV software. Although FRAD sites seriously threaten users who have been suffering from cyber threats and need information for removing them, little attention has been given to revealing these sites. In this paper, we propose a system to automatically crawl the web and identify FRAD sites. To shed light on the pervasiveness of this type of attack, we performed a comprehensive analysis of both passively and actively collected data. Our system collected 2, 913 FRAD sites in 31 languages, which have 73.5 million visits per month in total. We show that FRAD sites occupy search results when users search for cyber threats, thus preventing the users from obtaining the correct information.
Chinese Event Detection Combining BERT Model with Recurrent Neural Networks

With the rapid development of computer technology and the scale of the Internet, it is particularly important to extract useful information from the increasing amount of information on the Internet. Among them, event extraction is one of the research hotspots in the field of natural language processing and an important research direction in the field of information extraction. Event detection is the first step of the event extraction task, which plays a decisive role in the subsequent event extraction work. The article adopts BERT for word vector training, joint lexical vector, named entity vector, and semantic dependency vector as the input of Bi-LSTM, and then input them into the CRF sequence tagging layer after acquiring the features of sentences to achieve the recognition of event trigger words and the classification of event categories. The CEC corpus is selected as the training and test set, and experiments show that the method is effective in event detection, with F-values up to more than 70%.
Canet: Context-Aware Loss for Descriptor Learning

Research on designing local feature descriptors has gradually shifted to deep learning. Different from other computer vision tasks, the biggest challenge for local descriptor learning lies with the formulation of loss functions. Existing methods solve the problem by leveraging Siamese loss or triplet loss and improve the performance of the learned descriptors by a significant margin. However, the widely used Siamese loss and triplet loss cannot fully utilize the context information. In this paper, we propose a novel loss function to introduce more context information to facilitate training. After incorporating the proposed loss function into training, our learned descriptor demonstrates state-of-the-art performance in patch verification, image matching and patch retrieval benchmarks. The pretrained model will be publicly available at https://github.com/clelouch/CANet.
Adapting Fitts’ Law and N-Back to Assess Hand Proprioception

Proprioception is the body’s ability to sense the position and movement of each limb, as well as the amount of effort exerted onto or by them. Methods to assess proprioception have been introduced before, yet there is little to no study on assessing the degree of proprioception on body parts for use cases like gesture recognition wearable computing. We propose the use of Fitts’ law coupled with the N-Back task to evaluate proprioception of the hand. We evaluate 15 distinct points at the back of the hand and assess the musing extended 3D Fitts’ law. Our results show that the index of difficulty of tapping point from thumb to pinky increases gradually with a linear regression factor of 0.1144. Additionally, participants perform the tap before performing the N-Back task. From these results, we discuss the fundamental limitations and suggest how Fitts’ law can be further extended to assess proprioception
Study On the Temporal Pooling Used In Deep Neural Networks For Speaker Verification

The x-vector architecture has recently achieved state-of-the-art results on the speaker verification task. This architecture incorporates a central layer, referred to as temporal pooling, which stacks statistical parameters of the acoustic frame distribution. This work proposes to highlight the significant effect of the temporal pooling content on the training dynamics and task performance. An evaluation with different pooling layers is conducted, that is, including different statistical measures of central tendency. Notably, 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> and 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> moment-based statistics (skewness and kurtosis) are also tested to complete the usual mean and standard-deviation parameters. Our experiments show the influence of the pooling layer content in terms of speaker verification performance, but also for several classification tasks (speaker, channel or text related), and allow to better reveal the presence of external information to the speaker identity depending on the layer content.
Exploring the Complementary Features of Audio and Text Notes for Video-based Learning in Mobile Settings

In this paper, we compared audio and text note-taking behaviors for video-based learning in various mobile settings. We designed and implemented a note-taking tool and conducted a task-based study to examine how users took audio and text notes differently. Our results show that participants’ audio notes were significantly longer than text notes; longer audio notes were taken to capture unfamiliar video content and participants’ emotions. However, audio notes also raised several privacy concerns. Text notes allowed participants to revise for better accuracy and deeper reflection. Our findings of the complementary features of audio and text notes for video-based learning shed light on designing future note-taking tools that can be used to facilitate learning in varied mobile settings.
Risk Analysis Based on Factors of Failure in Small and Medium-sized Company Projects

Under the limited number of staff in small and medium-sized companies with little opportunity to learn project management, it is difficult for the staff to carry out projects in a systematic manner. New methods and tools for support are needed to prevent project failure in situations where project management knowledge and experience are scarce. Therefore, we have so far collected information on cases of failed projects for small and medium-sized company, and analyzed and categorized factors of failure. In this research, the factors of failure revealed from our previous research were regarded as risk factors, and risk analysis for failure avoidance was performed based on the risk assessment widely used in risk management.
Ego-Based Entropy Measures for Structural Representations on Graphs

Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs). Most of the proposed GNNs are based on the node homophily, i.e neighboring nodes share similar characteristics. However, in many complex networks, nodes that lie to distant parts of the graph share structurally equivalent characteristics and exhibit similar roles (e.g chemical properties of distant atoms in a molecule, type of social network users). A growing literature proposed representations that identify structurally equivalent nodes. However, most of the existing methods require high time and space complexity. In this paper, we propose VNEstruct, a simple approach, based on entropy measures of the neighborhood’s topology, for generating low-dimensional structural representations, that is time- efficient and robust to graph perturbations. Empirically, we observe that VNEstruct exhibits robustness on structural role identification tasks. Moreover, VNEstruct can achieve state- of-the-art performance on graph classification, without incorporating the graph structure information in the optimization, in contrast to GNN competitors.
On Indirectly Dependent Documentation in the Context of Code Evolution: A Study

A software system evolves over time due to factors such as bug-fixes, enhancements, optimizations and deprecation. As entities interact in a software repository, the alterations made at one point may require the changes to be reflected at various other points to maintain consistency. However, often less attention is given to making appropriate changes to the documentation associated with the functions. Inconsistent documentation is undesirable, since documentation serves as a useful source of information about the functionality. This paper presents a study on the prevalence of function documentations that are indirectly or implicitly dependent on entities other than the associated function. We observe a substantial presence of such documentations, with 62% of the studied Javadoc comments being dependent on other entities, as studied in 11 open-source repositories implemented in Java. We comprehensively analyze the nature of documentation updates made in 1288 commit logs and study patterns to reason about the cause of dependency in the documentation. Our findings from the observed patterns may be applied to suggest documentations that should be updated on making a change in the repository.
Word Spotting as a Service for Handwritten Documents

In this paper, a segmentation-free and training-free word spotting method is proposed that allows mobile device users to search instances of a query word in handwritten document collections. The method is based on a document-oriented keypoint and feature extraction pipeline together with a fast feature matching method that permits the pipeline to be effectively employed in the cloud and to be introduced as a service in modern mobile device units. Evaluation results on two segmentation-free historical handwritten datasets show the efficiency of the proposed method in terms of matching accuracy along with its fast retrieval time.
A survey of Interactive Technology in Augmented Reality

Interaction technology is one of the core technologies of augmented reality. With the maturity of augmented reality hardware technology and the expansion of application field, interaction technology has been paid more attention and development. This paper sorts out the classification methods of interaction technology, focuses on the research status of interaction technology based on computer vision, external devices and multi-modal, as well as the selection of 3D objects, introduces the latest typical application of interaction technology. Finally, the interaction technology is summarized and prospected.
Labeling nonograms: Boundary labeling for curve arrangements

Slanted and curved nonograms are a new type of picture puzzles introduced by Van de Kerkhof et al. (2019). They consist of an arrangement of lines or curves within a frame B, where some of the cells need to be colored in order to obtain the solution picture. For solving the puzzle, up to two clues need to be attached as numeric labels to each line on either side of B. In this paper we study the algorithmic problem of optimizing or deciding the existence of a placement of the given clue labels to such a nonogram. We provide polynomial-time algorithms for restricted cases and prove NP-completeness in general.
Differences in Visual Attention for HDR and SDR Content

High Dynamic Range (HDR) technology has recently captured the attention of camera and display manufacturers, as it is capable of closely matching the properties of the human visual system. Understanding the visual attention in HDR is significant to the fields of compression and quality evaluation, but so far only a limited number of studies focused on this topic. In this work we performed a thorough evaluation of the saliency maps of a large number of representative HDR and SDR images. Results show that for non-center biased images, visual attention for HDR differs for areas that primarily have higher contrast levels and relatively higher luminance than the SDR counterparts.
Aesthetic consciousness as a predictor of the effectiveness of art therapy in coping with stress

The subject of this research is the aesthetic consciousness as a predictor of the effectiveness of art therapy in coping with stress. The goal of this article consists in studying the aesthetic consciousness as a predictor of the effectiveness of art therapy in dealing with stressful conditions of the individual. The following empirical methods were employed in the course of research: free descriptions technique of D. A. Leontiev, the scale of resistance to sources of stress of E. V. Raspopin; and Spearman's rank correlation criterion for statistical verification of determined trends and data analysis. The research involved 44 respondents &amp;ndash; the students of the faculty of Philology of Sevastopol State University, aged from 17 to 23. It is established that resistance to certain sources of stress is clearly associated with the peculiarities of aesthetic consciousness of the individual; the following dependency is traced: the freer the subject feels in describing an aesthetic object, and the more diverse are his aesthetic reasoning, the higher is his stress resistance level in accordance with the component &amp;ldquo;Self-others&amp;rdquo; , and at the same time the lower is his resistance to stressors &amp;ldquo;the Self&amp;rdquo;. Therefore, in implementation of art therapy techniques for dealing with stressful conditions of the individual, it is necessary to take into account the peculiarities of the development of aesthetic consciousness of the individual, readiness and ability to work with the complex visual stimuli. The scientific novelty of lies in revealing the role of aesthetic consciousness of the individual as a predictor of the effectiveness of art therapy in coping with stress.