Reviewerly/exHarmony huggingface dataset:
(Mis)communication through stickers in online group discussions: A multiple-case study

Sticker use is an increasingly popular part of daily messaging activity. However, little is known regarding the types, functions, and outcomes of sticker use in authentic online communications. To investigate these phenomena, we analysed sticker use in five small mobile-messaging-facilitated discussion groups initiated by students for course projects in an Asian university. The students used four types of stickers, among which ‘animated picture without text’ was the most frequent. Sticker functions fell into two main categories: as a tone indicator with scope over a textual message, and as a stand-alone illocutionary act. Based on interviews with seven participants, we found disparities between the sender’s intention and the receiver’s interpretation for 34.7% of the stickers, but these disparities did not adversely affect the communication. Implications of the findings are discussed.
Learning complementary Siamese networks for real-time high-performance visual tracking

Recently, Siamese based methods have made a breakthrough in the visual tracking field. However, the existing trackers still cannot take full advantage of the deep features. In this work, we improve the performances of Siamese trackers by complementary learning with different types of matching features. Specifically, a Matching Activation Network (MAN) is firstly designed to highlight the matching regions of the search image given a template. Since only sparse parts of feature maps contribute to the matching result, an important design choice is to emphasize the weak-matching features by erasing the strong-matching ones and learn complementary classifiers from both types of features. Then we propose a novel complementary region proposal network (CoRPN) to take complementary features as inputs and their outputs complement to each other, which are fused to improve the performance. Experiments show that our proposed tracker achieves leading performances on five tracking datasets while retaining real-time speed.
Ensemble methods comparison to predict the Power produced by Photovoltaic Panels

Artificial intelligence is considered as a true driver of Industry 4.0 revolution. Combined with the power of connected objects and Big data, it is already revolutionizing the industrial sector thus allowing the realization of short, medium and long-term predictions. In fact, to exploit the significant quantities of data from the entire industrial ecosystem, it is crucial to rely fundamentally on these technologies especially the use of Artificial intelligence-based algorithms. Therefore, the main goal of this paper is to use Machine learning algorithms as part of Artificial intelligence, to forecast the hourly power provided by photovoltaic panels. In particular, a comparison study of Ensemble methods including Bootstrap aggregating (Bagging), Random Forests, and Boosting is performed to decide which models have the best forecasting outcomes. The investigated models are then assessed using the most commonly evaluation criteria: R-squared, Root Mean Square Error, and Mean Absolute Error. Furthermore, model reliability is determined by examining residual plots of the respective predictive models.
Mix-Up Augmentation for Oracle Character Recognition with Imbalanced Data Distribution

Oracle bone characters are probably the oldest hieroglyphs in China. It is of significant impact to recognize such characters since they can provide important clues for Chinese archaeology and philology. Automatic oracle bone character recognition however remains to be a challenging problem. In particular, due to the inherited nature, oracle characters are typically very limited and also seriously imbalanced in most available oracle datasets, which greatly hinders the research in automatic oracle bone character recognition. To alleviate this problem, we propose to design the mix-up strategy that leverages information from both majority and minority classes to augment samples of minority classes such that their boundaries can be pushed away towards majority classes. As a result, the training bias resulted from majority classes can be largely reduced. In addition, we consolidate our new framework with both the softmax loss and triplet loss on the augmented samples which proves able to improve the classification accuracy further. We conduct extensive evaluations w.r.t. both total class accuracy and average class accuracy on three benchmark datasets (i.e., Oracle-20K, Oracle-AYNU and OBC306). Experimental results show that the proposed method can result in superior performance to the comparison approaches, attaining a new state of the art in oracle bone character recognition.
A Measurement for Distortion Induced Saliency Variation in Natural Images

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Protecting Your Offloading Preference: Privacy-aware Online Computation Offloading in Mobile Blockchain

The high computational capacity demanded in blockchain mining hinders the involvement of mobile devices due to their limited computation power. Offloading the blockchain mining task to base stations (BSs) is a promising solution for mobile blockchain. Recently, many reinforcement learning (RL)-based approaches achieve the long-term performance of Quality of Service (QoS) during online computation offloading, but they fail to consider the risk of privacy leakage. Existing works for privacy preserving share an unresolved problem that they provide a private mechanism by adding private constraints into the value function of RL algorithm but neglect to protect the value function itself. Hence, we investigate a novel privacy issue caused by value function leakage, named offloading preference leakage. To solve this issue, we propose a privacy-aware deep RL method (PA-DRL) for computation offloading over the mobile blockchain. Specifically, a functional noise is generated, then added to the exploring and policy updating processes of DRL. Furthermore, we adopt a cooperative exploring mechanism and prioritized experience replay (PER) to improve the convergence rate of the proposed method. We provide the theoretical analysis for privacy preserving and convergence. Finally, simulation results show that our method can perform cost-efficient computation offloading, compared with benchmark methods.
19.3: Evaluation of Entrance Pupil Location in Measuring VR\AR Eyewear Displays: Theoretical and Experimental Analyses in Field of View

The conventional display measurements are no longer satisfying the need for eyewear displays. The measurement of VR/AR type display requires light measuring devices (LMDs) to simulate the characteristics of the human eye. Factors like the position of the pupil, field of view, and the motion of the eye are often discussed. One of the factors to be considered is the choice of entrance pupil location (aperture stop setting) of the LMD. In this paper, a preliminary study was carried out to compare the deviations of FOV and luminance with the choice of entrance pupil location. The experiment demonstrated that it exists as the most accurate eye‐viewing when the entrance pupil is located at the front focal point.
Research and Practice of Traditional Handicraft Interactive Learning Mode Based on User Interaction Behavior

After investigation and analysis of traditional handicraft digital applications, it is found that most applications pay more attention to digital content presentation and visual effects, and lack of practice and research on the display methods and interactive behavior design of traditional handicraft content with practical characteristics. Under the guidance of behavioral and psychological theories, follow the principle that the design should be consistent with human cognition and behavior habits, and combine the core elements in interactive behaviors to build and construct a traditional handicraft interaction mode that is consistent with user behavior logic. This article combines the five elements of interactive behavior (people, purpose, action, tool / media, and scene) with the traditional handicrafts and inheritance content, redefining participants, positioning behavioral motivation, planning behavioral processes, seeking new methods, and creating new scenarios to reconstruct interactive behaviors and build new models for interactive learning experiences with traditional handicrafts. Finally, take Cantonese Porcelain as an example to design the interactive learning mode. Then, through User interviews and experience evaluations, learn more about users' feelings and expectations for this model.
Spectral Clustering With Adaptive Neighbors for Deep Learning

Spectral clustering is a well-known clustering algorithm for unsupervised learning, and its improved algorithms have been successfully adapted for many real-world applications. However, traditional spectral clustering algorithms are still facing many challenges to the task of unsupervised learning for large-scale datasets because of the complexity and cost of affinity matrix construction and the eigen-decomposition of the Laplacian matrix. From this perspective, we are looking forward to finding a more efficient and effective way by adaptive neighbor assignments for affinity matrix construction to address the above limitation of spectral clustering. It tries to learn an affinity matrix from the view of global data distribution. Meanwhile, we propose a deep learning framework with fully connected layers to learn a mapping function for the purpose of replacing the traditional eigen-decomposition of the Laplacian matrix. Extensive experimental results have illustrated the competitiveness of the proposed algorithm. It is significantly superior to the existing clustering algorithms in the experiments of both toy datasets and real-world datasets.
Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.
Hoax Identification on Tweets in Indonesia Using Doc2Vec

In this paper, we present our work on hoax detection on a collection of Tweets. We tackle the hoax detection as a text classification problem, with Doc2Vec as the text representation method and SVM as the classifier. We collected and annotated 5000 Tweets that consist of 2500 hoax Tweets and 2500 truth Tweets. The experimental results show that the accuracy of our proposed hoax detection on Tweets is 93.4%.
Role of Interface Design: A Comparison of Different Online Learning System Designs

Mobile devices are becoming an indispensable part of the daily lives and learning habits of older adults with the easy access of the Internet. It enhances the connection between old users and online education, which supplies an approach to cultivate them with innovative concepts and entrepreneurship of education. However, the complicated navigation of information systems (IS) confuses older adults, and gets them disoriented in searches of information, in addition, to influencing online activities for older adults. This study aims to investigate what kind of navigation of IS is suitable for older adults. A 2 × 2 (2 factors, 2 levels) mixed experimental design was employed. The two factors were, respectively, cognitive load (CL) and navigation structure (NS). A sample of 40 older participants (mean age = 64.37, SD = 4.03) performed online learning tasks in terms of innovative concept using linear hierarchical or mixed NSs under different time pressures. The results showed that linear hierarchical navigation is more appropriate for the elderly when learning and generating innovative concepts on smartphones, as the interaction between CL and NS exists. Overall, the findings combined suggest that the linear hierarchical NS, compared to mixed hierarchical navigation, obtained better usability in terms of task efficiency, CL, and subjective ratings. The findings can provide theoretical support for designers to design and develop mobile websites for older adults.
VADAF: Visualization for Abnormal Client Detection and Analysis in Federated Learning

Federated Learning (FL) provides a powerful solution to distributed machine learning on a large corpus of decentralized data. It ensures privacy and security by performing computation on devices (which we refer to as clients) based on local data to improve the shared global model. However, the inaccessibility of the data and the invisibility of the computation make it challenging to interpret and analyze the training process, especially to distinguish potential client anomalies. Identifying these anomalies can help experts diagnose and improve FL models. For this reason, we propose a visual analytics system, VADAF, to depict the training dynamics and facilitate analyzing potential client anomalies. Specifically, we design a visualization scheme that supports massive training dynamics in the FL environment. Moreover, we introduce an anomaly detection method to detect potential client anomalies, which are further analyzed based on both the client model’s visual and objective estimation. Three case studies have demonstrated the effectiveness of our system in understanding the FL training process and supporting abnormal client detection and analysis.
The Evaluation of Block Chain Technology within the Scope of Ripple and Banking Activities

Abstract Technological developments have always led to changes in all aspects of our lives. Crypto currency is one of those changes. As a result of those changes, thousands of currencies such as bitcoin, ripple, litecoin and ethereum have evolved and have found a use in business. The present study focuses upon Ripple and tries to explain its effects on banks and business theoretically. It has been stated that the money transfer performed through Ripple is faster and more economical when compared to present systems. Additionally, it has been realised that the present SWIFT system has been influenced by that speed and economy, and therefore taken considerable technologic steps with an effort to improve its system.
Knowledge-Based Diverse Feature Transformation for Few-Shot Relation Classification

Few-shot relation classification is to classify novel relations having seen only a few training samples. We find it is unable to learn comprehensive relation features with information deficit caused by the scarcity of samples and lacking of significant distinguishing features. Existing methods ignore the latter problem. What’s worse, while there is a big difference between the source domain and the target domain, the generalization performance of existing methods is poor. And existing methods can not solve all these problems. In this paper, we propose a new model called Knowledge-based Diverse Feature Transformation Prototypical Network (KDFT-PN) for information deficit, lacking of significant distinguishing features and weak generalization ability. To increase semantic information, KDFT-PN introduces the information of knowledge base to fuse with the sample information. Meanwhile, we propose a novel Hierarchical Context Encoder based on prototypical network, which can enhance semantic interaction and improve cross-domain generalization ability. Moreover, this method has been evaluated on cross-domain and same-domain datasets. And experimental results are comparable with other state-of-the-art methods.
Linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA: A normal reference <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e10324" altimg="si5.svg"><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>-norm based test

A general linear hypothesis testing (GLHT) problem in heteroscedastic one-way MANOVA for high-dimensional data is considered and a normal reference L2-norm based test for the problem is proposed. Different from a few existing methodologies on the GLHT problem which impose strong assumptions on the underlying covariance matrices so that the associated tests’ null distributions are asymptotically normal, it is shown that under some regularity conditions, the proposed test statistic under the null hypothesis and a chi-square type mixture have the same normal or non-normal limiting distributions. It is then suggested to approximate the test’s null distribution using the distribution of the chi-square type mixture, which can be further approximated by the Welch–Satterthwaite chi-square-approximation with approximation parameters consistently estimated. Several simulation studies and a real data application are presented to demonstrate the good performance of the proposed test.
Image block generation from block-based SMRT in colour image encryption and its performance analysis

The development of an efficient and fast cryptosystem has become significant with the widespread use of internet and advancements in networking technology. A colour image encryption/decryption technique using image block generation from block-based Sequency-based Mapped Real Transform (SMRT) is introduced in this paper to achieve this goal. The encryption architecture uses a confusion-diffusion approach and begins by partitioning individual colour components of plain image into fixed-size blocks. This is followed by XOR operation between generated diffusion keys and the image blocks to achieve diffusion. SMRT of the diffused image block is found and confusion induced by image block operations. Encrypted image is obtained through inverse SMRT and compilation of encrypted colour components. The proposed cryptosystem demonstrates excellent features including high sensitivity, complex keystream, strong security, high encryption and decryption efficiency and simple structure.
Revisiting the Coco Panoptic Metric to Enable Visual and Qualitative Analysis of Historical Map Instance Segmentation

Segmentation is an important task. It is so important that there exist tens of metrics trying to score and rank segmentation systems. It is so important that each topic has its own metric because their problem is too specific. Does it? What are the fundamental differences with the ZoneMap metric used for page segmentation, the COCO Panoptic metric used in computer vision and metrics used to rank hierarchical segmentations? In this paper, while assessing segmentation accuracy for historical maps, we explain, compare and demystify some the most used segmentation evaluation protocols. In particular, we focus on an alternative view of the COCO Panoptic metric as a classification evaluation; we show its soundness and propose extensions with more "shape-oriented" metrics. Beyond a quantitative metric, this paper aims also at providing qualitative measures through precision-recall maps that enable visualizing the success and the failures of a segmentation method.
The WRF-Solar Ensemble Prediction System To Provide Solar Irradiance Probabilistic Forecasts

In this study, we introduce the recently developed WRF-Solar Ensemble System (WRF-Solar EPS) and a calibration method. The performances of forecast models are evaluated using the National Solar Radiation Data Base (NSRDB) observational analysis for day-ahead solar irradiance predictions. The results demonstrate that the ensemble forecast improves the quality of the forecasts by taking into account the uncertainty of each ensemble member. The Analog Ensemble (AnEn) calibration contributed to the reduction of positive bias and an overall improvement in the probabilistic attributes such as reliability and statistical consistency.
Impact of Bullying on Academic Performance: A Case Study for the Community of Madrid

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BERT for Arabic Named Entity Recognition

In the recent year, transfer learning and contextual word Embedding models have majorly improved the performance of several NLP tasks for many natural languages. In this paper, we tackle the Arabic NER task while exploiting the effectiveness of using a context-dependent and transformer based representational language model. We train Arabic transformer-based representational language models using BERT [1], and compare them to the pre-trained multi-lingual model by building and evaluating the resulted Arabic Named Entity Recognition systems. Our approach utilizes BERT as multi-layer bidirectional transformer encoder that helped in learning deep bidirectional Arabic word representations, by training and fine-tuning the pre-trained model to generate the most accurate NER system for our datasets. Our system achieved the accuracy of 0.91 which demonstrated that creating a language-specific model can improve the performance of a specific NLP task in comparison to already trained Multi-language model.
Compound Twin Beams Without the Need of Genuine Photon-Number-Resolving Detection

A scheme for building stronger multimode twin beams from a greater number of identical twin beams sufficiently weak so that single-photon-sensitive on-off detectors suffice for their detection is studied. The statistical properties of these compound twin beams involving nonclassicality are analyzed for intensities up to hundreds of photon pairs. Their properties are compared with those of genuine twin beams that require photon-number-resolving detectors for their experimental investigation. The use of such compound twin beams for the generation of sub-Poissonian light and for the measurement of absorption with sub-shot-noise precision is analyzed. A suitable theoretical model for compound twin beams is developed to interpret the experimental data.
Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

Autonomous intelligence plays a significant role in aviation security. Since most aviation accidents occur in the take-off and landing stage, accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely. In this study, an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron. Firstly, based on channels of color histogram, the pre-estimated object probability map is employed to reduce searching computation, and the optimization of the disturbance suppression options can make good resistance to similar areas around the object. Then the object score of probability map is obtained by the sliding window, and the candidate window with the highest probability map score is selected as the new object center. Thirdly, according to the new object location, the probability map is updated, the scale estimation function is adjusted to the size of real object. From qualitative and quantitative analysis, the comparison experiments are verified in representative video sequences, and our approach outperforms typical methods, such as distraction-aware online tracking, mean shift, variance ratio, and adaptive colour attributes.