Reviewerly/exHarmony huggingface dataset:
USING PEIRCE (AND DELEUZE’S PEIRCE) TO THINK ABOUT #FOODPORN AND OTHER INSTAGRAM SIGNS

This essay applies Peirce's and Deleuze's semiotics to "food porn" on Instagram, and this sign is an exemplar of the structure of all signs on this platform. Food porn is defined as triadic: (1) the food porn image plus the comments are representative of (2) the profile who posted the image (poster) for (3) another profile (ambient viewer) who thinks this relationship. From Peirce and Deleuze there are two main ways of thinking in signs and I apply them to food porn: from Peirce, comprehension is formal and the ambient viewer draws logical conclusions about the poster of the sign; from Deleuze, comprehension is creative and the ambient viewer can invent brilliant new concepts about the poster. It is not possible to extend my discussion to an evaluation of which mode of thinking is more typical today. This paper explicates Peirce's and Deleuze's semiotic systems to explain how we think with Instagram signs. Once we understand what we can do with social media signs, we can be more considered in what we're doing with it.
Medical Image Tagging by Deep Learning and Retrieval

Radiologists and other qualified physicians need to examine and interpret large numbers of medical images daily. Systems that would help them spot and report abnormalities in medical images could speed up diagnostic workflows. Systems that would help exploit past diagnoses made by highly skilled physicians could also benefit their more junior colleagues. A task that systems can perform towards this end is medical image classification, which assigns medical concepts to images. This task, called Concept Detection, was part of the ImageCLEF 2019 competition. We describe the methods we implemented and submitted to the Concept Detection 2019 task, where we achieved the best performance with a deep learning method we call ConceptCXN. We also show that retrieval-based methods can perform very well in this task, when combined with deep learning image encoders. Finally, we report additional post-competition experiments we performed to shed more light on the performance of our best systems. Our systems can be installed through PyPi as part of the BioCaption package.
Deep Bidirectional Correlation Filters for Visual Object Tracking

Visual Object Tracking (VOT) is an essential task for many computer vision applications. VOT becomes challenging when a target object faces severe occlusion, drastic illumination changes, and scale variation problems. In the literature, Discriminative Correlation Filters (DCFs)-based tracking methods have achieved promising results in terms of accuracy and efficiency in many complex VOT scenarios. A plethora of DCFs trackers have been proposed which exploit information observed in past frames to create and update DCFs for VOT. To adapt to target appearance variations, the DCFs are enhanced by incorporating spatial and temporal consistency constraints. Nevertheless, the performance degradation is observed for these methods because of the aforementioned limitations. To address these issues, we propose a novel algorithm based on bidirectional DCFs for VOT. In this algorithm, we propose the original idea of leveraging information from both past and future frames. The proposed algorithm first tracks the target object forward in the video sequence and then its uses the predicted location of the last window frame and track the target object backward towards the current frame. We design an appearance consistency loss function by taking the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}$</tex> norm between the regression target of the forward tracking and response map of the backward tracking to obtain the resulting response map. Our proposed algorithm realizes a highly accurate DCFs because forward and backward tracking information are fused together for consistent VOT. Although, a result will be output with some small delay because information is taken from a future to the present period, our proposed algorithm has the merit of addressing the drastic appearance variations VOT challenges. We evaluate our proposed tracker using deep features on three publicly available challenging datasets. Our results demonstrate the superior performance of the proposed tracker compared to the existing state-of-the-art trackers.
GeLaiGeLai: A visual platform for analysis of Classical Chinese Poetry based on Knowledge Graph

Classical Chinese poetry contains many precious historical and cultural information. However, the knowledge of classical Chinese poetry is highly fragmented. The statistics of imageries and allusions are often incomplete. Most of related works do not analyse the knowledge of poetry from the perspective of archaic Chinese words. It is hard to determine whether words are semantically related. Therefore, to solve these problems, “GeLaiGeLai” has been set up here, which is a system for data analysis of classical Chinese poetry based on knowledge graph. On the one hand, the platform is able to quickly and accurately find new words in ancient Chinese corpus through AP-LSTM-CRF, which is a new word detection method that first generates frequent character sequences using improved Apriori algorithm and then uses Bi-LSTM-CRF model which could generate the segmentation probability of every position of the sentence to further judge whether each frequent character sequence is a true new word. On the other hand, we visualize the knowledge graph and analyse the commonly-used word and emotions of poets. At the same time, the platform complements the characteristics of poetry, using knowledge graph to solve the problem of knowledge fragmentation and making it more systematic. With the knowledge graph, the performance of many reasoning and analysis tasks about classical Chinese poetry can be improved, such as determining the theme of poetry and analyzing the emotion of poetry, which proves the knowledge graph is helpful to understand classical Chinese poetry.
A New Leader-follower Model for Bayesian Tracking

This paper introduces a novel leader-follower model for tracking a group of manoeuvring objects under a probabilistic framework. The proposed model develops on the conventional leader-follower model in which the followers are driven stochastically towards the velocity and position of the leader. Here we consider the dynamic of followers as a mean-reverting process and express it in a continuous-time stochastic differential equation. Instead of using a standard global Cartesian or polar system, an intrinsic coordinate model is utilised for the leader where piecewise constant forces are applied relative to the heading of the leader. Followers then mean revert towards the heading angle and speed of the leader, leading to a more realistic behavioural modelling than the more conventional global coordinate systems. Such a dynamical model is readily incorporated into tracking algorithms using for example the variable rate particle filtering framework which can accurately capture and estimate the manoeuvres of the leader and followers. The simulation results verify its efficacy under challenging group tracking scenarios and future work will explore automatic identification of group structure and leadership from measurements of groups of moving objects.
Research on Scene Text Recognition Algorithm Basedon Improved CRNN

Image-based sequence recognition has always been a longstanding research topic in computer vision. Scene text recognition is one of the most important and challenging tasks in image-based sequence recognition. End-to-end scene text recognition based on deep learning now mainly transforms text recognition into sequence recognition problems. This paper improves the current advanced end-to-end trainable variable length recognition method CRNN [1]. Adding the IBN-Net [2] structure can improve the accuracy and generalization of the model without increasing the amount of calculation. Experiments in the public datasets ICDAR 2003 [3] and ICDAR 2013 [4] demonstrate the effectiveness of the proposed method. In addition, the proposed algorithm performs well in non-dictionary text recognition tasks, which clearly confirms its generalization.
Finding patterns and periods in Cartesian tree matching

We introduce a new metric of match, called Cartesian tree matching, which means that two strings match if they have the same Cartesian trees. Based on Cartesian tree matching, we define single pattern matching, multiple pattern matching, and indexing problems. We propose a linear time algorithm for single pattern matching, and randomized linear time algorithms for multiple pattern matching and indexing. We also define three types of periods, called full period, initial period, and general period. We propose O(n) time, O(nlog⁡log⁡n) time, and O(nlog⁡n) time algorithms for finding all the full periods, initial periods, and general periods of a string of length n, respectively. Our efficient algorithms use a representation of the Cartesian tree, called parent-distance representation.
Random Bit Generator Mechanism Based on Elliptic Curves and Secure Hash Function

Pseudorandom bit generators (PRBG) can be designed to take the advantage of some hard number theoretic problems such as the discrete logarithm problem (DLP). Such type of generators will have good randomness and unpredictability properties as it is so difficult to find an easy solution to the regarding mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to achieve various security strengths. In this paper, a pseudorandom bit generator mechanism that is based mainly on the elliptic curve discrete logarithm problem (ECDLP) and hash derivation function is proposed. The cryptographic hash functions are used in consuming applications that require various security strengths. In a good hash function, finding whatever the input that can be mapped to any pre-specified output is considered computationally infeasible. The obtained pseudorandom bits are tested with NIST statistical tests and it also could fulfill the up-to-date standards. Moreover, a 256 × 256 grayscale images are encrypted with the obtained pseudorandom bits following by necessary analysis of the cipher images for security prove.
Covariance Estimation for Factor Graph Based Bayesian Estimation

When attempting to estimate the state of a dynamic system, one of the common assumptions is that the uncertainty information for the inputs (measurements and dynamics) is known a-priori. Unfortunately, this is not a valid assumption in many cases, leading to the development of multiple covariance estimation techniques for both the Kalman filter and smoothing techniques. This paper presents a novel method to estimate the covariances of the inputs in a factor-graph formulation of the Bayesian estimation problem. A general solution, based on covariance estimation in linear regression problems, is presented that gives unbiased estimators of multiple variances from measured data. An iteratively re-weighted least squares (IRLS) algorithm is then used to estimate the input variances of a non-linear system using factor graph optimization. Simulation studies using a robot localization problem demonstrate the efficacy of our proposed techniques.
Action recognition using spatially distributed radar setup through microdoppler signature

Small form factor off-the shelf radar sensor nodes are being investigated for various privacy preserving non-contact sensing applications. This paper, presents a novel method, based on a system of spatially distributed radar setup(panel radar), for real time action recognition. Proposed method uses spatially distributed two single channel Continuous Wave (CW) radars to classify actions. For classification, a unique two layered classifier, is employed on novel features. Layer I performs coarse limb level classification followed by finer action detection in Layer II. For validation of the proposed system, 7 actions were targeted and data was collected for 20 people. Accuracy of 88.6 % was obtained, with a precision and recall of 0.9 and 0.89 respectively, hence proving the efficacy of this novel approach.
Temporal Constraint Background-Aware Correlation Filter With Saliency Map

Correlation filter (CF) based trackers have recently drawn great attention in visual tracking community due to their impressive performance and computational efficiency on benchmark datasets. However, the performance of most existing trackers using correlation filter is hampered by two aspects: i) Included background information in the selected rectangular target patch is considered as part of the target, and they are treated as important as the real target in training new filter model, it causes the filter easily drift when target shape changes dramatically. ii) Existing filters use a moving average operation with an empirical weight to update the filter model in each frame, such per frame adaptation constantly introduces new information of the target patch, but never consider the consistence of the historical information and the newly obtained one, further increases the risk of drifting. This paper presents a new framework including saliency map and a novel CF regression model. We reformulate the original optimization problem, and provide a closed form solution for multidimensional features which is solved efficiently using alternating direction method of multipliers (ADMM) and accelerated using Sherman-Morrison lemma, our algorithm as a new framework can be easily integrated into CF base trackers to boost their tracking performance. We perform comprehensive experiments on five benchmarks: OTB-2015, VOT2016, VOT2018, UAV123, and TempleColor-128. Results show that the proposed method performs favorably against lots of state-of-the-art methods with a speed close to real-time. Our method with deep features performs much better on all 5 datasets. Our code will be released to facilitate further researches.
VADR: Discriminative Multimodal Explanations for Situational Understanding

The focus of this paper is on the generation of multimodal explanations for information fusion tasks performed on multimodal data. We propose that separating modal components in saliency map explanations provides users with a better understanding of how convolutional neural networks process multimodal data. We adapt established state-of-the-art explainability techniques to mid-level fusion networks in order to better understand (a) which modality of the input contributes most to a model's decision and (b) which parts of the input data are most relevant to that decision. Our method separates temporal from non-temporal information to allow a user to focus their attention on salient elements of the scene that are changing in multiple modalities. The work is experimentally tested on an activity recognition task using video and audio data. In view of the fact that explanations need to be tailored to the type of user in a User Fusion context, we focus on meeting explanation requirements for system creators and operators respectively.
Cross-Domain Helpfulness Prediction of Online Consumer Reviews by Deep Learning Model

Customer reviews provide helpful information such as usage experiences or critiques; these are critical information resource for future customers. Since the amount of online review is getting bigger, people need a way to find the most helpful ones automatically. Previous studies addressed on the prediction of the percentage of the helpfulness voting results based on a regression model or classified them into a helpful or unhelpful classes. However, the voting result of an online review is not a constant over time, and we also find that there are many reviews getting zero vote. Therefore, we collect the voting results of the same online customer reviews over time, and observe the change of votes to find a better learning target. We collected a dataset with online reviews in five different product categories (“Apple”, “Video Game”, “Clothing, Shoes & Jewelry”, “Sports & Outdoors”, and “Prime Video”) from Amazon.com with the voting result on the helpfulness of the reviews, and monitor the helpfulness voting for six weeks. Experiments are conducted on the dataset to get a reasonable classification on the zero and non-zero vote reviews. We construct a classification system that can classify the online reviews via the deep learning model BERT. The results show that the classifier can get good result on the helpfulness prediction. We also test the classifier on cross-domain prediction and get promising results.
Blockchain-Based Privacy-Preserving Dynamic Spectrum Sharing

For improving the utilization and alleviating the shortage of spectrum resources, centralized database-based dynamic spectrum sharing system has been proposed. However, the centralized architecture is often considered to be nontransparent and more vulnerable to be attacked. To address the above issues, in this work, we propose a blockchain-based dynamic spectrum sharing framework. The distributed architecture based on blockchain technology can bring advantages including decentralization, openness, transparency, immutability and auditability. Considering that the introduction of blockchain technology in spectrum sharing will also bring privacy issues, we design a differential privacy-based privacy-preserving double auction mechanism. The auction can be implemented in smart contracts running on the blockchain. The proposed double auction mechanism is proved to satisfy differential privacy, individual rationality, computational efficiency, and truthfulness. The extensive experiments demonstrate the effectiveness of the proposed dynamic spectrum sharing scheme.
Studies on Learning Effects of AR-Assisted and PPT-Based Lectures

Abstract While common, computer presentations given during classroom lectures do not always improve learning effects; thus, this study incorporated three elements into technology-assisted classroom lectures: emphasis, augmentation, and integration. These three elements cannot be implemented simultaneously when using PowerPoint (PPT) presentations during classroom lectures. Therefore, the virtual and physical integration of augmented reality (AR) was employed to establish an assisted course lecturing tool for implementing these three elements. Teachers can refer to important content from textbooks (emphasis) while lecturing, and students can then use an AR device to scan the content and to call out related supplementary materials (augmentation) in facilitating their learning. These scanning and calling out functions of AR enable teachers to integrate technology-assisted tools with textbooks to enhance the effectiveness of classroom lectures. The pre- and posttest quasi-experimental research design was used to determine differences in the learning outcomes of two groups of AR-assisted and PPT-based course lectures. The experimental results indicate that the AR-assisted lecture was significantly more effective than the PPT-based lecture, and a similar result was obtained from a delayed test. According to interviews held with students, during the AR-assisted lecture, the students tended to focus on only one teaching medium and on the textbook content. By contrast, students of the PPT-based lecture became distracted while focusing on two different media sources simultaneously, resulting in the textbook content often being neglected.
Retrieving Semantic Image Using Shape Descriptors and Latent-Dynamic Conditional Random Fields

Abstract This paper introduces a new approach to semantic image retrieval using shape descriptors as dispersion and moment in conjunction with discriminative classifier model of latent-dynamic conditional random fields (LDCRFs). The target region is firstly localized via the background subtraction model. Then the features of dispersion and moments are employed to k-means clustering to extract object’s feature as second stage. After that, the learning process is carried out by LDCRFs. Finally, simple protocol and RDF (resource description framework) query language (i.e. SPARQL) on input text or image query is to retrieve semantic image based on sequential processes of query engine, matching module and ontology manager. Experimental findings show that our approach can be successful to retrieve images against the mammal’s benchmark with retrieving rate of 98.11%. Such outcomes are likely to compare very positively with those accessible in the literature from other researchers.
Semisupervised Multiple Choice Learning for Ensemble Classification

Ensemble learning has many successful applications because of its effectiveness in boosting the predictive performance of classification models. In this article, we propose a semisupervised multiple choice learning (SemiMCL) approach to jointly train a network ensemble on partially labeled data. Our model mainly focuses on improving a labeled data assignment among the constituent networks and exploiting unlabeled data to capture domain-specific information, such that semisupervised classification can be effectively facilitated. Different from conventional multiple choice learning models, the constituent networks learn multiple tasks in the training process. Specifically, an auxiliary reconstruction task is included to learn domain-specific representation. For the purpose of performing implicit labeling on reliable unlabeled samples, we adopt a negative l1 -norm regularization when minimizing the conditional entropy with respect to the posterior probability distribution. Extensive experiments on multiple real-world datasets are conducted to verify the effectiveness and superiority of the proposed SemiMCL model.
A Comparative Study of Augmented Reality Platforms for Building Educational Mobile Applications

Teachers and educators are always trying to find new ways to use the available technology around them to impart knowledge to their students. This includes exposing them to activities that engage them in learning through innovative ways. Augmented Reality (AR) is a technology that is starting to affect the conventional learning process since it has many potential uses and capabilities to introduce new ways and methods. AR technology may make classes more engaging and information more easily conceivable. Nowadays most young people own smartphones and use them in order to access social platforms, play games and connect with friends and their family. Thus, the combination of smartphones and AR for education is promising and still has to be fully discovered. This study aims to categorize the plethora of platforms available for building mobile AR applications and experiences, based on their distinct features and the capabilities that they provide. The findings of this study will assist teachers, educators and developers in choosing the appropriate platform to use when building educational mobile AR applications.
Paper Recommend Based on LDA and PageRank

The explosive growth of the number of papers leads to the over-expansion of the paper information, resulting in the problem of information overload, causing a lot of trouble for researchers to find the papers they need. This paper implements a paper recommendation system based on LDA and PageRank. In this system, we recommend papers in the same field to researchers by modeling and analyzing paper data. Therefore, we can provide researchers with a quick and effective reference for related papers in the research field. In this paper, the probability distribution calculation and keywords extraction of paper topics and words are carried out according to the LDA of papers in the paper pool. At the same time, word2dec is used to represent the topic vector, doc2vec is used to represent the paper vector, and by calculating the system similarity between documents and topics, we get the top N papers that are most similar to the topic input by users. We use the PageRank algorithm to reorder the options in the reference network to get the final recommendation results. Under the experimental data set, the accuracy of system recommendation is more than 60%, which has high reliability and achieves the corresponding recommendation effect.