Reviewerly/exHarmony huggingface dataset:
Cooperative Multi-Agent Deep Reinforcement Learning with Counterfactual Reward

In partially observable fully cooperative games, agents generally tend to maximize global rewards with joint actions, so it is difficult for each agent to deduce their own contribution. To address this credit assignment problem, we propose a multi-agent reinforcement learning algorithm with counterfactual reward mechanism, which is termed as CoRe algorithm. CoRe computes the global reward difference in condition that the agent does not take its actual action but takes other actions, while other agents fix their actual actions. This approach can determine each agent's contribution for the global reward. We evaluate CoRe in a simplified Pig Chase game with a decentralised Deep Q Network (DQN) framework. The proposed method helps agents learn end-to-end collaborative behaviors. Compared with other DQN variants with global reward, CoRe significantly improves learning efficiency and achieves better results. In addition, CoRe shows excellent performances in various size game environments.
Design of flow cytometer liquid circuit control system based on incremental PID algorithm

Abstract In the application and design of the flow cytometry, the problem of controlling the flow rate of liquid sample should be considered, which is directly related to the test performance of the instrument. In this paper, a flow cytometer liquid path controlling system based on incremental PID algorithm is designed. The STM32F429 is used as the core control chip, and a negative feedback correction network is introduced. At the same time, the incremental PID algorithm is used to control the speed of electronic motor. Combining with the hardware circuit, the automatic regulation and control of the flow rate of liquid path sample are realized. The experimental results show that the real-time sample flow can approach to the set value quickly and stably after the sampling started in this system. When different target values are set, the error between the measured value and the set value is less than 3%. The system is simple in design, high accuracy and stability, and can meet the requirements of intelligent control and high efficiency and precision in modern industrial instruments.
Sentiment Analysis on E-Learning Using Machine Learning Classifiers in Python

In today’s virtual world, E-learning frameworks are becoming more and more popular. Online courses turn out to be very trendy as it provides a virtual online educational platform where anyone can take education at their place. Online education also provides all types of courses to all age groups. But it is also observed that students dropout and low completion rate in E-learning has emerged. This paper review some papers on the opinion of students on E-learning and Mooc Data is taken from twitter with the help of a scraper written in python. Around 500 tweets were taken from twitter. This paper is focused on finding the accuracy of sentiment polarity as positive or negative. After finding the polarity, accuracy percent has been calculated with the help of three classifiers that are Naïve Bayes, SVC, and Logistic Regression. Hence, this technique may help online education providers to enhance and exceed the growth of services.
Cloud-based Secure Personal Health Record Management System using Mixnode and Blockchain

Security and privacy of personal health information are the two major concerns for the users of an e-health system. For privacy reasons, it is desired that unauthorized users including the third parties cannot link the outsourced e-health documents to their owners. This work proposes a symmetric key-based scheme that employs mix networks for achieving un-observable outsourced data access and Blockchain for achieving outsourced data integrity. An important security requirement named forward secrecy is implemented on Blockchains so that a medical health care provider is restricted to access any future health document after her session expires. Procedures for publishing and accessing e-health documents are proposed and analyzed.
MetaCGAN: A Novel GAN Model for Generating High Quality and Diversity Images with Few Training Data

Given a large amount of data in the base classes and a small number of data in the new classes, meta-learning can learn prior experience from the base classes and transfer knowledge to the new classes by generating network parameters for data generation. In this paper, we propose a novel generative adversarial network called MetaCGAN for generating high quality and diversity images to achieve data augmentation for the new classes with few data. In particular, MetaCGAN consists of two modules, the conditional GAN (CGAN) and MetaNet modules. The CGAN module is our skeleton network that is applied to generate images, while the MetaNet module is our auxiliary network that is applied to provide deconvolutional weights for the generator of CGAN. Experimental results on the MNIST, Fashion MNIST and CelebA data sets demonstrate the superiority of MetaCGAN over baseline models. Both qualitative and quantitative results show that the MetaNet module can learn prior knowledge and transfer it from the base classes to the new classes, which is beneficial for generating high quality and diversity images to the new classes with few images.
BIMP: Blockchain-Based Incentive Mechanism with Privacy Preserving in Location Proof

Location based services (LBS), as an important part of people’s everyday life, rely on current/historical location information to offer services. However, malicious users may generate some fake information to cheat providers to obtain more profits. Currently, existing schemes directly leverage GPS or a centralized party to claim location information, which may be easily counterfeited or result in leakage of users’ privacy. To authenticate the location information, an interactive scheme can be introduced to allow a mobile user as prover to generate proofs of location by exploiting neighboring nodes as witnesses. However, owing to the selfishness of users, it is a great challenge to encourage witnesses to generate proofs. Moreover, witnesses will not be willing to generate proofs unless that their privacy is under well protection. To tackle the above issues, we propose a blockchain-based incentive mechanism with privacy preserving in location proof. First, a novel blockchain system is introduced for interactive location proof to generate secure and trustworthy proofs through the use of distributed ledgers and cryptocurrency. Second, a proof of points protocol is proposed to efficiently reach consensus to build location proof blockchain and incentivise users. Third, motivated by security problem, a novel concept of traceable-detectable-prefix is developed to resist collusion attacks while protecting users’ privacy. Finally, theoretical analysis and simulation experiments are provided, which demonstrate that the proposed scheme can provide complete location proof while protecting privacy of users.
Zero-Shot Object Detection with Textual Descriptions Using Convolutional Neural Networks

Zero-shot object detection aims to detect and recognize objects unobserved in training samples from images. Previous studies generally utilized concept names or textual descriptions to build relationships between seen and unseen classes. However, these works rarely exploited the valuable information in textual descriptions for optimizing the network. Actually, textual descriptions contain much valuable information related to categories. Exploiting this information can help training the network and improve the detection performance. Besides, textual descriptions usually contain the names of objects that need to be detected. By using this character, we can narrow the scope of candidate unseen categories, thus can improve the detection accuracy. In this regard, we propose a novel framework that incorporates both images and their text descriptions for zero-shot object detection. In particular, we employ text convolutional neural network (CNN) and Faster R-CNN to extract text features and image features respectively, and combine them to optimize the regions that contain objects in images and to classify those newly detected objects simultaneously. Besides, we try extracting potential object labels directly from textual descriptions and introducing online hard example mining (OHEM) to assist with object classification and network optimization. Our extensive experiments on two public datasets demonstrate the superior performance of our approach to state-of-the-art methods.
DELEX

Recent advances in Machine Learning have unveiled interesting possibilities for real-time investigating about user characteristics and expressions like, but not limited to, age, sex, body posture, emotions and moods. These new opportunities lay the foundations for new HCI tools for interactive applications that adopt user emotions as a communication channel.
Learning Representations using Spectral-Biased Random Walks on Graphs

Several state-of-the-art neural graph embedding methods are based on short random walks (stochastic processes) because of their ease of computation, simplicity in capturing complex local graph properties, scalability, and interpretibility. In this work, we are interested in studying how much a probabilistic bias in this stochastic process affects the quality of the nodes picked by the process. In particular, our biased walk, with a certain probability, favors movement towards nodes whose neighborhoods bear a structural resemblance to the current node's neighborhood. We succinctly capture this neighborhood as a probability measure based on the spectrum of the node's neighborhood subgraph represented as a normalized Laplacian matrix. We propose the use of a paragraph vector model with a novel Wasserstein regularization term. We empirically evaluate our approach against several state-of-the-art node embedding techniques on a wide variety of real-world datasets and demonstrate that our proposed method significantly improves upon existing methods on both link prediction and node classification tasks.
Important Scene Detection Of Baseball Videos Via Time-Lag Aware Deep Multiset Canonical Correlation Maximization

This paper presents a new important scene detection method of baseball videos based on correlation maximization between heterogeneous modalities via time-lag aware deep multiset canonical correlation analysis (Tl-dMCCA). The technical contributions of this paper are twofold. First, textual, visual and audio features calculated from tweets and videos are adopted as multi-view time series features. Since Tl-dMCCA which utilizes these features includes the unsupervised embedding scheme via deep networks, the proposed method can flexibly express the relationship between heterogeneous features. Second, since there is the time-lag between posted tweets and the corresponding multiple previous events, Tl-dMCCA considers the time-lag relationships between them. Specifically, we newly introduce the representation of such time-lags into the derivation of their covariance matrices. By considering time-lags via Tl-dMCCA, the proposed method correctly detects important scenes.
Quaternion Harris For Multispectral Keypoint Detection

We present a new keypoint detection method that generalizes Harris corners for multispectral images by considering the input as a quaternionic matrix. Standard keypoint detectors run on scalar-valued inputs, neglecting input multimodality and potentially missing highly distinctive features. The proposed detector uses information from all channel inputs by defining a quaternionic autocorrelation matrix that possesses quaternionic eigenvectors and real eigenvalues, for the computation of which channel cross-correlations are also taken into account. We have tested the proposed detector on a variety of multispectral images (color, near-infrared), where we have validated its usefulness.
Matching LIWC with Russian Thesauri: An Exploratory Study

In Author Profiling research, there is a growing interest in lexical resources providing various psychologically meaningful word categories. One of such instruments is Linguistic Inquiry and Word Count, which was compiled manually in English and translated into many other languages. We argue that the resource contains a lot of subjectivity, which is further increased in the translation process. As a result, the translated lexical resource is not linguistically transparent. In order to address this issue, we translate the resource from English to Russian semi-automatically, analyze the translation in terms of agreement and match the resulting translation with two Russian linguistic thesauri. One of the thesauri is chosen as a better match for the psychologically meaningful categories in question. We further apply the linguistic thesaurus to analyze the psychologically meaningful word categories in two Author Profiling tasks based on Russian texts. Our results indicate that linguistically-motivated thesauri not only provide objective and linguistically motivated content, but also result in significant correlates of certain psychological states, replicating evidence obtained with hand-crafted lexical resources.
Convolutional Transformer with Sentiment-aware Attention for Sentiment Analysis

Given certain data available for training, the keys to improving a sentiment analysis system lie in developing a good model that is capable of capturing both local and global features of texts, as well as incorporating external knowledge into the model effectively. In this paper, we propose a multi-window Convolutional Transformer (ConvTransformer) that takes the advantages of both Transformer and CNN for sentiment analysis. The proposed ConvTransformer is able to capture important local n-gram features effectively while preserving sequential information of texts. Furthermore, we propose a sentiment-aware attention mechanism to incorporate the sentiment intensity information of each word by utilizing an external knowledge base, SentiWordNet. The sentiment-aware attention mechanism takes both sentiment and position information of each token into consideration when computing attention weights, resulting in a global feature for final classification. Comparing with CNN, RNN and attention-based baseline models, our model achieves the best performance on multiple sentiment analysis datasets.
Broad Learning System with Proportional-Integral-Differential Gradient Descent

Broad learning system (BLS) has attracted much attention in recent years due to its fast training speed and good generalization ability. Most of the existing BLS-based algorithms use the least square method to calculate its output weights. As the size of the training data set increases, this approach will cause the training efficiency of the model to be seriously reduced, and the solution of the model will also be unstable. To solve this problem, we have designed a new gradient descent method (GD) based on the proportional-integral-differential technique (PID) to replace the least square operation in the existing BLS algorithms, which is called PID-GD-BLS. Extensive experimental results on four benchmark data sets show that PID-GD can achieve faster convergence rate than traditional optimization algorithms such as Adam and AdaMod, and the generalization performance and stability of the PID-GD-BLS are much better than that of BLS and its variants. This study provides a new direction for BLS optimization and a better solution for BLS-based data mining.
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.
Predictive Coding Networks Meet Action Recognition

Action recognition is a key problem in computer vision that labels videos with a set of predefined actions. Most of the state-of-the-art methods rely on RGB frames for extracting the semantics and pre-computed optical flow fields as a motion cue. Then, both are combined using deep neural networks. Yet, it has been argued that such models are not able to leverage the motion information extracted from the optical flow, but instead the optical flow allows for better recognition of people and objects in the video. This urges the need to explore different cues or models that can extract motion in a more informative fashion. To tackle this issue, we propose to explore the predictive coding network, so called PredNet, a recurrent neural network that propagates predictive coding errors across layers and time steps. We analyze whether PredNet can better capture motions in videos by estimating over time the representations extracted from pre-trained networks for action recognition. In this way, the model only relies on the video frames, and does not need pre-processed optical flows as input. We report the effectiveness of our proposed model on UCF101 and HMDB51 datasets.
Method for Improving the Efficiency of Online communication Systems Based on Adaptive Multiscale Transformation

The article considers the issues of the development of multi-scale analysis taking into account a spline as the basic function. The algorithm of image data compression with the adaptive calculation of the detailing coefficients is proposed. The estimation of its advantages and prospects of use was conducted.
Assessing Ontology Alignment Quality - Analysis and Recommendations

There is a lot of research on ontology mapping. The importance of ontology alignment quality is obvious in the context of usage of systems of mapped ontologies in many applications. The assessment of the quality of the alignments or mapping algorithms have many dimensions. In this paper we will analyze ontology mapping evaluation, classify approaches and problems and discuss its applicability in various contexts.
Social Media Emerging as a Third Eye !! Decoding Users' Sentiment on Government Policy: A Case Study of GST

Micro blogging sites and other social networking platforms have become the primary means of communication and knowledge sharing with progressing technological trends. People across the globe express their views on products & services, predict share price and present feedback on the policies of the regimes. Everything that is shared on social networks may not be authentic or denote the truth. However, it definitely forms a basis to investigate and comprehend the public sentiments. Public sentiments are capable of affecting the economic landscape via foreign investments and stock markets among having other financial and social impacts. In this paper, we have analyzed public sentiments on the Goods and Services tax, popularly known as GST in India. GST subsumes eight central and nine state taxes thereby integrating the absolute indirect tax framework in the country which paves the way for varied opinions & reactions imperative to analyze a collective sentiment. We used a hybrid approach to do the sentiment analysis which uses a combination of lexicon-based method and supervised machine learning approach to determine public sentiments. We accumulated 163,373 tweets over a span of three weeks from July 4th to 25th, 2017 after GST was implemented in India w.e.f. July 1st, 2017. A spatio-temporal analysis was performed on the collected tweets. In this research, we annotated 22,000 unique tweets with the help of a lexicon-based method and thenceforth applied supervised machine learning techniques with a set of six distinct algorithms to train and predict the polarity on the complete data set. K-fold cross validation technique, for K in range of 3-10, was used to assess the model for an independent data set. Subsequently, it was found that accuracy, precision, recall and F1 score of all the models provided the best results when K approached 10. Resultantly, we observed that SVM and Logistic Regression could predict the polarity of new incoming tweets with an accuracy of 77.6% and 79.31% respectively.
HDR and WCG Image Quality Assessment Using Color Difference Metrics

High dynamic range (HDR) and wide color gamut (WCG) imagery is now mainstream across content creation. Thus, having a reliable way of evaluating HDR systems is essential. One common way of measuring the quality of an HDR system is measuring the color errors introduced along the imaging pipeline. Unfortunately, development of color difference metrics has been based on test patches, as opposed to natural imagery, with evaluation mostly limited to standard dynamic range (SDR) databases. In this paper, we evaluate several color difference metrics on five publicly available HDR distortion databases consisting of natural images and subjective scores. Distortion types include lower frequency distortions such as those from tone mapping as well as higher-frequency distortions resulting from compression artifacts by the various compression schemes such as Joint Photographic Experts Group (JPEG). We analyze these databases with the well-established CIE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{L}^\star \text{a}^\star \text{b}^\star $ </tex-math></inline-formula> metrics ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{00}, \Delta E_{94}$ </tex-math></inline-formula> ) as well as two HDR-specific metrics: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{z} (j_{z}a_{z}b_{z})$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{ITP} (IC_{T}C_{P})$ </tex-math></inline-formula> . To quantify the performance, we use four standard performance evaluation procedures. We observe that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{ITP}$ </tex-math></inline-formula> outperforms the other color difference metrics on four out of five databases. We also perform statistical analysis, which demonstrates the effectiveness of using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{ITP}$ </tex-math></inline-formula> to assess the overall image quality of HDR/WCG content. Weaknesses of all metrics for one database suggest that more advanced spatio-chromatic visual models are worth pursuing.