Reviewerly/exHarmony huggingface dataset:
A Personalized Learning System for Parallel Intelligent Education

Technological advancement has given education a new definition-parallel intelligent education-resulting in fundamentally new ways of teaching and learning. This article exemplifies an important component of parallel intelligent education-artificial education system in a narrative game environment to offer personalized learning. The system collects data on the player's actions while they play, assessing their concept knowledge via k-nearest-neighbor (kNN) classification, and provides tailored feedback to that student as they play the game. Based on an empirical evaluation, the kNN-based game system is shown to accurately provide players with differentiated instructions to guide them through the learning process based on the estimation of their knowledge levels.
Towards Effective Deep Embedding for Zero-Shot Learning

Zero-shot learning (ZSL) can be formulated as a cross-domain matching problem: after being projected into a joint embedding space, a visual sample will match against all candidate class-level semantic descriptions and be assigned to the nearest class. In this process, the embedding space underpins the success of such matching and is crucial for ZSL. In this paper, we conduct an in-depth study on the construction of embedding space for ZSL and posit that an ideal embedding space should satisfy two criteria: intra-class compactness and inter-class separability. While the former encourages the embeddings of visual samples of one class to distribute tightly close to the semantic description embedding of this class, the latter requires embeddings from different classes to be well separated from each other. Towards this goal, we present a simple but effective two-branch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier. Furthermore, we extend our method to a transductive setting to better handle the model bias problem in ZSL (i.e., samples from unseen classes tend to be categorized into seen classes) with minimal extra supervision. Specifically, we propose a pseudo labeling strategy to progressively incorporate the testing samples into the training process and thus balance the model between seen and unseen classes. Experimental results on five standard ZSL datasets show the superior performance of the proposed method and its transductive extension.
A dynamic and hierarchical access control for IoT in multi-authority cloud storage

Internet-of-Things (IoT) is massively growing and introducing several benefits that are able to transform the world. Also, cloud computing has become significantly important due to its ability to handle large sets of data. IoT and cloud computing present a stalwart combination that can enrich several analytics and applications. Nevertheless, cloud data security and users privacy can have gaps. Especially when the IoT and cloud systems deal with a dynamically behaving users within a centralized and costly environment. Therefore, there is an urgent need for a dynamic access control scheme that considers the users’ behavior while making the access control decisions within a reasonable time. In this paper, we introduce a Multi-Dimensional Access Control (MD-AC) scheme for dynamically authorizing and revoking users in the cloud with multiple authorities. The experimental results indicate that MD-AC can evaluate access requests within reasonable and acceptable processing times. By considering very hard experimental conditions and numerous transactions, the average encryption and decryption times are 18 and 10 ms respectively. Furthermore, the proposed scheme is validated and compared with recent state-of-art schemes. The results demonstrate that the proposed scheme is fast and robust against different well-known attacks. Moreover, MD-AC can be used for keeping the privacy of IoT services over the cloud environment.
Web-Shaped Model for Head Pose Estimation: An Approach for Best Exemplar Selection

Head pose estimation is a sensitive topic in video surveillance/smart ambient scenarios since head rotations can hide/distort discriminative features of the face. Face recognition would often tackle the problem of video frames where subjects appear in poses making it quite impossible. In this respect, the selection of the frames with the best face orientation can allow triggering recognition only on these, therefore decreasing the possibility of errors. This paper proposes a novel approach to head pose estimation for smart cities and video surveillance scenarios, aiming at this goal. The method relies on a cascade of two models: the first one predicts the positions of 68 well-known face landmarks; the second one applies a web-shaped model over the detected landmarks, to associate each of them to a specific face sector. The method can work on detected faces at a reasonable distance and with a resolution that is supported by several present devices. Results of experiments executed over some classical pose estimation benchmarks, namely Point '04, Biwi, and AFLW datasets show good performance in terms of both pose estimation and computing time. Further results refer to noisy images that are typical of the addressed settings. Finally, examples demonstrate the selection of the best frames from videos captured in video surveillance conditions.
Rnn-Transducer with Stateless Prediction Network

The RNN-Transducer (RNNT) outperforms classic Automatic Speech Recognition (ASR) systems when a large amount of supervised training data is available. For low-resource languages, the RNNT models overfit, and can not directly take advantage of additional large text corpora as in classic ASR systems.We focus on the prediction network of the RNNT, since it is believed to be analogous to the Language Model (LM) in the classic ASR systems. We pre-train the prediction network with text-only data, which is not helpful. Moreover, removing the recurrent layers from the prediction network, which makes the prediction network stateless, performs virtually as well as the original RNNT model, when using wordpieces. The stateless prediction network does not depend on the previous output symbols, except the last one. Therefore it simplifies the RNNT architectures and the inference.Our results suggest that the RNNT prediction network does not function as the LM in classical ASR. Instead, it merely helps the model align to the input audio, while the RNNT encoder and joint networks capture both the acoustic and the linguistic information.
A Hybrid Deep Grouping Algorithm for Large Scale Global Optimization

Many real-world problems contain a large number of decision variables which can be modeled as large scale global optimization (LSGO) problems. One effective way to solve an LSGO problem is to decompose it into smaller subproblems to solve. The existing works mainly focused on designing methods to decompose separable problems, while seldom focused on the decomposition of nonseparable large scale problems. Also, the existing decomposition methods only learn the interaction (correlation or interdependence) among variables to make the decomposition. In this article, we make the decomposition deeper: we not only consider the variable interaction but also take the essentialness of the variable into account to form a deep grouping method. To do this, we first design an essential/trivial variable detection scheme to support the deep decomposition for both separable problems and nonseparable problems. Based on it, we propose a new decomposition method called deep grouping method. Then, we design a new differential evolution (DE) algorithm with a new mutation strategy. By integrating all these, we propose a hybrid deep grouping (HDG) algorithm. Finally, the experiments are conducted on the widely used and most challenging LSGO benchmark suites, and the comparison results of the proposed algorithm with the state-of-the-art algorithms indicate the proposed algorithm is more effective.
Evolution strategies for continuous optimization: A survey of the state-of-the-art

Evolution strategies are a class of evolutionary algorithms for black-box optimization and achieve state-of-the-art performance on many benchmarks and real-world applications. Evolution strategies typically evolve a Gaussian distribution to approach the optimum. In this paper, we present a survey of recent advances in evolution strategies. We summarize the techniques, extensions, and practical considerations of evolution strategies for various optimization problems. We discuss some important open questions and promising topics that desire further research. Many of the discussed techniques and principles are applicable to other algorithms.
Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.
A New Method for Specific Emitter Identification With Results on Real Radar Measurements

Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitters by extracting unique features from the precise measurements of their emitted signals. A novel signal processing scheme with two stages is proposed for the identification of specific radar emitters. In the first stage, the received radar pulses are accurately time aligned and coherently integrated in order to increase the Signal-to-Noise (SNR) ratio. Using this technique, measurements with SNR improvements of more than 25 dB are obtained, enabling detection of subtle differences between different emitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received signal into a set of modes. Then, these mode signals are characterized by using a group of features for identification. We demonstrate highly successful identification performance with the proposed method on real radar datasets.
GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier

User reviews on social networking platforms like Twitter, Facebook, and Google+, etc. have been gaining growing interest on account of their wide usage in sentiment analysis which serves as the feedback to both public and private companies, as well as, governments. The analysis of such reviews not only plays a noteworthy role to improve the quality of such services and products but helps to devise marketing and financial strategies to increase the profit for companies and customer satisfaction. Although many analysis models have been proposed, yet, there is still room for improving the processing, classification, and analysis of user reviews which can assist managers to interpret customers feedback and elevate the quality of products. This study first evaluates the performance of a few machine learning models which are among the most widely used models and then presents a voting classifier Gradient Boosted Support Vector Machine (GBSVM) which is constituted of gradient boosting and support vector machines. The proposed model has been evaluated on two different datasets with term frequency and three variants of term frequency-inverse document frequency including uni-, bi-, and tri-gram as features. The performance is compared with other state-of-the-art techniques which prove that GBSVM outperforms these models.
CETAnalytics: Comprehensive effective traffic information analytics for encrypted traffic classification

Encrypted traffic classification is of great significance for advanced network services. Though encryption methods seem unbroken in protecting users’ privacy, existing studies have demonstrated that with sophisticated designed approaches utilizing the methods of machine learning or deep learning, the traffic can be identified as generated from which application type or even the specific application. However, most of the previous approaches either lack the generalization ability in different tasks or can hardly achieve the precise performance. One of the reasons is that they perform the classification from an incomplete perspective. To our best knowledge, none of which consider combing the payload content and payload statistics for encrypted traffic classification. Hence, in this paper, we propose the comprehensive effective traffic information analytics (CETAnalytics) framework to tackle the problem. Firstly, the comprehensive effective traffic information is specified and the motivation for combing the two aspects of the traffic is introduced. Based on the specification, the CETAnalytics framework utilizing the consolidated information and its devising implementation details are elaborated. Briefly, the implementation is totally built on the neural network for its high flexibility and powerful functionality to integrate the two dimensional analytics. Among the challenges tackled in the implementation, a substructure network Attract designed with the purpose of matching the traffic structure is proposed to realize the payload content analytics, which is one of the highlights of our work. For evaluation, several solid experiments are conducted using three designed tasks originated from the ISCXVPN2016 dataset. The experiment results show that: (i) the effectiveness of the framework design for encrypted traffic classification; (ii) our implementation can achieve both high precision and robust generalization performance at the same time.
Tikhonov Regularization within Ensemble Kalman Inversion

Ensemble Kalman inversion is a parallelizable methodology for solving inverse or parameter estimation problems.Although it is based on ideas from Kalman filtering, it may be viewed as a derivative-free optimization method.In its most basic form it regularizes ill-posed inverse problems through the subspace property: the solution found is in the linear span of the initial ensemble employed.In this work we demonstrate how further regularization can be imposed, incorporating prior information about the underlying unknown.In particular we study how to impose Tikhonov-like Sobolev penalties.As well as introducing this modified ensemble Kalman inversion methodology, we also study its continuous-time limit, proving ensemble collapse; in the language of multi-agent optimization this may be viewed as reaching consensus.We also conduct a suite of numerical experiments to highlight the benefits of Tikhonov regularization in the ensemble inversion context.
Data Classification with k-fold Cross Validation and Holdout Accuracy Estimation Methods with 5 Different Machine Learning Techniques

Classification of documents is measured in terms of accuracy by comparing the actual labels with predicted labels for the classes. There are many machine learning techniques, which can be used to build a classifier, it is almost difficult to manually predict which technique should be used for classification, especially when we are working on Indic languages and there is no reliable method, which can give good results. Such area still need to be explored by processing documents with natural language processing, then applying machine learning techniques to build the classifier. This research article has collected 154 Hindi Poetries from web; processed it using NLP techniques, extracted features to build classifier with 5 Machine Learning methods namely support vector machine, naïve bayes, decision tree algorithm, random forest and k-nearest neighbors. The results were examined in accuracy with holdout accuracy estimator and k-fold cross validation accuracy estimator to check the reliability of the methods. The results of this research will help to select the best document classifier for Hindi Poetries and increase accuracy by working on various feature extraction techniques and NLP techniques. The result of the experiment shows that the results of SVM, NB and random forest methods are better as compared to DTT and K-NN for used data set available in this experiment.
Single Shot Video Object Detector

Single shot detectors that are potentially faster and simpler than two-stage detectors tend to be more applicable to object detection in videos. Nevertheless, the extension of such object detectors from image to video is not trivial especially when appearance deterioration exists in videos, e.g., motion blur or occlusion. A valid question is how to explore temporal coherence across frames for boosting detection. In this paper, we propose to address the problem by enhancing per-frame features through aggregation of neighboring frames. Specifically, we present Single Shot Video Object Detector (SSVD) - a new architecture that novelly integrates feature aggregation into a one-stage detector for object detection in videos. Technically, SSVD takes Feature Pyramid Network (FPN) as backbone network to produce multi-scale features. Unlike the existing feature aggregation methods, SSVD, on one hand, estimates the motion and aggregates the nearby features along the motion path, and on the other, hallucinates features by directly sampling features from the adjacent frames in a two-stream structure. Extensive experiments are conducted on ImageNet VID dataset, and competitive results are reported when comparing to state-of-the-art approaches. More remarkably, for $448 \times 448$ input, SSVD achieves 79.2% mAP on ImageNet VID, by processing one frame in 85 ms on an Nvidia Titan X Pascal GPU. The code is available at https://github.com/ddjiajun/SSVD.
Deep balanced discrete hashing for image retrieval

Hashing has been widely used for large-scale multimedia retrieval because of its advantages in storage and retrieval efficiency. Traditional supervised hash methods represent an image as a feature vector and then perform a separate quantization step to generate a binary code. Due to the difficulty of discrete optimization of hash codes, continuous relaxation is generally used to replace discrete optimization. However, the process of continuous relaxation leads to inevitable quantization error. To avoid this drawback, a deep balanced discrete hashing method is proposed, which uses discrete gradient propagation with the straight-through estimator. The proposed method does not use the traditional continuous relaxation strategy, thereby reducing the quantization error caused by continuous relaxation. And the proposed method uses supervised information to directly guide the discrete coding and deep feature learning process. In the proposed method, the last layer of the Convolutional Neural Network (CNN) outputs the binary code directly. In the loss function, discrete values are calculated by combining the pairwise loss and a balance controlling term. The learned binary hash code maintains the similar relationship and label consistency at the same time. While maintaining the pairwise similarity, the proposed method keeps the balance of hash codes to improve retrieval performance. Extensive experiments show that the proposed method outperforms the state-of-the-art hashing methods on four image retrieval benchmark datasets.
Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads

Transporting suspended payloads is challenging for autonomous aerial vehicles because the payload can cause significant and unpredictable changes to the robot's dynamics. These changes can lead to suboptimal flight performance or even catastrophic failure. Although adaptive control and learning-based methods can in principle adapt to changes in these hybrid robot-payload systems, rapid mid-flight adaptation to payloads that have a priori unknown physical properties remains an open problem. We propose a meta-learning approach that "learns how to learn" models of altered dynamics within seconds of post-connection flight data. Our experiments demonstrate that our online adaptation approach outperforms non-adaptive methods on a series of challenging suspended payload transportation tasks. Videos and other supplemental material are available on our website: https://sites.google.com/view/meta-rl-for-flight
Variational quantum eigensolver for frustrated quantum systems

Hybrid quantum-classical algorithms have been proposed as a potentially viable application of quantum computers. A particular example---the variational quantum eigensolver, or VQE---is designed to determine a global minimum in an energy landscape specified by a quantum Hamiltonian, which makes it appealing for the needs of quantum chemistry. Experimental realizations have been reported in recent years and theoretical estimates of its efficiency are a subject of intense effort. Here we consider the performance of the VQE technique for a Hubbard-like model describing a one-dimensional chain of fermions with competing nearest- and next-nearest-neighbor interactions. We find that recovering the VQE solution allows one to obtain the correlation function of the ground state consistent with the exact result. We also study the barren plateau phenomenon for the Hamiltonian in question and find that the severity of this effect depends on the encoding of fermions to qubits. Our results are consistent with the current knowledge about the barren plateaus in quantum optimization.
Gender and Age Influences on Interpretation of Emoji Functions

An online survey, the Understanding Emoji Survey , was conducted to assess how English-speaking social media users interpret the pragmatic functions of emoji in examples adapted from public Facebook comments, based on a modified version of [15]’s taxonomy of functions. Of the responses received (N = 519; 351 females, 120 males, 48 “other”; 354 under 30, 165 over 30, age range 18--70+), tone modification was the preferred interpretation overall, followed by virtual action , although interpretations varied significantly by emoji type. Female and male interpretations were generally similar, while “other” gender respondents differed significantly in dispreferring tone and preferring multiple functions . Respondents over 30 often did not understand the functions or interpreted the emoji literally, while younger users interpreted them in more conventionalized ways. Older males were most likely, and younger females were least likely, to not understand emoji functions and to find emoji confusing or annoying, consistent with previously reported gender and age differences in attitudes toward, and frequency of, emoji use.