Reviewerly/exHarmony huggingface dataset:
A Survey of Some Interdisciplinary Methods and Tools to Measure Learners' Emotions in Intelligent Tutoring Systems

Emotion measurement in Educational technology is an important topic which amalgamates the computer-based education (specifically the intelligent tutoring systems ITS) and the emotion measurement. This will have a positive effect for gradually improving the performance of the educational process. It will be fruitful for the educational environment to adopt the measuring emotions techniques, emotion intelligence, and emotion recognition. This is useful for collecting information about students' emotions and directs the educational process to better performance. Adopting such technologies will face some challenges. Some of these challenges include data acquisition from the physiological and psychological tools, handling of different data types, manipulation of data, decision to recover the detected problem, cost of both the physiological and psychological tools,....etc. The researchers are interested in this field of studies to adapt instruction to develop the responding of a student's affective state. So, these studies will take student gradually to the best performance in his educational material.
Interactive Design of Geofences for Proactive Location-Based Services in Smart Cities

Recently, location-based services for Smart Cities became capable to proactively notify mobile users about hyperlocal information or location-based reminders once users enter clearly-specified geographical regions, better known as geofences. Up to now, geofences are mostly designed by humans through the interactive design of circular or polygonal shapes on 2-dimensional digital maps. In future Smart City applications, geofences will not only represent flat regions but also arbitrary 3-dimensional spaces such as no-fly zones for drones. This work investigates the applicability of using head-mounted displays together with touch controllers to interactively design 3-dimensional geofences within a virtual 3D representation of the world. It presents and discusses a proof of concept that enables humans to examine, create, modify, move, scale, and rotate 3-dimensional geofences and to edit its meta information within a generated 3D virtual environment of the inner city of Berlin.
A Comparative study for Feature Extraction and Classification of Images

Digital images increased rapidly due to the usage of users to the Internet, digital camera and their cell phones. The huge amounts of images are used for different purposes: business, education, recording/sharing events, remembering, etc. Most commercial and online shopping companies rely on images to attract consumers to visit and buy their products. Image classification is a core task for most of such applications. The performance of a classifier depends on two main factors: the features extracted from the images and the classification techniques. Many feature extraction algorithms are proposed by researchers such as Local Binary Pattern, Maximally Stable Extermal Regions, Speed Up Robust Features and Deep Convolutional Neural Networks. This paper tries to experimentally compare among these different feature extraction algorithms by applying different classifiers with each one. These classifiers are Support Vector Machine (SVM), Decision Tree, k-Nearest Neighbors and Naive Bayes. The classifiers were learned from a dataset of eight categories: animals, food, nature, building, tourism, people, sport and apparatus. Our experiments show that SVM achieves 93.89% accuracy when Residual Neural Network is used as a feature detector and SVM as a classifier.
Entanglement dynamics of two atoms coupling to a quantum cavity

Recently, strong coupling dominating dissipative and decoherence effects between the atom and a quantum cavity has been investigated experimentally. In this paper, we study the entanglement dynamics of two two-level atoms coupling to a quantum cavity where each mirror is formed by a chain of atoms trapped near a one-dimensional waveguide. We consider the effects of both Markovian dynamics and non-Markovian dynamics on the entanglement evolution of the two central atoms. Our results show that the steady entanglement can be generated between the two central atoms whether the system is under the Markov regime or non-Markov regime when the number of atoms of the atomic mirrors is finite. When the number of the atomic mirrors is large enough, the entanglement of two central atoms takes oscillating behavior without a stable value. The effect of detuning and dissipation on entanglement dynamics is also discussed and the results imply that the detuning plays an important role in improving the entanglement amplitude.
Scalable Kernel Learning Via the Discriminant Information

Kernel approximation methods create explicit, low-dimensional kernel feature maps to deal with the high computational and memory complexity of standard techniques. This work studies a supervised kernel learning methodology to optimize such mappings. We utilize the Discriminant Information criterion, a measure of class separability with a strong connection to Discriminant Analysis. By generalizing this measure to cover a wider range of kernel maps and learning settings, we develop scalable methods to learn kernel features with high discriminant power. Experimental results on several datasets showcase that our techniques can improve optimization and generalization performances over state of the art kernel learning methods.
Super-resolution Restoration of Single Vehicle Image Based on ESPCN-VISR Model

Abstract In the process of image acquisition monitoring in vehicle license plate recognition system, the accuracy of license plate recognition will decrease which is caused by the complicated application scenarios, poor performance of the equipment and system, and the degeneration of surveillance images. In order to overcome the shortcoming, a super-resolution restoration method for single vehicle image based on efficient sub-pixel convolutional neural network (ESPCN) model is proposed (hereinafter referred to as ESPCN-VISR method). In the experiments, ESPCN-VISR method was proved to be superior by four indexes of quantitative assessment which are peak signal to noise ratio (PSNR), structural similarity (SSIM), vehicle license plate recognition accuracy (VLPRA), and calculation time of reconstruction (CTR). Compared with the methods based on sparse dictionary learning and deep convolutional neural network SRCNN, the ESPCN-VISR method can improve the performance of vehicle license plate recognition system with vehicle license plate sample image dataset LPI-1000.
Optimum Kernel Particle Filter for Asymmetric Laplace Noise

In this paper we present on-line Bayesian filtering methods for time series models corrupted by asymmetric Laplace noise. An optimum kernel particle filter is designed for the general asymmetric case, and its performance is compared to that of a traditional bootstrap filter and a newly designed Rao-Blackwellised particle filter for the symmetric linear case. The optimum kernel is shown to improve performance and reduce degeneracy in the filter for a non-linear time-series model, and both the Rao-Blackwellised and the optimum kernel filter show significant advantages over a traditional bootstrap particle filter for a linear model.
Dilated Convolutional Neural Networks for Panoramic Image Saliency Prediction

Saliency prediction is an important way to understand human's behavior and has a wide range of applications. Although lots of algorithms have been designed to predict saliency for planar images, there are few works for 360° images. In this paper, we propose an encoder-decoder network for panoramic image saliency prediction. Dilated convolutional layers are deployed in the network, which can extract more representative features and improve the accuracy of saliency prediction. To deal with the image distortions in 360° images, our network takes cube map format as input and processes six faces of cube map simultaneously. Respecting the saliency distribution of ground truth, we also propose a new data augmentation method to train the network, which is validated to be helpful for performance improvement. Extensive experiments show that our method gives new state-of-the-art results on 360° image saliency prediction.
Affects in Tweets with Real Time Emotions using Deep Learning Techniques: A Novel Approach

Twitter is an online microblogging tool that has 400 million messages per day. SemEval-2018 Tasks have already been presented and explored in the previous years by the name of “Affect in Tweets” but the scope for improvement never ends. So, in this research paper, we come up with deep learning architecture which is extremely coherent for the given task of extracting emotion intensity and classes from tweets (description of the task is given on www.codalab.com for details). Deep learning models are productive due to their automatic learning capability and automatic feature extraction. This research paper highlights the implementation of deep learning-based models such as convolutional neural networks and LSTM for classifications. The implemented tasks are-:1. emotion intensity regression 2. Emotion intensity ordinal classification,z 3. Multilabel emotion classification. We have expressed that the fine-grained intensity scores that we have obtained are reliable. Our dataset is beneficial for testing supervised machine learning algorithms for multi-label classification, intensity regression, sleuthing ordinal category of intensity of feeling (low, moderate, etc.). We have implemented various machine learning and deep learning-based models and achieved an accuracy of 77.64% in E-oc (Emotion ordinal classification) task, which is the highest among all competitors.
Pattern-Adaptive Time Series Prediction via Online Learning and Paralleled Processing using CUDA

In this paper we study the problem of prediction of time series data in a computational efficient way. We proposed a SARIMA (Seasonal AutoRegressive Integrated Moving Average) based-online learning algorithm to update the model parameters via Gradient Descent based algorithm every time when a new data comes, based on the gap between prediction and true value of the time series data. In this way, our model can adapt to the new pattern of the time series if the pattern changes, and generates more accurate prediction results comparing with existing SARIMA implementations. Besides, it has the capability of learning the true pattern and converge to optimal model even if the start point is far from optimal. We also implement our algorithm on CUDA (Compute Unified Device Architecture) platform to support training millions of different online learning models concurrently by leveraging the GPU's capability of parallel computing. The performance of our online learning algorithm is evaluated with simulation results.
Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices

Continuous authentication systems allow users not to possess or remember something to authenticate themselves. These systems perform a permanent authentication that improves the security level of traditional mechanisms, which just authenticate from time to time. Despite the benefits of continuous authentication, the selection of dimensions and characteristics modelling of user's behaviour, and the creation and management of precise models based on Machine learning, are two important open challenges. This chapter proposes a continuous and adaptive authentication system that uses Machine Learning techniques based on the detection of anomalies. Applications usage and the location of the mobile device are considered to detect abnormal behaviours of users when interacting with the device. The proposed system provides adaptability to behavioural changes through the insertion and elimination of patterns. Finally, a proof of concept and several experiments justify the decisions made during the design and implementation of this work, as well as demonstrates its suitability and performance.
Game Theoretical Study on a Client-Controlled Deduplication Scheme

Data deduplication eliminates redundant data and is receiving increasing attention in cloud storage services due to the proliferation of big data and the demand for efficient storage. Various deduplication schemes have been proposed mainly from technological points of view. However, a deduplication scheme often involves multiple parties with conflict interests. How to design incentive mechanisms for data deduplication schemes and study their acceptance by all relevant stakeholders remain important open issues. In this paper, we propose an incentive mechanism for a client-controlled deduplication scheme and apply game theory to analyze its acceptance by all stakeholders. Besides theoretical analysis, we also conduct extensive experiments based on a real-world dataset to demonstrate the effectiveness of our incentive mechanism. The experiment results show that the incentive mechanism can increase deduplication percentage and suppress free-riding behaviors of data users.
Computer Laws Consideration on Smart City Data Planning of Chongli 2022

Smart Cities make a good use of modern ICT and innovation idea to provide systematically creative, integrated of variety of services. In order to serve citizen's life a good quality, optimize management and services, enhance the efficiency of resource usage, a good smart city planning is in a big demand. Since 2014, professionals have started to consider Smart City planning and design for Chongli, in order to prepare the 2022 Winter Olympics there properly. Nevertheless, there is a shortage of cyber security consideration in current Smart City planning and designing. Although Yu et al (2018) discussed the Smart Cities development, but more works are still needed. Legal preparation actually is very important for Smart Chongli.
High Speed Approximate Cognitive Domain Ontologies for Constrained Asset Allocation based on Spiking Neurons

Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). One problem that is often tasked to CDOs is asset allocation. Given the number of possible solutions in this allocation problem, determining the optimal solution via CDO can be very time and energy consuming. A grid of isolated spiking neurons is capable of generating solutions to this problem very quickly, although some degree approximation is required to achieve the speedup. The approximate spiking approach presented in this work was able to complete nearly all allocation simulations with greater than 98% accuracy. Our results in this work show that constraining the possible solution space by creating specific rules for a scenario can alter the quality of the allocation result. We present a study compares allocation score and computation time for three different constraint implementation cases. Given the vast increase in speed, as well as the reduction computational requirements, the presented algorithm is ideal for moving asset allocation to low power embedded hardware.
Crowdsourcing-Based Ranking Aggregation for Person Re-Identification

Person re-identification (re-ID) is widely applied in surveillance and criminal detection applications. The existing research focus on devising the stand-alone re-ID methods, ignoring their practical application in the multi-person collaboration scenario. To improve the search efficiency, a group of investigators are usually assigned the same task to re-identify a suspect from a shared gallery set. Due to their personalized viewpoints and search feedback operations, different investigators may obtain diverse search results of the same query target. In this case, merging different rankings and generating an improved result is of great importance. To this end, this paper proposes a crowdsourcing-based ranking aggregation to adaptively fuse multiple ranking lists for re-ID problem. The method estimates the reliability of individual investigators, with a specifically designed long tail distribution to fit the top ranking demand, and is feasible for human-machine interaction. Extensive experiments conducted on four dataset-s demonstrate the superiority of the proposed method.
Dilated Residual Spatial Attentive Generative Adversarial Network for Single Image De-raining

Abstract This paper propose a dilated residual spatial attentive generative adversarial network for single image de-raining. The improved spatial attentive mechanism is combined with the dilated convolution residual module to optimize the Condition Generative Adversarial Network(CGAN) structure, to solve two problems still exist in the task of removing rain from a single image: First, the rain streaks contained in the dataset we can use are limited, and in the case of real rainy days, the rain streak density is diverse, it is impossible to simulate them completely and accurately. Then, the existing rain removal models cannot remove rain streaks properly for images with different rain streak density which attend to over or under rain removal. In our methods, Firstly, the dilated convolution module is used to enhance the feature extraction of rain streaks, and then an attention map is generated by the spatial attention mechanism module to accurately locate the position of rain streaks, and the rain streaks are extracted as the foreground information by combining the two parts of the network to guide the subsequent rain removal operation; Then the rain streaks is removed through the Contextual auto-encoder, combining with PatchGAN discriminator. The experimental results on two synthetic dataset and real-world rain image show that the network model proposed in this paper has high generalization ability under different rainfall conditions. it improves the rain removal effect of the model on the most realistic public dataset, and improves the recovery ability of image details while effectively removing rain on other public datasets, which verifies the effectiveness of the model in improving the generalization ability and improving the image quality after rain removal. At the same time, the effect of rain removal is better than other methods on the real rain images, which proves that the method proposed in this paper has high practical application value.
Dynamic Mixture Ratio Model

Finite mixtures of probability densities with components from exponential family serve as flexible parametric models of high-dimensional systems. However, with a few specialized exceptions, these dynamic models assume data-independent weights of mixture components. Their use is illogical and restricts the modeling applicability. The requirement for closeness with respect to conditioning, the basic learning operation, leads to a novel class of models: the mixture ratios. The paper justified them and shows their ability to model truly dynamic systems.
Discrete Wasserstein Autoencoders for Document Retrieval

Learning to hash via generative models has became a promising paradigm for fast similarity search in document retrieval. The binary hash codes are treated as Bernoulli latent variables when training a variational autoencoder (VAE). However, the prior of discrete distribution (i.e., Bernoulli distribution) is short of some structure regularization to generate more effi-cient binary codes. In this paper, we present an end-to-end Wasserstein Autoencoder (WAE) for text hashing to avoid in-differentiable operators in the reparameterization trick, where the latent variables can be imposed to any discrete priors we can sample by using adversarial learning. Moreover, we can generate more efficient discrete codes by imposing a structural constraint on priors like the bit balance constraint. Our experiments show that the proposed model is competitive to the state of the art methods on both unsupervised and supervised scenarios.
Detection of early gastric cancer with wavelets

The early stages of gastric cancer are not recognized due to its asymptomatic development. Detection of patho- logical changes can be achieved using the photodynamic diagnostics, but this approach provides rather low tumor selectivity. Additional opportunities to improve the detection of precancerous stages may be provided by ana- lyzing blood circulation in the vessels of the stomach. We consider the possibilities of multiresolution analysis in recognizing changes in blood circulation in rats with different levels of the fluorescent signal in the stomach associated with precancerous stages.
Multi-layer Joint Classification-Metric Deep Learning for Top View Image Person Re-identification

Person re-identification (ReID) is the problem of identifying pedestrian images among multiple non-overlapping cameras. Most of the researches are based on the front view or side view of the human body. However, there are many challenges, such as occlusion, posture changes. Therefore, based on top view images, we propose a person reidentification method, called ML-CM (multi-layer joint classification-metric deep learning). The proposed method uses classification models and integrates them into the Siamese network, which is the unification of classification and metric learning. It is more suitable for top view images. In this way, minimum intra-class variance and maximum inter-class variance are achieved. We have collected the top view images of 1008 people as TVPR (top view person re-identification) dataset and conducted experiments, demonstrating that the deep features obtained by our approach can get a competitive result.
Defending Multimedia Content Embedded in Online Social Networks (OSNs) Using Digital Watermarking

In global internet usage, increasing multimedia message, which includes video, audio, images, and text documents, on the web raised a lot of consequences related to copyright. For copyright protection, authentication purpose and forgery detection digital watermarking is the robust way in social network content protection. In this technique, the privacy information is embedded inside the multimedia content like image and video. The protected content embedded inside multimedia content is called watermark-enabled information. To make more effective the process of watermarking, the content encrypted before embedding to the image. Basically, the digital watermarking embedded process implemented in two different domains called spatial and frequency domain. In spatial domain digital watermarking, the watermark information is embedded in the least significant bit of the original image on the basis of bit plane selected and on the basis of the pixels of image, embedding, and detection is performed.
Detecting Anomalies in Dismount Tracking Data

This effort develops an approach for detecting behavioral anomalies using tracks of pedestrians. This research develops physically meaningful features that are understandable by an operator. The features can be used with standard classifiers such as the one class support vector machine that is used in this research. The one class support vector machine is very stable for this application and provides significant insight into the nature of its decision boundary. Its stability and ease of system use stems from a unique automatic tuning approach that is computationally efficient and compares favorably with competing approaches. This automatic tuning approach is believed to be novel and was developed as part of this research. Results are provided using hand-tracked measured video data.