Reviewerly/exHarmony huggingface dataset:
Research on the construction of teaching platform of drama film and television literature based on IoT

The Internet of things is known as the third wave of the world information industry after computer and Internet. With its connectivity and intelligence, the Internet of Things has unique advantages in breaking the time and space constraints, expanding learning resources and improving the depth of exploration. Its integration with the teaching practice curriculum of drama, film and television literature specialty is conducive to the improvement of the above problems. Based on this, through the intelligent optimization algorithm and the theoretical framework of experiential learning, this paper constructs a teaching model of drama, film and Television Literature Specialty Based on experiential learning theory under the Internet of Things environment, thus creating a professional teaching practice platform, and makes a thorough study on the optimization algorithm of drama, film and television literature specialty. Now the teaching and learning optimization algorithm based on the Internet of Things can be well used in the teaching practice platform of drama, film and television literature specialty.
Performance analysis of wind generators and PV systems in industrial small-scale applications

In this paper, some aspects of the performance of photovoltaic systems and wind generators are discussed. The performance of technologies for different types of wind generators is briefly explained, including the advantages and disadvantages of each type, and for different wind speeds. Technologies and techniques that improve the performance of different photovoltaic systems are also presented in a general way. In addition, a case study is presented to evaluate the use of wind generators and photovoltaic systems for small-scale industrial applications in the Tomar region (Portugal). The approach presented puts the focus on the small-scale applications for small/medium scale industries. These industries have a small/medium power consumption and most of them occupy limited spaces and the environmental conditions are those which exist in the place. The simulations show that there is a significant financial risk associated with the small-scale wind and photovoltaic systems. The purpose of this comparison is to ensure their technical and economic evaluation, leading to a better choice towards achieving zero net energy in buildings. The results obtained for the two technologies allow some important conclusions regarding their applications: photovoltaic systems have better performance than wind generators, photovoltaic systems have high-energy density, which leads to less need for space.
Generative Imputation and Stochastic Prediction

In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also over target class assignments that require careful consideration. In this paper, we propose a simple and effective method for imputing missing features and estimating the distribution of target assignments given incomplete data. In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish. Following this, a predictor network is trained using the imputed samples from the generator network to capture the classification uncertainties and make predictions accordingly. The proposed method is evaluated on CIFAR-10 and MNIST image datasets as well as five real-world tabular classification datasets, under different missingness rates and structures. Our experimental results show the effectiveness of the proposed method in generating imputations as well as providing estimates for the class uncertainties in a classification task when faced with missing values.
Automated surface feature detection using fringe projection: An autoregressive modeling-based approach

An automatic surface defect detection algorithm is proposed in a fringe projection profilometry setup. An exponential phase field associated with the fringe projected surface image is analyzed using two-dimensional auto-regressive (AR) model. The AR model coefficients capture the local fringe frequency information. The variation in the fringe frequency from the defect-free to defective surface region is utilized as a signature for defect identification and localization. The fringe frequency threshold required for the image segmentation into defective and defect free regions is derived from the mean frequency computed over the same image which obviates the need for the reference image. In order to enhance the computation efficiency of the algorithm the image is divided into small patches and analysis is performed patch-wise. The simulation and experimental results demonstrate the advantages of the proposed phase based surface evaluation method over the commonly used intensity based methods.
Eye detection and localization in a facial image based on partial geometric shape of iris and eyelid under practical scenarios

The accurate detection and localization of an eye in a facial image is important for many computer vision applications, such as face recognition, fatigued driving detection, and gaze estimation. Although research on eye detection has matured, most existing eye detection methods produce poor performance in various practical scenarios where there exists variation in facial expressions or illumination, people wearing clear eyeglasses, and so on. We have proposed a method that can locate eyes under the above-mentioned varied environmental conditions. The proposed approach follows two steps: eye candidate detection and eye candidate verification. In the first step, two features, namely semicircular edge shape and semiellipse edge shape features, are proposed to detect the eye candidates. In the second step, all of the selected eye candidates are verified using a support vector machine trained with the fusion of local binary pattern, cell mean intensity, and histogram of oriented gradients features. The proposed method has been tested under different conditions of AR, the Chinese Academy of Sciences’ Pose, Expression, Accessories, and Lighting, and the facial recognition technology databases. The experimental results suggest that the proposed method provides better performance as compared to the existing methods in terms of precision and recall.
From Free-text User Reviews to Product Recommendation using Paragraph Vectors and Matrix Factorization

Recent theoretical and practical advances have led to the emergence of review-based recommender systems, where user preference data is encoded in at least two dimensions; the traditional rating scores in a predefined discrete scale and the user-generated reviews in the form of free-text. The main contribution of this work is the presentation of a new technique of incorporating those reviews into collaborative filtering matrix factorization algorithms. The text of each review, of arbitrary length, is mapped to a continuous feature space of fixed length, using neural language models and more specifically, the Paragraph Vector model. Subsequently, the resulting feature vectors (the neural embeddings) are used in combination with the rating scores in a hybrid probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The proposed methodology is then compared to three other similar approaches on six datasets in order to assess its performance. The obtained results demonstrate that the new formulation outperforms the other systems on a set of two metrics, thereby indicating the robustness of the idea.
Fault Tolerant Control Using Interval Type-2 Takagi-Sugeno Fuzzy Controller for Nonlinear System

A novel Fault Tolerant Control (FTC) strategy is proposed based on interval type-2 Takagi-Sugeno controller and conventional PID controller, in this paper it has been presented for a nonlinear system. The main advantage of this strategy is, it can handle the faults, uncertainties in describing the nonlinear systems and give rigorous fuzzy rules. This study assesses the design of the robust controller which can tolerate the system component and actuator faults into the system and maintain the performance of the system at an acceptable level. To validate the proposed FTC strategy three-tank interacting level control system is considered. Finally, the simulation of the three-tank interacting level process model demonstrates the superiority of the proposed strategy using two integral error performance index Integral Absolute Error (IAE) and Integral Square Error (ISE).
A Modified Inexact Arithmetic Median Filter for Removing Salt-and-Pepper Noise From Gray-Level Images

We have recently proposed approximate median filters (APMFs). They are based on the sorting network and achieve acceptable image quality under low-cost hardware. In this brief, we develop a specific comparator to improve the capabilities of those filters in noise elimination. The architecture of our inexact median filters (IMFs) is regular and modular. Also, we introduce the histogram-based error dispersion plot as a new error evaluation method to have a better assessment of IMF performance. Simulation results show that the proposed filter is effectively low cost in power, area, and speed. Despite the tradeoff between the filtering accuracy and circuit characteristics, the output quality of the filter is largely similar to that of the precise one. Also, the degradation is almost not noticeable to the human eye.
Translations Diversification for Expert Finding

Expert finding is the task of retrieving and ranking knowledgeable people in the subject of user’s query. It is a well-studied problem that has attracted the attention of many researchers. The most important challenge in expert finding is to determine the similarity between query words and documents authored by candidate experts. One of the most important challenges in Information Retrieval (IR) community is the issue of vocabulary gap between queries and documents. In this study, a translation model based on words clustering in two query and co-occurrence spaces is proposed to overcome this problem. First, the words that are semantically close, are clustered in a query space and then each cluster in this space are clustered again in a co-occurrence space. Representatives of each cluster in the co-occurrence space are considered as a diverse subset of the parent cluster. By this method, the query translations are expected to be diversified in the query space. Next, a probabilistic model, that is based on the belonging degree of word to cluster and similarity of cluster to query in the query space, is used to consider the problem of vocabulary gap. Finally, the corresponding translations to each query are used in conjunction with a combination model for expert finding. Experiments on Stack Overflow dataset show the effectiveness of the proposed method for expert finding.
Geometric Quantum Computation with Shortcuts to Adiabaticity

Abstract Realization of a large‐scale quantum computation relies on fast and high‐fidelity quantum control. Geometric quantum control is thought to be a promising candidate for quantum computation which consolidates the robustness against both random noise (through the global geometrical feature) and systematic errors (through adiabatic characteristic). The adiabatic process of geometric control can be accelerated through an auxiliary Hamiltonian in different scenarios by means of shortcuts to adiabaticity (STA). Especially, the auxiliary Hamiltonian can be absorbed into the original interacting configuration in most of the cases, which allows STA to coalesce with the Abelian or the non‐Abelian geometric control. As a consequence, geometric quantum control can have an enhanced robustness against decoherence after speeding up by STA. Here, the recent theoretical and experimental advances in geometric quantum computation based on STA are reviewed.
Non-malleable Codes for Decision Trees

We construct efficient, unconditional non-malleable codes that are secure against tampering functions computed by decision trees of depth $$d= n^{1/4-o(1)}$$ . In particular, each bit of the tampered codeword is set arbitrarily after adaptively reading up to d arbitrary locations within the original codeword. Prior to this work, no efficient unconditional non-malleable codes were known for decision trees beyond depth $$O(\log ^2 n)$$ . Our result also yields efficient, unconditional non-malleable codes that are $$\exp (-n^{\varOmega (1)})$$ -secure against constant-depth circuits of $$\exp (n^{\varOmega (1)})$$ -size. Prior work of Chattopadhyay and Li (STOC 2017) and Ball et al. (FOCS 2018) only provide protection against $$\exp (O(\log ^2n))$$ -size circuits with $$\exp (-O(\log ^2n))$$ -security. We achieve our result through simple non-malleable reductions of decision tree tampering to split-state tampering. As an intermediary, we give a simple and generic reduction of leakage-resilient split-state tampering to split-state tampering with improved parameters. Prior work of Aggarwal et al. (TCC 2015) only provides a reduction to split-state non-malleable codes with decoders that exhibit particular properties.
Face Recognition Algorithm Based on VGG Network Model and SVM

Abstract The problem that the dimension of facial features is too large does exist with the Deep learning face recognition. This paper proposes a face recognition algorithm based on SVM combined with VGG network model extracting facial features, which can not only accurately extract face features, but also reduce feature dimensions and avoid irrelevant features to participate in the calculation. Firstly, the VGG-16 model is obtained by training the training data set, which is used for feature extraction, on top of this, principal component analysis method (PCA) is used for feature dimensionality reduction, and last, the face recognition is performed by SVM classifier with linear kernel function. In this paper, we conduct a comparative experiment on CelebA dataset and find that the accuracy reaches its peak when the feature dimension is reduced to 400.The experiment is carried out on LFW dataset using 400-dimensional feature data, and comparing with other algorithms, the results show that the algorithm in this paper has reached the level of state-of-art.
Online Learned Siamese Network with Auto-Encoding Constraints for Robust Multi-Object Tracking

Multi-object tracking aims to estimate the complete trajectories of objects in a scene. Distinguishing among objects efficiently and correctly in complex environments is a challenging problem. In this paper, a Siamese network with an auto-encoding constraint is proposed to extract discriminative features from detection responses in a tracking-by-detection framework. Different from recent deep learning methods, the simple two layers stacked auto-encoder structure enables the Siamese network to operate efficiently only with small-scale online sample data. The auto-encoding constraint reduces the possibility of overfitting during small-scale sample training. Then, the proposed Siamese network is improved to extract the previous-appearance-next vector from tracklet for better association. The new feature integrates the appearance, previous, and next stage motions of an element in a tracklet. With the new features, an online incremental learned tracking framework is established. It contains reliable tracklet generation, data association to generate complete object trajectories, and tracklet growth to deal with missing detections and to enhance the new feature for tracklet. Benefiting from discriminative features, the final trajectories of objects can be achieved by an efficient iterative greedy algorithm. Feature experiments show that the proposed Siamese network has advantages in terms of both discrimination and correctness. The system experiments show the improved tracking performance of the proposed method.
CSCC: Convolution Split Compression Calculation Algorithm for Deep Neural Network

Convolutional Neural Networks (CNNs) have become one of the most successful machine learning techniques for image and video processing. The most computationally intensive part of the CNN is the convolutional layers, which have the multi-channel image and multiple kernels. However, due to the network pruning operation and the application of RELU activation function operation in the training process, numerous zero values are generated in the network. This paper proposes the convolution split compression calculation (CSCC) algorithm, which improves the performance of the convolution layer by utilizing the sparse characteristic of the feature map. In the CSCC algorithm, first, the feature map is directly converted into a sparse matrix of compressed sparse row (CSR) format, which avoids expanding feature map to an intermediate matrix and reduces the memory space consumption. Second, the convolution kernel is converted into a vector. Finally, the convolution result is obtained by the sparse matrix vector multiplication (SpMV). The experimental results show that the CSCC algorithm has a good advantage in computation speed and memory consumption compared with the other convolution algorithms.
Student Concentration Evaluation Index in an E-learning Context Using Facial Emotion Analysis

Analysis of student concentration can help to enhance the learning process. Emotions are directly related and directly reflect students’ concentration. This task is particularly difficult to implement in an e-learning environment, where the student stands alone in front of a computer. In this paper, a prototype system is proposed to figure out the concentration level in real-time from the expressed facial emotions during a lesson. An experiment was performed to evaluate the prototype system that was implemented using a client-side application that uses the C# code available in Microsoft Azure Emotion API. We have found that the emotions expressed are correlated with the concentration of the students, and devised three distinct levels of concentration (high, medium, and low).
Clustered eye movement similarity matrices

Eye movements recorded for many study participants are difficult to interpret, in particular when the task is to identify similar scanning strategies over space, time, and participants. In this paper we describe an approach in which we first compare scanpaths, not only based on Jaccard (JD) and bounding box (BB) similarities, but also on more complex approaches like longest common subsequence (LCS), Frechet distance (FD), dynamic time warping (DTW), and edit distance (ED). The results of these algorithms generate a weighted comparison matrix while each entry encodes the pairwise participant scanpath comparison strength. To better identify participant groups of similar eye movement behavior we reorder this matrix by hierarchical clustering, optimal-leaf ordering, dimensionality reduction, or a spectral approach. The matrix visualization is linked to the original stimulus overplotted with visual attention maps and gaze plots on which typical interactions like temporal, spatial, or participant-based filtering can be applied.
Real-time thinning algorithms for 2D and 3D images using GPU processors

The skeletonization of binary images is a common task in many image processing and machine learning applications. Some of these applications require very fast image processing. We propose novel techniques for efficient 2D and 3D thinning of binary images using GPU processors. The algorithms use bit-encoded binary images to process multiple points simultaneously in each thread. The simpleness of a point is determined based on Boolean algebra using only bitwise logical operators. This avoids computationally expensive decoding and encoding steps and allows for additional parallelization. The 2D algorithm is evaluated using a dataset of handwritten characters images. It required an average computation time of 3.53 ns for 32×32 pixels and 0.25 ms for 1024×1024 pixels. This is 52 to 18,380 times faster than a multi-threaded border-parallel algorithm. The 3D algorithm was evaluated based on clinical images of the human vasculature and required computation times of 0.27 ms for 128×128×128 voxels and 20.32 ms for 512×512×512 voxels, which is 32 to 46 times faster than the compared border-sequential algorithm using the same GPU processor. The proposed techniques enable efficient real-time 2D and 3D skeletonization of binary images, which could improve the performance of many existing machine learning applications.
Clausal Abstraction for DQBF

Dependency quantified Boolean formulas (DQBF) is a logic admitting existential quantification over Boolean functions, which allows us to elegantly state synthesis problems in verification such as the search for invariants, programs, or winning regions of games. In this paper, we lift the clausal abstraction algorithm for quantified Boolean formulas (QBF) to DQBF. Clausal abstraction for QBF is an abstraction refinement algorithm that operates on a sequence of abstractions that represent the different quantifier levels. For DQBF we need to generalize this principle to partial orders of abstractions. The two challenges to overcome are: (1) Clauses may contain literals with incomparable dependencies, which we address by the recently proposed proof rule called Fork Extension, and (2) existential variables may have spurious dependencies, which we prevent by tracking consistency requirements during the execution. Our implementation $$\textsc {dCAQE}$$ solves significantly more formulas than the existing DQBF algorithms.
Resource semantics

The Logic of Bunched Implications (BI) was introduced by O'Hearn and Pym. The original presentation of BI emphasised its role as a system for formal logic (broadly in the tradition of relevant logic) that has some interesting properties, combining a clean proof theory, including a categorical interpretation, with a simple truth-functional semantics. BI quickly found significant applications in program verification and program analysis, chiefly through a specific theory of BI that is commonly known as 'Separation Logic'. We survey the state of work in bunched logics - which, by now, is a quite large family of systems, including modal and epistemic logics and logics for layered graphs - in such a way as to organize the ideas into a coherent (semantic) picture with a strong interpretation in terms of resources. One such picture can be seen as deriving from an interpretation of BI's semantics in terms of resources, and this view provides a basis for a systematic interpretation of the family of bunched logics, including modal, epistemic, layered graph, and process-theoretic variants, in terms of resources. We explain the basic ideas of resource semantics, including comparisons with Linear Logic and ideas from economics and physics. We include discussions of BI's λ-calculus, of Separation Logic, and of an approach to distributed systems modelling based on resource semantics.
LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models

Simultaneously inferring both the structure and parameters of Ordinary Differential Equations (ODEs) for a complex dynamic system is more practical in many systems identification problems, but it remains challenging due to the complexity of the underlying search space. In this research, we propose a novel algorithm based on Particle Swarm Optimization (PSO) and Latin Hypercube Sampling (LHS) to address the above problem. The proposed algorithm is termed LatinPSO, and it can be effectively used for inferring the structure and parameters of ODE models through time course data. To start with, the real Human Immunodeficiency Virus (HIV) model and several synthetic models are used for evaluating the performance of LatinPSO. Experimental results demonstrated that LatinPSO could find satisfactory candidate ODE models with appropriate structure and parameters.