Reviewerly/exHarmony huggingface dataset:
Semantic Relation Preserving Knowledge Distillation for Image-to-Image Translation

Generative adversarial networks (GANs) have shown significant potential in modeling high dimensional distributions of image data, especially on image-to-image translation tasks. However, due to the complexity of these tasks, state-of-the-art models often contain a tremendous amount of parameters, which results in large model size and long inference time. In this work, we propose a novel method to address this problem by applying knowledge distillation together with distillation of a semantic relation preserving matrix. This matrix, derived from the teacher’s feature encoding, helps the student model learn better semantic relations. In contrast to existing compression methods designed for classification tasks, our proposed method adapts well to the image-to-image translation task on GANs. Experiments conducted on 5 different datasets and 3 different pairs of teacher and student models provide strong evidence that our methods achieve impressive results both qualitatively and quantitatively.
Twitter Sentiment Analysis Using Machine Learning Algorithms: A Case Study

Sentiment analysis, also referred to as opinion mining or emotion extraction is the classification of emotions within a textual data. This technique has been widely used over the years in order to determine the sentiments, emotions within a particular textual data. Twitter is a social media platform that has been mostly used by people to express emotions for particular events. In this paper, we have collected tweets for a number of events, analyzed them using a number of Machine Learning algorithms like Naïve Bayes, SVM, Random Forest classifier and LSTM and compared the results.
An Initiative To Prevent Japanese Encephalitis Using Genetic Algorithm And Artificial Neural Network

Japanese Encephalitis primarily affects children. Most adults in endemic countries have natural immunity after childhood infection, but individuals of any age may be affected. This work deals with the data of those who are affected. The primary step is studying the data obtained to Figure out the unique and similar symptoms which are present in Japanese Encephalitis in comparison with normal Viral Fever. Machine Learning algorithms are used to carry out this work. The Genetic Algorithm is used for optimization and generation of fittest string for the input data. To obtain precise results along with the justification, the Attribute Selection algorithm is also used. The main objective of the work is to create preventive awareness of the disease at the initial stage. Extract the essential features of biotest from the affected person, which is taken into consideration with the genetic algorithm and Attribute Selection algorithm. Genetic algorithms give higher quality for the optimized problem and produce an approximate result using the Attribute Selection algorithm with factor analysis. The percentage of improvement on using these algorithms is 96%. OpenCV color change detection and Artificial Neural Network (ANN) is used to detect the change in the color and infection information of the Brain cell. The results outperform with the existing methodologies to detect whether the cell is parasitized or uninfected. The percentage of omprovement on using this algorithm is 99%.
LCSCNet: Linear Compressing-Based Skip-Connecting Network for Image Super-Resolution

In this paper, we develop a concise but efficient network architecture called linear compressing based skipconnecting network (LCSCNet) for image super-resolution. Compared with two representative network architectures with skip connections, ResNet and DenseNet, a linear compressing layer is designed in LCSCNet for skip connection, which connects former feature maps and distinguishes them from newly-explored feature maps. In this way, the proposed LCSCNet enjoys the merits of the distinguish feature treatment of DenseNet and the parametereconomic form of ResNet. Moreover, to better exploit hierarchical information from both low and high levels of various receptive fields in deep models, inspired by gate units in LSTM, we also propose an adaptive element-wise fusion strategy with multisupervised training. Experimental results in comparison with state-of-the-art algorithms validate the effectiveness of LCSCNet.
Software Defect Prediction Using Neural Network Based SMOTE

Software defect prediction is a practical approach to improve the quality and efficiency of time and costs for software testing by focusing on defect modules. The dataset of software defect prediction naturally has a class imbalance problem with very few defective modules compared to non-defective modules. This situation has a negative impact on the Neural Network, which can lead to overfitting and poor accuracy. Synthetic Minority Over-sampling Technique (SMOTE) is one of the popular techniques that can solve the problem of class imbalance. However, Neural Network and SMOTE both have hyperparameters which must be determined by the user before the modelling process. In this study, we applied the Neural Networks Based SMOTE, a combination of Neural Network and SMOTE with each hyperparameter of SMOTE and Neural Network that are optimized using random search to solve the class imbalance problem in the six NASA datasets. The results use a 5*5 cross-validation show that increases Bal by 25.48% and Recall by 45.99% compared to the original Neural Network. We also compare the performance of Neural Network-based SMOTE with “Traditional” Machine Learning-based SMOTE. The Neural Network-based SMOTE takes first place in the average rank.
An automatic coloring method for ethnic costume sketches based on generative adversarial networks

In this paper, we present an automatic coloring model for ethnic costume sketches based on a generative adversarial networks. The proposed model is composed of a 6-layer U-net structure generator and a discriminator with 5-layer convolutional neural network. Then the loss function and the weights of true reliability value in the discriminator are optimized. And, finally the constructed sketch database is used for training to get an automatic coloring model. The results of a large number of sketch coloring experiments show that the model has good learning ability on exploring color law of ethnic costumes and can achieve better coloring effects.
Reference-frame-independent, measurement-device-independent quantum key distribution using fewer quantum states

Reference-frame-independent quantum key distribution (RFI-QKD) provides a practical way to generate secret keys between two remote parties without sharing common reference frames. On the other hand, measurement-device-independent QKD (MDI-QKD) offers a high level of security, as it is immune to all quantum hacking attempts to measurement devices. The combination of these two QKD protocols, i.e., RFI-MDI-QKD, is one of the most fascinating QKD protocols, since it holds advantages of both practicality and security. For further practicality of RFI-MDI-QKD, it is beneficial to reduce the implementation complexity. Here, we show that RFI-MDI-QKD can be implemented using fewer quantum states than those of its original proposal. We find that, in principle, the number of quantum states for one of the parties can be reduced from six to three without compromising security. Compared to conventional RFI-MDI-QKD where both parties transmit six quantum states, it significantly simplifies the implementation of the QKD protocol. We also verify the feasibility of the scheme with a proof-of-principle experiment.
On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification

State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks. Throughout the efforts to make the models robust against adversarial example attacks, it has been found to be a very difficult task. While many defense approaches were shown to be not effective, adversarial training remains as one of the promising methods. In adversarial training, the training data are augmented by “adversarial” samples generated using an attack algorithm. If the attacker uses a similar attack algorithm to generate adversarial examples, the adversarially trained network can be quite robust to the attack. However, there are numerous ways of creating adversarial examples, and the defender does not know what algorithm the attacker may use. A natural question is: Can we use adversarial training to train a model robust to multiple types of attack? Previous work have shown that, when a network is trained with adversarial examples generated from multiple attack methods, the network is still vulnerable to white-box attacks where the attacker has complete access to the model parameters. In this paper, we study this question in the context of black-box attacks, which can be a more realistic assumption for practical applications. Experiments with the MNIST dataset show that adversarially training a network with an attack method helps defending against that particular attack method, but has limited effect for other attack methods. In addition, even if the defender trains a network with multiple types of adversarial examples and the attacker attacks with one of the methods, the network could lose accuracy to the attack if the attacker uses a different data augmentation strategy on the target network. These results show that it is very difficult to make a robust network using adversarial training, even for black-box settings where the attacker has restricted information on the target network.
Quality of internal representation shapes learning performance in feedback neural networks

A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Despite the simplicity of this learning scheme, the factors contributing to or hindering the success of training in reservoir networks are in general not well understood. In this work, we study non-linear feedback networks trained to generate a sinusoidal signal, and analyze how learning performance is shaped by the interplay between internal network dynamics and target properties. By performing exact mathematical analysis of linearized networks, we predict that learning performance is maximized when the target is characterized by an optimal, intermediate frequency which monotonically decreases with the strength of the internal reservoir connectivity. At the optimal frequency, the reservoir representation of the target signal is high-dimensional, de-synchronized, and thus maximally robust to noise. We show that our predictions successfully capture the qualitative behaviour of performance in non-linear networks. Moreover, we find that the relationship between internal representations and performance can be further exploited in trained non-linear networks to explain behaviours which do not have a linear counterpart. Our results indicate that a major determinant of learning success is the quality of the internal representation of the target, which in turn is shaped by an interplay between parameters controlling the internal network and those defining the task.
Photonic quantum walks with four-dimensional coins

The dimensionality of the internal coin space of discrete-time quantum walks has a strong impact on the complexity and richness of the dynamics of quantum walkers. While two-dimensional coin operators are sufficient to define a certain range of dynamics on complex graphs, higher dimensional coins are necessary to unleash the full potential of discrete-time quantum walks. In this work we present an experimental realization of a discrete-time quantum walk on a line graph that, instead of two-dimensional, exhibits a four-dimensional coin space. Making use of the extra degree of freedom we observe multiple ballistic propagation speeds specific to higher dimensional coin operators. By implementing a scalable technique, we demonstrate quantum walks on circles of various sizes, as well as on an example of a Husimi cactus graph. The quantum walks are realized via time-multiplexing in a Michelson interferometer loop architecture, employing as the coin degrees of freedom the polarization and the traveling direction of the pulses in the loop. Our theoretical analysis shows that the platform supports implementations of quantum walks with arbitrary $4 \times 4$ unitary coin operations, and usual quantum walks on a line with various periodic and twisted boundary conditions.
Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation

Vision-and-Language Navigation (VLN) is a natural language grounding task where an agent learns to follow language instructions and navigate to specified destinations in realworld environments.A key challenge is to recognize and stop at the correct location, especially for complicated outdoor environments.Existing methods treat the STOP action equally as other actions, which results in undesirable behaviors that the agent often fails to stop at the destination even though it might be on the right path.Therefore, we propose Learning to Stop (L2STOP), a simple yet effective policy module that differentiates STOP and other actions.Our approach achieves the new state of the art on a challenging urban VLN dataset TOUCHDOWN, outperforming the baseline by 6.89% (absolute improvement) on Success weighted by Edit Distance (SED).
Classifier Based Breast Cancer Segmentation

Breast cancer occurs as a result of erratic growth and proliferation cells that originate in the breast. In this paper, the classifiers were used to identify the abnormalities on mammograms to get the region of interest (ROI). Before classifier based segmentation, noise, pectoral muscles, and tags were removed for a successful segmentation process. Then the proposed approach extracted the brightest regions using modified k-means. From the extracted brightest regions, shape and texture features were extracted and given to classifiers (KNN and SVM) and marked as ROI only those non-overlapping abnormal regions. The ROIs obtained using the proposed classifier-based segmentation algorithm was compared with the ground truth annotated by the radiologists. The datasets used to evaluate the performance of the proposed algorithm was public (MIAS) and local datasets (BGH and DADC).
Partial multi-label learning with mutual teaching

Partial Multi-label Learning (PML) tackles the problem where each training instance is associated with a set of candidate labels that include both the relevant ground-truth labels and irrelevant false positive labels. Most of the existing PML methods try to iteratively update the confidence of each candidate label, while the estimated label confidence may be not reliable due to the cumulative error induced in the confidence updating process, especially when false positive labels dominate. In this paper, we propose a simple yet effective model called PML-MT (Partial Multi-label Learning with Mutual Teaching), in which a couple of prediction networks as well as the corresponding teacher networks are adopted to learn collaboratively and teach each other throughout the training process. Specially, the proposed PML-MT model iteratively refines the label confidence matrix through a couple of self-ensemble teacher networks and trains two prediction networks simultaneously in a mutual teaching manner. Moreover, we propose a novel regularization term to further exploit label correlations from the outputs of the prediction networks under the supervision of the refined label confidence matrix. In addition, a co-regularization term is introduced to maximize the agreement on the outputs of the couple prediction networks, so that the predictions of each network would be more reliable. Extensive experiments on synthesized and real-world PML datasets demonstrate that the proposed approach outperforms the state-of-the-art counterparts.
Ethnic Insults in YouTube Comments: Social Contagion and Selection Effects During the German “Refugee Crisis”

Abstract In this article, we investigate the extent to which ethnic insults propagate through comment networks in YouTube videos from four German political talk shows with the largest audience reach. We argue that comments using ethnic insults signal social norms and embolden others to emulate offensive behaviour, therefore potentially contributing the contagiousness of insulting commenting. Moreover, periods of highly salient intergroup conflict (i.e. sexual assaults and terrorist attacks), may further reduce inhibitions to publicly post insulting content further multiplying the spread of this behaviour. To evaluate our claims, we construct a repeated cross-sectional dataset from the comment networks in YouTube videos as well as a pseudo-panel of highly active users which is used to gauge the impact of social selection. Results indicate that the use of ethnic insults in online comment sections appears socially contagious. Specifically, we find that a larger presence of ethnic insults in preceding comments increases the prevalence of insulting comments by 2 percentage points which increases to 7 percentage points in the aftermath of violent incidents. However, findings based on the pseudo-user panel do show that using ethnic insults is socially contagious thus suggesting that the patterns found for the full sample are largely a function of social selection processes.
FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions -- specified with function-based mapping languages like FunUL and RML+FnO -- can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.
Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems

For supervised classification problems involving design, control, and other practical purposes, users are not only interested in finding a highly accurate classifier but they also demand that the obtained classifier be easily interpretable. While the definition of interpretability of a classifier can vary from case to case, here, by a humanly interpretable classifier, we restrict it to be expressed in simplistic mathematical terms. As a novel approach, we represent a classifier as an assembly of simple mathematical rules using a nonlinear decision tree (NLDT). Each conditional (nonterminal) node of the tree represents a nonlinear mathematical rule (split-rule) involving features in order to partition the dataset in the given conditional node into two nonoverlapping subsets. This partitioning is intended to minimize the impurity of the resulting child nodes. By restricting the structure of the split-rule at each conditional node and depth of the decision tree, the interpretability of the classifier is ensured. The nonlinear split-rule at a given conditional node is obtained using an evolutionary bilevel optimization algorithm, in which while the upper level focuses on arriving at an interpretable structure of the split-rule, the lower level achieves the most appropriate weights (coefficients) of individual constituents of the rule to minimize the net impurity of two resulting child nodes. The performance of the proposed algorithm is demonstrated on a number of controlled test problems, existing benchmark problems, and industrial problems. Results on 2-500 feature problems are encouraging and open up further scopes of applying the proposed approach to more challenging and complex classification tasks.
Convolutional Network Embedding of Text-Enhanced Representation for Knowledge Graph Completion

In recent years, knowledge graph representation learning has prompted extensive research. Machine learning models are used to map entity and relational data in knowledge graphs to vector representations in low-dimensional spaces to predict and analyze potential relationships. Current works mainly focus on the knowledge representation of the triple structure and relationship path in knowledge graphs without fully utilizing external textual information to semantically supplement knowledge representation. However, the existing knowledge inventory, such as that for smart health and emotion care systems, is relatively meager, and structural knowledge is incomplete; therefore, knowledge graph completion is essential. In this article, we propose a novel joint representation learning model that introduces text description information and extracts reliable feature information from text data by using a convolutional neural network (CNN) model. Furthermore, being based on the attention mechanism, the proposed model distinguishes the characteristic credibility of different relationships, enhances the representation of the entity relationship structure vector in the existing knowledge graph, and obtains rich semantic information. Finally, the 2-D convolution operation is used to process the joint representation vectors of entities and relationships to obtain nonlinear features, and the knowledge graph is completed by completing the calculation of the score function of the joint representation vector of the entity and the relationship. Experiments performing tasks, such as link prediction and triple classification, on the FreeBase (FB15k), WordNet (WN18) and Yet Another Great Ontology (YAGO3-10) data sets reveal that our model performs better than the benchmark model and has some degree of scalability.
A method of VR-EEG scene cognitive rehabilitation training

Virtual reality technology can intuitively provide patients of neuropsychological diseases with an almost real environment for cognitive rehabilitation training . In this paper, virtual reality technology is used to construct specific scenes that are universal and related to MCI patients to restore and train patients' scene memory cognitive ability to help patients strengthen or gradually restore scene memory cognitive ability. The construction of virtual reality scenes with different contents such as life, environment, transportation and tourism, real-time detection is carried out in combination with EEG signals of patients in different scenes. The experimental results of the analysis of EEG signals of patients shows that memory rehabilitation training is strengthened by using specific stimulation scenes.