Reviewerly/exHarmony huggingface dataset:
Regularized Wasserstein Means for Aligning Distributional Data

We propose to align distributional data from the perspective of Wasserstein means. We raise the problem of regularizing Wasserstein means and propose several terms tailored to tackle different problems. Our formulation is based on the variational transportation to distribute a sparse discrete measure into the target domain. The resulting sparse representation well captures the desired property of the domain while reducing the mapping cost. We demonstrate the scalability and robustness of our method with examples in domain adaptation, point set registration, and skeleton layout.
Facial expression detection of all emotions and face recognition system

Nowadays we have seen that the study of facial expression recognition became most important.In humans, the way of interaction will possible through text and voice.Apart from that, facial expressions play an important role in smooth communication among individuals.We can recognize the emotion of a human, based on facial expressions.First of all, facial areas were removed from the original images.Meanwhile, by using the different types of optimizers we have trained the convolutional neural network mainly focused on the face extraction.The accuracy of other methods like 2D PCA is 78.61%,Adaboost is 83.12%,SVM is 87.15%.In this paper, the method we proposed is softmax classifier by which we obtained an accuracy of 94.82% which is the highest among all.Here this softmax classifier we have used in this experiment, suggests the combination of face detection and emotion detection using CNN.
Automated Spectral Kernel Learning

The generalization performance of kernel methods is largely determined by the kernel, but spectral representations of stationary kernels are both input-independent and output-independent, which limits their applications on complicated tasks. In this paper, we propose an efficient learning framework that incorporates the process of finding suitable kernels and model training. Using non-stationary spectral kernels and backpropagation w.r.t. the objective, we obtain favorable spectral representations that depends on both inputs and outputs. Further, based on Rademacher complexity, we derive data-dependent generalization error bounds, where we investigate the effect of those factors and introduce regularization terms to improve the performance. Extensive experimental results validate the effectiveness of the proposed algorithm and coincide with our theoretical findings.
Question-Driven Purchasing Propensity Analysis for Recommendation

Merchants of e-commerce Websites expect recommender systems to entice more consumption which is highly correlated with the customers' purchasing propensity. However, most existing recommender systems focus on customers' general preference rather than purchasing propensity often governed by instant demands which we deem to be well conveyed by the questions asked by customers. A typical recommendation scenario is: Bob wants to buy a cell phone which can play the game PUBG. He is interested in HUAWEI P20 and asks “can PUBG run smoothly on this phone?” under it. Then our system will be triggered to recommend the most eligible cell phones to him. Intuitively, diverse user questions could probably be addressed in reviews written by other users who have similar concerns. To address this recommendation problem, we propose a novel Question-Driven Attentive Neural Network (QDANN) to assess the instant demands of questioners and the eligibility of products based on user generated reviews, and do recommendation accordingly. Without supervision, QDANN can well exploit reviews to achieve this goal. The attention mechanisms can be used to provide explanations for recommendations. We evaluate QDANN in three domains of Taobao. The results show the efficacy of our method and its superiority over baseline methods.
Ensembles of Locally Independent Prediction Models

Ensembles depend on diversity for improved performance. Many ensemble training methods, therefore, attempt to optimize for diversity, which they almost always define in terms of differences in training set predictions. In this paper, however, we demonstrate the diversity of predictions on the training set does not necessarily imply diversity under mild covariate shift, which can harm generalization in practical settings. To address this issue, we introduce a new diversity metric and associated method of training ensembles of models that extrapolate differently on local patches of the data manifold. Across a variety of synthetic and real-world tasks, we find that our method improves generalization and diversity in qualitatively novel ways, especially under data limits and covariate shift.
NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations

We propose improving the cross-target and cross-scene generalization of visual navigation through learning an agent that is guided by conceiving the next observations it expects to see. This is achieved by learning a variational Bayesian model, called NeoNav, which generates the next expected observations (NEO) conditioned on the current observations of the agent and the target view. Our generative model is learned through optimizing a variational objective encompassing two key designs. First, the latent distribution is conditioned on current observations and the target view, leading to a model-based, target-driven navigation. Second, the latent space is modeled with a Mixture of Gaussians conditioned on the current observation and the next best action. Our use of mixture-of-posteriors prior effectively alleviates the issue of over-regularized latent space, thus significantly boosting the model generalization for new targets and in novel scenes. Moreover, the NEO generation models the forward dynamics of agent-environment interaction, which improves the quality of approximate inference and hence benefits data efficiency. We have conducted extensive evaluations on both real-world and synthetic benchmarks, and show that our model consistently outperforms the state-of-the-art models in terms of success rate, data efficiency, and generalization.
Learning Triple Embeddings from Knowledge Graphs

Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem of directly learning triple embeddings. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the endpoint nodes. The goal of this paper is to introduce Triple2Vec, a new technique to directly embed knowledge graph triples. We leverage the idea of line graph of a graph and extend it to the context of knowledge graphs. We introduce an edge weighting mechanism for the line graph based on semantic proximity. Embeddings are finally generated by adopting the SkipGram model, where sentences are replaced with graph walks. We evaluate our approach on different real-world knowledge graphs and compared it with related work. We also show an application of triple embeddings in the context of user-item recommendations.
Stochastic Loss Function

Training deep neural networks is inherently subject to the predefined and fixed loss functions during optimizing. To improve learning efficiency, we develop Stochastic Loss Function (SLF) to dynamically and automatically generating appropriate gradients to train deep networks in the same round of back-propagation, while maintaining the completeness and differentiability of the training pipeline. In SLF, a generic loss function is formulated as a joint optimization problem of network weights and loss parameters. In order to guarantee the requisite efficiency, gradients with the respect to the generic differentiable loss are leveraged for selecting loss function and optimizing network weights. Extensive experiments on a variety of popular datasets strongly demonstrate that SLF is capable of obtaining appropriate gradients at different stages during training, and can significantly improve the performance of various deep models on real world tasks including classification, clustering, regression, neural machine translation, and objection detection.
Face Super-Resolution by Learning Multi-view Texture Compensation

Single face image super-resolution (SR) methods using deep neural network yields decent performance. Due to the posture of face images, multi-view face super-resolution task is more challenging than single input. Multi-view face images contain complement information from different view. However, it is hard to integrate texture information from multi-view low-resolution (LR) face images. In this paper, we propose a novel face SR using multi-view texture compensation to combine multiple face images to yield a HR image as output. We use texture attention mechanism to transfer high-accurate texture compensation information to fixed view for better visual performance. Experimental results conform that the proposed neural network outperforms other state-of-the-art face SR algorithms.
PERSONAL SELF-DEVELOPMENT IN THE CONTEXT OF GLOBAL EDUCATION: THE TRANSFORMATION OF VALUES AND IDENTITY

The article discusses the issues of pedagogical support for personal self-development and the problems of human self-development, acting as one of the leading trends in improving education and training in thecontext of globalization of the modern world and modern education. It considers the limitations and opportunities(pedagogical resources) of additional and global education in the field of personal self-development and theimplementation of pedagogical ideas of Russian and foreign scientists. Global education is a complex idea that helpsa person better understand the world. Thanks to global education, a person can comprehend the problems of relationsbetween peoples and other social groups, the problems of war and migration, the problems of corruption and socialjustice, the problems of poverty and homelessness, the bioethical and biotechnological problems of mankind, theproblems of ecology and culture, science and art, the problems of gender relations and generations, career and professional issues, science and education, other global aspects. He can comprehend them in the context of internationalrepresentations. In addition, he can touch the “timeless” Vedic knowledge of his ancestors about the unity of theuniverse, its laws, etc. This can be done both through contact with the Vedic sources and texts proper, and in thecontext of understanding the ideas of modern cosmic scientists. Global (as Vedic) education is able to transform theway people understand themselves and the world, it helps them (trans) to shape themselves and the world in the mostharmonious, authentic and congruent way. This will naturally manifest itself in the transformation of values andidentities of pupils and students facilitate the proactive participation of people and their mutual learning andupbringing in the process of solving significant problems for their community and the context of the evolution ofhumankind, preventing and blocking involutionary processes.
SMGEA: A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories

Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of source models transfer to other target models and, thus, pose a security threat to black-box applications (when attackers have no access to the target models). Current transfer-based ensemble attacks, however, only consider a limited number of source models to craft an adversarial example and, thus, obtain poor transferability. Besides, recent query-based black-box attacks, which require numerous queries to the target model, not only come under suspicion by the target model but also cause expensive query cost. In this article, we propose a novel transfer-based black-box attack, dubbed serial-minigroup-ensemble-attack (SMGEA). Concretely, SMGEA first divides a large number of pretrained white-box source models into several "minigroups." For each minigroup, we design three new ensemble strategies to improve the intragroup transferability. Moreover, we propose a new algorithm that recursively accumulates the "long-term" gradient memories of the previous minigroup to the subsequent minigroup. This way, the learned adversarial information can be preserved, and the intergroup transferability can be improved. Experiments indicate that SMGEA not only achieves state-of-the-art black-box attack ability over several data sets but also deceives two online black-box saliency prediction systems in real world, i.e., DeepGaze-II (https://deepgaze.bethgelab.org/) and SALICON (http://salicon.net/demo/). Finally, we contribute a new code repository to promote research on adversarial attack and defense over ubiquitous pixel-to-pixel computer vision tasks. We share our code together with the pretrained substitute model zoo at https://github.com/CZHQuality/AAA-Pix2pix.
Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base

Word embeddings have developed into a major NLP tool with broad applicability. Understanding the semantic content of word embeddings remains an important challenge for additional applications. One aspect of this issue is to explore the interpretability of word embeddings. Sparse word embeddings have been proposed as models with improved interpretability. Continuing this line of research, we investigate the extent to which human interpretable semantic concepts emerge along the bases of sparse word representations. In order to have a broad framework for evaluation, we consider three general approaches for constructing sparse word representations, which are then evaluated in multiple ways. We propose a novel methodology to evaluate the semantic content of word embeddings using a commonsense knowledge base, applied here to the sparse case. This methodology is illustrated by two techniques using the ConceptNet knowledge base. The first approach assigns a commonsense concept label to the individual dimensions of the embedding space. The second approach uses a metric, derived by spreading activation, to quantify the coherence of coordinates along the individual axes. We also provide results on the relationship between the two approaches. The results show, for example, that in the individual dimensions of sparse word embeddings, words having high coefficients are more semantically related in terms of path lengths in the knowledge base than the ones having zero coefficients.
StegoCrypt Scheme using LSB-AES Base64

Many people use the internet in their daily communication. But the risks of data theft on the internet are quite high, so sending security is a very important thing for data. Cryptography and steganography is a technique used to secure data to minimize data theft and access by unauthorized people. Combination of Least Significant Bit (LSB) - Advanced Encryption Standard (AES) - Base64 is proposed in this study to provide protection for messages and various file formats embedded in digital images. Secret messages are encrypted with the AES and Base64 methods before being inserted into the image using the LSB method. The study also analyzed the performance of the LSB-AES-Base64 combination of algorithms on various files and the size of the cover image. Cover images used an image with RGB channels. For measurement of imperceptibility performance used Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and histogram analysis. Based on the results of testing, the proposed method can work well with the value of PNSR and MSE in a very good category. The stego image histogram is also identic to the original image.
STORYTELLING IN TEACHING LITERACY: BENEFITS AND CHALLENGES

Literacy has become a crucial position in education, especially in Indonesia. It is used as one of ways to develop education through developing the culture of reading, writing, and counting. The study aims to find out benefits and challenges of implementing storytelling in teaching literacy. The study employed a qualitative descriptive research design. The data were obtained from students� interview and observation. The findings found some benefits in implementing teaching program which include using two languages as the medium of interaction, providing suitable learning materials and various media, using of dramatization to show a character�s expression, asking moral value of the story as an evaluation instrument, developing students� literacy ability, arousing students� imagination, enriching students� vocabulary, and developing students� reading awareness and interest. Meanwhile, some challenges found in implementing teaching program are students� proficiency and length of texts. Based on those findings, it is suggested to provide more times to truly explore students� literacy ability and their preference in story.
Scientific Literature Summarization Using Document Structure and Hierarchical Attention Model

Scientific literature summarization aims to summarize related papers into a survey. It can help researchers, especially newcomers, to quickly know the current situation of their professional area from massive literature. Since the structure of documents is very important for scientific literature summarization, and it is obviously observed that there is a relationship and semantic information hidden in document structures of scientific literature, therefore, we employ a hierarchical attention model to learn document structure from all the papers for summarization. In particular, we utilize attention mechanism to capture relations among document structure and learn semantic information on document discourse levels, and judge the importance of each sentence according to its surroundings by the information obtained. Moreover, we automatically construct a scientific literature data set consisting of surveys and their references. We evaluate our proposed model on this dataset with ROUGE metrics, experiments prove that our approach is effective, and our model outperforms several baselines.
Weakly-Supervised Video Re-Localization with Multiscale Attention Model

Video re-localization aims to localize a sub-sequence, called target segment, in an untrimmed reference video that is similar to a given query video. In this work, we propose an attention-based model to accomplish this task in a weakly supervised setting. Namely, we derive our CNN-based model without using the annotated locations of the target segments in reference videos. Our model contains three modules. First, it employs a pre-trained C3D network for feature extraction. Second, we design an attention mechanism to extract multiscale temporal features, which are then used to estimate the similarity between the query video and a reference video. Third, a localization layer detects where the target segment is in the reference video by determining whether each frame in the reference video is consistent with the query video. The resultant CNN model is derived based on the proposed co-attention loss which discriminatively separates the target segment from the reference video. This loss maximizes the similarity between the query video and the target segment while minimizing the similarity between the target segment and the rest of the reference video. Our model can be modified to fully supervised re-localization. Our method is evaluated on a public dataset and achieves the state-of-the-art performance under both weakly supervised and fully supervised settings.
Cloud Infrastructure Estimation and Auto-Scaling Using Recurrent Cartesian Genetic Programming-Based ANN

Use of cloud resources has increased with the increasing trend of organizations and governments towards cloud adaptation. This increase in cloud resource usage, leads to enormous amounts of energy consumption by cloud data center servers. Energy can be conserved in a cloud server by demand-based scaling of resources. But reactive scaling may lead to excessive scaling. That, in turn, results in enormous energy consumption by useless scale up and scale down. The scaling granularity can also result in excessive scaling of the resource. Without a proper mechanism for estimating cloud resource usage may lead to significant scaling overheads. To overcome, such inefficiencies, we present Cartesian genetic programming based neural network for resource estimation and a rule-based scaling system for IaaS cloud server. Our system consists of a resource monitor, a resource estimator and a scaling mechanism. The resource monitor takes resource utilizations and feeds to the estimator for efficient estimation of resources. The scaling system uses the resource estimator's output for scaling the resource with the granularity of a CPU core. The proposed method has been trained and tested with real traces of Bitbrains data center, producing promising results in real-time. It has shown better prediction accuracy and energy efficiency than predictive scaling systems from literature.
Real-Time Dynamic 3D Shape Reconstruction with SWIR InGaAs Camera

In this paper, a real-time, dynamic three-dimensional (3D) shape reconstruction scheme based on the Fourier-transform profilometry (FTP) method is achieved with a short-wave infrared (SWIR) indium gallium arsenide (InGaAs) camera for monitoring applications in low illumination environments. A SWIR 3D shape reconstruction system is built for generating and acquiring the SWIR two-dimensional (2D) fringe pattern of the target. The depth information of the target is reconstructed by employing an improved FTP method, which has the advantages of high reconstruction accuracy and speed. The maximum error in depth for static 3D shape reconstruction is 1.15 mm for a plastic model with a maximum depth of 36 mm. Meanwhile, a real-time 3D shape reconstruction with a frame rate of 25 Hz can be realized by this system, which has great application prospects in real-time dynamic 3D shape reconstruction, such as low illumination monitoring. In addition, for real-time dynamic 3D shape reconstruction, without considering the edge areas, the maximum error in depth among all frames is 1.42 mm for a hemisphere with a depth of 35 mm, and the maximum error of the average of all frames in depth is 0.52 mm.
Automated sentiment analysis of Free-Comment: An indirect liking measurement?

• A sentiment analysis algorithm trained with machine learning was applied to four Free-Comment datasets. • The computation of sentiment scores did not require any manual intervention. • The sentiment scores were positively correlated with liking but were less discriminant. • The results were too contrasted to consider sentiment scores an indirect measure of liking.
Breast Cancer Detection Using CNN on Mammogram Images

The most commonly occurring form of cancer is breast cancer and thereby requires urgent attention. In our paper, we work on these CT images of breast cancer to detect cancer in early stages. These images are classified into benign (non-cancerous) and malignant (cancerous) using our proposed method of Convolution Neural Network (CNN) following which medical care can be given. In our paper, image segmentation along with the image preprocessing is used which provides enhanced images. This helps in increasing the overall accuracy of the system. To conclude, the paper compares the trade-off between accuracy and time to train the network obtained in different execution environment i.e. Tensorflow and Matlab. Tensorflow gave 87.98% accuracy with training time of 6 h and Matlab gave 84.02% accuracy with training time of 45 min when the neural network’s architecture remains the same.