Reviewerly/exHarmony huggingface dataset:
Time-varying Item Feature Conditional Variational Autoencoder for Collaborative Filtering

The factors impacting on what users purchase can be classified as an internal factor which is time-invariant user's unique taste and an external factor which is time-varying item characteristic. However, the current recommendation system has a limitation of making recommendations based only on the user's history without taking into account the item features trends of the time, which prevents precise recommendations. In this paper, the recommendation system that reflects inter-items trends of time-based bin is proposed. We focus on creating a hybrid recommender system that could effectively combine time-varying content data with the users history data. Specifically, we use Conditional Variational Autoencoder (VAE) to add a time dynamic item features to user-item implicit feedback data. In this case, distributed representation of items in the specific period is used as a condition that is added to input and latent variable of VAE respectively. In detail, the distributed representation per time bin can be extracted using LSTM. By applying a condition into VAE, a hybrid recommendation system can be created to reflect the item time-varying features. The proposed model in this paper differs from current studies in that it reflects the changing characteristics inherent of the products and utilizes it for recommendation. The Movielens 1M data and Amazon women's clothing dataset are used for the evaluation of the proposed model.
Anuj@IEEE BigData 2019: A Novel Code-Switching Behavior Analysis in Social Media Discussions Natural Language Processing

With internet and social media breaking the ice, more and more people across the globe have started to use social media platforms like Facebook, Twitter, Instagram etc. Most people follow Multilingualism as a mode of communication to convey information across the globe. They share topics over the common forum to converse, with the use of multiple languages being spoken either by individual speaker or group of speakers. This essentially makes the context more complex to understand and it makes even more harder for processing various Natural Language Processing (NLP) tasks. Such user behavior of mixing multiple languages in one single discussion topic, having multiple community inclusion is referred as code-switching. At IEEE 2019 Big data conference, a Shared Task (Understanding Multilingual Communities through Analysis of code-switching Behaviors in Social Media Discussions) is conducted as a track of Big Data Cup. Firstly, Tasks is to detect the language of each post given in the discussion forum with the help of multiple languages. Secondly, to detect relevance score of a post by determining how much the content is closely connected or appropriate in the discussion. This paper proposes a novel approach to detect the language of each word in the post using Natural Language Processing (NLP) techniques involving linguistics, Python package(langdetect) and various other approaches. It also explains how Machine Learning is applied to figure out relevance of a post and other metrics required for prediction. Code-Mixing detection is an important step for any NLP application to determine the language of a post at first place in order to perform any NLP task over social media.
A novel approach for component based application logic event attack modeling

An Event that targets a particular system is required to identify through a novel approach of vulnerability modeling. Current research does not support Event Attack Modeling in component based application logic vulnera-bilities. To find such vulnerabilities, it is important to identify the component that triggered the Event to exploit the system. This research proposes the Event Based Attack Modeling, especially in a scenario of component based software subversion logic attack category Business Application Logic. This will help to design and reuse of component from existing application's functional logic.
Robust stereo matching using improved ZNCC combined SAD-LBP

Stereo matching aims to obtain depth information of scene from captured images, which becomes an active research topic in the field of computer vision. Most stereo matching cost algorithms are based on a common assumption, which is the intensity or color value of corresponding pixels are same. However, in real-world applications, the colors of the objects observed in the recorded image data are affected by radiometric variations. In this paper, using a novel similarity measure we propose a robust stereo matching method, which has robust performance to noise, illumination condition changes, and exposure changes between left and right images. The proposed stereo matching cost combines improved zero mean normalized cross-correlation (ZNCC) model and the absolute difference of local binary pattern (LBP) of windows to get both the color and texture similarity of windows to be matched. Based on Middleburry data set, we verify the effectiveness of the proposed algorithm. Computed results show that the proposed algorithm is more robust to illumination changes and noise than related stereo matching algorithms.
A survey on image and video cosegmentation: Methods, challenges and analyses

Image and video cosegmentation is a newly emerging and rapidly progressing area, which aims at delineating common objects at pixel-level from a group of images or a set of videos. Plenty of related works have been published and implemented in varied applications, but there lacks a systematic survey on both image and video cosegmentation. This paper provides a comprehensive overview including the existing methods, applications, and challenges. Specifically, different cosegmentation problem settings are described, the formulation details of the methods are summarized and their potential applications are listed. Moreover, the benchmark datasets and standard evaluation metrics are also given; and the future directions and unsolved challenges are discussed.
Hybrid Recommendation System for Young Football Athletes Customized Training

Information and communication technologies (ICTs) are been increasingly used in sports over the past decades, especially in professional football, with the goal of enhancing preparation and improve the athletes’ performance. Training programs, however, are not accessible for young and amateur athletes. Most of the systems available, don’t have learning skills to adjust, develop and find new suggestions for training, specifically designed for each athlete. In this paper we present the Smart Coach architecture and user adaptation model and describe our hybrid recommendation system to help the development of young athletes. It simplifies the relationship between the team’s technical staff leaders and their young athletes, enhancing the counselling of the young person and their development as an athlete. The system allows performance evaluation for young athletes utilizing various measurements. The match information is captured intuitively and adaptively by acquaintances, relatives and staff, using a comfortable smartphone interface.
RL4HIN: Representation Learning for Heterogeneous Information Networks

Effectively analyzing and mining large-scale heterogeneous information networks (HINs) by adopting network representation learning (NRL) approaches have received increasing attention. The abundant semantic and structural information contained in HINs not only facilitates network analysis and downstream tasks, but also poses special challenges to well capture that rich information. With the intention to preserve such rich yet potential information during HIN embedding, we first discuss the latent dependence existed in indirect neighbors, then study the different abilities of forward layer and backward layer of bidirectional recurrent neural network to remain semantic of HINs. And finally, we propose a novel representation learning model for HIN, namely RL4HIN. RL4HIN utilizes a skip-dependence strategy for enhancing the latent dependence between farther neighbors, and then develops a proposed weighted loss function in order to balance such difference between forward and backward layer. Extensive experiments, including node classification and visualization, have been conducted on two large- scale and real-world HINs. The experimental results show that RL4HIN significantly outperforms several state-of-the-art NRL approaches.
Optimization of Kernel Method-Based Autoencoder Using Chaotic Evolution Algorithm

We use a chaotic evolution algorithm to optimize the parameter of Gaussian kernel function in the kernel methodbased autoencoder. Kernel method-based autoencoder is an unsupervised learning algorithm with the objective of learning a representation for a set of data. Kernel methods play an important role in building a kernel method-based autoencoder. There are some options for selecting kernel functions, such as Gaussian kernel, polynomial kernel, and Laplacian kernel, etc. In each case, we are required to identify the parameters satisfying the specified requirements or problems. Unfortunately, in some cases, because of a large range of parameters, we can not select proper parameters manually. Chaotic evolution algorithm is one of the optimization algorithms, intending to obtain optimal solutions for a problem, given its certain solution search range. We take advantage of chaotic evolution algorithm to tune parameters automatically for Gaussian kernel function in this work. We found that the proposed method is an efficient and effective tool to solve the selection issue of kernel method-based autoencoder.
An Efficient Feature Selection Algorithm Based on Kernel Function

The traditional feature selection algorithms do not consider the nonlinear relationships between features because their objective functions are linear. We propose a feature selection algorithm for capturing nonlinear relationships between features. By introducing a kernel function, the algorithm maps the original data into the high-dimensional kernel space for calculation, then it can consider the relationship among the features. We use L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -norm for the regularization factor to double-constrain, not only improves the accuracy of the algorithm but also makes the regularization factor W more sparse. Because two kinds of norms are used simultaneously, they make the objective function difficult to directly derive, this paper designed a superior method to optimize. Experimental results show that the classification performance and stability are stronger than other similar algorithms.
A robust, efficient, and balanced parallel algorithm for finding connected components

Finding connected components in an undirected graph has many practical applications. For example in a graph representing a social network, a connected component represents a group of related individuals with common interest. Also, finding connected components forms the basis for other clustering algorithms. In this paper, we will present a parallel algorithm which uses the well known sequential algorithm as the basis for finding connected components in an undirected graph. The algorithm can be adopted to run on a single computer with multiple cores or MapReduce. It is robust in the sense that it honors memory limits. This is important in today's containerized environments. It balances the workload even in the presence of data skew. For the best known algorithm running in MapReduce, the number of iterations is the square of the logarithmic function of the number of vertices in the graph. For our algorithm, we will prove that the upper bound on the number of iterations is a logarithmic function of the maximum size of a connected component. In each iteration, the amount of data read from or written to a file system is bounded by four times the number of edges in the graph.
Auction Based Power Aware Real-Time Scheduler for Heterogeneous FPGA Cloud Platform

Auction based scheduling strategies in current literature are essentially associated with software for cloud environments, as the underlying hardware is considered generic and not re-configurable at runtime. However, recent cloud infrastructures like Amazon EC2 F1 services and Microsoft Azure deploy field programmable gate arrays (FPGAs) as an integral component, for its property of dynamic re-configuration at runtime. Auction based scheduling strategies for FPGA based cloud platforms is still not explored, where the goal is to optimize power dissipation. In this work, we try to deploy an auction based power aware scheduling mechanism for real-time task scheduling in heterogeneous FPGA based cloud platforms, which we term "Auction Based Power Aware Real-Time Scheduler" (AB-PARTS). In this mechanism, a local scheduler is invoked to execute requested tasks (periodic and non-periodic) to meet its real-time requirements. If tasks cannot be guaranteed execution in the local processing elements (PEs) or FPGAs, switch to a distributed scheduling approach with an auction scheme is made. In the auction scheme, task details are broadcasted to other schedulers, which send back an acknowledgment with reward value based on the dynamic status of the PEs of the schedulers. Task is dispatched to the scheduler, which generates the maximum reward value. The scheduler allocates the task to its PEs in such a manner so that the power consumption is optimized. For experimental purpose, we deploy a cloud platform with heterogeneous Altera FPGA boards, where performance of the proposed strategy is tested with standard tasks of the EPFL benchmark suite. Related results depict that the strategy is quite capable to achieve high resource utilization with low power consumption over different simulation scenarios.
Facial Expression Recognition Using Random Forest Classifier

The face though seems an easy object to be recognized by retina, but the artificial intelligence is not yet intelligent enough to do the task easily. As the source of a face is generally an image capturing object, there are a lot of variations and complexions that persists with the image like (e.g., noise, rotation). There are many techniques that use some algorithms to find similarity in face model and the test image, and most of them are successful on their part to attain better test similarities. However, considering the diverse scale of applications and mode of image sourcing, a single algorithm cannot get maximum efficiency everywhere. Even after using the best algorithm for a particular task, an application has to counter with challenges of face recognition. This paper analyzes the hybrid approach of Gabor wavelet and Harris corner features for facial expression recognition. A subspace is created by this algorithm for training of feature vectors, and random forest classifier calculates the similarity score for performance evaluation which will provide improved results in terms of recognition accuracy.
Image Generation and Style Transfer Using Conditional Generative Adversarial Networks

Generative Adversarial Networks have been utilized as a general-purpose solution to image domain transfer problems because of their ability to learn almost any data distribution. However, in most cases, the network architecture is tailored towards a specific application, and there has not been much exploration on using GANs for applications involving mixed datasets. In this work, we have studied the generalizability of Conditional Generative Adversarial Networks by testing their performance on a combination of image datasets, using the same type of input-output mapping. We show that a significant consequence of using multiple datasets is that the model can transfer characteristics such as style and design from one dataset to another. In order to facilitate this, we have modified the conditional GAN model to handle multiple conditional inputs, taking both the input image map and a class label. The results of our experiments show that a conditional GAN can successfully generalize to different types of input and transfer features of one class to others.
PLDSD

A vast amount of data that can be easily read by machines have been published in freely accessible and interconnected datasets, creating the so-called Linked Open Data cloud. This phenomenon has opened opportunities for the development of semantic applications, including recommender systems. In this paper, we propose Personalized Linked Data Semantic Distance (PLDSD), a novel similarity measure for linked data that personalizes the RDF graph by adding weights to the edges, based on previous user's choices. Thus, our approach has the purpose of minimizing the sparsity problem by ranking the best features for a particular user, and also, of solving the item cold-start problem, since the feature ranking task is based on features shared between old items and the new item. We evaluate PLDSD in the context of a LOD-based Recommender System using mixed data from DBpedia and MovieLens, and the experimental results indicate better accuracy of recommendations compared to a non-personalized baseline similarity method.
Neural Machine Translation with Acoustic Embedding

Neural machine translation (NMT) has successfully redefined the state of the art in machine translation on several language pairs. One popular framework models the translation process end-to-end using attentional encoder-decoder architecture and treats each word in the vectors of intermediate representation. These embedding vectors are sensitive to the meaning of words and allow semantically similar words to be near each other in the vector spaces and share their statistical power. Unfortunately, the model often maps such similar words too closely, which complicates distinguishing them. Consequently, NMT systems often mistranslate words that seem natural in the context but do not reflect the content of the source sentence. Incorporating auxiliary information usually enhances the discriminability. In this research, we integrate acoustic information within NMT by multi-task learning. Here, our model learns how to embed and translate word sequences based on their acoustic and semantic differences by helping it choose the correct output word based on its meaning and pronunciation. Our experiment results show that our proposed approach provides more significant improvement than the standard text-based transformer NMT model in BLEU score evaluation.
Multiscale Analysis of Textual Content Using Eyegaze

Reading of a textual content involves a complex coordination between various parts of brain responsible for visual inputs, language processing, cognitive functions and motor response. In addition, psychological factors like attention and perception play a major role in understanding of the content. Many of these factors get reflected in the behaviour of eye movement, as the content is read. In this paper, we present an approach for analysing a textual content in various scales using eyegaze. The scales include (i) individual fixation characteristics, (ii) saccades and fixations within a line (iii) overall difficulty score of the content. An affordable infrared eye tracking device is used to capture the gaze characteristics in an unobtrusive manner. Two types (easy and difficult) of textual contents are designed for the experiment which are benchmarked using standard readability indices. The fixation characteristics include fixation duration, change in drift direction within a fixation and spatial area of a fixation. Using Analysis of Variance (ANOVA), the former two are found to be statistically significant in distinguishing the two types of contents. Within a line, the spatial distance between fixations and the number of switching between saccades and fixations characterize the flow during a reading where the later is found to be statistically significant. A mixture of two partial sigmoid is used as a mapping function to compute the difficulty score of a content from the significant features. For a given content, the variation of these scores among individual readers, enables us to get deeper insights into their cognitive and psychological aspects.
Rain streak removal using under-/over-exposed images

When we take a photograph in outdoors, which is often having unexpected rain streaks and under-/over-exposed pixels in bright and dark regions. To address these problems, we introduce a new blending method based on multi-scale image decomposition for an image sequence taken with varying exposure time. Specifically, under-/over-exposed images are blended for extending the dynamic range of pixels in the wavelet domain, while removing rain streaks.
Eyebox centering using chromatic aberrations of virtual reality head-mounted displays

We demonstrate that transverse chromatic aberration (TCA) measurements can be used to determine the eyebox of a virtual reality head-mounted display using a digital test pattern, which consists of red, green, and blue bars placed horizontally and vertically at ±5°, ±10°, ±15° in the field of view. The pattern also features a white cross in the center of the field of view to determine the horizontal and vertical resolution. This provides simultaneous measurements of the TCAs and resolution for a given position of the camera in the eyebox. A map of the eyebox was generated by raster-scanning the position of the camera over the eyebox. The results for the Oculus Rift show that resolution has approximately a quadratic dependence on the position in the eyebox whereas the response to TCAs is linear. Therefore, TCA provides a more sensitive eyebox measurement at small displacements allowing for repeatable centering within 0.5 mm.
Interpretability for Neural Networks from the Perspective of Probability Density

Currently, most of works about interpretation of neural networks are to visually explain the features learned by hidden layers. This paper explores the relationship between the input units and the output units of neural network from the perspective of probability density. For classification problems, it shows that the probability density function (PDF) of the output unit can be expressed as a mixture of three Gaussian density functions whose mean and variance are related to the information of the input units, under the assumption that the input units are independent of each other and obey a Gaussian distribution. The experimental results show that the theoretical distribution of the output unit is basically consistent with the actual distribution.