Reviewerly/exHarmony huggingface dataset:
Saliency detection using a deep conditional random field network

Saliency detection has made remarkable progress along with the development of deep learning. While how to integrate the low-level intrinsic context with high-level semantic information to keep the object boundary sharp and restrain the background noise is still a challenging problem. Many attempts on network structures and refinement strategies have been explored, such as using Conditional Random Field (CRF) to improve the accuracy of saliency map, but it is independent from the deep network and cannot be trained end-to-end. To tackle this issue, we propose a novel Deep Conditional Random Field network (DCRF) to take both deep feature and neighbor information into consideration. First, Multi-scale Feature Extraction Module (MFEM) is adopted to capture the low level texture and high level semantic features, multi-stacks of deconvolution layers are employed to improve the spatial resolution of deep layers. Then we employ Backward Optimization Module (BOM) to guide shallower layers by high-level location and shape information derived from deeper layers, which intrinsically enhance the representational capacity of low-level features. Finally, a Deep Conditional Random Field Module (DCRFM) with unary and pairwise potentials is designed to concentrate on spatial neighbor relations to obtain a compact and uniformed saliency map. Extensive experimental results on 5 datasets in terms of 6 evaluation metrics demonstrate that the proposed method achieves state-of-the-art performance.
Sentiment Analysis for Konkani Language: Konkani Poetry, a Case Study

Sentiment analysis is a part of NLP research. In the present work, we increased the existing corpus of Konkani senti-words by adding 75% more words and employed the Naïve Bayes classifier for the automatic senti-tagging of Konkani poems. We obtained 82% accuracy over a set of 50 poems that have been written by 22 contemporary poets, for which all the words were ensured to be the part of our corpus. We got 70% accuracy for tagging of a set of 10 randomly selected poems. The results are comparable with those of research on automatic classification of Telugu songs (Harika et al. in Proceedings of the 4th workshop on sentiment analysis where AI meets psychology, IJCAI, pp 48–52, 2016, [15]). The performance of the model could rank it third in the list of similar works reported in the literature.
Semi-supervised Classification-based Local Vertex Ranking via Dual Generative Adversarial Nets

Real-world graphs are usually very sparse in terms of inadequate edges and labels as well as have poor quality due to a large amount of noisy data. In this paper, we propose a classification-based local vertex ranking architecture through dual generative adversarial networks in the semi-supervised setting, DQGAN, for analyzing sparse noisy graphs with rarely labeled data. First, we develop a quadruple generative adversarial ClassNet model to address the noisy data and data sparsity issues as well as to classify each vertex into K classes by automatically creating imaginary/real-looking supplementary labeled vertices with the quite different/similar distributions as real vertices, without the high cost of multi-step graph propagation, heterogeneous graph mining, and iterative weight learning. In addition, the vertex label vicinity is incorporated into the classification model to capture the pairwise vertex closeness based on the labeling and align the vertex label vicinity with the well-known vertex homophily for preserving the original structural semantics in the classification space. Second, we present a quintuple generative adversarial RankNet framework to locally rank each vertex on each of K classes by designing the game of multiple competitors utilizing the mix of real and noisy data to fight against each other, for improving the robustness of local vertex ranking to noisy data with few help from human efforts. The cycle ranking consistency strategy is designed to make the ranking quality verifiable through the bidirectional information-lossless translations between the original features and the ranking features. We propose to utilize the relaxed local PageRank property to produce high-quality local vertex ranking results in the context of information networks. Third but last, extensive evaluation on real graph datasets demonstrates that DQGAN outperforms existing representative methods in terms of both classification and ranking in the semi-supervised setting.
Target Classification and Recognition for High-Resolution Remote Sensing Images: Using the Parallel Cross-Model Neural Cognitive Computing Algorithm

Target classification and recognition (TCR) are important information-extraction techniques for high-resolution remote sensing images (HRIs). However, because methods with high accuracy usually have higher time complexity, the massive remote sensing image has brought great difficulties for realtime application. In this article, we propose a hybrid, heterogeneous parallel processing algorithm to improve the efficiency TCR based on the Crossmodel Neural Cognitive Computing (CNCC) algorithm [parallel TCR based on MapReduce of hierarchical CNCC (PTCR-C)]. Parallel programming technologies used in this article include Open Multiprocessing (OpenMP), Compute Unified Device Architecture (CUDA), and the message passing interface (MPI). Experiments on public remote sensing image data sets show that the PTCR-C algorithm can effectively improve the efficiency of the original TCR algorithm. The PTCR-C algorithm has better augmented ability and provides the reference for further real-time TCR of remote sensing applications.
A Classification Method of Oracle Materials Based on Local Convolutional Neural Network Framework

The classification of materials of oracle bone is one of the most basic aspects for oracle bone morphology. However, the classification method depending on experts' experience requires long-term learning and accumulation for professional knowledge. This article presents a multiregional convolutional neural network to classify the rubbings of oracle bones. First, we detected the “shield grain” and “tooth grain” on the oracle bone rubbings, then complete the division of multiple areas on an image of oracle bone. Second, the convolutional neural network is used to extract the features of each region and we complete the fusion of multiple local features. Finally, the classification of tortoise shell and animal bone was realized. Utilizing the image of oracle bone provided by experts, we conducted an experiment; the result show our method has better classification accuracy. It has made contributions to the progress of the study of oracle bone morphology.
Local-Density Subspace Distributed Clustering for High-Dimensional Data

Distributed clustering is emerging along with the advent of the era of big data. However, most existing established distributed clustering methods focus on problems caused by a large amount of data rather than caused by the large dimension of data. Consequently, they suffer the “curse” of dimensionality (e.g., poor performance and heavy network overhead) when high-dimensional (HD) data are clustered. In this article, we propose a distributed algorithm, referred to as Local Density Subspace Distributed Clustering (LDSDC) algorithm, to cluster large-scale HD data, motivated by the idea that a local dense region of a HD dataset is usually distributed in a low-dimensional (LD) subspace. LDSDC follows a local-global-local processing structure, including grouping of local dense regions (atom clusters) followed by subspace Gaussian model (SGM) fitting (flexible and scalable to data dimension) at each sub-site, merging of atom clusters at every sub-site according to the merging result broadcast from the global site. Moreover, we propose a fast method to estimate the parameters of SGM for HD data, together with its convergence proof. We evaluate LDSDC on both synthetic and real datasets and compare it with four state-of-the-art methods. The experimental results demonstrate that the proposed LDSDC yields best overall performance.
Exploring the Effects of Aggregation Choices on Untrained Visualization Users' Generalizations From Data

Abstract Visualization system designers must decide whether and how to aggregate data by default. Aggregating distributional information in a single summary mark like a mean or sum simplifies interpretation, but may lead untrained users to overlook distributional features. We ask, How are the conclusions drawn by untrained visualization users affected by aggregation strategy? We present two controlled experiments comparing generalizations of a population that untrained users made from visualizations that summarized either a 1000 record or 50 record sample with either single mean summary mark, a disaggregated view with one mark per observation or a view overlaying a mean summary mark atop a disaggregated view. While we observe no reliable effect of aggregation strategy on generalization accuracy at either sample size, users of purely disaggregated views were slightly less confident in their generalizations on average than users whose views show a single mean summary mark, and less likely to engage in dichotomous thinking about effects as either present or absent. Comparing results from 1000 record to 50 record data set, we see a considerably larger decrease in the number of generalizations produced and reported confidence in generalizations among viewers who saw disaggregated data relative to those who saw only mean summary marks.
Preliminary Investigation of Visualizing Human Activity Recognition Neural Network

Owing to the growing demand for wearable context-aware applications, activity recognition technologies have attracted great attention. A neural network has been recently used as a recognition algorithm because of its discrimination and feature extraction ability. While understanding the network provides us useful information to improve its performance, visualization techniques for neural networks have been not explored yet in the human activity recognition field. We propose a visualization method tailored to human activity recognition that generates acceleration signals which maximize the activation of a unit in a neural network. We introduce a new regularization method based on a low pass filter to suppress high-frequency components induced in the generation process to improve the interpretability of the signals.
A Patent Text Classification Model Based on Multivariate Neural Network Fusion

In order to improve the efficiency and accuracy of automatic classification of patent texts, a patent text classification model (C3-BIGRU-AT) based on multivariate neural network fusion was proposed. Firstly, patent text is segmented and represented by text preprocessing. Then, the text features of different levels and different characteristics are extracted through word embedding layer, convolution layer, BIGRU layer and Attention layer, and text category recognition is carried out through soft Max layer. Finally, case studies show that C3-BIGRU-AT model has a high ability of patent text recognition, and can meet the requirements of accurate and efficient classification of a large number of patent texts.
Text Co-Detection in Multi-View Scene

Multi-view scene analysis has been widely explored in computer vision, including numerous practical applications. The texts in multi-view scenes are often detected by following the existing text detection method in a single image, which however ignores the multi-view corresponding constraint. The multi-view correspondences may contain structure, location information and assist difficulties induced by factors like occlusion and perspective distortion, which are deficient in the single image scene. In this paper, we address the corresponding text detection task and propose a novel text co-detection method to identify the co-occurring texts among multi-view scene images with compositions of detection and correspondence under large environmental variations. In our text co-detection method, the visual and geometrical correspondences are designed to explore texts holding high pairwise representation similarity and guide the exploitation of texts with geometrical correspondences, simultaneously. To guarantee the pairwise consistency among multiple images, we additionally incorporate the cycle consistency constraint, which guarantees alignments of text correspondences in the image set. Finally, text correspondence is represented by a permutation matrix and solved via positive semidefinite and low-rank constraints. Moreover, we also collect a new text co-detection dataset consisting of multi-view image groups obtained from the same scene with different photographing conditions. The experiments show that our text co-detection obtains satisfactory performance and outperforms the related state-of-the-art text detection methods.
Adaptation of Classical Machine Learning Algorithms to Big Data Context: Problems and Challenges : Case Study: Hidden Markov Models Under Spark

Big Data Analytics presents a great opportunity for scientists and businesses. It changed the methods of managing and analyzing the huge amount of data. To make big data valuable, we often use Machine Learning algorithms. Indeed, these algorithms have shown, in the past, their processing speed, efficiency and accuracy. But today, with the complex characteristics of big data, new problems have emerged and we are facing new challenges when developing and designing a new Machine Learning algorithm for Big Data Analytics. Therefore, it is essential to review the classical algorithms to adapt them to this new context. One of the methods of adaptation is the coupling between new technologies (i.e., distributed computing by GPU, Hadoop, Spark) and the Machine Learning algorithms to reduce the computational cost of data analysis. This paper highlights main challenges of adaptation of Machine Learning algorithms to the Big Data context and describes a novel method to make these algorithms efficient and fast in Big Data processing by taking as a case study the Hidden Markov Models using Spark framework. The results of complexity comparison of classical algorithms and those adapted to the Big Data context using Spark show a great improvement.
MobileNet-Tiny: A Deep Neural Network-Based Real-Time Object Detection for Rasberry Pi

In this paper, we present a new neural network architecture, MobileNet-Tiny that can be used to harness the power of GPU based real-time object detection in raspberry-pi and also in devices with the absence of a GPU and limited graphic processing capabilities such as mobile phones, laptops, etc. MobileNet-Tiny trained on COCO dataset running on a non-Gpu laptop dell XPS 13, achieves an accuracy of 19.0 mAP and a speed of 19.4 FPS which is 3 times as fast as MobileNetV2, and when running on a raspberry pi, it achieves a speed of 4.5 FPS which is up to 7 times faster than MobileNetV2. MobileNet-Tiny was modeled to offer a compact, quick, and well-balanced object detection solution to a variety of GPU restricted devices.
Image Denoising With Linear and Non-linear Filters

A very huge part of computerized picture preparing is given to image restoration. Picture denoising is the governor of the picture information to deliver an outwardly excellent picture. Distinctive commotion models incorporate filter as added substance and multiplicative sort. They consolidate Gaussian disturbance, salt and pepper clamor, Brownian commotion and spot filter. De noising calculation is application subordinate for example the utilization of a particular channel is useful against a particular sort of clamor. In this paper, the separating approach has been nominated when the picture is ruined with salt and pepper noise. There are different channels that can expel the commotion from pictures and save picture subtleties. By and large various channels are utilized for disposing of various commotions like Mean channel evacuates the motivation noise, Median filter removes "salt and pepper" noise and Weiner channel evacuates noise that has tainted a sign. It depends on a factual methodology. we have advised that Median filter delivers best results against impulsive noise.
BLE Bluetooth Beacon based Solution to Monitor Egress of Alzheimer’s Disease Sufferers from Indoors

With the ageing people community, Alzheimer's disease (AD) is the most common disease in prevalence. It affects the memory of the affected individual by causing neuron degeneration. Such cognitive disability leads people to roam around places since they are ignorant about location. They may go afar from the safe area to traffic prone areas which cause serious, life threatening issues to them. Proposed study introduces a technology that involves a wearable device for Alzheimer disease affected elderly patients and listening device given to care takers or doctors. This system detects and notifies caregivers about the departure of people to risk locations from their residence zone. This system uses Bluetooth Low energy (BLE) communication technology that utilizes transmitter and receiver fixed to elderly individual as wearable device and in residence respectively. Based on the signal strength between the transmitter and receiver, the distance estimation between the elderly and the receiver is calculated with the help of a Raspberry Pi(Rpi) and an alert notification is sent to doctors/caretakers when the distance exceeds safety limit.
Item recommendation by predicting bipartite network embedding of user preference

With the development of e-commerce, various methodologies have studied to improve recommendation performance. Recently, many deep learning based network embedding approaches are applied to the recommendation domain. However, these approaches still have several limitations, such as the problem of data sparseness and the changing in user preference over time, which cannot be considered. In this paper, we propose a novel method for item recommendation based on network embedding. First, we apply a bipartite network embedding to address the data sparsity problem. Bipartite network embedding is a vector representation method that reflects explicit (i.e., observed data) and implicit relations (i.e., unobserved data). Bipartite network embedding methodology can address the data sparsity problem by using implicit relationship information from applying the random walk approach. Second, we predict future bipartite network embedding of user preference by adopting a Kalman filter to consider the changes in user preferences. We have conducted experiments to evaluate the effectiveness and performance of the proposed recommendation method. Through experimentation, the proposed recommendation method is validated as outperforming than the existing approaches including existing network embedding methods.
Deep Learning with Domain Randomization for Optimal Filtering

Filtering is the process of recovering a signal, x(t), from noisy measurements z(t). One common filter is the Kalman Filter, which is proven to be the conditional minimum variance estimator of x(t) when the measurements are a Gaussian random processes. However, in practice (a) the measurements are not necessarily Gaussian and (b) an estimation of the measurement covariance is problematic and often tuned using cross-validation and domain knowledge. In order to address the Kalman Filter's suboptimal performance in situations where non-Gaussian noise is present, we train a deep autoencoder to learn a mapping of the noisy measurements to a "learned" noise process covariance R(t) and measurements z(t), which are passed to a Kalman Filter. The Kalman Filter's output is then mapped to the original measurement space via the decoder portion of the autoencoder, where this output and original measurements are compared. Training this autoencoder-Kalman Filter (AEKF) via domain randomization on simulated noisy sensor responses, we show the AEKF's estimate of x(t), in the majority of cases, achieves a lower MSE than both a standard Kalman Filter and a Long Short-Term Memory (LSTM) recurrent deep neural network. Most significantly, the AEKF outperforms all methods in the case of simulated Cauchy noise.
Automatic lecture video skimming using shot categorization and contrast based features

Video skimming is one of the recently, getting popular technique for preparing preview for long watching video sequences. Most of the video skimming techniques developed in the literature uses manual intervention of users to prepare the review. Mostly the literature reported video skimming for sports and movie industries. In sports the portion of video where audience claps are used and in movie important contents are manually selected for preparing the preview. However in literature rarely any work reported for skimming of lecture video sequences. Lecture videos are generally, recorded indoor, low illuminated, noisy environment condition and contents of the scene rarely changes much. Hence designing an automatic skimming scheme is quite difficult task. In this article, we put forward an intelligent expert video skimming technique for lecture video sequences, where human intervention is not required. In the proposed scheme, initially the lecture video is segmented into a number of shots. We proposed the use of radiometric correlation technique for lecture video segmentation or finding the shot transitions. After getting the shot transitions in a video, the shots are recognized. The fuzzy K-nearest neighborhood technique is proposed to recognize the shots in a video. The shots are recognized into three categories: title slides, written texts/displayed slides and talking heads/writing hands. Three contrast based features: one existing i.e., average sharpness (AS) and two newly proposed: relative height (RH) and edge potential (EP) are used to find the contents of a frame. The frames with different contrast values are categorized to prepare the video skimming or the capsule. The media recreation is achieved by selecting a set of frames around these selected content frames. The effectiveness of the proposed scheme is demonstrated in this paper using five test sequences, including three NPTEL and two non NPTEL. It is also observed that the capsule prepared by the proposed scheme, provides a better preview of the actual sequence. The performance of the proposed scheme is tested by comparing it against three state-of-the-art techniques. The evaluation of the proposed scheme is carried out by using three evaluation measures. It is also observed that the proposed scheme is found to be better than that of the existing schemes.
Hybrid Method for Moving Object Exploration in Video Surveillance

Moving object in a video could be explored using hybrid methodologies as one among the enticing field of vision in computers. It is extensively applied in video surveillances and target identification system. Extracting reliable information accurately is a rigorous task in a challenging environment. This paper investigates the problem of detecting an object in dynamic scenes. We suggest two method 1) feature extraction using FBF 2) Image matching using ISURF. The ISURF (Improved Speeded up Robust Feature detection) is the improvised method of original SURF algorithm. In this the matching duration is reduced by limiting the total number of features to be compared. The FBF (Fast Bilateral Filtering) algorithm is suggested for feature extraction and denoising the captured key frames. Thus this paper proposes a hybrid method for moving object exploration in a dynamic scene with reduced time.