Reviewerly/exHarmony huggingface dataset:
Sentence Selective Neural Extractive Summarization with Reinforcement Learning

In this work we employed a common Recurrent Neural Network (RNN) based sequence model for single document summarization, composed of encoder-extractor hierarchical network architecture. We develop a sentence level selective encoding mechanism to select important feature before extracting sentences, and use a novel reinforcement learning based training algorithm to extend the sequence model. Besides, for single document extractive summarization task, most of researchers only pay attention to the main part of document. We analyze and explore the side information such as the headline and image caption in both CNN and Daily Mail news datasets. Empirical experiment results show the effect that our model outperforms the baseline model, and can be comparable with the state-of-the-art extractive systems when automatically evaluated in the ROUGE metric. The statistics analysis of the data set verifies our experiment results.
Joint Embedding of Emoticons and Labels Based on CNN for Microblog Sentiment Analysis

Microblog sentiment analysis is a technique to analyze the emotion types of microblog texts, which is beneficial to control the direction of the public's attitudes or emotions. Different researches are being developed to guide sentiment analysis. However, these methods do not consider or do not give enough attention to emoticons and labels present in the microblog sentences, which cause a loss of potential information. To solve this problem, the research proposes a novel model, called joint embedding of Emoticons and Labels based on CNN (EL-CNN). EL-CNN applies an input encoder to convert input sequences into fix-length semantic vectors, a label encoder to convert labels into fix-length semantic vectors and a matcher to calculate a matching score between the fix-length semantic vectors obtained from the input encoder and the label encoder, which turns the sentiment classification tasks into vector matching tasks. The work maintains the interpretability of word embedding and enjoys a built-in ability to capture sentiment information from emoticons and labels. Experimental results on two public microblog benchmark corpora (NLPCC2013 and NLPCC2014) show that the EL-CNN model achieves significant improvements as compared to all start-of-the-art methods.
Accuracy Enhancement of a Blind Image Steganalysis Approach Using Dynamic Learning Rate-Based CNN on GPUs

Blind image steganalysis is the classification problem of determining whether an image contains any hidden data or not. This blind process doesn't need any prior information about the embedding algorithm which is used to hide data on the examined images. Recently, Convolutional Neural Network (CNN) is presented to deal with the blind image steganalysis classification problem. Most of the CNN-based image steganalysis approaches can't cope with low payloads. Improved Gaussian Convolutional Neural Network (IGNCNN) is presented with a transfer learning method in order to deal with stego-images with low payloads. IGNCNN contains a pre-processing layer which is consisted of a fixed coefficients (data-set independent) high pass filter (HPF). IGNCNN also is a fixed learning rate based-CNN. In this paper, a dynamic learning rate-based CNN approach is proposed, in order to highly minimize the detection error cost. Nevertheless, the proposed approach uses a dataset dependent-based Gaussian HPF instead, as a preprocessing layer, in order to well-choose a cutoff frequency depending on the training dataset. Experiments are performed on graphical processing units (GPUs) with the standard BOSSbase 1.01 dataset exposed to the S-UNIWARD and WOW image steganographic algorithms. Results show that the proposed approach outperforms computing approaches, GNCNN, improved GNCNN, SRM and SRM+EC, by an average increase of 7.4%, 5.3%,4.1% and 2.8% respectively in terms of accuracy metric.
Autoencoder based sample selection for self-taught learning

Self-taught learning is a technique that uses a large number of unlabeled data as source samples to improve the task performance on target samples. Compared with other transfer learning techniques, self-taught learning can be applied to a broader set of scenarios due to the loose restrictions on the source data. However, knowledge transferred from source samples that are not sufficiently related to the target domain may negatively influence the target learner, which is referred to as negative transfer. In this paper, we propose a metric for the relevance between a source sample and the target samples. To be more specific, both source and target samples are reconstructed through a single-layer autoencoder with a linear relationship between source samples and reconstructed target samples being simultaneously enforced. An ℓ2,1-norm sparsity constraint is imposed on the transformation matrix to identify source samples relevant to the target domain. Source domain samples that are deemed relevant are assigned pseudo-labels reflecting their relevance to target domain samples, and are combined with target samples in order to provide an expanded training set for classifier training. Local data structures are also preserved during source sample selection through spectral graph analysis. Promising results in extensive experiments show the advantages of the proposed approach.
Medical Image Analysis: Human and Machine

Images play a critical role in science and medicine. Until recently, analysis of scientific images, specifically medical images, was an exclusively human task. With the evolution from analog to digital image data and the development of sophisticated computer algorithms, machines (computers) as well as humans can now analyze these images. Though different in many ways, these alternative means of analysis share much in common: we can model the newer computational methods on the older human-observer approaches.
The battle for truth: How online newspaper commenters defend their censored expressions

The presence of hate speech in the commentary field of online newspapers is a pressing challenge for free speech policy. We have conducted interviews with 15 people whose comments were censored for posting comments of a racist, discriminatory or hateful nature. What characterizes their self-understanding and enemy images? We found that central to their motivation for writing such comments was an understanding of themselves as particularly knowledgeable people. They see themselves as people who fight for the revelation of the truth, in contrast to the lies spread by politicians and the media. Furthermore, they regard politicians and the media as corrupt elites that are leading our society into destruction by their naïve support of liberal migration policies. By linking up to alternative news media, these individuals support various forms of racialized conspiracy theories, but also a form of radical right-wing populism in their concern that politics should be acted out by people themselves. As such, our study adds to the literature on conspiracy theories in general and racialized conspiracy theories in particular, but also to the literature on online far-right activists. Our contribution lies both in the newness of focusing on the self-perceptions, but also in opening up for a modification of existing literature on the far right.
Integrating Augmented Reality in Training and Industrial Applications

Industries face significant challenges in today's economy, finding qualified workers with the requisite qualifications to meet the job skills requirements. Companies are researching and implementing new technologies to improve training, production process productivity, cost reduction, and improve product quality. One of the technologies being considered for implementation into the production environment is augmented reality. Many companies are researching and experimenting with this AR technology as it shows promise to improve skill development, productivity, and product quality, thus reducing production costs. The integration of AR technologies into the production environment will require a significant investment in the development of new training content, improved processes, and procedures using these new digital capabilities. The implementation of the AR technologies in the manufacturing environment will result in a major paradigm shift in the traditionally manual labor skills demands on the factory floor.
Recommending Exception Handling Code

Exception handling is an advanced programming technique to prevent run-time errors or crashes for modern software systems. However, inexperienced programmers might fail to write proper exception handling code in their programs. In this paper, we introduce ExAssist, a code recommendation tool for exception handling. ExAssist can predict what types of exception could occur in a given piece of code and recommend proper exception handling code for such exceptions. Preliminary evaluation of ExAssist suggests that it provides highly accurate recommendations. The tool's website is at rebrand.ly/exassist. The video demo of ExAssist could be found at rebrand.ly/exassistdemo.
On Hermitian LCD codes and their Gray image

We provide methods and algorithms to construct Hermitian linear complementary dual (LCD) codes over finite fields. We study existence of self-dual basis with respect to Hermitian inner product, and as an application, we construct Euclidean LCD codes by projecting the Hermitian codes over such a basis. Many optimal quaternary Hermitian and ternary Euclidean LCD codes are obtained. Comparisons with classical constructions are made.
Video Dialog via Multi-Grained Convolutional Self-Attention Context Multi-Modal Networks

Video dialog is a new and challenging task, which requires an AI agent to maintain a meaningful dialog with humans in natural language about video contents. Specifically, given a video, a dialog history and a new question about the video, the agent has to combine video information with dialog history to infer the answer. However, the existing methods of image dialog and video question answering, which fail to process the complexity of video information and establish the logical dependency of history contexts, are inappropriate to be applied directly to video dialog. In this paper, we propose a novel approach for video dialog called multi-grained convolutional self-attention context network, which combines video information with dialog history. Instead of using RNN to encode the sequence information, we design a multi-grained convolutional self-attention mechanism to capture both element and segment level interactions that contain multi-grained sequence information. Moreover, a hierarchical dialog history encoder is designed to learn the context-aware question representation. Finally, we establish two decoders in multiple-choice and open-ended forms respectively, which utilize different strategies to get the multi-model context-aware video representation and to generate human-like answers. We evaluate our method on two large-scale datasets. Due to the flexibility and parallelism of the new attention mechanism, our method can achieve higher time efficiency, and the extensive experiments also show the effectiveness of our method.
Opinion Mining Using Live Twitter Data

Opinion mining and extracting Sentiments of people is the need of today. This is the era of Big data. Because of social networking sites, it's easy to analyze sentiments of people. Sentiment analysis is a technique to extract opinions of people regarding any product, issue or personality. This paper is about extracting live twitter data regarding any topic and converting it into structured form from un-structured one. Opinions are extracted from the text data and polarity is assigned against each tweet. Polarity of data can be positive, negative or neutral. Most recent and popular opinions can be extracted. It is useful for both market analyzers as well as customers. Customers get to know honest reviews about any product and companies can know their customer's interests. Furthermore, predictions are also given based on the classified data.
Combining object detection and causality mining for efficient development of augmented reality-based on-the-job training systems in hotel management

The purpose of this study is to propose a methodology for establishing an augmented reality (AR)-based model for efficient OJT through object detection and causality mining, a novel text analysis method. Articles on hotel management published in the last decade and useful for OJT were collected, information on the causal relationships between them extracted, and related rules saved to a rule base. Using the same data, we detected various objects through SSD (Single Shot Multibox Detector), a real-time object detection system. Then we matched sets of causalities and displayed them to trainees wearing AR devices. This methodology reduces development and maintenance costs required to operate OJT programmes. Trainees are immersed in the training environment, which improves the effectiveness of the training. To show the feasibility of the proposed method, we developed a prototype AR-OJT system for hotel management training, automatically extracting knowledge on hotel management from articles according to the proposed method. The results demonstrate that the AR-OJT group shows better performance in terms of learning motivation and self-regulated learning than the control OJT group. No significant difference in learning performance was found between the two groups, which implies that traditional OJT can be substituted with AR-OJT.
Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the two source and target visual domains is minimized. We use the output space of a shared cross-domain deep encoder to model the embedding space and use the Sliced-Wasserstein Distance (SWD) to measure and minimize the distance between the embedded distributions of two source and target domains to enforce the embedding to be domain-agnostic. Additionally, we use the source domain labeled data to train a deep classifier from the embedding space to the label space to enforce the embedding space to be discriminative. As a result of this training scheme, we provide an effective solution to train the deep classification network on the source domain such that it will generalize well on the target domain, where only unlabeled training data is accessible. To mitigate the challenge of class matching, we also align corresponding classes in the embedding space by using high confidence pseudo-labels for the target domain, i.e. assigning the class for which the source classifier has a high prediction probability. We provide experimental results on UDA benchmark tasks to demonstrate that our method is effective and leads to state-of-the-art performance.
A blockchain model for preserving intellectual property

The number of applications that blockchain era suggests are vastly growing each day in almost all industries. One of the most dialectical research areas using blockchain concepts is the aspects of intellectual property and copy rights. In this paper, we propose a novel framework of a hybrid model which combines consortium blockchain and private blockchain properties coupled with the definition of attribute-based access control to produce a suggested benchmark in preserving intellectual property law and practice. This hybrid-based model can help in solving many problems detected in Intellectual Property management using blockchain, such as provenance and authenticity.
A Kinect-Based Augmented Reality Game for Lower Limb Exercise

Augmented reality (AR) is where 3D virtual objects are integrated into a 3D real environment in real time. The augmented reality applications such as medical visualization, maintenance and repair, robot path planning, entertainment, military aircraft navigation, and targeting applications have been proposed. This paper introduces the development of an augmented reality game which allows the user to carry out lower limb exercise using a natural user interface based on Microsoft Kinect. The system has been designed as an augmented game where users can see themselves in a world augmented with virtual objects generated by computer graphics. The player sitting in a chair just has to step on a mole that appears and disappears by moving upward and downward randomly. It encourages the activities of a large number of lower limb muscles which will help prevent falls. It is also suitable for rehabilitation.
Bayesian Inferred Self-Attentive Aggregation for Multi-Shot Person Re-Identification

Person re-identification is a challenging retrieval task that aims to match pedestrians from multiple non-overlapping cameras. In this paper, we introduce a deep multi-instance learning framework to aggregate instance-level images to boost retrieval performance. Considerable annotation inconsistency inevitably happens in many current person re-identification datasets due to unconcerned of annotations or dramatic varieties in surveillance scenarios, thereby leading to model drifting. To alleviate this issue, we formulate the person re-identification problem in a weakly supervised setting, and propose a self-inspired attention model based on Bayesian inference, to adaptively evaluate regional features with their global dependencies across instances, which we refer to as Bayesian Inferred Self-Attentive Aggregation (BISAA). The evaluation mechanism is parameterized by neural networks to provide an insight into the contribution of each instance and semantic human part to set-level labels. Furthermore, to facilitate aggregation across a set of instances, we propose a new collective aggregation function to make the model more robust to outliers, by adjusting the activation threshold, to allow some non-informative instances to be ignored while paying more attention to the discriminative ones. Extensive experiments with ablation analysis show the effectiveness of our method and the proposed method outperforms many related state-of-the-art techniques on four benchmark datasets: PRID2011, iLIDS-VID, Market-1501 and MSMT17.
Stochastic Virtual Machine Placement for Cloud Data Centers Under Resource Requirement Variations

In cloud computing environment, the optimal placement of virtual machines (VMs) onto physical servers has been of great importance to improving the resource utilization and energy efficiency of data centers. In this work, we study the VM placement problem for minimizing the total energy consumption in a data center under the uncertainty of resource requirements demanded by the VMs. Instead of using deterministic values to represent the resource requirements, as in most existing placers, we propose a stochastic placement approach in which the resource requirement variations are modeled as random variables. We further formulate the uncertainty-aware VM placement problem as a stochastic optimization model, of which the optimization objective is to minimize the total energy consumed by all physical machines (PMs). In the presence of varying resource requirements, the optimization model is subject to a probabilistic constraint on resource overflow probability on each PM (i.e., the probability of demanded CPU/memory exceeding the maximum capacity the PM can provide). To solve this stochastic optimization problem, we develop an efficient metaheuristic to seek for an optimized VM placement solution that minimizes the total energy cost while satisfying the probabilistic resource constraint. Moreover, by incorporating a solution initialization procedure and a neighborhood search strategy, we can further improve the effectiveness of the metaheuristic in solution space exploration. Extensive simulations are performed to justify the proposed approach, in terms of both solution feasibility and energy efficiency. By taking into account the uncertainty of resource requirements, the stochastic method can achieve more energy-efficient placement solutions compared with the deterministic VM placement algorithm.
Video Summarization by Learning Relationships between Action and Scene

We propose a novel deep architecture for video summarization in untrimmed videos that simultaneously recognizes action and scene classes for every video segments. Our networks accomplish this through a multi-task fusion approach based on two types of attention modules to explore semantic correlations between action and scene in the videos. The proposed networks consist of the feature embedding networks and attention inference networks to stochastically leverage the inferred action and scene feature representations. Additionally, we design a new center loss function that learns the feature representations by enforcing to minimize the intra-class variations and to maximize the inter-class variations. Our model achieves a score of 0.8409 for summarization and accuracy of 0.7294 for action and scene recognition on test set of CoVieW'19 dataset, which is ranked 3rd.