Reviewerly/exHarmony huggingface dataset:
Remote Sensing Target Tracking in UAV Aerial Video Based on Saliency Enhanced MDnet

Remote sensing target tracking in the aerial video from unmanned aerial vehicles (UAV) plays a key role in public security. As the UAV aerial video has rapid changes in scale and perspective, few pixels in the target region, and multiple similar disruptors, and the main tracking methods in this research field generally have relatively low tracking performance and timeliness, we propose a remote sensing target tracking method for the UAV aerial video based on a saliency enhanced multi-domain convolutional neural network (SEMD). First, in the pre-training stage, we combine the least squares generative adversarial networks (LSGANs) with a multi-orientation Gaussian Pyramid to augment typical easily confused negative samples for enhancing the capacity to distinguish between targets and the background. Then, a saliency module was integrated into our tracking network architecture to boost the saliency of the feature map, which can improve the representation power of a rapid dynamic change target. Finally, in the stage for generating tracking samples, we implemented a local weight allocation model to screen for hard negative samples. This approach can not only improve the stability in tracking but also boost efficiency. The comprehensive evaluations of public and homemade hard datasets demonstrate that the proposed method can achieve high accuracy and efficiency results compared with state-of-the-art methods.
Blockchain: Analysis of the New Technological Components as Opportunity to Solve the Trust Issues in Supply Chain Management

The issues of trust in the area of supply chain management are an immense concern among the stakeholders cooperating in the supply chain. For a sustainable process of logistics and transportation, an efficient information sharing is considered crucial. The current systems that serve as a base for the operations in supply chain have several drawbacks in terms of data security and trust among stakeholders, who share information as part of their cooperation. Information is shared in a paper-based or semi-digitalized way due to the lack of trust or risk of competitive disadvantages in the current systems. This paper aims to analyze the trust issues in supply chain management and propose new ways of improving trust by considering these issues at the design level. Technological components are analyzed for designing new models and to support the implementation of new architectures that improve trust in a systems which manage supply chain processes.
An Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model

In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on. The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator. We also explore two approaches to accomplish the conditional discriminator: (1) phredGANa, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) phredGANd, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance. To demonstrate the superior performance of phredGAN over the persona Seq2Seq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends. Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation. We also explore the trade-offs from using either variant of phredGAN on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).
Improving Relation Extraction by Pre-trained Language Representations

Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of relation extraction to novel languages. Similarly, pre-processing introduces an additional source of error. To address these limitations, we introduce TRE, a Transformer for Relation Extraction, extending the OpenAI Generative Pre-trained Transformer [Radford et al., 2018]. Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions. TRE allows us to learn implicit linguistic features solely from plain text corpora by unsupervised pre-training, before fine-tuning the learned language representations on the relation extraction task. TRE obtains a new state-of-the-art result on the TACRED and SemEval 2010 Task 8 datasets, achieving a test F1 of 67.4 and 87.1, respectively. Furthermore, we observe a significant increase in sample efficiency. With only 20% of the training examples, TRE matches the performance of our baselines and our model trained from scratch on 100% of the TACRED dataset. We open-source our trained models, experiments, and source code.
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and node features are successfully leveraged in existing GCNs, however, attributes that graph links may carry are commonly ignored, as almost all of these models simplify graph links into binary or scalar values describing node connectedness. In our paper instead, links are reverted to hypostatic relationships between entities with descriptional attributes. We propose GCN-LASE (GCN with Link Attributes and Sampling Estimation), a novel GCN model taking both node and link attributes as inputs. To adequately captures the interactions between link and node attributes, their tensor product is used as neighbor features, based on which we define several graph kernels and further develop according architectures for LASE. Besides, to accelerate the training process, the sum of features in entire neighborhoods are estimated through Monte Carlo method, with novel sampling strategies designed for LASE to minimize the estimation variance. Our experiments show that LASE outperforms strong baselines over various graph datasets, and further experiments corroborate the informativeness of link attributes and our model's ability of adequately leveraging them.
Adaptive Importance Sampling Unscented Kalman Filter based SAR image super resolution

Enhancing Synthetic Aperture Radar (SAR) images for sharp and detailed images through super-resolution (SR) is of great significance in many remote sensing applications. Due to the inherent resolution limitations and the cost incurred for further development of sensor devices, image enhancements through image processing techniques have become more popular. However, in these techniques, the soft edges present in the images are not reconstructed completely, thereby marring the clarity of the generated image. This paper presents a method to restore more details in the image by adaptively adjusting the measurement noise covariance and process noise covariance into the intensity estimation framework for SAR image super-resolution. For this, an Adaptive Importance Sampling Unscented Kalman Filter (ISUKF) framework is implemented using the covariance matching technique. Experimental results indicate that the super-resolution using ISUKF framework performs better, regarding denoising and feature preservation, when accounted for the measurement and process noise covariance. It also outperforms the other recent and standard SR techniques covered in the literature.
Usability of Chatbots: A Systematic Mapping Study

Background: The use of chatbots has increased considerably in recent years.These are used in different areas and by a wide variety of users.Due to this fact, it is essential to incorporate usability in their development.Aim: Our objective is to identify the state of the art in chatbot usability and applied human-computer interaction techniques, to analyze how to evaluate chatbots usability.Method: A systematic mapping study has been conducted, searching the main scientific databases.The search retrieved 170 citations and 19 articles were retained as primary studies.Results: The works were categorized according to four criteria: usability techniques, usability characteristics, research methods and type of chatbots.Conclusions: Chatbot usability is a very incipient field of research, where the published studies are mainly surveys, usability tests, and rather informal experimental studies.Hence, it becomes necessary to perform more formal experiments to measure user experience, and exploit these results to provide usability-aware design guidelines.
Limits of private learning with access to public data

We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where all examples are private) and classical learning (where all examples are public). We study the limits of learning in this setting in terms of private and public sample complexities. We show that any hypothesis class of VC-dimension $d$ can be agnostically learned up to an excess error of $\alpha$ using only (roughly) $d/\alpha$ public examples and $d/\alpha^2$ private labeled examples. This result holds even when the public examples are unlabeled. This gives a quadratic improvement over the standard $d/\alpha^2$ upper bound on the public sample complexity (where private examples can be ignored altogether if the public examples are labeled). Furthermore, we give a nearly matching lower bound, which we prove via a generic reduction from this setting to the one of private learning without public data.
Practical Two-Step Lookahead Bayesian Optimization

Expected improvement and other acquisition functions widely used in Bayesian optimization use a one-step assumption: they value objective function evaluations assuming no future evaluations will be performed. Because we usually evaluate over multiple steps, this assumption may leave substantial room for improvement. Existing theory gives acquisition functions looking multiple steps in the future but calculating them requires solving a high-dimensional continuous-state continuous-action Markov decision process (MDP). Fast exact solutions of this MDP remain out of reach of today's methods. As a result, previous two- and multi-step lookahead Bayesian optimization algorithms are either too expensive to implement in most practical settings or resort to heuristics that may fail to fully realize the promise of two-step lookahead. This paper proposes a computationally efficient algorithm that provides an accurate solution to the two-step lookahead Bayesian optimization problem in seconds to at most several minutes of computation per batch of evaluations. The resulting acquisition function provides increased query efficiency and robustness compared with previous two- and multi-step lookahead methods in both single-threaded and batch experiments. This unlocks the value of two-step lookahead in practice. We demonstrate the value of our algorithm with extensive experiments on synthetic test functions and real-world problems.
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification

Supervised learning models often perform poorly at low-shot tasks, i.e. tasks for which little labeled data is available for training.One prominent approach for improving low-shot learning is to use unsupervised pre-trained neural models.Another approach is to obtain richer supervision by collecting annotator rationales (explanations supporting label annotations).In this work, we combine these two approaches to improve lowshot text classification with two novel methods: a simple bag-of-words embedding approach; and a more complex context-aware method, based on the BERT model.In experiments with two English text classification datasets, we demonstrate substantial performance gains from combining pre-training with rationales.Furthermore, our investigation of a range of train-set sizes reveals that the simple bag-of-words approach is the clear top performer when there are only a few dozen training instances or less, while more complex models, such as BERT or CNN, require more training data to shine.
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model

This paper focuses on the problem of computing an $\epsilon$-optimal policy in a discounted Markov Decision Process (MDP) provided that we can access the reward and transition function through a generative model. We propose an algorithm that is initially agnostic to the MDP but that can leverage the specific MDP structure, expressed in terms of variances of the rewards and next-state value function, and gaps in the optimal action-value function to reduce the sample complexity needed to find a good policy, precisely highlighting the contribution of each state-action pair to the final sample complexity. A key feature of our analysis is that it removes all horizon dependencies in the sample complexity of suboptimal actions except for the intrinsic scaling of the value function and a constant additive term.
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations

Systemic bias in word embeddings has been widely reported and studied, and efforts made to debias them; however, new contextualized embeddings such as ELMo and BERT are only now being similarly studied.Standard debiasing methods require large, heterogeneous lists of target words to identify the "bias subspace".We show that using new contextualized word embeddings in conceptor debiasing allows us to more accurately debias word embeddings by breaking target word lists into more homogeneous subsets and then combining ("Or'ing") the debiasing conceptors of the different subsets.
Exploiting Structural and Semantic Context for Commonsense Knowledge Base Completion

Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes, resulting in orders of magnitude more nodes compared to conventional KBs (18x more nodes in ATOMIC compared to Freebase (FB15K-237)). Importantly, this implies significantly sparser graph structures - a major challenge for existing KB completion methods that assume densely connected graphs over a relatively smaller set of nodes. In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. Specifically, we investigate two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge. We describe our method to incorporate information from both these sources in a joint model and provide the first empirical results for KB completion on ATOMIC and evaluation with ranking metrics on ConceptNet. Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1.5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency. Further analysis on model predictions shines light on the types of commonsense knowledge that language models capture well.
Joint Shape Representation and Classification for Detecting PDAC

We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D volume classification task with little training data. We propose a two-stage framework, which first segments the pancreas into a binary mask, then compresses the mask into a shape vector and performs abnormality classification. Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting. Experiments are performed on 300 normal scans and 136 PDAC cases. We achieve a specificity of $$90.2\%$$ (false alarm occurs on less than 1/10 normal cases) at a sensitivity of $$80.2\%$$ (less than 1/5 PDAC cases are not detected), which show promise for clinical applications.
A novel fuzzy k-means latent semantic analysis (FKLSA) approach for topic modeling over medical and health text corpora

Medical and health text documents pose a challenge for data handling and retrieving the relevant and meaningful documents. Automatically retrieval of significant knowledge with a better understanding of medical and health documents is a challenging task. One popular approach for thematically unders tand the medical and health text documents and finding the topics from these documents is topic modeling. In this research, we propose a novel topic modeling approach Fuzzy k-means latent semantic analysis (FKLSA) by using the fuzzy clustering. Our method generates local and global term frequencies through the bag of words (BOW) model. Principal component analysis is used for removing high dimensionality negative impact on global term weighting. Previous work shows that in medical and health documents redundancy issue has a negative impact on the quality of text mining. Therefore, the main achievement of FKLSA is the handling of the redundancy issue in medical and text documents and discover semantically more precise topics. FKLSA is socially utilized for finding the themes from medical and health text corpus. These topics are further used for text classification and clustering tasks in text mining. Experimental results show that FKLSA performs better than LDA and RedLDA for redundant corpora. FKLSA’s time performance is also stable with an increase in number of topics and thus better than LDA and LSA on a big twitter heath dataset. Quantitative evaluations of the real-world dataset for health and medical documents show that FKLSA gives a higher performance as compared to state-of-the-art topic models like Latent Dirichlet allocation and Latent semantic analysis.
A naked eye 3D display and interaction system for medical education and training

To provide natural simulated objects and intuitive user interaction in medical education and training, we propose a naked eye 3D display and interaction system. The current 3D rendering algorithms for naked eye 3D displays are not suitable for medical use, due to the requirements of displaying and interacting with high quality medical images and simulating soft tissues. Because the traditional 3D rendering procedure and vertex indexing in collision detection require substantial computing power when using a naked eye 3D display, the current method cannot achieve fluent displays and interactions. Thus, we develop a novel octree-based 3D rendering and interaction algorithm for high quality medical models to improve the rendering rate and obtain smooth human machine interactions when using the naked eye 3D display device. We also valuate the soft-body phantom simulation of the naked eye 3D display device by combining the traditional 3D rendering algorithm with the elastic 3D simulation to simulate deformable tissues. We integrate an incremental interaction method and a Kalman filter-based hand tracking method to achieve a larger user interaction range and robust hand tracking. We used the proposed system to perform human-computer interactions with rigid phantoms and soft-body phantoms. The experimental results showed that the proposed rendering algorithm for rigid phantoms could achieve higher rendering performance (50 FPS) than the traditional rendering algorithm (9.8 FPS). The user experiments showed that the 3D simulation system equipped with the enhanced rendering algorithm could achieve fluent interactions when using the naked eye 3D display, thus promoting education experiences and reducing task completion times.
Mineral Mapping with Hyperspectral Image Based on an Improved K-Means Clustering Algorithm

Mineral mapping with hyperspectral images has been demonstrated as an effective way for land resources survey. K-means, as a typical clustering algorithm, is commonly used to process the object identification of hyperspectral images. However, due to the influence of mixed pixel, the matching of data points and cluster centers of the traditional k-means clustering algorithm is very difficult. Therefore, this paper proposes an improved k-means clustering algorithm to identify the mineral types from the AVIRIS hyperspectral image of Cuprite mining area. This algorithm uses three methods to select the initial cluster centers and spectral information divergence instead of Euclidean distance for better measuring the similarity. Finally, by matching the clustering results with the mineral distribution map of this region and USGS mineral spectral library, it was found that the improved k-means clustering algorithm can get better clustering results and higher mineral mapping accuracy than the traditional algorithm.
The camera offset space

Potential visibility has historically always been of importance when rendering performance was insufficient. With the rise of virtual reality, rendering power may once again be insufficient, e.g., for integrated graphics of head-mounted displays. To tackle the issue of efficient potential visibility computations on modern graphics hardware, we introduce the camera offset space (COS). Opposite to how traditional visibility computations work---where one determines which pixels are covered by an object under all potential viewpoints---the COS describes under which camera movement a sample location is covered by a triangle. In this way, the COS opens up a new set of possibilities for visibility computations. By evaluating the pairwise relations of triangles in the COS, we show how to efficiently determine occluded triangles. Constructing the COS for all pixels of a rendered view leads to a complete potentially visible set (PVS) for complex scenes. By fusing triangles to larger occluders, including locations between pixel centers, and considering camera rotations, we describe an exact PVS algorithm that includes all viewing directions inside a view cell. Implementing the COS is a combination of real-time rendering and compute steps. We provide the first GPU PVS implementation that works without preprocessing, on-the-fly, on unconnected triangles. This opens the door to a new approach of rendering for virtual reality head-mounted displays and server-client settings for streaming 3D applications such as video games.