Reviewerly/exHarmony huggingface dataset:
High-Efficient Reed-Solomon Decoder Based on Deep Learning

Deep learning recently shows outstanding potential in channel decoding optimization, but its effect on the decoding of Reed-Solomon (RS) codes has yet to be explored. In this paper, we propose a RS decoder based on deep learning for the first time, and pave a new way to improve the existing RS decoding algorithms. We exploit a deep neural network (DNN) to estimate the error numbers of the received codewords, and according to the estimation results, a novel decoder is designed, which can adjust the most suitable decoding method to each received codeword automatically. Experiments show that for (7, 3), (15, 9) and (63, 55) RS codes, the average computational complexity of our decoder can be reduced by 68.96 %, 62.38 %, 50.61 % respectively compared with the HDD-LCC algorithm.
Investigating a Design Space for Developing Design Thinking in Electronic Healthcare Records

In recent years, most hospitals have introduced an Electronic Health Record (EHR) systems to replace paper patient records. The satisfaction with these systems was lower than expected due to the poor organization of system data and the difficulty with obtaining actionable information. Data visualization and interface design are not as advanced in some parts of clinical medicine as it is in other scientific disciplines and commercial industries. In addition, the designers of electronic health record interfaces are confronted with a dizzying array of design choices. In this paper, we propose an 8-dimensional design space to provide a framework when designing interfaces for accessing a patient’s record. We present our methodology to define this design space: first we used the 5W1H method to set the dimensions, then we studied existing systems and interviewed doctors to define main values or categories of each dimension, we present many illustrative examples of these categories. Finally, we showcase the utility of our work for designers of EHR systems through a scenario. Overall, our design space can help building systems which will improve health data visualization and interface design.
Maskpan: Mask Prior Guided Network For Pansharpening

Pansharpening aims to generate the high spatial resolution multispectral (HRMS) images by fusing the spatial and spectral information from the low resolution multispectral (LRMS) images and high resolution panchromatic (PAN) images. Although existing pansharpening methods excel at achieving visual pleasing HRMS, they are limited in providing discriminability for visual tasks. To address this problem, this paper proposes a mask prior guided network (MaskPan) for pansharpening, which incorporates high-level semantic features with low-level detail information to improve the visual discrimination and quality of pansharpened images simultaneously. To make full use of the mask prior, the spatial and spectral features in conjunction with the semantic features are firstly fused in feature domain, and then promoted by an attention mechanism. In addition, the semantic segmentation task is introduced as a new metric to evaluate the visual discrimination of pansharpened images. Experimental results show that the proposed MaskPan can effectively enhance image quality and visual discrimination, thereby improving the pansharpening performance.
Robust Video Facial Authentication With Unsupervised Mode Disentanglement

Deep learning-based video facial authentication has limitations when it comes to real-world applications, due to large mode variations such as illumination, pose, and eyeglasses variations in real-life situations. Many of existing mode-invariant facial authentication methods need labels of each mode. However, the label information could not be always available in practice. To alleviate this problem, we develop an unsupervised mode disentangling method for video facial authentication. By matching both disentangled identity features and dynamic features of two facial videos, our proposed method shows significant face verification and identification performances on three publicly available datasets, KAIST-MPMI, UVA-NEMO, and YTF.
Exaggerated Learning For Clean-And-Sharp Image Restoration

Deep learning has become a methodology of choice for image restoration tasks, including denoising, super-resolution, deblurring, exposure correction, etc., because of its superiority to traditional methods in reconstruction quality. However, the published deep learning methods still have not solve the old dilemma between low noise level and detail sharpness. We propose a new CNN design strategy, called exaggerated deep learning, to reconcile two mutually conflicting objectives: noise free and detail sharpness. The idea is to deliberately overshoot for the desired attributes in the CNN optimization objective function; the cleanness or sharpness is overemphasized according to different semantic contexts. The exaggerated learning approach is experimented on the restoration tasks of super-resolution and low light correction. Its effectiveness and advantages have been empirically affirmed.
Image Co-segmentation with Multi-Scale Dual-Cross Correlation Network

Considering that the global correlation between images is very important for image co-segmentation, we propose a multi-scale Dual-Cross Correlation Network (DCNet) that can efficiently capture global matching information across images to obtain segmentation results. Specifically, the low-dimensional index feature is used to calculate the correlation and the high-dimensional content features are combined with the correlation matrix for final segmentation. Meanwhile, we specially design a Dual-Cross Correlation Module (DCCM) which harvests the spatial and channel correlation with the adjacent pixels of another image on the cross path to enhance the representation of correlation efficiently. By utilizing a further loop operation, each feature can capture the global dependencies from all pixels of another feature. Furthermore, we fuse multi-scale correlation and features into the decoder, which is called Multi-scale Correlation Fusing Decoder (MCFD), to refine the final segmentation results. Moreover, we introduce a new dice loss function to train the whole network by averaging the dice loss value of the foreground and background. Finally, we validate our method on three co-segmentation benchmarks and the results show that our method achieves the state-of-the-art performance.
Credit Card Fraud Prevention Planning using Fuzzy Cognitive Maps and Simulation

For any financial organization, development and management of fraud prevention plan is a complex yet crucial process. Most of the organizations opt for fraud detection and prevention plans to sustain their marketplace. With the advancement of technology and diversification in user's demand for high security and privacy, it is important to improve fraud prevention planning for financial organizations and banks. Different processes like fraud detection, fraud risk assessment, fraud perception, monitoring and control are involved in fraud prevention planning. It is difficult for Chief Investigating Officer (CIO) to grasp the dependencies among all processes. Similarly, the effect of inefficiencies may raise severe concerns to the planning model and organization as well. This paper considers the formulation of fraud prevention plan for credit cards through implementation of Fuzzy Cognitive Maps (FCM). Furthermore, our aim is to represent the complexity of relationships that exists in different processes of fraud prevention model. The model has been validated through mathematical simulation using log sigmoid function and then risk prediction has been done on single what-if scenario. The contribution of this paper is to analyse and predict risks related to fraud prevention planning in credit cards and help in better decision making.
Advisory Framework to Interconnect Distributed Water Bodies Targeting Agriculture Farms

There has been several instances of uneven distribution of water resources in villages, towns and cities of developing countries of South-east Asia, such as India, Indonesia, etc. Interconnection of distributed water bodies such that excess water from one water source to other deficit source of a stressed agriculture farm is always a challenge. Planning and implementation of efficient laying in already connected pipeline laid mechanisms are costly and time consuming. In this regard, this paper proposes a novel advisory framework named as Zobhana Jala Sambaddha (ZJS) to address the above problems and provide an automated solution as compared to manual pipe laying mechanisms. The proposed advisory Framework ZJS arrives at a solution space that connects multiple source and destination points. This multipath solution between any source and destination points are derived considering different parameters of interest such as: low-cost, less distance, low pumping power, geographical constraints etc. An image processing based data abstraction with multi constrained optimization framework provides an advisory solution. It is observed from different test case runs carried out in Sri City, that the proposed framework is more generic and automated; eliminates the need for time consuming surveys for pipe-laying; and in fact, reduces pipe-laying infrastructure by upto 36%.
Bridging the divide between API users and API developers by mining public code repositories

Software application programming interfaces (APIs) are a ubiquitous part of Software Engineering. The evolution of these APIs requires constant effort from their developers and users alike. API developers must constantly balance keeping their products modern whilst keeping them as stable as possible. Meanwhile, API users must continually be on the lookout to adapt to changes that could break their applications. As APIs become more numerous, users are challenged by a myriad of choices and information on which API to use. Current research attempts to provide automatic documentation, code examples, and code completion to make API evolution more scalable for users. Our work will attempt to establish practical and scalable API evolution guidelines and tools based on public code repositories, to aid both API users and API developers.
Signature Extraction Using Connected Component Labeling

Signature is a means of self-identification and authentication for humans. There are various algorithms used in order to extract the signature from several documents like bank cheques, certificates, and many more. Signatures have to be accepted only from the intended person. Different works have been carried out in the fields of offline recognition systems over the past few years. There are many algorithms and approaches used till date which have their own advantages and disadvantages. The algorithm used here is “Connected Component Analysis.” It is used to detect the signature from the complete document provided.
Augmenting Kannada Educational Video with Indian Sign Language Captions Using Synthetic Animation

The project aims for a machine translation system for augmenting Kannada educational video by providing a translation to Indian Sign Language (ISL) using virtual animation approach along with the subtitles. The system accepts Kannada educational video clips as input and output is delivered as 3D character animation. The first phase approach of the proposed system makes use of SpeechRecognition like kaldi an open source for converting the input video files to their respective text which are used to generate the subtitles and these texts are further pre-processed in order to meet the sign language norms. The right SiGML file for these pre-processed words is picked from the database. These SiGML files consist of XML tags which correspond to the respective HamNoSys Notations. HamNoSys is an alphabetic system that mostly phonetically describes signs. A synthetic animation of a human in the tool in the URL APP, JA SiGML takes input as these SiGML files and generates signs of the respective pre-processed words. Synchronization of inputted video with the sign language and the subtitle generation is vital for the clear and clean flow. This system is Tested by professional sign language trainer/interpreter for the sign language generated by the avatar and is found effective. The accuracy of sign language shown was found out to be 63%.
Facing Big Data by an Agent-Based Multimodal Evolutionary Approach to Classification

Multi-agent systems recently gained a lot of attention for solving machine learning and data mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing when large datasets have to be analyzed. In this paper, we propose a multi-agent classification system able to tackle large datasets where each agent independently explores a random small portion of the overall dataset, searching for meaningful clusters in proper subspaces where they are well-formed (i.e., compact and populated). This search is orchestrated by means of a genetic algorithm able to act in a multi-modal fashion, since meaningful clusters might lie in different subspaces. Furthermore, since agents operate independently one another, their execution is parallelized across different computational units. Tests show that the proposed algorithm, E-ABC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , is able to deal with large datasets, returning satisfactory results in terms of scalability and performances, especially when compared with our previous baseline versions.
Modeling coherence and diversity for image paragraph captioning

Image paragraph captioning task aims to provide detailed and objective multi-sentence descriptions of a target image. Existing models suffer from the problems of the monotony of sentence structure, the mechanical repetition of sentence meanings and stiff inter-sentence transitions in paragraph-level descriptions. In this paper, we consider modeling human concerned evaluation criteria, namely, diversity and coherence, and use them as explicit rewards in the proposed Reinforcement Learning based training strategy. Ablation study on the standard paragraph description dataset shows the necessity of leveraging Self-critical Sequence Training strategy and the effectiveness of our novel designed reward combined with CIDEr. Final experimental results show that our model improves the current state-of-the-art model in terms of four out of six standard evaluation metrics.
Multiple-source Domain Adaptation in Rule-based Neural Network

Domain adaptation uses the previously acquired knowledge (source domain) to support predicted tasks in the current domain without sufficient labeled data (target domain).Although many methods have been developed in domain adaptation, one issue hasn't been solved: how to implement knowledge transfer when more than one source domain is available.In this paper we present a neural network-based method which extracts domain knowledge in the form of rules to facilitate knowledge transfer, merge rules from all source domains and further select related rules for target domain and clip redundant rules.The method presented is validated on datasets that simulate the multi-source scenario and the experimental results verify the superiority of our method in handling multi-source domain adaptation problems.
Latent Context Based Soft Actor-Critic

The performance of deep reinforcement learning methods prone to degenerate when applied to tasks requiring relatively longer horizon memory or with highly variable dynamics. In this paper, we utilize the probabilistic latent context variables motivated by recent Meta-RL materials, and propose the Latent Context based Soft Actor-Critic (LC-SAC) approach to address aforementioned issues. The latent context is capable to encode information about both the agent's previous behaviors and the dynamics of the current undergoing environment, which empirically believed to be beneficial for efficient policy optimization. Experiment results demonstrate that LC-SAC can achieve comparable performance with SAC on a collection of continuous control benchmarks and outperforms SAC in some particular tasks with above two characteristics. Moreover, we also introduce a simple but general procedure to integrate LC-SAC with diverse-quality demonstrations to enable efficient reuse of human prior knowledge, and finally achieve competitive performance with comparatively small number of interactions with environments.
A named entity recognition model based on ensemble learning

Knowledge Graph has gradually become one of core drivers advancing the Internet and AI in recent years, while there is currently no normal knowledge graph in the field of agriculture. Named Entity Recognition (NER), one important step in constructing knowledge graphs, has become a hot topic in both academia and industry. With the help of the Bidirectional Long Short-Term Memory Network (Bi-LSTM) and Conditional Random Field (CRF) model, we introduce a method of ensemble learning, and implement a named entity recognition model ELER. Our model achieves good results for the CoNLL2003 data set, the accuracy and F1 value in the best experimental results are respectively improved by 1.37% and 0.7% when compared with the BiLSTM-CRF model. In addition, our model achieves an F1 score of 91% for the agricultural data set AgriNER2018, which proves the validity of ELER model for small agriculture sample data sets and lays a foundation for the construction of agricultural knowledge graphs.
Caarvida

We report on an interdisciplinary visual analytics project wherein automotive engineers analyze test drive videos. These videos are annotated with navigation-specific augmented reality (AR) content, and the engineers need to identify issues and evaluate the behavior of the underlying AR navigation system. With the increasing amount of video data, traditional analysis approaches can no longer be conducted in an acceptable timeframe. To address this issue, we collaboratively developed Caarvida, a visual analytics tool that helps engineers to accomplish their tasks faster and handle an increased number of videos. Caarvida combines automatic video analysis with interactive and visual user interfaces. We conducted two case studies which show that Caarvida successfully supports domain experts and speeds up their task completion time.
Research on the Input Methods of Cardboard

Virtual Reality (VR) is a new technology developed in the 20th century and the demands for VR from all walks of life are increasing, and Cardboard plays an important role in the demands. Facing the current situation that the existing Cardboard input methods cannot meet the actual needs well, aiming at the four typical input methods of Cardboard: buttons, gaze, voice and gestures, the paper conducts an experiment on typical object selection tasks and analyzes the experimental results in the sparse distribution, dense distribution, and sheltered dense distribution. The research results have important practical value for the design and development of the input of Cardboard based applications.
Proximal Stochastic AUC Maximization

This work considers a stochastic optimization problem for maximizing the AUC (area under the ROC curve). The AUC metric has proven to be a reliable performance measure for evaluating a model learned on imbalanced data. The batch pairwise learning methods (e.g., rankSVM) can achieve a quadratic convergence to the optimal solution. However, the batch learning paradigm hinders the scalability of these methods. Recently different online and stochastic AUC maximization algorithms are developed. While these can scale well for large-scale data, they either cannot generalize as good as the batch AUC methods or suffer from slow convergence, which minimizes their scalability. A recent stochastic pairwise learning algorithm for AUC maximization suggests to schedule both the regularization and the averaging steps to improve the generalization capability and the convergence speed. Building on this algorithm, we develop a simple proximal stochastic AUC maximization algorithm. The proposed algorithm uses a proximal operator of the pairwise hinge loss function, which encourages small update steps. Averaging these adjacent weights has a significant improvement on the converges rate of the final model. Experiments on several benchmark data sets show that the proposed algorithm can achieve AUC classification accuracy on par with that of the batch method while being considerably efficient. The proposed algorithm also outperforms state-of-the-art online and stochastic algorithms in terms of generalization performance and convergence rate.
Optical Flow-Guided Mask Generation Network for Video Segmentation

The purpose of video segmentation is to segment foreground objects from a video sequence. In this paper, we propose a CNN based method for the semi-supervised video object segmentation, where a hybrid encoder-decoder network is designed to generate pixel-wise foreground object segmentation in use of both spatial and temporal information. In order to minimize cumulative error of the network as much as possible, we develop a two-stage training scheme: alternate training and back-propagation-through-time training. Then the performances of our method and other state-of-the-art ones are compared on two annotated video segmentation databases. Furthermore, we also run an extensive ablation study to test the effects of different components from our method.
Phoneme based Domain Prediction for Language Model Adaptation

Automatic Speech Recognizer (ASR) and Natural Language Understanding (NLU) are the two key components for any voice assistant. ASR converts the input audio signal to text using acoustic model (AM), language model (LM) and Decoder. NLU further processes this text for sub-tasks like predicting domain, intent and slots. Since input to NLU is text, any error in ASR module will propagate in NLU sub-tasks. ASR generally process speech in small duration windows and first generates phonemes using Acoustic Model (AM) and then Word Lattices using Decoder, Dictionary and Language Model (LM). Training and maintaining a generic LM, which fits the distribution of data of multiple domains is a difficult task. So our proposed architecture uses multiple domain specific LMs to rescore word lattice and has a way to select LMs for rescoring. In this paper, we are proposing a novel Multistage CNN architecture to classify the domain from partial phoneme sequence and use it to select top K domain LMs. The accuracy of multistage classification model based on phoneme input for top three domains has achieved state-of-the-art results on 2 open datasets, 97.76% in ATIS and 99.57% in Snips.
Convolutional Bayesian Models for Anatomical Landmarking on Multi-dimensional Shapes

The anatomical landmarking on statistical shape models is widely used in structural and morphometric analyses. The current study focuses on leveraging geometric features to realize an automatic and reliable landmarking. The existing implementations usually rely on classical geometric features and data-driven learning methods. However, such designs often have limitations to specific shape types. Additionally, calculating the features as a standalone step increases the computational cost. In this paper, we propose a convolutional Bayesian model for anatomical landmarking on multi-dimensional shapes. The main idea is to embed the convolutional filtering in a stationary kernel so that the geometric features are efficiently captured and implicitly encoded into the prior knowledge of a Gaussian process. In this way, the posterior inference is geometrically meaningful without entangling with extra features. By using a Gaussian process regression framework and the active learning strategy, our method is flexible and efficient in extracting arbitrary numbers of landmarks. We demonstrate extensive applications on various publicly available datasets, including one brain imaging cohort and three skeletal anatomy datasets. Both the visual and numerical evaluations verify the effectiveness of our method in extracting significant landmarks.