Reviewerly/exHarmony huggingface dataset:
E2F-GAN: Eyes-to-Face Inpainting via Edge-Aware Coarse-to-Fine GANs

Face inpainting is a challenging task aiming to fill the damaged or masked regions in face images with plausibly synthesized contents. Based on the given information, the reconstructed regions should look realistic and more importantly preserve the demographic and biometric properties of the individual. The aim of this paper is to reconstruct the face based on the periocular region (eyes-to-face). To do this, we proposed a novel GAN-based deep learning model called Eyes-to-Face GAN (E2F-GAN) which includes two main modules: a coarse module and a refinement module. The coarse module along with an edge predictor module attempts to extract all required features from a periocular region and to generate a coarse output which will be refined by a refinement module. Additionally, a dataset of eyes-to-face synthesis has been generated based on the public face dataset called CelebA-HQ for training and testing. Thus, we perform both qualitative and quantitative evaluations on the generated dataset. Experimental results demonstrate that our method outperforms previous learning-based face inpainting methods and generates realistic and semantically plausible images. We also provide the implementation of the proposed approach to support reproducible research via (<uri>https://github.com/amiretefaghi/E2F-GAN</uri>).
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval

Dense passage retrieval (DPR) models show great effectiveness gains in first stage retrieval for the web domain. However in the web domain we are in a setting with large amounts of training data and a query-to-passage or a query-to-document retrieval task. We investigate in this paper dense document-to-document retrieval with limited labelled target data for training, in particular legal case retrieval. In order to use DPR models for document-to-document retrieval, we propose a Paragraph Aggregation Retrieval Model (PARM) which liberates DPR models from their limited input length. PARM retrieves documents on the paragraph-level: for each query paragraph, relevant documents are retrieved based on their paragraphs. Then the relevant results per query paragraph are aggregated into one ranked list for the whole query document. For the aggregation we propose vector-based aggregation with reciprocal rank fusion (VRRF) weighting, which combines the advantages of rank-based aggregation and topical aggregation based on the dense embeddings. Experimental results show that VRRF outperforms rank-based aggregation strategies for dense document-to-document retrieval with PARM. We compare PARM to document-level retrieval and demonstrate higher retrieval effectiveness of PARM for lexical and dense first-stage retrieval on two different legal case retrieval collections. We investigate how to train the dense retrieval model for PARM on limited target data with labels on the paragraph or the document-level. In addition, we analyze the differences of the retrieved results of lexical and dense retrieval with PARM.
Fast Active Aerial Exploration for Traversable Path Finding of Ground Robots in Unknown Environments

This article proposes an autonomous aerial exploration framework for traversable path finding of ground robots in unknown environments to achieve a fast response in search and rescue (SAR) missions. Different from existing works, our method provides a task-oriented active exploration strategy that makes the aerial robot fully autonomous during the exploration and does not require human intervention. To identify the candidate regions which ground robots can go through, a fast incremental traversability estimation algorithm is presented, which can evaluate the traversability of the region of interest (ROI) efficiently. Based on the traversability map, we propose a highly efficient frontier detection algorithm that can guarantee both reachability and safety of the extracted frontiers. To reduce the total response time of the whole robot system, a new cost function considering both the time costs of the aerial and ground robots is designed that aims to avoid back-and-forth motion behavior during aerial exploration. The performance of our approach is evaluated in both simulation and real-world experiments. The results show that the proposed method outperforms existing methods in terms of traversability estimation and frontier detection time (about one order of magnitude faster) as well as the response time (at least 13% shorter on average).
A Sparse Reconstructive Evidential &lt;i&gt;&lt;/i&gt;-Nearest Neighbor Classifier for High-dimensional Data

The Evidential <i>K</i>-Nearest Neighbor (EK-NN) classification rule provides a global treatment of uncertainty and imprecision in class labels, and has been widely used in pattern recognition. Nevertheless, EK-NN still suffers from the fixed presupposition of hyper-parameter <i>K</i> without prior knowledge, due to the different spatial distribution of neighbors of each pattern in Euclidean space. More concretely, neighbors of some patterns may provide confusing information and then derive wrong classification results. To address this issue, we propose a sparse reconstructive evidential <i>K</i>-NN (SEK-NN) classifier, appropriately determining an individual <i>K</i> for each pattern and mapping the correlations between patterns from Euclidean space to a sparse reconstructed space. To match with this sparse reconstructed space, SEK-NN supersedes the Euclidean distance by correlation coefficients to measure the dissimilarities between patterns. When handling high-dimensional data, a parallel version of SEK-NN is implemented under the Apache Spark to speed up the parameter estimation. We respectively test SEK-NN and parallel SEK-NN over 19 middle dimensional datasets, 1 middle volume and 4 high-dimensional datasets that are up to 100 thousand of dimensions. Experimental results show that SEK-NN has great prediction performance and parallel SEK-NN is able to appropriately tackle high-dimensional datasets.
Augmented Intelligence in Mental Health Care: Sentiment Analysis and Emotion Detection with Health Care Perspective

Sentiment analysis or opinion mining has become one of the fastest growing areas now. Though the journey started since 1990s but huge outbreak of sentiment analysis occurred after 2004. With the increasing pressure of new era, new technology, more complex and busy lifestyle, mental health issues are also becoming more serious concerns. In this survey paper, a brief evolution history of sentiment analysis has been discussed. Briefly, emotion detection through facial expression has also been addressed. Commonly used approaches and technology used for sentiment analysis and emotion detection has been discussed with the comparison of available technologies. With the methodology used for sentiment analysis, an insight view of mental health concerns where sentiment analysis can play a vital role, has been discussed. Nowadays, our youth generation is using social website in a large scale to express their mental status, as a way of entertainment, to express their general opinion regarding any topic or issue. Hence the large web data now has gained the ability to show the overall mental condition for a large community. After corona outbreak mental issues have been increased significantly as well as use of social media also has been increased incredibly due to lockdown and work from home lifestyle. So by using the vast data of web platform, we can analyze the recent situation of mental health issues and also predict near future concerns. Although in accuracy of sentiment analysis, we are still facing many challenges with our existing algorithms, but there are a lot of future scope in this field.
Domain Neural Adaptation

Domain adaptation is concerned with the problem of generalizing a classification model to a target domain with little or no labeled data, by leveraging the abundant labeled data from a related source domain. The source and target domains possess different joint probability distributions, making it challenging for model generalization. In this article, we introduce domain neural adaptation (DNA): an approach that exploits nonlinear deep neural network to 1) match the source and target joint distributions in the network activation space and 2) learn the classifier in an end-to-end manner. Specifically, we employ the relative chi-square divergence to compare the two joint distributions, and show that the divergence can be estimated via seeking the maximal value of a quadratic functional over the reproducing kernel hilbert space. The analytic solution to this maximization problem enables us to explicitly express the divergence estimate as a function of the neural network mapping. We optimize the network parameters to minimize the estimated joint distribution divergence and the classification loss, yielding a classification model that generalizes well to the target domain. Empirical results on several visual datasets demonstrate that our solution is statistically better than its competitors.
Random CNN structure: tool to increase generalization ability in deep learning

Abstract The paper presents a novel approach for designing the CNN structure of improved generalization capability in the presence of a small population of learning data. Unlike the classical methods for building CNN, we propose to introduce some randomness in the choice of layers with a different type of nonlinear activation function. The image processing in these layers is performed using either the ReLU or the softplus function. This choice is random. The randomness introduced in the network structure can be interpreted as a special form of regularization. Experiments performed on the detection of images belonging to either melanoma or non-melanoma cases have shown a significant improvement in average quality measures such as accuracy, sensitivity, precision, and area under the ROC curve.
On the Design of Medical Data Ecosystem for Improving Healthcare Research and Commercial Incentive

This paper outlines a visionary approach for building a distributed federated medical data lake and ecosystem with commercial incentive across hospitals and personal health data generated from wearable medical devices at home. This article creates the ownership enforcement guideline as the basis for building the blockchain based ecosystem platform by reviewing various aspects of studies of data ownership rights. This blockchain based platform can enforce the data ownership, patient privacy, and provide a controlled and secure access to the medical data. It can also perform owner centric medical data exchange and effectively aggregate various related medical data sets from various distributed data sets from various hospitals. Moreover, this blockchain platform unlocks the academic and business value of the medical data by modeling medical data as Non-Fungible Token (NFT) which can provide incentives for all data driven value chain entities to make a medical data ecosystem possible.
Autoencoder-based Anomaly Detection in Smart Farming Ecosystem

The inclusion of Internet of Things (IoT) devices is growing rapidly in all application domains. Smart Farming uses IoT devices to increase efficiency and optimize farming operations. These devices can be used in a cloud or edge computing infrastructure which can provide remote control of watering and fertilization, real time monitoring of farm conditions, and provide solutions for more sustainable practices. These improvements to efficiency and ease of use come with added risks to security and privacy. Combining vulnerable IoT devices with the critical infrastructure of the agriculture domain broadens the attack surface for adversaries. Cyberattacks in a large coordinated manner could disrupt the economy of agriculture-dependent nations. To the sensors in a system, an attack may appear as anomalous behaviour. Additionally, there are possibilities of anomalies generated due to faulty hardware, issues in network connectivity (if present), or simply abrupt changes to the environment due to weather, human error, or other unforeseen circumstances. To make these systems more secure, it is imperative to detect such data discrepancies and trigger appropriate mitigation mechanisms. In this paper, we propose an anomaly detection model for Smart Farming using an unsupervised Autoencoder machine learning model. We chose to use an Autoencoder as our method of anomaly detection because it attempts to reconstruct normal data with a low reconstruction loss and anomalous data with a high loss. The high reconstruction loss value for a data point indicates that the data is not like the rest. Our model was trained and tested on data collected from our greenhouse test-bed. Our proposed Autoencoder based anomaly detection method achieved 98.98% and took 262 seconds to train and has a detection time of .0585 seconds.
In-lab demonstration of coherent one-way protocol over free space with turbulence simulation

Over the last decade, free-space quantum key distribution (QKD), a secure key sharing protocol, has risen in popularity due the adaptable nature of free-space networking and the near-term potential to share quantum-secure encryption keys over a global scale. While the literature has primarily focused on polarization based-protocols for free-space transmission, there are benefits to implementing other protocols, particularly when operating at fast clock-rates, such as in the GHz. In this paper, we experimentally demonstrate a time-bin QKD system, implementing the coherent one-way (COW) at 1 GHz clock frequency, utilizing a free-space channel and receiver. We demonstrate the receiver's robustness to atmospheric turbulence, maintaining an operational visibility of 92%, by utilizing a lab-based turbulence simulator. With a fixed channel loss of 16 dB, discounting turbulence, we obtain secret key rate (SKR) of 6.4 kbps, 3.4 kbps, and 270 bps for three increasing levels of turbulence. Our results highlight that turbulence must be better accounted for in free-space QKD modelling due to the additional induced loss.
Please be polite: Towards building a politeness adaptive dialogue system for goal-oriented conversations

Politeness embalms interactions, putting the parties in conversation at ease. Even the most well-intended communication can fall through if there is a manifestation of rudeness. Complementarily, even the most angst-prone situation can be communicated with much less hurt if lathered with politeness. In this paper, we propose a novel task named as Politeness Adaptive Dialogue System (PADS) to incorporate politeness feedback of agents’ actions according to user’s mood and demands. As there is no politeness labeled conversational data available, we annotate the recently released MultiDoGO dataset having six domains with appropriate politeness labels. The proposed end-to-end dialogue system comprises of a transformer-based politeness classifier that interacts with a reinforced learning framework with four different politeness-oriented reward algorithms, forcing the agent to adapt to polite actions upon encountering the user’s dissatisfaction in the dialogue. We train and evaluate PADS by building six different user simulators, each corresponding to a domain. Quantitative and qualitative analysis show that our proposed politeness based reward algorithms improve the task success rate and reduce the dialogue length with state-of-the-art results.
Progressive Feature Learning for Facade Parsing With Occlusions

Existing deep models for facade parsing often fail in classifying pixels in heavily occluded regions of facade images due to the difficulty in feature representation of these pixels. In this paper, we solve facade parsing with occlusions by progressive feature learning. To this end, we locate the regions contaminated by occlusions via Bayesian uncertainty evaluation on categorizing each pixel in these regions. Then, guided by the uncertainty, we propose an occlusion-immune facade parsing architecture in which we progressively re-express the features of pixels in each contaminated region from easy to hard. Specifically, the outside pixels, which have reliable context from visible areas, are re-expressed at early stages; the inner pixels are processed at late stages when their surroundings have been decontaminated at the earlier stages. In addition, at each stage, instead of using regular square convolution kernels, we design a context enhancement module (CEM) with directional strip kernels, which can aggregate structural context to re-express facade pixels. Extensive experiments on popular facade datasets demonstrate that the proposed method achieves state-of-the-art performance.
Optical flow for video super-resolution: a survey

Video super-resolution is currently one of the most active research topics in computer vision as it plays an important role in many visual applications. Generally, video super-resolution contains a significant component, i.e., motion compensation, which is used to estimate the displacement between successive video frames for temporal alignment. Optical flow, which can supply dense and sub-pixel motion between consecutive frames, is among the most common ways for this task. To obtain a good understanding of the effect that optical flow acts in video super-resolution, in this work, we conduct a comprehensive review on this subject for the first time. This investigation covers the following major topics: the function of super-resolution (i.e., why we require super-resolution); the concept of video super-resolution (i.e., what is video super-resolution); the description of evaluation metrics (i.e., how (video) super-resolution performs); the introduction of optical flow based video super-resolution; the investigation of using optical flow to capture temporal dependency for video super-resolution. Prominently, we give an in-depth study of the deep learning based video super-resolution method, where some representative algorithms are analyzed and compared. Additionally, we highlight some promising research directions and open issues that should be further addressed.
FCHP: Exploring the Discriminative Feature and Feature Correlation of Feature Maps for Hierarchical DNN Pruning and Compression

Pruning can remove the redundant parameters and structures of Deep Neural Networks (DNNs) to reduce inference time and memory overhead. As one of the important components of DNN, feature maps (FMs) have been widely used in network pruning. However, previous approaches do not fully investigate the discriminative features in FMs, and also do not explicitly utilize all the features associated with each layer in the pruning procedure. In this paper, we explore the discriminative feature of FMs and explicitly investigate the two-adjacent-layer features of each layer to propose a three-phase hierarchical pruning framework, dubbed as FCHP. Firstly, we decompose each FM into several components to extract the discriminative feature. After that, since pruning each layer is related to the FMs of adjacent layers, we explicitly calculate the feature correlation of discriminative features of two adjacent layers, and then use the feature correlation to cluster FMs into several hierarchies to guide subsequent pruning. Finally, we compute the content of discriminative features, and remove channels corresponding to FMs with fewer discriminative features in each hierarchy, respectively. In the experiment, we prune DNNs with the multiple types of architecture on different benchmarks, and the results have achieved the state-of-the-arts in terms of compressed parameters and FLOPs drop. For example, as for ResNet-56 on CIFAR-10, FCHP respectively obtains 50% of parameters and FLOPs reduction with negligible accuracy loss. Besides, as for ResNet-50 on ImageNet, FCHP reduces 40.5% of parameters and 44.1% of FLOPs with 0.43% of Top-1 accuracy drop.
COMET

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. Training wide and deep neural networks require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. Traditional memory saving techniques either suffer from performance overhead or are constrained by limited interconnect bandwidth or specific interconnect technology. In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger models or to accelerate training. Our framework purposely adopts error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we perform a theoretical analysis on the compression error propagation from the altered activation data to the gradients, and empirically investigate the impact of altered gradients over the training process. Based on these analyses, we optimize the error-bounded lossy compression and propose an adaptive error-bound control scheme for activation data compression. Experiments demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5X over the baseline training and 1.8X over another state-of-the-art compression-based framework, respectively, with little or no accuracy loss.
A comparison of topologically associating domain callers based on Hi-C data

Topologically associating domains (TADs) are local chromatin interaction domains, which have been shown to play an important role in gene expression regulation. TADs were originally discovered in the investigation of 3D genome organization based on High-throughput Chromosome Conformation Capture (Hi-C) data. Continuous considerable efforts have been dedicated to developing methods for detecting TADs from Hi-C data. Different computational methods for TADs identification vary in their assumptions and criteria in calling TADs. As a consequence, the TADs called by these methods differ in their similarities and biological features they are enriched in. In this work, we performed a systematic comparison of twenty-six TAD callers. We first compared the TADs and gaps between adjacent TADs across different methods, resolutions, and sequencing depths. We then assessed the quality of TADs and TAD boundaries according to three criteria: the decay of contact frequencies over the genomic distance, enrichment and depletion of regulatory elements around TAD boundaries, and reproducibility of TADs and TAD boundaries in replicate samples. Last, due to the lack of a gold standard of TADs, we also evaluated the performance of the methods on synthetic datasets. We discussed the key principles of TAD callers, and pinpointed current situation in the detection of TADs. We provide a concise, comprehensive, and systematic framework for evaluating the performance of TAD callers, and expect our work will provide useful guidance in choosing suitable approaches for the detection and evaluation of TADs.