Reviewerly/exHarmony huggingface dataset:
Ensemble Dehazing Networks for Non-homogeneous Haze

Image dehazing is one of the most challenging imaging inverse problems. Although deep learning methods produce compelling results, one of the most crucial practical challenge is that of non-homogeneous haze, which remains an open problem. To address this challenge, we propose 3 models that are inspired by ensemble techniques. First, we propose a DenseNet based single-encoder four-decoders structure denoted as EDN-3J, wherein among the four decoders, three of them output estimates of dehazed images (J <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , J <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , J <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) that are then weighted and combined via weight maps learned by the fourth decoder. In our second model called EDN-AT, the single-encoder four-decoders structure is maintained while three decoders are transformed to jointly estimate two physical inverse haze models that share a common transmission map t with two distinct ambient light maps (A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). The two inverse haze models are then weighted and combined for the final dehazed image. To endow two sub-models flexibility and to induce capability of modeling non-homogeneous haze, we apply attention masks to ambient lights. Both the weight maps and attention maps are generated from the fourth decoder. Finally, in contrast to the above two ensemble models, we propose an encoder-decoder-U-net structure called EDN-EDU, which is a sequential hierarchical ensemble of two different dehazing networks with different modeling capacities. Experiments performed on challenging benchmark image datasets of NTIRE'20 and NTIRE'19 demonstrate that the proposed models outperform many state-of-the-art methods and this fact is particularly demonstrated in the NTIRE-2020 contest where the EDN-AT model achieves the best result in the sense of the perceptual quality metric LPIPS.
Feature based video stabilization based on boosted HAAR Cascade and representative point matching algorithm

The success of handheld video capturing devices has further fueled the need of improved video stabilization. The videos often contain many foreground facial features like eyes, nose etc. These foreground features can be considered as feature points and may be used to stabilize videos. This paper proposes an innovative and effective digital video stabilization technique, which utilizes foreground features present in the video to produce consistent and stabilized output. It uses successive stages of Boosted HAAR cascade and representative point matching digital motion stabilization algorithm to identify and stabilize the video. The feature based tracking of object improves motion estimation accuracy between two frames thereby increasing the correlation calculation and compensation motion vector. This work achieves a significantly smoother sequence after the motion compensation. It also improves the robustness, precision and quality of the video when compared to traditional digital stabilization algorithms. The simulation results compared with pre-existing techniques reflect distinct improvements in Inter-Frame Transformation Fidelity values and Structural Similarity Index along with lesser standard deviation between image frames.
A Survey on Underwater Images Enhancement Techniques

The primary objective of underwater image enhancement is to recover the quality that has been degraded due to scatters and amalgamation within the underwater environment. These images suffer from strong absorption, low contrast, noise, and poor visibility. Thus to avoid aforementioned problems of the underwater images, enhancement is required. This paper discusses various image enhancement techniques like Histogram equalization, Adaptive Histogram Equalization(AHE), CLAHE, Histogram slicing, Contrast stretching, Dark Channel Prior, etc.
Positive-unlabeled learning for open set domain adaptation

Open Set Domain Adaptation (OSDA) focuses on bridging the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source as unknown. The challenges of this task are closely related to those of Positive-Unlabeled (PU) learning where it is essential to discriminate between positive (known) and negative (unknown) class samples in the unlabeled target data. With this newly discovered connection, we leverage the theoretical framework of PU learning for OSDA and, at the same time, we extend PU learning to tackle uneven data distributions. Our method combines domain adversarial learning with a new non-negative risk estimator for PU learning based on self-supervised sample reconstruction. With experiments on digit recognition and object classification, we validate our risk estimator and demonstrate that our approach allows reducing the domain gap without suffering from negative transfer.
Novel Multiperspective Hiring Framework for the Selection of Software Programmer Applicants Based on AHP and Group TOPSIS Techniques

The selection of software programmer applicants based on multiperspective evaluation criteria (grade point average (GPA) and soft skills of the applicants) is needed instead of an interview because an interview does not necessarily lead to hiring the best candidate amongst the applicants. The selection of a suitable software programmer is considered a challenging task owing to the following factors: (1) data variation, (2) multiple evaluation criteria and (3) criterion importance. A general framework for the selection of the best software programmer applicants is not available in the existing literature. The present study aims to propose a novel multiperspective hiring framework based on multicriteria analysis to select the best software programmer amongst several applicants. A decision matrix (DM) is constructed for the selection of the best programmer applicants according to multiple criteria, namely, structured programming, object-oriented programming, data structure, database system and courseware engineering. Each criterion includes two parameters, namely, GPA and soft skills, and these criteria cross over with programmer applicants as alternatives. The standard and expert opinion of the Software Engineering Body of Knowledge is used to distribute the criteria in the DM. The two commonly used techniques of multicriteria decision-making are analytic hierarchy process (AHP) for weighing the criteria and technique for order performance by similarity to ideal solution (TOPSIS) for ranking the alternatives (programmer applicants). The data used in this study include 60 software engineering students who graduated in 2016 from Universiti Pendidikan Sultan Idris. Results show that integrating multilayer analytic hierarchy process (MLAHP) and group TOPSIS are effective for solving applicant selection problems. Group TOPSIS uses different contexts — internal and external aggregation — and indicates similar results. Objective validation is used for the ranking of the results, which are equally divided into four parts. Furthermore, the applicants are systematically ranked. This study benefits application software, system software and computer programming tool companies by providing a method that improves software quality whilst reducing time and cost in the selection process.
Task support of electronic patient care report (ePCR) systems in emergency medical services: An elaboration likelihood model lens

This paper aims to investigate the antecedents of task support in the context of electronic patient care report (ePCR) in the emergency medical service (EMS). Although electronic health record has generated significant interest, the area of EMS has been largely overlooked in the literature. This study examines characteristics related to the ePCR system—information quality, security protection, and normative cues—and their impact on EMS task support, through the lens of the elaboration likelihood model. We further propose a moderating role of absorptive capacity and top management belief as elaboration likelihoods. Several theoretical contributions and managerial implications are discussed.
A framework for building cognitive process models

Abstract The term process model is widely used, but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that the following dimensions characterize process models: They have a scope that includes different levels of abstraction. They specify a hypothesized mental information transformation. They make predictions not only for the behavior of interest but also for processes. The models’ predictions for the processes can be derived from the input, without reverse inference from the output data. Moreover, the presumed information transformation steps are not contradicting current knowledge of human cognitive capacities. Lastly, process models require a conceptual scope specifying levels of abstraction for the information entering the mind, the proposed mental events, and the behavior of interest. This framework can be used for refining models before testing them or after testing them empirically, and it does not rely on specific modeling paradigms. It can be a guideline for developing cognitive process models. Moreover, the framework can advance currently unresolved debates about which models belong to the category of process models.
Comparing CNN-Based Object Detectors on Two Novel Maritime Datasets

Vessel detection studies conducted on inshore and offshore maritime images are scarce, due to a limited availability of domain-specific datasets. We addressed this need collecting two datasets in the Finnish Archipelago. They consist of images of maritime vessels engaged in various operating scenarios, climatic conditions and lighting environments. Vessel instances were precisely annotated in both datasets. We evaluated the out-of-the-box performance of three state-of-the-art CNN-based object detection algorithms (Faster R-CNN [1], R-FCN [2] and SSD [3]) on these datasets and compared them in terms of accuracy and run-time. The algorithms were previously trained on the COCO dataset [4]. We explore their performance based on different feature extractors. Furthermore, we investigate the effect of the object size on the algorithm performance. For this purpose, we group all objects in each image into three categories (small, medium and large) according to the number of occupied pixels in the annotated bounding box. Experiments show that Faster R-CNN with ResNet101 as feature extractor outperforms the other algorithms.
Description Based Text Classification with Reinforcement Learning

The task of text classification is usually divided into two stages: {\it text feature extraction} and {\it classification}. In this standard formalization categories are merely represented as indexes in the label vocabulary, and the model lacks for explicit instructions on what to classify. Inspired by the current trend of formalizing NLP problems as question answering tasks, we propose a new framework for text classification, in which each category label is associated with a category description. Descriptions are generated by hand-crafted templates or using abstractive/extractive models from reinforcement learning. The concatenation of the description and the text is fed to the classifier to decide whether or not the current label should be assigned to the text. The proposed strategy forces the model to attend to the most salient texts with respect to the label, which can be regarded as a hard version of attention, leading to better performances. We observe significant performance boosts over strong baselines on a wide range of text classification tasks including single-label classification, multi-label classification and multi-aspect sentiment analysis.
Trajectory planning of quadrotor using sliding mode control with extended state observer

This paper presents the trajectory planning of an under-actuated quadcopter unmanned aerial vehicle. To control the complete structure of the rotorcraft, the main model is divided into two sub-models, namely inner model and external model. The inner model is for the attitude control model controlled by the sliding mode controller and the outer model is altitude control model governed by the extended state observer. The quadrotor unmanned aerial vehicle is a type of multivariable, multi-degree-of-freedom and nonlinear in nature. Planning the trajectory of the unmanned aerial vehicle and stabilizing its flight are complex tasks because of its ability to maneuver quickly. Due to these stated issues, the tuning of this type of dynamic system is a difficult task. This paper deals with these issues by designing the aforementioned dual controller scheme. In addition, the effectiveness of the proposed controller is apparent in simulations performed in MATLAB, Simulink 2016. The designed controller shows better results and robustness than traditional controllers do.
An improved approximation algorithm for ATSP

We revisit the constant-factor approximation algorithm for the asymmetric traveling salesman problem by Svensson, Tarnawski, and Végh [STOC 2018]. We improve on each part of this algorithm. We avoid the reduction to irreducible instances and thus obtain a simpler and much better reduction to vertebrate pairs. We also show that a slight variant of their algorithm for vertebrate pairs has a much smaller approximation ratio. Overall we improve the approximation ratio from 506 to 22+ε for any ε > 0. This also improves the upper bound on the integrality ratio from 319 to 22.
Learning Identity-Invariant Motion Representations for Cross-ID Face Reenactment

Human face reenactment aims at transferring motion patterns from one face (from a source-domain video) to an-other (in the target domain with the identity of interest).While recent works report impressive results, they are notable to handle multiple identities in a unified model. In this paper, we propose a unique network of CrossID-GAN to perform multi-ID face reenactment. Given a source-domain video with extracted facial landmarks and a target-domain image, our CrossID-GAN learns the identity-invariant motion patterns via the extracted landmarks and such information to produce the videos whose ID matches that of the target domain. Both supervised and unsupervised settings are proposed to train and guide our model during training.Our qualitative/quantitative results confirm the robustness and effectiveness of our model, with ablation studies confirming our network design.
Visual Parsing with Query-Driven Global Graph Attention (QD-GGA): Preliminary Results for Handwritten Math Formula Recognition

We present a new visual parsing method based on convolutional neural networks for handwritten mathematical formulas. The Query-Driven Global Graph Attention (QD-GGA) parsing model employs multi-task learning, and uses a single feature representation for locating, classifying, and relating symbols. First, a Line-Of-Sight (LOS) graph is computed over the handwritten strokes in a formula. Second, class distributions for LOS nodes and edges are obtained using query-specific feature filters (i.e., attention) in a single feed-forward pass. Finally, a Maximum Spanning Tree (MST) is extracted from the weighted graph. Our preliminary results show that this is a promising new approach for visual parsing of handwritten formulas. Our data and source code are publicly available.
Skeleton-Based Interactive Graph Network For Human Object Interaction Detection

The human-object interaction detection (HOI) task aims to localize human and objects in an input image and predict their relationships, which is essential for understanding human behaviors in complex scenes. Due to the human-centric nature of the HOI task, it is beneficial to make use of human-related knowledge such as human skeletons to infer fine-grained human-object interactions. However, previous works simply embed skeletons via convolutional networks, which fail to capture structured connections in human skeletons and ignore the object influence. In this work, we propose a Skeleton-based Interactive Graph Network (SIGN) to capture fine-grained human-object interactions via encoding interactive graphs between keypoints in human skeletons and object from spatial and appearance aspects. Experimental results demonstrate the effectiveness of our SIGN model, which achieves significant improvement over baselines and outperforms other state-of-the-art methods on two benchmarks.
Why do we retrace our visual steps? Semantic and episodic memory in gaze reinstatement

When we look at repeated scenes, we tend to visit similar regions each time—a phenomenon known as resampling . Resampling has long been attributed to episodic memory, but the relationship between resampling and episodic memory has recently been found to be less consistent than assumed. A possibility that has yet to be fully considered is that factors unrelated to episodic memory may generate resampling: for example, other factors such as semantic memory and visual salience that are consistently present each time an image is viewed and are independent of specific prior viewing instances. We addressed this possibility by tracking participants’ eyes during scene viewing to examine how semantic memory, indexed by the semantic informativeness of scene regions (i.e., meaning ), is involved in resampling. We found that viewing more meaningful regions predicted resampling, as did episodic familiarity strength. Furthermore, we found that meaning interacted with familiarity strength to predict resampling. Specifically, the effect of meaning on resampling was attenuated in the presence of strong episodic memory, and vice versa. These results suggest that episodic and semantic memory are each involved in resampling behavior and are in competition rather than synergistically increasing resampling. More generally, this suggests that episodic and semantic memory may compete to guide attention.
Training Effective Neural CLIR by Bridging the Translation Gap

We introduce Smart Shuffling, a cross-lingual embedding (CLE) method that draws from statistical word alignment approaches to leverage dictionaries, producing dense representations that are significantly more effective for cross-language information retrieval (CLIR) than prior CLE methods. This work is motivated by the observation that although neural approaches are successful for monolingual IR, they are less effective in the cross-lingual setting. We hypothesize that neural CLIR fails because typical cross-lingual embeddings "translate" query terms into related terms -- i.e., terms that appear in a similar context -- in addition to or sometimes rather than synonyms in the target language. Adding related terms to a query (i.e., query expansion) can be valuable for retrieval, but must be mitigated by also focusing on the starting query. We find that prior neural CLIR models are unable to bridge the translation gap, apparently producing queries that drift from the intent of the source query.
Ensemble Generative Cleaning With Feedback Loops for Defending Adversarial Attacks

Effective defense of deep neural networks against adversarial attacks remains a challenging problem, especially under powerful white-box attacks. In this paper, we develop a new method called ensemble generative cleaning with feedback loops (EGC-FL) for effective defense of deep neural networks. The proposed EGC-FL method is based on two central ideas. First, we introduce a transformed deadzone layer into the defense network, which consists of an orthonormal transform and a deadzone-based activation function, to destroy the sophisticated noise pattern of adversarial attacks. Second, by constructing a generative cleaning network with a feedback loop, we are able to generate an ensemble of diverse estimations of the original clean image. We then learn a network to fuse this set of diverse estimations together to restore the original image. Our extensive experimental results demonstrate that our approach improves the state-of-art by large margins in both white-box and black-box attacks. It significantly improves the classification accuracy for white-box PGD attacks upon the second best method by more than 29% on the SVHN dataset and more than 39% on the challenging CIFAR-10 dataset.
Real-World Super-Resolution using Generative Adversarial Networks

Robust real-world super-resolution (SR) aims to generate perception-oriented high-resolution (HR) images from the corresponding low-resolution (LR) ones, without access to the paired LR-HR ground-truth. In this paper, we investigate how to advance the state of the art in real-world SR. Our method involves deploying an ensemble of generative adversarial networks (GANs) for robust real-world SR. The ensemble deploys different GANs trained with different adversarial objectives. Due to the lack of knowledge about the ground-truth blur and noise models, we design a generic training set with the LR images generated by various degradation models from a set of HR images. We achieve good perceptual quality by super resolving the LR images whose degradation was caused by unknown image processing artifacts. For real-world SR on images captured by mobile devices, the GANs are trained by weak supervision of a mobile SR training set having LR-HR image pairs, which we construct from the DPED dataset which provides registered mobile-DSLR images at the same scale. Our ensemble of GANs uses cues from the image luminance and adjusts to generate better HR images at low-illumination. Experiments on the NTIRE 2020 real-world super-resolution dataset show that our proposed SR approach achieves good perceptual quality.
Knowledge Enhanced Personalized Search

This paper presents a knowledge graph enhanced personalized search model, KEPS. For each user and her queries, KEPS first con- ducts personalized entity linking on the queries and forms better intent representations; then it builds a knowledge enhanced profile for the user, using memory networks to store the predicted search intents and linked entities in her search history. The knowledge enhanced user profile and intent representation are then utilized by KEPS for better, knowledge enhanced, personalized search. Furthermore, after providing personalized search for each query, KEPS leverages user's feedback (click on documents) to post-adjust the entity linking on previous queries. This fixes previous linking errors and improves ranking quality for future queries. Experiments on the public AOL search log demonstrate the advantage of knowledge in personalized search: personalized entity linking better reflects user's search intent, the memory networks better maintain user's subtle preferences, and the post linking adjustment fixes some linking errors with the received feedback signals. The three components together lead to a significantly better ranking accuracy of KEPS.
American Sign Language Alphabets Recognition using Hand Crafted and Deep Learning Features

Sign language is evolving as an inevitable communication method for the hearing impaired persons. The basic element of the sign language is the sign language alphabets. In this paper, a combination of hand crafted features and deep learning method is used to classify the signs in a more accurate manner. The skin color based YCbCr segmentation method and local binary pattern is applied for accurate shape segmentation and for texture features or local shape information. The transfer learning framework (VGG-19) is fine-tuned to obtain the features that are then fused with hand crafted features by serial based fusion technique. Finally, these features are given to a SVM classifier to classify the signs.