Reviewerly/exHarmony huggingface dataset:
Transfer Vision Patterns for Multi-Task Pixel Learning

Multi-task pixel perception is one of the most important topics in the field of machine intelligence. Inspired by the observation of cross-task interdependencies of visual patterns, we propose a multi-task vision pattern transformation (VPT) method to adaptively correlate and transfer cross-task visual patterns by leveraging the powerful transformer mechanism. To better transfer visual patterns, specifically, we build two types of pattern transformation based on the statistic prior that the affinity relations across tasks are correlated. One aims to transfer feature patterns for the integration of different task features; the other aims to exchange structure patterns for mining and leveraging the latent interaction cues. These two types of transformations are encapsulated into two VPT units, which provide universal matching interfaces for multi-task learning, complement each other to guide the transmission of feature/structure patterns, and finally realize an adaptive selection of important patterns across tasks. Extensive experiments on the joint learning of semantic segmentation, depth prediction and surface normal estimation demonstrate that our proposed method is more effective than those baselines and achieve the state-of-that-art performance in three pixel-level visual tasks.
RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer

In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches.
Image compression–encryption scheme using SPIHT and chaotic systems

The world is moving towards digitization for all forms of data. Images are one of the most used data that contains a lot of information. With the increasing network technology, the sharing of data across the internet has also manifold. Certain images are confidential and require security for storing or transmitting on an insecure communication channel. Digital images usually have highly correlated data where compression can be performed to reduce the load on the transmission channel or storage memory. The paper proposes an algorithm to compress the digital images and encryption is performed on the compressed data to provide security. Compression of the image is performed using the Set partitioning in hierarchical trees algorithm. The compressed image is encrypted using a chaotic sequence generated using the Henon map and the cipher image is scrambled using a permutation table obtained from Piecewise Linear chaotic map. To reduce the computation while generating the chaotic sequence, a base conversion operation is used. The initial parameters of the chaotic systems are generated from the elliptic curve point are shared securely using ElGamal based elliptic curve cryptosystem. Security analyses are conducted along with a comparison with other existing systems. The proposed algorithm is secured against various attacks and outperforms existing techniques concerning various statistical and security aspects of an enciphering algorithm.
End-to-end Multi-Instance Robotic Reaching from Monocular Vision

Multi-instance scenes are especially challenging for end-to-end visuomotor (image-to-control) learning algorithms. "Pipeline" visual servo control algorithms use separate detection, selection and servo stages, allowing algorithms to focus on a single object instance during servo control. End-to-end systems do not have separate detection and selection stages and need to address the visual ambiguities introduced by the presence of an arbitrary number of visually identical or similar objects during servo control. However, end-to-end schemes avoid embedding errors from detection and selection stages in the servo control behaviour, are more dynamically robust to changing scenes and are algorithmically simpler. In this paper, we present a reactive real-time end-to-end visuomotor learning algorithm for multi-instance reaching. The proposed algorithm uses a monocular RGB image and the manipulator&#x2019;s joint angles as the input to a light-weight fully-convolutional network (FCN) to generate control candidates. A key innovation of the proposed method is identifying the optimal control candidate by regressing a control-Lyapunov function (cLf) value. The multi-instance capability emerges naturally from the stability analysis associated with the cLf formulation. We demonstrate the proposed algorithm effectively reaching and grasping objects from different categories on a table-top amid other instances and distractors from an over-the-shoulder monocular RGB camera. The network is able to run up to &#x223C;160 fps during inference on one GTX 1080 Ti GPU.
Imbalanced Classification Problems: A Comparative Study of Non-ensemble and Ensemble-Based Approaches

The imbalanced classification problem presents significant challenges for machine learning techniques as it directly affects the efficiency of the classification model. This problem exists when one class has fewer instances as compared with other classes. Most classification algorithms are usually inclined to the majority class and reject the minority class by considering it an outlier. This problem is common in many fields such as medical science, fraud detection, network intrusion, etc., which has received large attraction from data mining researchers and practitioners during these years. This paper presents a formal description of imbalanced learning and empirical comparison between non-ensemble and ensemble-based methods. We develop a brief experimental analysis, which compares various representative methods chosen from different families. The results indicate that UnderBagging (ensemble-based) is a promising and effective method on 44 real-world datasets using G-mean as a performance index.
Evolving Secret Sharing in Almost Semi-honest Model

Evolving secret sharing is a special kind of secret sharing where the number of shareholders is not known beforehand, i.e., at time \(t=0\). In classical secret sharing such a restriction was assumed inherently i.e., the number of shareholders was given to the dealer’s algorithm as an input. Evolving secret sharing relaxes this condition. Pramanik and Adhikari left an open problem regarding malicious shareholders in the evolving setup, which we answer in this paper. We introduce a new cheating model, called the almost semi-honest model, where a shareholder who joins later can check the authenticity of share of previous ones. We use collision resistant hash function to construct such a secret sharing scheme with malicious node identification. Moreover, our scheme preserves the share size of Komargodski et al. (TCC 2016).
A self-adaptive learning method for motion blur kernel estimation of the single image

The estimation of blur kernel is the first and principal steps in the deconvolution of single blurred image. The quality of image restoration highly depends on its estimation accuracy. We then propose a new modified-Radon-transform approach along with a low-high-pass filtering method to estimate the motion blur parameters by a self-adaptive learning strategy, which greatly improved the deblurring quality of the blurred image. The Gaussian low-pass and high-pass filters are adopted to reduce the noise level in blurred image, and the batch normalization and self-adaptive method are considered to eliminate the interference from the noise stripes. It is noted that the estimation of blur angle plays an important cue for the exploration of blur kernel. The experimental evaluation is conducted on both synthetic VOC2012 database as well as the natural-real motion blurred single image with or without noise. The experimental results show that our proposed method can obtain more accurate and more reliable blur parameters than other approaches.
Understanding User Expertise Through Lived Experience

This experience report discusses the design of “HazardAware,” a website being developed to provide interactive tools for individuals and community decision-makers in the U.S. Gulf of Mexico states to learn about housing-related natural risk and mitigation options. As part of the user-centered design process, we conducted interviews with target users. We describe how participants’ experiences with natural hazards and hazard preparation helped them develop situated expertise, and discuss how sharing this expertise with the interdisciplinary project team has helped us make the website more accountable to users.
HTML-LSTM: Information Extraction from HTML Tables in Web Pages Using Tree-Structured LSTM

In this paper, we propose a novel method for extracting information from HTML tables with similar contents but with a different structure. We aim to integrate multiple HTML tables into a single table for retrieval of information containing in various Web pages. The method is designed by extending tree-structured LSTM, the neural network for tree-structured data, in order to extract information that is both linguistic and structural information of HTML data. We evaluate the proposed method through experiments using real data published on the WWW.
On the benefits of defining vicinal distributions in latent space

The vicinal risk minimization (VRM) principle is an empirical risk minimization (ERM) variant that replaces Dirac masses with vicinal functions. There is strong numerical and theoretical evidence showing that VRM outperforms ERM in terms of generalization if appropriate vicinal functions are chosen. Mixup Training (MT), a popular choice of vicinal distribution, improves generalization performance of models by introducing globally linear behavior in between training examples. Apart from generalization, recent works have shown that mixup trained models are relatively robust to input perturbations/corruptions and at same time are calibrated better than their non-mixup counterparts. In this work, we investigate the benefits of defining these vicinal distributions like mixup in latent space of generative models rather than in input space itself. We propose a new approach - VarMixup (Variational Mixup) - to better sample mixup images by using the latent manifold underlying the data. Our empirical studies on CIFAR-10, CIFAR-100 and Tiny-ImageNet demonstrates that models trained by performing mixup in the latent manifold learned by VAEs are inherently more robust to various input corruptions/perturbations, are significantly better calibrated and exhibit more local-linear loss landscapes.
P2DF

The extensive use of digital devices by individuals generates a significant amount of private data which creates challenges for investigation agencies to protect suspects' privacy. Existing digital forensics models illustrate the steps and actions to be followed during an investigation, but most of them are inadequate to investigate a crime with all the processes in an integrated manner and do not protect suspect's privacy. In this paper, we propose the development of a privacy-preserving digital forensics (P2DF) framework, which facilitates investigation through maintaining confidentiality of the suspects through various privacy standards and policies. It includes an access control mechanism which allows only authorized investigators to access private data and identified digital evidences. It is also equipped with a digital evidence preservation mechanism which could be helpful for the court of law to ensure the authenticity, confidentiality, and reliability of the evidences, and to verify whether privacy of the suspect was preserved during the investigation process.
Selective Multibranch Attention Network With Material Constraint for Baggage Reidentification

Baggage reidentification (re-ID) expects to retrieve the unique baggage across multiple nonoverlapping cameras between the precheck and postcheck areas. This critical challenge task lies in two folds: intraclass dissimilarity by pose variations and interclass similarity due to the identical style. In this article, we solve the mentioned problems by a selective multibranch deep network (SMBNet) architecture with the attention mechanism. Specifically, SMBNet incorporates one branch for global feature representations and multiple-part representations corresponding to different local regions. Meanwhile, we plugged the attention block into the part branch to further help the network capture the dependence of the context on local features. Depending on the class response to each part, we propose an adaptive part selection (APS) to automatically select the appropriate part branch and joint with the global one to build a comprehensive baggage descriptor. It effectively blurs unreliable parts and significantly improves representational performance. Finally, we couple classification supervision and material signal in triplet loss to learn a discriminative embedding space that can compact class and material-invariant features and better separate specific representations. Extensive experiments demonstrate the superiority of SMBNet, which achieves 84.7% mean average precision (mAP) and 82.8% Rank-1 on the MVB dataset and significantly outperforms the state-of-the-art methods.
Real-Time Embedded HPC Based Earthquake Damage Mapping Using 3D LiDAR Point Clouds

In the early hours following the earthquake, supporting humanitarian actions like rescue operations and relief distribution is the primary objective of the rescue managers. The damage mapping can be performed using reliable data that can be obtained from high-resolution satellite imagery but obtaining satellite imagery can be challenging for some days post disaster due to revisit time. Considering the disaster response timing, Unmanned Aerial Vehicles (UAV) are used because ground transportation systems are ineffective due to road blockage. In this work, we make use of Light Detection and Ranging (LiDAR) 3D point cloud data obtained for Haiti Earthquake. The focus of our work is to develop and implement an approach for LiDAR data classification to enable Earthquake damage mapping and detection. This is obtained by running our deep learning network on NVIDIA Jetson Nano embedded supercomputing platform. This approach takes the advantage of embedded High-Performance computing and low power consumption capabilities of Jetson Nano which enhances the classification and promotes rapid response which is the key to manage post-disaster activities. Jetson Nano is a feasible option which provides a GPU architecture that is optimized for running energy-aware deep learning models and which generates the results in real or near-real time. We envisage that our work could be extended to perform near real-time classification of LiDAR point clouds in a post earthquake scenario.
Protocol Design Issues for Object Density Estimation and Counting in Remote Sensing

Generic object density estimation and counting plays an important role in various remote sensing applications. Therefore, this work discusses available Earth observation based datasets for both tasks. Critical aspects of the current evaluation protocol are identified and analyzed, which yields novel design issues for an optimal future protocol. Moreover, the parameters for an ideal benchmark dataset are defined which allows appropriate evaluation and ranking of methodologies.
Collision Free Curved Path Following for Small Fixed-Wing UAVs

Curved path following is an essential capability for small fixed-wing unmanned aerial vehicles (UAVs), while faced with many difficulties because of the varying curvature of the reference path and nonlinear constrained kinematics of the UAV. Besides, the dynamic flight environment requires extra ability of the UAV for real-time obstacle avoidance, especially in complex airspace such as mountainous and urban area. Therefore, this paper studies the problem of curved path following with obstacle avoidance for small fixed-wing UAVs. A predictive controller guiding the UAV to minimize the distance between the virtual target point and the UAV position projected on the reference path and to avoid collision with obstacles, is developed with specially designed objective function. The dynamics of the virtual target point is designed using the Lyapunov method, which ensures the tracking stability. Simulation results verify the superiority of the proposed controller compared with several other classical path following algorithms and the obstacle avoidance effectiveness.
A Fragile Watermarking in Ciphertext Domain Based on Multi-Permutation Superposition Coding for Remote Sensing Image

The current studies of ciphertext watermarking for remote sensing image are mostly focused on the copyright protection. However, the ciphertext data of remote sensing image is at risk of being destroyed, how to ensure the integrity of it is also a problem to be solved. Therefore, aiming at the problem of integrity verification of remote sensing image ciphertext, this paper proposes a ciphertext domain fragile watermarking algorithm by using the multi-permutation superposition coding and the probabilistic encryption. According to the experimental results, the proposed algorithm is highly sensitive to the tamper of ciphertext and can locate the tamper accurately. In addition, the embedding capacity of the proposed ciphertext watermarking is large enough for the copyright protection or other applications, meanwhile, the proposed algorithm will not affect the original remote sensing image date and its future application.
Sparse Representation Based Image Super-resolution Combining Rotation Strategy and Nonlocal Self-similarity

This paper proposes an improved image super-resolution method based on sparse representation. A pair of dictionaries are learned by minimizing the sparse recovery error of high-resolution sample patches. In the reconstruction phase, the learned dictionaries and residual compensation are used to reconstruct the high-resolution image. In order to further reduce the sparse recovery error, a rotation strategy is introduced. Moreover, the image nonlocal self-similarity is exploited to constrain super-resolution reconstruction. Experimental results show that the proposed method can effectively improve PSNR, SSIM and visual effects.
A Novel Teaching Video Speech Recognition Method Based on HMM Model

To facilitate users in information retrieval based on teaching video content, a teaching video recognition training system with waveform display, pronunciation evaluation and other functions is developed by using speech recognition technology. In this paper, the basic principle of speech recognition is described in detail from the aspects of speech signal preprocessing, feature parameter extraction and HMM model matching. HMM, Viterbi decoding and speech evaluation algorithm are designed and applied. Then, based on the online collaborative learning platform, the recognition model is established by HMM, and the training resources in the experimental database are tested and analyzed. The results show that the improved speech recognition model can enhance the robustness and efficiency of the system, and improve the accuracy and error correction rate of English video teaching score.
Detecting Distance between Surfaces of Large Transparent Material Based on Low-Cost TOF Sensor and Deep Convolutional Neural Network

Detecting distance between surfaces of transparent materials with large area and thickness has always been a difficult problem in the field of industry. In this paper, a method based on low-cost TOF continuous-wave modulation and deep convolutional neural network technology is proposed. The distance detection between transparent material surfaces is converted to the problem of solving the intersection of the optical path and the transparent material’s front and rear surfaces. On this basis, the Gray code encoding and decoding operations are combined to achieve distance detection between surfaces. The problem of holes and detail loss of depth maps generated by low-resolution TOF depth sensors have been also effectively solved. The entire system is simple and can achieve thickness detection on the full surface area. Besides, it can detect large transparent materials with a thickness of over 30 mm, which far exceeds the existing optical thickness detection system for transparent materials.
Chain logic and Shelah’s infinitary logic

For a cardinal of the form κ = בκ, Shelah's logic L 1 has a characterisation as the maximal logic above $${ \cup _{\lambda < \kappa }}{L_{\lambda ,\omega }}$$ satisfying a strengthening of the undefinability of well-order. Karp's chain logic [20] L is known to satisfy the undefinability of well-order and interpolation. We prove that if κ is singular of countable cofinality, Karp's chain logic [20] is above L 1 . Moreover, we show that if κ is a strong limit of singular cardinals of countable cofinality, the chain logic $$L_{ < \kappa , < \kappa }^c{ \cup _{\lambda < \kappa }}L_{\lambda ,\lambda }^c$$ is a maximal logic with chain models to satisfy a version of the undefinability of well-order. We then show that the chain logic gives a partial solution to Problem 1.4 from Shelah's [28], which asked whether for κ singular of countable cofinality there was a logic strictly between $${L_{{\kappa ^ + },\omega }}$$ and $${L_{{\kappa ^ + },{\kappa ^ + }}}$$ having interpolation. We show that modulo accepting as the upper bound a model class of Lκ, κ, Karp's chain logic satisfies the required properties. In addition, we show that this chain logic is not κ-compact, a question that we have asked on various occasions. We contribute to further development of chain logic by proving the Union Lemma and identifying the chain-independent fragment of the logic, showing that it still has considerable expressive power. In conclusion, we have shown that the simply defined chain logic emulates the logic L 1 in satisfying interpolation, undefinability of well-order and maximality with respect to it, and the Union Lemma. In addition it has a completeness theorem.
A Novel Oversampling Method Based on SMOTE and Local Sets for Imbalanced Classification

Learning a classifier from imbalanced data continues to be a challenging issue. The oversampling methods can improve the imbalanced classification from the perspective of data preprocessing. Different oversampling methods have been proposed. Nevertheless, most tend to generate unnecessary noise, create redundant synthetic samples in the class center and heavily rely on the parameter k. To solve the above issues, this work presents an oversampling method based on local sets and SMOTE (LS-SMOTE). First, the local sets are searched to describe the local characteristic of imbalanced data. Second, a local sets-based noise filter is designed to remove noise and smooth the class boundary. Finally, on each local set, the interpolation of SMOTE between a base sample and a selected sample closest to the majority class is employed to create the synthetic samples. Experimental results with 12 real data sets have proved that LS-SMOTE outperforms representative oversampling methods in training k nearest neighbor classifier.