Reviewerly/exHarmony huggingface dataset:
The influence of mood induction by music or a soundscape on presence and emotions in a virtual reality park scenario

Music and background sound are often used in virtual realities for creating an emotional atmosphere. The present study investigates how music or an ambient soundscape influence presence, the feeling of "being there", as well as positive and negative affect. Fifty-one subjects participated, taking a stroll through a virtual park presented via a head-mounted display while they were walking on a treadmill. Sound was varied within subjects in four audio conditions: In a randomized sequence, participants experienced silence, a nature soundscape and music of positive or negative valence. In addition, time of day (daytime vs. nighttime walk) in the virtual environment was varied between subjects. Afterwards they were asked to rate their experience of presence and the positive and negative affect experienced. Results indicated that replaying any kind of sound lead to higher presence ratings compared to no sound at all, but there was no difference between playing a soundscape or music. Background music, however, tended to induce the expected emotions, though somewhat dependent on the musical pieces chosen. Further studies might evaluate whether it is possible to induce emotions through positive or negative (non-musical) soundscapes as well.
BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy

Local differential privacy (LDP) has received much interest recently. In existing protocols with LDP guarantees, a user encodes and perturbs his data locally before sharing it to the aggregator. In common practice, however, users would prefer not to answer all the questions due to different privacy-preserving preferences for some questions, which leads to data missing or the loss of data quality. In this paper, we demonstrate a new approach for addressing the challenges of data perturbation with consideration of users' privacy preferences. Specifically, we first propose BiSample: a bidirectional sampling technique value perturbation in the framework of LDP. Then we combine the BiSample mechanism with users' privacy preferences for missing data perturbation. Theoretical analysis and experiments on a set of datasets confirm the effectiveness of the proposed mechanisms.
POSIT

Software developers use a mix of source code and natural language text to communicate with each other: Stack Overflow and Developer mailing lists abound with this mixed text. Tagging this mixed text is essential for making progress on two seminal software engineering problems --- traceability, and reuse via precise extraction of code snippets from mixed text. In this paper, we borrow code-switching techniques from Natural Language Processing and adapt them to apply to mixed text to solve two problems: language identification and token tagging. Our technique, POSIT, simultaneously provides abstract syntax tree tags for source code tokens, part-of-speech tags for natural language words, and predicts the source language of a token in mixed text. To realize POSIT, we trained a biLSTM network with a Conditional Random Field output layer using abstract syntax tree tags from the CLANG compiler and part-of-speech tags from the Standard Stanford part-of-speech tagger. POSIT improves the state-of-the-art on language identification by 10.6% and PoS/AST tagging by 23.7% in accuracy.
Refining and modifying the EFCAMDAT

Abstract This report outlines the development of a new corpus, which was created by refining and modifying the largest open-access L2 English learner database – the EFCAMDAT. The extensive data-curation process, which can inform the development and use of other corpora, included procedures such as converting the database from XML to a tabular format, and removing problematic markup tags and non-English texts. The final dataset contains two corresponding samples, written by similar learners in response to different prompts, which represents a unique research opportunity when it comes to analyzing task effects and conducting replication studies. Overall, the resulting corpus contains ~406,000 texts in the first sample and ~317,000 texts in the second sample, written by learners representing diverse L1s and a large range of L2 proficiency levels.
Approximate Manifold Defense Against Multiple Adversarial Perturbations

Existing defenses against adversarial attacks are typically tailored to a specific perturbation type. Using adversarial training to defend against multiple types of perturbation requires expensive adversarial examples from different perturbation types at each training step. In contrast, manifold-based defense incorporates a generative network to project an input sample onto the clean data manifold. This approach eliminates the need to generate expensive adversarial examples while achieving robustness against multiple perturbation types. However, the success of this approach relies on whether the generative network can capture the complete clean data manifold, which remains an open problem for complex input domain. In this work, we devise an approximate manifold defense mechanism, called RBF-CNN, for image classification. Instead of capturing the complete data manifold, we use an RBF layer to learn the density of small image patches. RBF-CNN also utilizes a reconstruction layer that mitigates any minor adversarial perturbations. Further, incorporating our proposed reconstruction process for training improves the adversarial robustness of our RBF-CNN models. Experiment results on MNIST and CIFAR-10 datasets indicate that RBF-CNN offers robustness for multiple perturbations without the need for expensive adversarial training.
Stochastic Adversarial Learning for Domain Adaptation

Learning across domains is challenging especially when test data in target domain are sparse, heterogeneous and unlabeled. This challenge is even severe when building a deep stochastic neural model. This paper presents a stochastic semi-supervised learning for domain adaptation by using labeled data from source domain and unlabeled data from target domain. There are twofold novelties in the proposed method. First, a graphical model is constructed to identify the random latent features for classes as well as domains which are learned by variational inference. Second, we learn the class features which are discriminative among classes and simultaneously invariant to both domains. An adversarial neural model is introduced to pursue domain invariance. The domain features are explicitly learned to purify the extraction of class features for an improved classification. The experiments on sentiment classification illustrate the merits of the proposed stochastic adversarial domain adaptation.
Convolutional Neural Network Pruning Using Filter Attenuation

Filters are the essential elements in convolutional neural networks (CNNs). Filters generate feature maps and form the main part of the computational and memory requirements of the convolutional networks. In filter pruning methods, a filter with all of its components, including channels and connections, are removed. The removal of a filter can cause a drastic change in the network's performance. Also, the removed filters cannot come back to the network structure. We want to address these problems in this paper. We propose a CNN pruning method based on filter attenuation in which weak filters are not abruptly removed. Instead, weak filters are attenuated and gradually removed. In the proposed attenuation approach, there is a chance for weak filters to return to the network. The filter attenuation method is assessed using the VGG model for the Cifar10 image classification task. Simulation results show that the filter attenuation works well based on different pruning criteria, and better results are obtained in comparison with the conventional pruning methods.
Joint Content-Adaptive Dictionary Learning And Sparse Selective Extrapolation For Cross-Spectral Image Reconstruction

Numerous applications deal with distorted images, e.g., during transmission over lossy channels in image coding, or to reconstruct occlusions in multi-color multi-view imaging scenarios. In many cases, not all spectral channels are distorted, or the losses distribute differently between the channels. Recently, efforts were made to develop reference guided approaches to reconstruct distorted spectral content. However, these methods are only able to exploit information from a single reference, even if there are multiple spectral components available that could be used for reconstruction. To overcome this limitation, a novel method is proposed in this paper, which introduces a content-adaptive dictionary learning approach that is applicable to an arbitrary number of references. With the novel approach, an average PSNR gain of approx. 1.5 dB is achieved in comparison to the best recently published state-of-the-art methods for a single reference component. Moreover, the presence of multiple guidance channels can enhance the reconstruction by another 6 dB on average. At the same time, the complexity of the novel algorithm is significantly lower than for previously published methods.
On the Power of Amortization in Secret Sharing

Consider the following secret-sharing problem: A file s should be distributed between n servers such that (d-1)-subsets cannot recover the file, (d+1)-subsets can recover the file, and d -subsets should be able to recover s if and only if they appear in some pre-defined list L . The goal is to minimize the information ratio—that is, the number of bits stored on a server per each bit of the secret. We show that for any constant d and any pre-defined list L , if the file is sufficiently long (exponential in n d ), the problem can be solved with a constant asymptotic information ratio of c d that does not grow with the number of servers n . This result is based on a new construction of d -party conditional disclosure of secrets for arbitrary predicates over an n -size domain in which each party communicates at most four bits per secret bit. In both settings, previous results achieved a non-constant information ratio that grows asymptotically with n , even for the simpler special case of d = 2 . Moreover, our constructions yield the first example of an access structure whose amortized information ratio is constant, whereas its best-known non-amortized information ratio is sub-exponential, thus providing a unique evidence for the potential power of amortization in the context of secret sharing. Our main result applies to exponentially long secrets, and so it should be mainly viewed as a barrier against amortizable lower-bound techniques. We also show that in some natural simple cases (e.g., low-degree predicates), amortization kicks in even for quasi-polynomially long secrets. Finally, we prove some limited lower bounds and point out some limitations of existing lower-bound techniques.
Learning Label-Relational Output Structure for Adaptive Sequence Labeling

Sequence labeling is a fundamental task of natural language understanding. Recent neural models for sequence labeling task achieve significant success with the availability of sufficient training data. However, in practical scenarios, entity types to be annotated even in the same domain are continuously evolving. To transfer knowledge from the source model pre-trained on previously annotated data, we propose an approach which learns label-relational output structure to explicitly capturing label correlations in the latent space. Additionally, we construct the target-to-source interaction between the source model M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> and the target model M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> and apply a gate mechanism to control how much information in M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> and M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> should be passed down. Experiments show that our method consistently outperforms the state-of-the-art methods with a statistically significant margin and effectively facilitates to recognize rare new entities in the target data especially.
High Performance Visual Tracking With Siamese Actor-Critic Network

Object tracking is one of the fundamental tasks of computer vision and it is still a major challenge that trackers can balance between real-time speed and high performance. In this paper, a novel Siamese Actor-Critic network (SiamAC) is proposed to improve the accuracy and robustness of tracking while performing with real-time. Specifically, SiamAC consists of a fully convolutional siamese matching network for similarity learning and an Actor-Critic framework trained by reinforcement learning. The Actor is aimed to infer the optimal action in continuous space, while the Critic produces a Q-value to guide effectively the offline training of both Actor and Critic networks. During inference, according to the response map produced by the matching network, the most similar positions and scaled candidate patches are selected as the input of Actor-Critic. Subsequently, Actor can effectively search more precise location of these candidates and the Critic acts as a validator to decide the final tracking results with the highest confidence. Benefiting from this refinement, traditional multi-scale test and certain hyper-parameters in Siamese trackers can be discarded. Evaluations on popular benchmarks demonstrate that the proposed SiamAC achieves state-of-the-art performance with a real-time speed.
A diversified shared latent variable model for efficient image characteristics extraction and modelling

An object can be consisting of various attributes, such as illuminance, appearance, shape, orientation, etc. Separately extract these attributes has enormous value in visual effects modeling, attribute-specific retrieval and recognition. Essentially, these attributes can be fairly abstract and thus need labels to extract. However, sometimes the labels of these attributes may not be available with training data. A solution to this problem is projecting the observed data into a lower dimension latent subspace, such that each observed data can be represented by a latent variable. After that, the dimensions of a latent variable can be segmented into different parts by weighting the kernel automatic relevance determination (ARD) parameters. Consequently, the latent variable is segmented into different parts each of which corresponds to the main attribute. In real life scenery, the attributes of an object may vary significantly from case to case. For instance, a single face can probably be under different illuminance conditions. Taking into account the diversity of these attribute variations, we propose the Diversified Shared Latent Variable Model (DSLVM) to extract and manipulate object attributes in an unsupervised way. More specifically, we initially set up two views that share the same latent variables. Then, two Diversity Encouraging (DE) priors are applied to the inducing points of each model view. Here, the inducing points are a small representative dataset that explains the observed data in its entirety. Meanwhile, the exploited diversity encouraging priors are able to cover more diverse characteristics of the attributes. The defined objective function is computed by variational inference. Extensive experiments on different datasets demonstrate that our method can accurately deal with various object.
Medical Images Security Using Watermarking, Hashing and RGB Displacement

Healthcare organizations produce large volume of data every day in the form of images that need to be stored. Now days, medical imaging storage has become prominent area of research and the greatest agent of information exchange in healthcare. To address the storage issues, cloud storage has been widely used and provides scalable computational resources as a service. But one of the biggest concerns about cloud is security. On cloud, images are stored in encrypted form to provide security but a small modification may change the actual meaning of the image. In this paper, a review on cloud services for healthcare, issues faced by cloud and medical images and techniques to secure medical images has been done. Also, a technique using watermarking and hashing has been proposed, which provides security and minimum distortion of medical images.
Label Noise Robust Curriculum for Deep Paraphrase Identification

In this paper, we study the effect of label noise on deep learning models for paraphrase identification. Curriculum learning, a learning paradigm that learns easy samples first and then gradually proceeds with hard ones, has shown excellent results in dealing with label noise for deep neural networks (DNNs). However, most previous studies focus on image classification, and design their curriculum only based on training losses of samples, ignoring domain-specific knowledge. In this paper, we propose a predefined curriculum learning based framework, incorporating both training losses of samples and domain-specific knowledge, to train robust deep models for paraphrase identification (PI) with label noise. Through extensive experiments on two popular PI benchmarks, we show that 1) the performance of the deep paraphrase identification model can drop sharply at the case of severe label noise; 2) our approach can significantly improve generalization performance of deep networks trained on corrupted data especially at extremely high levels of label noise; 3) our method can outperform several state-of-the-art label corruption robust methods.
Dynamic Actor-Advisor Programming for Scalable Safe Reinforcement Learning

Real-world robots have complex strict constraints. Therefore, safe reinforcement learning algorithms that can simultaneously minimize the total cost and the risk of constraint violation are crucial. However, almost no algorithms exist that can scale to high-dimensional systems to the best of our knowledge. In this paper, we propose Dynamic Actor-Advisor Programming (DAAP), as an algorithm for sample-efficient and scalable safe reinforcement learning. DAAP employs two control policies, actor and advisor. They are updated to minimize total cost and risk of constraint violation intertwiningly and smoothly towards each other's direction by using the other as the baseline policy in the Kullback-Leibler divergence of Dynamic Policy Programming framework. We demonstrate the scalability and sample efficiency of DAAP through its application on simulated robot arm control tasks with performance comparisons to baselines.
Coordinate-Free Dynamics and Differential Flatness of a Class of 6DOF Aerial Manipulators

In this work, we derive a coordinate-free formulation of the coupled dynamics of a class of 6DOF aerial manipulators consisting of an underactuated quadrotor equipped with a 2DOF articulated manipulator, and demonstrate that the system is differentially flat with respect to the end effector pose. In particular, we require the center of mass of the entire system to be fixed in the end effector frame, suggesting a reasonable mechanical design criterion. We make use of an inertial decoupling transformation to demonstrate differential flatness, allowing us to plan dynamically feasible trajectories for the system in the space of the 6DOF pose of the end effector, which is ideal for achieving precise manipulator tasks. Simulation results validate the flatness-based planning methodology for our dynamic model, and its usefulness is demonstrated in a simulated aerial videography task.
Segmentation and 3D Reconstruction of NON-RIGID Shape from RGB Video

In this paper we propose a unsupervised and unified approach to simultaneously recover time-varying 3D shape, camera motion, and temporal clustering into deformations, all of them, from partial 2D point tracks in a RGB video and without assuming any pre-trained model. As the data are drawn from a sequentially ordered images, we fully exploit this information to constrain all model parameters we estimate. We present an energy-based formulation that is efficiently solved and allows to estimate all model parameters in the same loop via augmented Lagrange multipliers in polynomial time, enforcing similarities between images at any level. Validation is done in a wide variety of human video sequences, including articulated and continuous motion, and for dense and missing tracks. Our approach is shown to outperform state-of-the-art solutions in terms of 3D reconstruction and clustering.
Development of an Autonomous Robotics Platform for Road Marks Painting Using Laser Simulator and Sensor Fusion Technique

SUMMARY The design and experimental works of an autonomous robotic platform for road marks painting are presented in this paper as the first autonomous system of its kind. The whole system involves two main sub-systems, namely: an autonomous mobile robot navigation system which is used for recognizing the roads and estimating the position of road marks, and automatic road marks painting system that is attached to the mobile robot platform to control the spray of the paint on the road’s surface. The experimental results show the capability of the proposed system to perform the task of autonomous road marks painting with accuracy of ±10 cm.
Co-evolution of simulink models in a model-based product line

Co-evolution of metamodels and conforming models is a known challenge in model-driven engineering. A variation of co-evolution occurs in model-based software product line engineering, where it is needed to efficiently co-evolve various products together with the single common platform from which they are derived. In this paper, we aim to alleviate manual efforts during this co-evolution process in an industrial setting where Simulink models are partially reused across various products. We propose and implement an approach providing support for the co-evolution of reusable model fragments. A demonstration on a realistic example model shows that our approach yields a correct co-evolution result and is feasible in practice, although practical application challenges remain. Furthermore, we discuss insights from applying the approach within the studied industrial setting.