Reviewerly/exHarmony huggingface dataset:
Employing dropout regularization to classify recurring drifted data streams

Streaming data analysis is currently a rapidly growing research direction. One of the serious problems hindering the data stream classification is the fact that during the exploitation of the model, its probabilistic characteristics may change. This phenomenon is called concept drift. Until today, multiple methods have been proposed to overcome their negative influence on model performance during learning in dynamic environments. This work introduces a new streaming data classifier based on a dropout technique that can significantly reduce model restoration time and performance loss and can improve its overall score in the presence of recurring concept drifts. The usefulness of the proposed algorithm is evaluated based on extensive experimental study and backed-up with thorough statistical analysis.
SAR Image Specle Reduction based on a Generative Adversarial Network

Synthetic aperture radar (SAR) image despeckling is recognized as the basis for SAR image processing and interpretation. Over the past decades, many impressed speckle reduction methods have been developed and achieved good performance under certain circumstances. However, how to suppress speckle noise in a homogeneous region while more effectively protecting details and avoid distortion of data features caused by homomorphic transformation is still an urgent problem. In this paper, a novel speckle reduction algorithm based on generative adversarial network (GAN) is proposed, which contains a generator and a discriminator. For the generator that is used directly for subsequent noise reduction, a total variation (TV) loss function is added. Meanwhile, we directly learn the mapping between the input image and the ground truth rather than the logarithmic transformation. Indeed, the improved lightweight discriminative network will also provide learning guidance for the generator. Experiments on simulatedSAR images and real SAR images demonstrate the improvement in visual and statistical performance comparing to the state-of-the-art despeckling algorithms.
Motion Blur Prior

Priors play an important role of regularizers in image deblurring algorithms. Image priors are frequently studied and many forms were proposed in the literature. Blur priors are considered less important and the most common forms are simple uniform distributions with domain constraints. We propose a more informative blur prior based on the notion of atomic norm which favors blurs composed of line segments and is suitable for motion blur. The prior is formulated as a linear program that can be inserted into any optimization task. Evaluation is conducted on blind deblurring of moving objects.
Did you mix me? Formally Verifying Verifiable Mix Nets in Electronic Voting

Verifiable mix nets, and specifically proofs of (correct) shuffle, are a fundamental building block in numerous applications: these zero-knowledge proofs allow the prover to produce a public transcript which can be perused by the verifier to confirm the purported shuffle. They are particularly vital to verifiable electronic voting, where they underpin almost all voting schemes with non-trivial tallying methods. These complicated pieces of cryptography are a prime location for critical errors which might allow undetected modification of the outcome.The best solution to preventing these errors is to machine-check the cryptographic properties of the design and implementation of the mix net. Particularly crucial for the integrity of the outcome is the soundness of the design and implementation of the verifier (software). Unfortunately, several different encryption schemes are used in many different slight variations which makes it infeasible to machine-check every single case individually. However, a particular optimised variant of the Terelius-Wikström mix net is, and has been, widely deployed in elections including national elections in Norway, Estonia and Switzerland, albeit with many slight variations and several different encryption schemes.In this work, we develop the logical theory and formal methods tools to machine-check the design and implementation of all these variants of Terelius-Wikström mix nets, for all the different encryption schemes used; resulting in provably correct mix nets for all these different variations. We do this carefully to ensure that we can extract a formally verified implementation of the verifier (software) which is compatible with existing deployed implementations of the Terelius-Wikström mix net. This gives us provably correct implementations of the verifiers for more than half of the national elections which have used verifiable mix nets.Our implementation of a proof of correct shuffle is the first to be machine-checked to be cryptographically correct and able to verify proof transcripts from national elections. We demonstrate the practicality of our implementation by verifying transcripts produced by the Verificatum mix net system and the CHVote e-voting system from Switzerland.
Recognition of Sign Language Symbols using Templates

This paper deals with recognition of Sign Language symbols of limited vocabulary. Earlier various techniques were used for sign language recognition for gestures, various postures recognition on different Sign Languages like American, Korean, and British Sign Languages. This work helps us to recognize and learn various sign language symbols used by hearing impaired people for communication. The proposed system works with static images of Sign Language symbols made using face and hands only, system uses the standard symbols of Sign language. Since human beings tend to bear no resemblance in terms of size and shape, the most challenging problem was to correctly classify the information gathered from the input data, captured by webcam and using image processing toolbox of MATLAB.
Multi-Label classifier based on Kernel Random Vector Functional Link Network

In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.
TERG: Topic-Aware Emotional Response Generation for Chatbot

A more intelligent chatbot should be able to express emotion, in addition to providing informative responses. Despite much works in designing neural dialogue generation systems in recent years, few studies consider both emotion to be expressed and topic relevance in the generation process. To address this problem, we present a Topic-aware Emotional Response Generation (TERG) model, which can not only exactly generate desired emotional response but perform well in topic relevance. Specifically, TERG equips an encoder-decoder structure with an emotion aware module to control the emotional sentence generation and a topic aware module to enhance topic relevance. We evaluate our model on a large real-world dataset of conversations from social media. Experimental results show that our model obtains a significant improvement against several strong baseline methods on both automatic and human evaluation.
Perceptually Inspired Weighted MSE Optimization Using Irregularity-Aware Graph Fourier Transform

In image and video coding applications, distortion has been traditionally measured using mean square error (MSE), which suggests the use of orthogonal transforms, such as the discrete cosine transform (DCT). Perceptual metrics such as Structural Similarity (SSIM) are typically used after encoding, but not tied to the encoding process. In this paper, we consider an alternative framework where the goal is to optimize a weighted MSE metric, where different weights can be assigned to each pixel so as to reflect their relative importance in terms of perceptual image quality. For this purpose, we propose a novel transform coding scheme based on irregularity-aware graph Fourier transform (IAGFT), where the induced IAGFT is orthogonal, but the orthogonality is defined with respect to an inner product corresponding to the weighted MSE. We propose to use weights derived from local variances of the input image, such that the weighted MSE aligns with SSIM. In this way, the associated IAGFT can achieve a coding efficiency improvement in SSIM with respect to conventional transform coding based on DCT. Our experimental results show a compression gain in terms of multi-scale SSIM on test images.
Tight Security Analysis of 3-Round Key-Alternating Cipher with a Single Permutation

The tight security bound of the KAC (Key-Alternating Cipher) construction whose round permutations are independent from each other has been well studied. Then a natural question is how the security bound will change when we use fewer permutations in a KAC construction. In CRYPTO 2014, Chen et al. proved that 2-round KAC with a single permutation (2KACSP) has the same security level as the classic one (i.e., 2-round KAC). But we still know little about the security bound of incompletely-independent KAC constructions with more than 2 rounds. In this paper, we will show that a similar result also holds for 3-round case. More concretely, we prove that 3-round KAC with a single permutation (3KACSP) is secure up to $$\varTheta (2^{\frac{3n}{4}})$$ queries, which also caps the security of 3-round KAC. To avoid the cumbersome graphical illustration used in Chen et al.'s work, a new representation is introduced to characterize the underlying combinatorial problem. Benefited from it, we can handle the knotty dependence in a modular way, and also show a plausible way to study the security of rKACSP. Technically, we abstract a type of problems capturing the intrinsic randomness of rKACSP construction, and then propose a high-level framework to handle such problems. Furthermore, our proof techniques show some evidence that for any r, rKACSP has the same security level as the classic r-round KAC in random permutation model.
Line follower robot &amp; obstacle detection using PID controller

Navigation is the significant feature in applications of Mobile Robots. Various navigation tools are available based on accuracy & complexity. PID Controller is used for designing a Line follower Robot. Array of multiple sensors detects the black surface and travel along the line. Arduino Uno continuously monitors the signal from the sensors and turns the Robot as the line getting detected. Obstacle detection is performed by Ultrasonic sensor. Line follower Robots are applicable in military, intruder alarm systems and so on.
Generating Visibility-Aware Trajectories for Cooperative and Proactive Motion Planning

The safety of an autonomous vehicle not only depends on its own perception of the world around it, but also on the perception and recognition from other vehicles. If an ego vehicle considers the uncertainty other vehicles have about itself, then by reducing the estimated uncertainty it can increase its safety. In this paper, we focus on how an ego vehicle plans its trajectories through the blind spots of other vehicles. We create visibility-aware planning, where the ego vehicle chooses its trajectories such that it reduces the perceived uncertainty other vehicles may have about the state of the ego vehicle. We present simulations of traffic and highway environments, where an ego vehicle must pass another vehicle, make a lane change, or traverse a partially-occluded intersection. Emergent behavior shows that when using visibility-aware planning, the ego vehicle spends less time in a blind spot, and may slow down before entering the blind spot so as to increase the likelihood other vehicles perceive the ego vehicle.
Ring Reservoir Neural Networks for Graphs

Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance.
A Survey of the Development of Wearable Devices

With the rapid development of wearable technologies, wearable devices are gradually entering people's daily life. As a novel way of human-computer interaction, wearable devices have brought more and more convenience and assistance to people than ever before. In this paper, we firstly make a classification of wearable devices based on their functions and how they are worn. An introduction to the current development of wearable devices is also presented. Then, the challenges in the area of wearable devices and their possible solutions are discussed. Finally, the conclusions are drawn on the development and future trends of wearable devices and technologies.
Blinded by novelty

Interacting with a novel technology or a known technology in a novel context can influence a user's interaction with said technology in both a positive or negative way. Research is often primarily interested in capturing interaction under regular conditions, rendering such influences less desirable. Depending on the actual novelty of any given technology, such halo or novelty effects might be weaker or stronger, and thereby more difficult to mitigate. In this paper, we want to share a number of experiences related to participant curiosity and technology novelty in interaction with automated vehicles. We intend to use our experiences from several studies to open the discussion on a potential underestimation of technology novelty and its effects on user research in vehicle automation technology and want to particularly highlight the role, researchers, manufacturers, and media play in shaping such effects.
Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution

Super-Resolution (SR) techniques can compensate for the missing information of low-resolution images and further promote experts and algorithms to make accurate diagnosis decisions. Although the existing pixel-loss based SR works produce high-resolution images with impressive objective metrics, the over-smoothed contents that lose high-frequency information would disturb the visual experience and the subsequent diagnosis. To address this issue, we propose a joint Spatial-Wavelet super-resolution Network (SWD-Net) with collaborative Dual-stream. In the spatial stage, a Refined Context Fusion (RCF) is proposed to iteratively rectify the features by a counterpart stream with compensative receptive fields. After that, the wavelet stage enhances the reconstructed images, especially the structural boundaries. Specifically, we design the tailor-made Wavelet Features Adaptation (WFA) to adjust the wavelet coefficients for better compatibility with networks and Wavelet-Aware Convolutional blocks (WAC) to exploit features in the wavelet domain efficiently. We further introduce the wavelet coefficients supervision together with the traditional spatial loss to jointly optimize the network and obtain the high-frequency enhanced SR images. To evaluate the SR for medical images, we build a benchmark dataset with histopathology images and evaluate the proposed SWD-Net under different settings. The comprehensive experiments demonstrate our SWD-Net outperforms state-of-the-art methods. Furthermore, SWD-Net is proven to promote medical image diagnosis with a large margin. The source code and dataset are available at https://github.com/franciszchen/SWD-Net.
ACED: Accurate And Edge-Consistent Monocular Depth Estimation

Single image depth estimation is a challenging problem. The current state-of-the-art method formulates the problem as that of ordinal regression. However, the formulation is not fully differentiable and depth maps are not generated in an end-to-end fashion. The method uses a native threshold strategy to determine per-pixel depth labels, which results in significant discretization errors. For the first time, we formulate a fully differentiable ordinal regression and train the network in end-to-end fashion. This enables us to include boundary and smoothness constraints in the optimization function, leading to smooth and edge-consistent depth maps. A novel per-pixel confidence map computation for depth refinement is also proposed. Extensive evaluation of the proposed model on challenging benchmarks reveals its superiority over recent state-of-the-art methods, both quantitatively and qualitatively. Additionally, we demonstrate practical utility of the proposed method for single camera bokeh solution using in-house dataset of challenging real-life images.
Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation

Multi-modal medical image segmentation plays an essential role in clinical diagnosis. It remains challenging as the input modalities are often not well-aligned spatially. Existing learning-based methods mainly consider sharing trainable layers across modalities and minimizing visual feature discrepancies. While the problem is often formulated as joint supervised feature learning, multiple-scale features and class-specific representation have not yet been explored. In this paper, we propose an affinity-guided fully convolutional network for multimodal image segmentation. To learn effective representations, we design class-specific affinity matrices to encode the knowledge of hierarchical feature reasoning, together with the shared convolutional layers to ensure the cross-modality generalization. Our affinity matrix does not depend on spatial alignments of the visual features and thus allows us to train with unpaired, multimodal inputs. We extensively evaluated our method on two public multimodal benchmark datasets and outperform state-of-the-art methods.
Exploring the Potential of Speech-based Virtual Assistants in Mixed Reality Applications for People with Cognitive Disabilities

Mixed Reality (MR) has been receiving increasing interest in the rehabilitation of people with Cognitive Disabilities. The power of MR in the context of therapies is the possibility to maintain a physical and psychological relationship with the surrounding environment while experiencing customized multimedia content and tasks that are appropriate for the needs of these users. The purpose of this work is to explore the potential of MR applications integrated with interactive speech-based Virtual Assistants. We present HoloLearn, an application for Microsoft HoloLens, which aims to help people with Cognitive Disabilities to improve their autonomy and learn simple activities of their everyday life (e.g., setting the table). The paper discusses the design features of the Virtual Assistant created and the results of a controlled study. The participants involved were 15 subjects with Cognitive Disabilities who used HoloLearn in two experimental conditions - with and without a Virtual Assistant. The results, although preliminary, indicate that enriching a MR experience with the presence of a speech-based Virtual Assistant would improve the user performance in the execution of the MR activities.
Asymptotic State Transformations of Continuous Variable Resources

We study asymptotic state transformations in continuous variable quantum resource theories. In particular, we prove that monotones displaying lower semicontinuity and strong superadditivity can be used to bound asymptotic transformation rates in these settings. This removes the need for asymptotic continuity, which cannot be defined in the traditional sense for infinite-dimensional systems. We consider three applications, to the resource theories of (I) optical nonclassicality, (II) entanglement, and (III) quantum thermodynamics. In cases (II) and (III), the employed monotones are the (infinite-dimensional) squashed entanglement and the free energy, respectively. For case (I), we consider the measured relative entropy of nonclassicality and prove it to be lower semicontinuous and strongly superadditive. One of our main technical contributions, and a key tool to establish these results, is a handy variational expression for the measured relative entropy of nonclassicality. Our technique then yields computable upper bounds on asymptotic transformation rates, including those achievable under linear optical elements. We also prove a number of results which guarantee that the measured relative entropy of nonclassicality is bounded on any physically meaningful state and easily computable for some classes of states of interest, e.g., Fock diagonal states. We conclude by applying our findings to the problem of cat state manipulation and noisy Fock state purification.
On Bringing Case-Based Reasoning Methodology to Deep Learning

The case-based reasoning community is successfully pursuing multiple approaches for applying deep learning methods to advance case-based reasoning. This “Challenges and Promises” paper argues for a complementary endeavor: pursuing ways that the case-based reasoning methodology can advance deep learning. Starting from challenges in deep learning and proposed neural-symbolic integrations based on specific technologies, it proposes studying how CBR ideas can inform choices of components for a new reasoning pipeline.
A Vertex-Edge Graph Convolutional Network for Skeleton-Based Action Recognition

The Graph Convolutional Network(GCN) methods for skeleton-based action recognition have achieved great success due to their ability of exploiting the joint information from the graph structure of the skeleton data. Recently, as a strong and complementary modality for action recognition, the bone information from skeleton data has attracted more attention. However, most existing GCN-based methods extract the bone and joint features with two separate GCN networks, ignoring the dependencies between joints and bones. In this work, a Vertex-Edge Graph Convolutional Network(VE-GCN) is proposed to reveal the information across joints, bones and their relationships simultaneously. In addition, we learn the additional connections among joints and bones for various action samples besides the natural connections of the skeleton. Then we conduct the convolution operation on joints and their neighbors based on these additional connections. Moreover, the conditional random field (CRF) is utilized as the loss function to achieve improved performance. Experimental results on two large-scale datasets NTU RGB+D and NTU RGB+D 120 show that the proposed model outperforms state-of-the-art models.