Reviewerly/exHarmony huggingface dataset:
Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization

Warping-based video stabilizers smooth camera trajectory by constraining each pixel's displacement and warp stabilized frames from unstable ones accordingly. However, since the view outside the boundary is not available during warping, the resulting holes around the boundary of the stabilized frame must be discarded (i.e., cropping) to maintain visual consistency, and thus does leads to a tradeoff between stability and cropping ratio. In this paper, we make a first attempt to address this issue by proposing a new Out-of-boundary View Synthesis (OVS) method. By the nature of spatial coherence between adjacent frames and within each frame, OVS extrapolates the out-of-boundary view by aligning adjacent frames to each reference one. Technically, it first calculates the optical flow and propagates it to the outer boundary region according to the affinity, and then warps pixels accordingly. OVS can be integrated into existing warping-based stabilizers as a plug-and-play module to significantly improve the cropping ratio of the stabilized results. In addition, stability is improved because the jitter amplification effect caused by cropping and resizing is reduced. Experimental results on the NUS benchmark show that OVS can improve the performance of five representative state-of-the-art methods in terms of objective metrics and subjective visual quality. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Single Backlit Image Enhancement Method For Improvement Of Visibility Of Dark Part

This study proposes a simple and fast backlit image enhancement method that improves the visibility of dark parts. The proposed method uses gamma correction and histogram equalization to make an intensity-adjusted image and a contrast-enhanced image, respectively. Then, an alpha-blended image of the two images is obtained. Finally, an output image is obtained by fusing the original image and the blended image using alpha blending to avoid causing artifacts in bright parts. The weight for fusion is calculated by using Otsu’s method and the guided filter. We verify the effectiveness of the proposed method through comparative experiments.
Two-to-one mappings and involutions without fixed points over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

In this paper, two-to-one mappings and involutions without any fixed point on finite fields of even characteristic are investigated. First, we characterize a closed relationship between them by implicit functions and develop an AGW-like criterion for 2-to-1 mappings. Using this criterion, some new constructions of 2-to-1 mappings are proposed and eight classes of 2-to-1 mappings of the form (x2k+x+δ)s+cx are obtained. Finally, a number of classes of involutions without any fixed point are derived from the known 2-to-1 mappings by the relation between them.
Membership Inference Attacks are Easier on Difficult Problems

Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e.g. to detect copyright abuses. We show that models with higher dimensional input and output are more vulnerable to MIA, and address in more detail models for image translation and semantic segmentation, including medical image segmentation. We show that reconstruction-errors can lead to very effective MIA attacks as they are indicative of memorization. Unfortunately, reconstruction error alone is less effective at discriminating between non-predictable images used in training and easy to predict images that were never seen before. To overcome this, we propose using a novel predictability error that can be computed for each sample, and its computation does not require a training set. Our membership error, obtained by subtracting the predictability error from the reconstruction error, is shown to achieve high MIA accuracy on an extensive number of benchmarks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Lifelong Twin Generative Adversarial Networks

In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs). LT-GANs learns a sequence of tasks from several databases and its architecture consists of three components: two identical generators, namely the Teacher and Assistant, and one Discriminator. In order to allow for the LT-GANs to learn new concepts without forgetting, we introduce a new lifelong training approach, namely Lifelong Adversarial Knowledge Distillation (LAKD), which encourages the Teacher and Assistant to alternately teach each other, while learning a new database. This training approach favours transferring knowledge from a more knowledgeable player to another player which knows less information about a previously given task.
Attention deep learning based model for predicting the 3D Human Body Pose using the Robot Human Handover Phases

This work proposes a human motion prediction model for handover operations. We use in this work, the different phases of the handover operation to improve the human motion predictions. Our attention deep learning based model takes into account the position of the robot’s End Effector (REE) and the phase in the handover operation to predict future human poses. Our model outputs a distribution of possible positions rather than one deterministic position, a key feature in order to allow robots to collaborate with humans. We provide results of the human upper body and the human right hand, also referred as Human End Effector (HEE).The attention deep learning based model has been trained and evaluated with a dataset created using human volunteers and an anthropomorphic robot, simulating handover operations where the robot is the giver and the human the receiver. For each operation, the human skeleton is obtained with an Intel RealSense D435i camera attached inside the robot’s head. The results shown a great improvement of the human’s right hand prediction and 3D body compared with other methods.
Lightweight Visual Question Answering using Scene Graphs

Visual question answering (VQA) is a challenging problem in machine perception, which requires a deep joint understanding of both visual and textual data. Recent research has advanced the automatic generation of high-quality scene graphs from images, while powerful yet elegant models like graph neural networks (GNNs) have shown great power in reasoning over graph-structured data. In this work, we propose to bridge the gap between scene graph generation and VQA by leveraging GNNs. In particular, we design a new model called Conditional Enhanced Graph ATtention network (CE-GAT) to encode pairs of visual and semantic scene graphs with both node and edge features, which is seamlessly integrated with a textual question encoder to generate answers through question-graph conditioning. Moreover, to alleviate the training difficulties of CE-GAT towards VQA, we enforce more useful inductive biases in the scene graphs through novel question-guided graph enriching and pruning. Finally, we evaluate the framework on one of the largest available VQA datasets (namely, GQA) with ground-truth scene graphs, achieving the accuracy of 77.87%, compared with the state of the art (namely, the neural state machine (NSM)), which gives 63.17%. Notably, by leveraging existing scene graphs, our framework is much lighter compared with end-to-end VQA methods (e.g., about 95.3% less parameters than a typical NSM).
An LSH-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si77.svg"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-representatives clustering method for large categorical data

Clustering categorical data remains a challenging problem in the era of big data, due to the difficulty in measuring dis/similarity meaningfully for categorical data and the high computational complexity of existing clustering algorithms that makes it difficult to be applied in practical use for big data mining applications. In this paper, we propose an integrated approach that incorporates the Locality-Sensitive Hashing (LSH) technique into the k-means-like clustering so as to make it capable of predicting the better initial clusters for boosting clustering effectiveness. To this end, we first utilize a data-driven dissimilarity measure for categorical data to construct a family of binary hash functions that are then used to generate the initial clusters. We also propose to use a nearest neighbor search at each iteration for cluster reassignment of data objects to improve the clustering complexity. These solutions are incorporated into the k-representatives algorithm resulting in the so-called LSH-k-representatives algorithm. Extensive experiments conducted on multiple real-world and synthetic datasets have demonstrated the effectiveness of the proposed method. It is shown that the newly developed algorithm yields comparable or better clustering results in comparison to the existing closely related works, yet it is significantly more efficient by a factor of between 2× and 32×.
Building personas from phenomenography: a method for user-centered design in education

Purpose Personas are lifelike characters that are driven by potential or real users’ personal goals and experiences when interacting with a product. Personas support user-centered design by focusing on real users’ needs. However, the use of personas in educational research and design requires certain adjustments from its original use in human-computer interface design. This paper aims to propose a process of creating personas from phenomenographic studies, which helps us create data-grounded personas effectively. Design/methodology/approach Personas have features that can help address design problems in educational contexts. The authors compare the use of personas with other common methodologies in education research, including phenomenology and phenomenography. Then, this study presents a six-step process of building personas using phenomenographic study as follows: articulate a design problem, collect user data, assemble phenomenographic categories, build personas, check personas and solve the design problem using personas. The authors illustrate this process with two examples, including the redesign of a professional development website and an undergraduate research program design. Findings The authors find that personas are valuable tools for educational design websites and programs. Phenomenography can productively help educational designers and researchers build sets of personas following the process the authors propose. Originality/value The use and method of personas in educational contexts are scarce and vague. Using the example contexts, the authors provide educational designers and researchers a clear method of creating personas that are relatable and applicable for their design problems.
Turnover-induced knowledge loss in practice

When contributors to a software project leave, the knowledge they hold may become lost, thus impacting code quality and team productivity. Although well-known strategies can be used to mitigate knowledge loss, these strategies have to be tailored to their target context to be effective. To help software development organizations mitigate turnover-induced knowledge loss, we sought to better understand the different contexts in which developers experience this knowledge loss, and the resulting implications. We conducted qualitative interviews with 27 professional developers and managers from three different companies that provide software products and services. Leveraging the experience of these practitioners, we contribute a framework for characterizing turnover-induced knowledge loss and descriptions of the implications of knowledge loss, synthesized into 20 observations. These observations about knowledge loss in practice are organized into four themes, validated by the participants, and discussed within the context of the research literature in software engineering.
Deep Audio-Visual Fusion Neural Network for Saliency Estimation

In this work, we propose a deep audio-visual fusion model to estimate the saliency of videos. The model extracts visual and audio features with two separate branches and fuses them to generate the saliency map. We design a novel temporal attention module to utilize the temporal information and a spatial feature pyramid module to fuse the spatial information. Then a multi-scale audio-visual fusion method is used to integrate different modalities. Furthermore, we propose a new dataset for audio-visual saliency estimation. The proposed dataset consists of 202 high quality video squences with a large range of motions, scenes and object types. Many of the videos have high audio-visual correspondence. Several experiments are conducted on different datasets. The results demonstrate that our model outperforms the previous state-of-the-art methods by a large margin and the proposed dataset can serve as a new benchmark for the audio-visual saliency estimation task.
Coherent many-body Rabi oscillations via superradiance and superabsorption and the mean-field approach for a superradiant laser

The experimental success achieved in recent decades in the coherent manipulation of several Rabi cycles has led to a new stage in the physics of radiation-matter interaction. Motivated by this perspective, we propose here the engineering of coherent many-body Rabi oscillations (CMBROs) induced from the interactions of a moderately dense atomic sample with the environment and a high-finesse cavity mode. The CMBRO follows from the interplay between superradiance and superabsorption: the collective decay and absorption of the atomic sample inside the cavity. The equations we have derived for this interplay, based on the mean-field approximation, describe a two-level superradiant laser in an appropriate parameter regime, and its properties are also discussed. The control of quantum coherence according to the method presented here can be adapted to the implementation of quantum information processes as well as for the investigation of the fundamentals of quantum mechanics.
Fast and Accurate Scene Parsing via Bi-Direction Alignment Networks

In this paper, we propose an effective method for fast and accurate scene parsing called Bidirectional Alignment Network (BiAlignNet). Previously, one representative work BiSeNet [1] uses two different paths (Context Path and Spatial Path) to achieve balanced learning of semantics and details, respectively. However, the relationship between the two paths is not well explored. We argue that both paths can benefit each other in a complementary way. Motivated by this, we propose a novel network by aligning two-path information into each other through a learned flow field. To avoid the noise and semantic gaps, we introduce a Gated Flow Alignment Module to align both features in a bidirectional way. Moreover, to make the Spatial Path learn more detailed information, we present an edge-guided hard pixel mining loss to supervise the aligned learning process. Our network achieves 80.1parcent and 78.5parcent mIoU in validation and test set of Cityscapes while running at 30 FPS with full resolution inputs. Code and models will be available at https://github.com/jojacola/BiAlignNet.
Optimal Packet Camouflage Against Traffic Analysis

Research has proved that supposedly secure encrypted network traffic is actually threatened by privacy and security violations from many aspects. This is mainly due to flow features leaking evidence about user activity and data content. Currently, adversaries can use statistical traffic analysis to create classifiers for network applications and infer users’ sensitive data. In this article, we propose a system that optimally prevents traffic feature leaks. In our first algorithm, we model the packet length probability distribution of the source app to be protected and that of the target app that the source app will resemble. We define a model that mutates the packet lengths of a source app to those lengths from the target app having similar bin probability. This would confuse a classifier by identifying a mutated source app as the target app. In our second obfuscation algorithm, we present an optimized scheme resulting in a trade-off between privacy and complexity overhead. For this reason, we propose a mathematical model for network obfuscation. We formulate analytically the problem of selecting the target app and the length from the target app to mutate to. Then, we propose an algorithm to solve it dynamically. Extensive evaluation of the proposed models, on real app traffic traces, shows significant obfuscation efficiency with relatively acceptable overhead. We were able to reduce a classification accuracy from 91.1% to 0.22% using the first algorithm, with 11.86% padding overhead. The same classification accuracy was reduced to 1.76% with only 0.73% overhead using the second algorithm.
Online Weight Pruning Via Adaptive Sparsity Loss

Pruning neural networks has regained interest in recent years as a means to compress state-of-the-art deep neural networks and enable their deployment on resource-constrained devices. In this paper, we propose a robust sparsity controlling framework that efficiently prunes network parameters during training with minimal computational overhead. We incorporate fast mechanisms to prune individual layers and build upon these to automatically prune the entire network under a user-defined budget constraint. Key to our end-to-end network pruning approach is the formulation of an intuitive and easy-to-implement adaptive sparsity loss used to explicitly control sparsity during training, enabling efficient budget-aware optimization.
Towards a Software-Defined, Fine-Grained QoS Framework for 5G and Beyond Networks

5G offers a slew of new features and capabilities to support a whole gamut of new applications. On the other hand, 5G new radio (NR), especially, high-band mmWave radio, also poses new challenges, as shown by recent measurement studies of commercial 5G services. In order to effectively support new classes of application such as extra low-latency and/or high-bandwidth applications, we argue that truly cross-layer network-application integration that exposes application semantics to enable 5G and beyond 5G (B5G) networks to make intelligent decisions, e.g., for dynamic radio resource allocation, is needed. Unfortunately the existing 5G flow-based framework is inadequate to support such cross-layer integration. We therefore advocate a software-defined, fine-grained QoS framework. We use ultra-high resolution (UHR) volumetric video streaming as a use case and conduct very preliminary experiments to demonstrate the potential benefits of the proposed framework. This position paper serves as a strawman to call for new intelligent architectural designs for B5G networks and next-generation wireless systems.
Intelligent and Adaptive Mixup Technique for Adversarial Robustness

Deep neural networks are generally trained using large amounts of data to achieve state-of-the-art accuracy in many possible computer vision and image analysis applications ranging from object recognition to natural language processing. It is also claimed that these networks can memorize the data which can be extracted from the network parameters such as weights and gradient information. The adversarial vulnerability of the deep networks is usually evaluated on the unseen test set of the databases. If the network is memorizing the data, then the small perturbation in the training image data should not drastically change its performance. Based on this assumption, we first evaluate the robustness of deep neural networks on small perturbations added in the training images used for learning the parameters of the network. It is observed that, even if the network has seen the images it is still vulnerable to these small perturbations. Further, we propose a novel data augmentation technique to increase the robustness of deep neural networks to such perturbations.
Blockchain side implementation of Pure Wallet (PW): An offline transaction architecture

This article proposes an electronic payment architecture named Pure Wallet (PW), which extends the concept of Blockchain cryptocurrency for offline transactions. The process is divided into three steps. The first step requires the use of Internet connection, to convert cryptocurrency into a token at the token manager. The token manager initiates a transaction that requires the information in the token to complete. Then offline transactions step is performed between electronic devices like mobile phones through a secure Near Field Communication (NFC) using the token in senders device. The financial value in the form of a token is encrypted by the sender and sent to the receiver’s device via NFC. In the third step, the receiver converts the received token into cryptocurrency in the presence of Internet connection by sending the information required to complete the transaction to the token manager. The goal is to propose an electronic payment architecture utilizing Blockchain, which will enable financial transactions without instant connection to the Internet. The Blockchain implementation in this work utilizes smart contract in Ethereum Blockchain. The result shows a successful transfer of value without instant Internet connection. The open issues related to Blockchain in offline transactions are listed for further research works.
A rank-based framework through manifold learning for improved clustering tasks

The relevance of diversified data preprocessing approaches for improving clustering tasks is remarkable. Once the effectiveness is direct impacted by feature representation and similarity definition, considerable attention from the research community has been drawn to this direction. More recently, rank-based manifold learning methods have been successfully explored in unsupervised similarity learning for retrieval scenarios. Such methods consider the underlying dataset manifold to compute a new similarity measure, which increases the separability of data from distinct classes. In this paper, a rank-based framework for clustering tasks is proposed based on contemporary manifold learning methods. A flexible model is employed, where ranking structures are the representation of similarity information among data samples. Subsequently, is made the exploration of unsupervised similarity learning. It is also possible to compute more effective similarity measures and clustering results. To assess the effectiveness of the proposed framework was conducted a comprehensive experimental evaluation. The tests involved various public image datasets, considering different manifold learning and clustering methods. The quantitative experiments take into consideration comparisons with traditional and recent state-of-the-art clustering approaches.