Reviewerly/exHarmony huggingface dataset:
Imitation Learning-Based Drone Motion Planning in Dense Obstacle Scenarios

For the drone motion planning problem in dense obstacle scenarios, we introduce a trajectory generation method based on imitation learning that does not require the establish-ment of a local map, which greatly increases the planning speed. This method utilizes only onboard sensors and depth camera perception. We specially made the Imitation Learning Planning-Drones (ILP-Drones) dataset for training. The kinodynamic and smoothness of the generated trajectory are improved with local nonlinear optimization. The uniform B-Spline parameterization is adopted to allocate a reasonable time interval for the generated trajectory. Ultimately, our method is able to plan high quality trajectories with excellent collision avoidance ability within mil-liseconds. This is demonstrated by comparative experiments with various advanced algorithms. At the same time, the flexibility and adaptability of our method are demonstrated by ablation experiments with different number of predicted points and different simulation environments.
GTRNet: a graph-based table reconstructed network

Tabular data, with an exceedingly effective data structure, can give us a more intuitive visual display. To under-stand well and make use of the spatial and logic dependencies of it, we propose an end-to-end, graph-based table reconstructed network, namely GTRNet, in this paper. Our model works differently from most existing models which treat tables as either a markup sequence problem or a graph structure of rows and columns. It can utilize a table as input and extract its features in the text, image and position coordinate to predict the dependencies of the text instances and well distinguish the spatial relationship to infer whether multiple text segments belong to the same merged cell. Optimized along with this network, we can then restore the structure of this table. Moreover, we also create a new Chinese benchmark dataset GraphTable for this task to tackle complex challenges on the table. The competitive results on ICDAR-2013, GraphTable, SciTSR and FinTab benchmarks further confirm the great effectiveness of GTRN et.
Convolutional Decoupled cVAE-GANs for Pseudo-Replay Based Continual Learning

Continual Learning is the concept of having a model able to sequentially learn to solve new tasks without losing the ability to solve previous tasks. Achieving this is challenging because neural networks usually suffer from catastrophic forgetting of the preceding tasks when they are learning new ones. To handle this issue, pseudo-replay approaches leverages the performance of generative networks using them to generate samples related to past data to serve as input to the model when it is learning new tasks. In this work, we propose an improved architecture and training strategy based on the state-of-the-art pseudo-replay IRCL method. We use a cVAE-GAN as the generative model and train it decoupled from the other components of the architecture. Also, we make use of convolutional layers for the architecture components instead of linear ones. Our experimental results show that the proposed method outperforms the state-of-the-art IRCL method by up to 10% in Average Accuracy and up to 8.3% in Average Backward Transfer on both Split MNIST and Split FashionMNIST datasets.
Learning an Interpretable Learning Rate Schedule via the Option Framework

Learning rate is a common and important hyperparameter in many gradient-based optimizers which are used for training machine learning models. Heuristic handcrafted learning rate schedules (LRSs) can work in many practical situations, but their design and tuning is a tedious work, and there is no guarantee that a given handcrafted LRS matches a given problem. Many works have been dedicated to automatically learning an LRS from the training dynamics of the optimization problem, but most of them share a common deficit: they borrow the algorithms designed elsewhere as methods for automatic outer-training, but those methods often lack interpretability in the context of learning an LRS. In this paper, we leverage the option framework, a generalization to the common rein-forcement learning framework, to automatically learn an LRS based on the dynamics of optimization, which takes the idea of temporal abstraction as an underlying interpretation. To meet the requirements of LLRS, the RL state is designed as consisting of the global state and the per-parameter state. We propose a policy architecture which processes these two parts according to their respective structures, and combines them to yield the input for functional computation. Experiments are carried out on classic machine learning tasks and test functions for numerical optimization to demonstrate the effectiveness and the interpretability of our method.
Towards Interpretable Feature Representation for Domain Adaptation Problem

Deep convolutional neural networks (CNNs) have witnessed a great progress in visual recognition over the past years. However, deep CNN models are still suffering from the domain adaptation problem. Most of the existing methods try to resolve this issue by creating more useful samples in the source domain for network training so that the well-trained CNN models can well adapt to more possible variations in the target domain. However, such methods are different with human visual mechanism. Human eyes can effectively recognize images with large variations that were never seen before, as long as human eyes are very familiar with partial contents of the input images. We simulate the visual mechanism of human eyes and make feature responses diverse as far as possible. We proposed a novel angular diversity loss, which contains a pair of angular Spatial Activation Diversity (A-SAD) losses by borrowing the idea of the angular losses. Besides concerning the recognition accuracy, we also focus on understanding deep CNNs. Recent works further pushed the interpretability into the training stage of the CNN models. This helps CNN models learn more meaningful feature representations. Extensive experiment on MNIST dataset and its six variation dataset show the effectiveness of the proposed A-SAD loss.
Partially Supervised Classification for Early Concept Drift Detection

As more and more data is generated and stored, and as longer data streams become available, concept drift detection is becoming crucial for most real world applications. We introduce Partially Supervised Drift Detection, PSDD, a drift detection method based on Decision Trees that does not suppose any knowledge of true class labels during inference. Our approach works in any number of dimensions and is able to distinguish real from virtual drift. We successfully evaluated our method with well established datasets in the drift detection field.
Achieving Both Model Accuracy and Robustness by Adversarial Training with Batch Norm Shaping

Adversarial training is an important approach to improving Deep Learning model robustness. It uses attack methods to generate adversarial samples that can maximize the chance of misclassification and updates model weight values accordingly to ensure these samples are not misclassified. It is difficult to retain model accuracy while improving robustness using adversarial training. In this paper, we study one of the important factors causing this undesirable effect - batch normalization. We find that batch normalization has three confoundings in adversarial training, which may cause model accuracy degradation and/or sub-optimal robustness improvement. We propose a novel adversarial training method called norm shaping, in which a model always uses batch norms, in both adversarial training and inference. It enforces that a batch (in both training and inference) should always have at least a dominating portion of clean samples such that the batch norms follow a distribution similar to that of clean sample batches. Our results show that it can substantially improve existing adversarial training methods (for models with batch normalization layers), such as PGD and TRADES. On CIFAR-10, it can achieve much better model accuracy and robustness on a list of existing attacks. For example, it can achieve 0.94 model accuracy and 0.81 robustness against PGD attack while TRADES and PGD adversarial trainings can achieve around 0.88 accuracy and 0.47 robustness. Our method also has 0.51 robustness against the strongest adaptive attack.
Path-aware Multi-hop Question Answering Over Knowledge Graph Embedding

Question answering over knowledge graph (KGQA) aims at answering questions posed over the knowledge graph (KG). Multi-hop KGQA requires multi-hop reasoning on KG to achieve the correct answer. Unfortunately, KGs are usually incomplete with many missing links, which poses additional challenges to KGQA. KG embedding-based KGQA methods have recently been proposed as a way to overcome this limitation. However, existing KG embedding-based KGQA methods fail to take full advantage of semantic correlations between questions and paths. Furthermore, their inference process is not easily explainable. To address these challenges, we propose a novel path-aware multi-hop KGQA model (PA-KGQA), which can fully capture semantic correlations between the paths and the questions in a feature-interactive manner. Specifically, we introduce a case-enhanced path retriever to evaluate the importance of paths between topic entities and candidate answer entities, and then propose an interactive convolutional neural network (ICNN) to model the interactions between paths and questions for mining richer correlation features. Experiments show that PA-KGQA achieves state-of-the-art results on multiple benchmark datasets and is explainable.
Authenticated Encryption for Very Short Inputs

We study authenticated encryption (AE) modes dedicated to very short messages, which are crucial for Internet-of-things applications. Since the existing general-purpose AE modes need at least three block cipher calls for non-empty messages, we explore the design space for AE modes that use at most two calls. We proposed a family of AE modes, dubbed $$\textsf {Manx}$$ , that work when the total input length is less than 2n bits, using an n-bit block cipher. Notably, the second construction of $$\textsf {Manx}$$ can encrypt almost n-bit plaintext and saves one or two block cipher calls from the standard modes, such as $$\textsf{GCM} $$ or $$\textsf{OCB} $$ , keeping the comparable provable security. We also present benchmarks on popular 8/32-bit microprocessors using AES. Our result shows the clear advantage of $$\textsf {Manx}$$ over the previous modes for such short messages.
Multi-Task Learning for Face Recognition via Mutual Information Minimization

In this work, we propose a novel multi-task learning framework which has better generalization ability and robustness compared to conventional multi-task methods. In the proposed approach, the downstream task-related information is extracted first from the original input by using a stochastic global representation. Afterwards, the task-specific representations is generated for learning downstream tasks. Moreover, the disentanglement between the task-specific representations are taken into account by using mutual information minimization to learn better representations. For computing the downstream losses, we take the homoscedastic task-related uncertainties into account to balance the downstream task losses. Finally, we test our methods on the public available dataset. The experimental results suggest that the proposed approach has better performance in terms of generalization ability and robustness compared to the existing methods.
Dynamic selection of classifiers based on complexity measures

Multiple Classifier Systems (MCS) have been an alternative to the monolithic approach to improve the performance of the classification task and increase the accuracy of pattern recognition. The dynamic selection (DS) has shown to be a promising strategy. The architecture in which classification systems are built influences their performance. However, the difficulty of the classification problem is usually neglected. Studies show that a better understanding of data complexity can be interesting for classifier selection. Thus, in this work, we evaluate the complexity measures as selection criteria for the dynamic selection of classifiers. Modifying classical literature approaches results in higher accuracy of 39.51% of the cases by adding the complexity measures while maintaining the original approach corresponds to 28.4%. Ties were present in 35.8%. This work also presents the information gain of 14 complexity measures, which suggest some drawbacks of using some of them.
U-Net Based Discriminator for Real-World Super-Resolution

In recent years, single image super-resolution (SISR) deep learning techniques have achieved remarkable improvements in recovering a high-resolution (HR) image from an observed low-resolution (LR) input. Nevertheless, these proposed methods fail in many real-world scenarios since their models are usually trained using a pre-defined degradation process from HR ground truth images to LR ones. To address this issue, new architectures have been proposed focusing on adopting more complicated degradation models to emulate real-world degradation achieving prominent performance but still limited to certain kinds of inputs and dropping considerably in other cases. In this paper, we present a GAN structure for blind super-resolution tasks, applying a technique that has not been very commonly used in SISR proposals: a U-Net architecture as a discriminator of the GAN network. Adding this structural change will encourage the discriminator to focus more on semantic and structural changes between real and fake images and to attend less to domain-preserving perturbations. In addition, the loss function of the generator was modified by adding the LPIPS loss function for the perceptual loss and a per-pixel consistency regularization technique based on the CutMix data augmentation. Numerous novel solutions that have been proposed recently involve powerful deep learning techniques. The proposed model was trained using the DF2K dataset employing a degradation framework for real-world images by estimating blur kernels and real noise distributions to obtain more realistic LR samples. Finally, we present a benchmark comparing our results with other methods in the state-of-the-art. The commonly-used evaluation metrics for image restoration PSNR, SSIM, and LPIPS were used for this evaluation.
Merkle Tree Ladder Mode: Reducing the Size Impact of NIST PQC Signature Algorithms in Practice

We introduce the Merkle Tree Ladder (MTL) mode of operation for signature schemes. MTL mode signs messages using an underlying signature scheme in such a way that the resulting signatures are condensable: a set of MTL mode signatures can be conveyed from a signer to a verifier in fewer bits than if the MTL mode signatures were sent individually. In MTL mode, the signer sends a shorter condensed signature for each message of interest and occasionally provides a longer reference value that helps the verifier process the condensed signatures. We show that in a practical scenario involving random access to an initial series of $$10{,}000$$ signatures that expands gradually over time, MTL mode can reduce the size impact of the NIST PQC signature algorithms, which have signature sizes of $$666$$ to $$\mathrm{49,856}$$ bytes with example parameters at various security levels, to a condensed signature size of 248 to 472 bytes depending on the selected security level. Even adding the overhead of the reference values, MTL mode signatures still reduce the overall signature size impact under a range of operational assumptions. Because MTL mode itself is quantum-safe, the mode can support long-term cryptographic resiliency in applications where signature size impact is a concern without limiting cryptographic diversity only to algorithms whose signatures are naturally short.
Compressing of Medium- to Low-Rate Transform Residuals With Semi-Extreme Sparse Coding as an Alternate Transform in Video Coding

Modern hybrid video codecs split their video content into blocks. These blocks are predicted and the difference between original and predicted block is calculated. This residual block is transformed by a discrete cosine transform (DCT) from the pixel domain into the frequency domain and quantized by a dead-zone quantizer (DZQ), which removes high frequency signals, depending on the quantization parameter (QP). DZQ has an extended zone around zero, which acts as a noise-gate, removing noise as intended but also removing useful signals. DCT and DZQ are not the optimal solution, they create distortions and side effects at lower bitrates. In contrast, sparse coding does not have these problems at lower bitrates, but it compresses inefficient at higher bit rates. We propose a method to add sparse coding as an alternate transform, which is controlled by a rate-distortion optimization (RDO) decision, in a semi-extreme sparse coding (SESC) setup and uses a semi-extreme dictionary training (SEDT) process. It is integrated into the High Efficiency Video Coding (HEVC) test model HM-16.18 and screen content coding (HEVC-SCC) test model HM-16.18+SCM-8.7. Experimental results demonstrate that the proposed method achieves a Bjontegaard rate difference (BD-rate) of up to 5.5% compared to the standard.
Kernel-Based Multilayer Graph Signal Recovery via Median Truncation of Gradient Descent

Complex structured data-driven applications frequently encompass a higher-order connectivity or interaction among data samples and can be represented by a multilayer graph. In this paper, we consider the problem of signal recovery on multilayer graphs that are corrupted by both noise and outliers within a general recovery framework. A robust and layer-aware multilayer graph signal recovery based on median truncation of the gradient descent, MT-GSR (Median Truncated-Graph Signal Recovery) is proposed. It adaptively truncates node values on each layer that diverge considerably from the sample median of its neighborhood measurements. A local joint-intra-inter-layer regularization operator that relies on graph kernels is formulated to capture the prior information of the layered network topology. The graph kernels exhibit the linear and nonlinear dependencies of the regularization operators. In addition, the layer-aware MT-GSR is extended to a layer-aware-distributed approach, the MT-LDGSR (MT-Layer-aware Distributed Graph Signal Recovery). The MT-LDGSR employs the local properties of each node on each layer and provides a robust signal recovery framework on multilayer networks. Numerical tests provide an analysis of performance guarantees and evaluate the proposed recovery methods on synthetic and real-world data to validate the proposed multilayer kernel-based signal recovery methods.
Improved Threshold Signature Scheme CSI-FiSh with Fast Secret Recovery

The paper presents an improved version of the CSI-FiSh threshold signature offered by L. De Feo and M. Meyer in 2020. In the proposed scheme, public and private keys are additionally updated avoiding the case of compromising a dealer. It is also proposed to eliminate the sequential information transfer between participants when signing and replace it with an assembly with the participation of the dealer. Experimental results showing the effectiveness of the proposed approach and the assessment of the resulting scheme safety are presented.
Politeness Strategies in Directives Speech Acts Found in Daily Conversations among Students

This study aims to find out the kinds of Brown and Levinson’s politeness strategy among the senior high school and how they use those strategies in their daily conversations. In this study applied Brown and Levinson’s politeness strategy. This study was a qualitative approach using descriptive method. The subject was the Senior High School Daarul Huda. The source of the data is the students’ spoken expressions which the writer transcribes into conversational texts. The researcher observed the students’ speaking classes, came to their classes before lecturing, and went to their boarding houses. Their conversation were recorded and transcribed. Then, data was analyzed based on Brown and Levinson’s politeness strategy. The result showed there were 166 utterances of Brown and Levinson's politeness strategy that were used by the students in their daily conversations. These are a bald on-record strategy, a positive politeness strategy, a negative politeness strategy, an off-record strategy and a do not do FTA/ keep the quiet strategy.
Service provision process scheduling using quantum annealing for technical product-service systems

Service provision processes in technical product-service systems (PSS) should be scheduled before they are performed according to the real process conditions. Incorrectly scheduled service provision processes will result in value losses for both customers and providers of PSS. We developed a quantum annealing (QA)-based service provision process scheduling approach and implemented it in a case study in the commercial vehicle industry. For small-size problems, the results show that our QA-based method delivers comparable results to the conventional computational method. For large-size problems, our QA-based method outperforms the conventional method in terms of computational efficiency and optimization accuracy.
Boolean function classes with high nonlinearity

In 2020, Liu (Q.Liu, The lower bounds on the second-order nonlinearity of three classes of Boolean functions, Advances in Mathematics of Communications, AIMS (
A study of data security using cryptography

To make better data transmission over networks cryptography is used with the internet having reached a level that merges with our lives, growing explosively during the last several decades; data security has become a main concern for anyone connected to the web.Data security ensures that our data is only accessible by the intended receiver and prevents any modification or alteration of data.In order to achieve this level of security, various algorithms and methods have been developed.Cryptography can be defined as techniques that cipher data, depending on specific algorithms that make the data unreadable to the human eye unless decrypted by algorithms that are predefined by the sender.
Mathematical Modeling of Hemodynamic Processes Taking into Account Effects of Border Deformation

A mathematical model is investigated that describes the dynamics of blood flow through blood vessels, taking into account the effects of wall deformation. A mathematical model is presented as a model of hydrodynamic processes in a blood vessel. In particular, variants of motion in plane and cylindrical coordinate systems are considered, taking into account the peculiarities of the effects of wall deformation. Functional dependencies are obtained for the amplitudes of the voltage acting on the surface of the blood vessel, which depends on the amplitude, wave number and oscillation frequency. The results obtained allow us to formulate a method for assessing blood dynamics from known values of pulse and pressure.