Reviewerly/exHarmony huggingface dataset:
Hyperspectral Image Denoising by Asymmetric Noise Modeling

In general, hyperspectral images (HSIs) are degraded by a mixture of complicated noise (i.e., mixture of Gaussian and sparse noise), and how to precisely model HSI noise plays a vital role in the task of HSI denoising. The most popular choices for encoding the noise distribution are Gaussian, Laplacian, and the mixture of Gaussians, but they are always incompatible with real-world HSI noise. By investigating histograms of the error map, we first explore that asymmetry is a typical and general feature of HSI noise. Inspired by this discovery, we find that a bandwise asymmetric Laplacian (AL) distribution can be finely used to model this type of noise. Equipped with the low-rank matrix factorization (LRMF) framework, we formulate a novel model by the maximum likelihood estimation (MLE) principle, which can be efficiently solved using the iterative optimization algorithm. Extensive experimental results on synthetic and real datasets demonstrate that the proposed model outperforms other counterparts. It is also found that scale and asymmetry parameters in the AL distribution can well interpret the pattern of real-world HSI noise.
Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm

In recent years, quantum computers and algorithms have made significant progress indicating the prospective importance of quantum computing (QC). Especially combinatorial optimization has gained a lot of attention as an application field for near-term quantum computers, both by using gate-based QC via the Quantum Approximate Optimization Algorithm and by quantum annealing using the Ising model. However, demonstrating an advantage over classical methods in real-world applications remains an active area of research. In this work, we investigate the robustness verification of ReLU networks, which involves solving many-variable mixed-integer programs (MIPs), as a practical application. Classically, complete verification techniques struggle with large networks as the combinatorial space grows exponentially, implying that realistic networks are difficult to be verified by classical methods. To alleviate this issue, we propose to use QC for neural network verification and introduce a hybrid quantum procedure to compute provable certificates. By applying Benders decomposition, we split the MIP into a quadratic unconstrained binary optimization and a linear program which are solved by quantum and classical computers, respectively. We further improve existing hybrid methods based on the Benders decomposition by reducing the overall number of iterations and placing a limit on the maximum number of qubits required. We show that, in a simulated environment, our certificate is sound, and provides bounds on the minimum number of qubits necessary to approximate the problem. Finally, we evaluate our method within simulations and on quantum hardware.
A QP-adaptive Mechanism for CNN-based Filter in Video Coding

Convolutional neural network (CNN)-based in-loop filtering have been very successful in video coding. For most existing works, however, a specific model was required for each quantization parameter (QP) band. In this paper, we introduce a generic method for helping CNN-filters deal with variable quantization noises. A feasible solution to this problem can be implemented on CNN by introducing a quantization step (Qstep) into the CNN. As the quantization noise changes, the CNN filter’s ability to suppress noise changes accordingly. The (vanilla) convolution layer can be replaced directly by this method in existing CNN filters. Compared with the VVenC anchor, only one CNN filter is used and achieves about 3.6% BD-rate reduction for the luminance component of random-access configuration. Also, about 0.8% BD-rate reduction has been achieved compared with the previous QP-map method.
Efficient and Anonymous Cross-Domain Authentication for IIoT Based on Blockchain

The rapid development of the Industrial Internet of Things (IIoT) has realized the intelligence of industrial manufacturing and improved production efficiency. For improved collaboration, devices from different management domains (e.g., factories) connected through various communication technologies exchange information and share resources. However, they face security and privacy issues when cross-domain communication requires authentication. The limitations of existing schemes include the risk of a single point of failure in a trusted center, leakage of device privacy, high certificate management costs, and low authentication efficiency. Because blockchain with features such as decentralization and tamper-proof can effectively solve some of these problems, we design an efficient and anonymous cross-domain authentication scheme based on blockchain to achieve reliable communication between cross-domain IIoT devices. Specifically, our scheme improves authentication efficiency while enabling device anonymity to ensure that identities are not linkable, and combines blockchain and dynamic accumulator technology to achieve fast authentication. Security analysis demonstrates that our scheme can resist common attacks, and a performance evaluation proves its feasibility and efficiency.
Robust Deep Reinforcement Learning Scheduling via Weight Anchoring

Questions remain on the robustness of data-driven learning methods when crossing the gap from simulation to reality. We utilize weight anchoring, a method known from continual learning, to cultivate and fixate desired behavior in Neural Networks. Weight anchoring may be used to find a solution to a learning problem that is nearby the solution of another learning problem. Thereby, learning can be carried out in optimal environments without neglecting or unlearning desired behavior. We demonstrate this approach on the example of learning mixed QoS-efficient discrete resource scheduling with infrequent priority messages. Results show that this method provides performance comparable to the state of the art of augmenting a simulation environment, alongside significantly increased robustness and steerability.
Playable Environments: Video Manipulation in Space and Time

We present Playable Environments-a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a video by providing a sequence of desired actions. The actions are learnt in an unsupervised manner. The camera can be controlled to get the desired viewpoint. Our method builds an environment state for each frame, which can be manipulated by our proposed action mod-ule and decoded back to the image space with volumetric rendering. To support diverse appearances of objects, we extend neural radiance fields with style-based modulation. Our method trains on a collection of various monocular videos requiring only the estimated camera parameters and 2D object locations. To set a challenging benchmark, we in-troduce two large scale video datasets with significant cam-era movements. As evidenced by our experiments, playable environments enable several creative applications not at-tainable by prior video synthesis works, including playable 3D video generation, stylization and manipulation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> willi-menapace.github.io/playable-environments-website.
A method of named entity recognition for Tigrinya

This paper proposes a method for Named-Entity Recognition (NER) for a low-resource language, Tigrinya, using a pre-trained language model. Tigrinya is a morphologically rich, although one of the underrepresented in the field of NLP. This is mainly due to the limited amount of annotated data available. To address this problem, we present the first publicly available datasets of NER for Tigrinya containing two versions, namely, (V1 and V2) annotated manually. The V1 and V2 datasets contain 69,309 and 40,627 tokens, respectively, where the annotations are based on the CoNLL 2003 Beginning, Inside, and Outside (BIO) tagging schema. Specifically, we develop a new pre-trained language model for Tigrinya based on RoBERTa, which we refer to as TigRoBERTa. Our model is then fine-tuned on down-stream tasks on a more specific target NER and POS tasks with limited data. Finally, we further enhance the model performance by applying semi-supervised self-training using unlabeled data. The experimental results show that the method achieved 84% F1-score for NER and 92% accuracy for POS tagging, which is better than or comparable to the baseline method based on the CNN-BiLSTM-CRF.
Heterogeneous Graph Neural Network With Multi-View Representation Learning

In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain node embeddings with versatility. To address the problem, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heterogeneous Graph Neural Network</i> for HG embedding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">within a Multi-View representation learning framework</i> (named MV-HetGNN), which consists of a view-specific ego graph encoder and auto multi-view fusion layer. MV-HetGNN thoroughly learns complex heterogeneity and semantics in the local structure to generate comprehensive and versatile node representations for HGs. Extensive experiments on three real-world HG datasets demonstrate the significant superiority of our proposed MV-HetGNN compared to the state-of-the-art baselines in various downstream tasks, e.g., node classification, node clustering, and link prediction.
Phishing Fraud Detection on Ethereum Using Graph Neural Network

Blockchain has widespread applications in the financial field but has also attracted increasing cybercrimes. Recently, phishing fraud has emerged as a major threat to blockchain security, calling for the development of effective regulatory strategies. Nowadays network science has been widely used in modeling Ethereum transaction data, further introducing the network representation learning technology to analyze the transaction patterns. In this paper, we consider phishing detection as a graph classification task and propose an end-to-end Phishing Detection Graph Neural Network framework (PDGNN). Specifically, we first construct a lightweight Ethereum transaction network and extract transaction subgraphs of collected phishing accounts. Then we propose an end-to-end detection model based on Chebyshev-GCN to precisely distinguish between normal and phishing accounts. Extensive experiments on five Ethereum datasets demonstrate that our PDGNN significantly outperforms general phishing detection methods and scales well in large transaction networks.
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation

3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art semantic segmentation models suffer from the part-misclassification problem, wherein parts of the same object are labelled incorrectly. Previous methods have utilized hierarchical, iterative methods to fuse semantic and instance information, but they lack learnability in context fusion, and are computationally complex and heuristic driven. This paper presents Segment-Fusion, a novel attention-based method for hierarchical fusion of semantic and instance in-formation to address the part misclassifications. The presented method includes a graph segmentation algorithmfor grouping points into segments that pools point-wise features into segment-wise features, a learnable attention-based net-work to fuse these segments based on their semantic and instance features, and followed by a simple yet effective connected component labelling algorithm to convert seg-ment features to instance labels. Segment-Fusion can be flexibly employed with any network architecture for semantic/instance segmentation. It improves the qualitative and quantitative performance of several semantic segmentation backbones by upto 5% on the ScanNet and S3DIS datasets.
Towards a Desktop-AR Prototyping Framework: Prototyping Cross-Reality Between Desktops and Augmented Reality

Augmented reality (AR) head-worn displays (HWDs) allow users to view and interact with virtual objects anchored in the 3D space around them. These devices extend users' digital interaction space compared to traditional desktop computing environments by both allowing users to interact with a larger virtual display and by affording new interactions (e.g., intuitive 3D manipulations) with virtual content. Yet, 2D desktop displays still have advantages over AR HWDs for common computing tasks and will continue to be used well into the future. Because of their not entirely overlapping set of affordances, AR HWDs and 2D desktops may be useful in a hybrid configuration; that is, users may benefit from being able to work on computing tasks in either environment (or simultaneously in both environments) while transitioning virtual content between them. In support of such computing environments, we propose a prototyping framework for bidirectional Cross-Reality interactions between a desktop and an AR HWD. We further implemented a proof-of-concept seamless Desktop-AR display space, and describe two concrete use cases for our framework. In future work we aim to further develop our proof-of-concept into the proposed framework.
A Complementary Dual-Branch Network for Appearance-Based Gaze Estimation From Low-Resolution Facial Image

Estimating gaze from a low-resolution (LR) facial image is a challenging task. Most current networks for gaze estimation focus on using face images of adequate resolution. Their performance degrades when the image resolution decreases due to information loss. This work aims to explore more helpful face and gaze information in a novel way to alleviate the problem of information loss in the LR gaze estimation task. Considering that all faces have a relatively fixed structure, it is feasible to reconstruct the residual information of face and gaze based on the solid constraint of the prior knowledge of face structure through learning an end-to-end mapping from pairs of low- and high-resolution (HR) images. This article proposes a complementary dual-branch network (CDBN) to achieve this task. A fundamental branch is designed to extract features of the major structural information from LR input. A residual branch is employed to reconstruct features containing the residual information as a supplement under the supervision of both the HR image and gaze direction. These two features are then fused and processed for gaze estimation. Experimental results on three widely used data sets, MPIIFaceGaze, EYEDIAP, and RT-GENE, show that the proposed CDBN achieves more accurate gaze estimation from the LR input image compared with the state-of-the-art methods.
A Novel Compressed Sensing-Based Graph Isomorphic Network for Key Node Recognition and Entity Alignment

In recent years, the related research of entity alignment has mainly focused on entity alignment via knowledge embeddings and graph neural networks; however, these proposed models usually suffer from structural heterogeneity and the large-scale problem of knowledge graph. A novel entity alignment model based on graph isomorphic network and compressed sensing is proposed. First, for the problem of structural heterogeneity, graph isomorphic network encoder is applied in knowledge graph to capture structural similarity of entity relation. Second, for the problem of large scale, key node and community are integrated for priority entity alignment to improve execution speed. However, the exiting node importance ranking algorithm cannot accurately identify key node in knowledge graph. So the compressed sensing is adopted in node importance ranking to improve the accuracy of identifying key node. The authors have carried out several experiments to test the effect and efficiency of the proposed entity alignment model.
AcroFOD: An Adaptive Method for Cross-Domain Few-Shot Object Detection

Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data. There exists two significant challenges: (1) Highly insufficient target domain data; (2) Potential over-adaptation and misleading caused by inappropriately amplified target samples without any restriction. To address these challenges, we propose an adaptive method consisting of two parts. First, we propose an adaptive optimization strategy to select augmented data similar to target samples rather than blindly increasing the amount. Specifically, we filter the augmented candidates which significantly deviate from the target feature distribution in the very beginning. Second, to further relieve the data limitation, we propose the multi-level domain-aware data augmentation to increase the diversity and rationality of augmented data, which exploits the cross-image foreground-background mixture. Experiments show that the proposed method achieves state-of-the-art performance on multiple benchmarks. The code is available at https://github.com/Hlings/AcroFOD .
A Survey on Data Poisoning Attacks and Defenses

With the widespread deployment of data-driven services, the demand for data volumes continues to grow. At present, many applications lack reliable human supervision in the process of data collection, which makes the collected data contain low-quality data or even malicious data. This low-quality or malicious data make AI systems potentially face much security challenges. One of the main security threats in the training phase of machine learning is data poisoning attacks, which compromise model integrity by contaminating training data to make the resulting model skewed or unusable. This paper reviews the relevant researches on data poisoning attacks in various task environments: first, the classification of attacks is summarized, then the defense methods of data poisoning attacks are sorted out, and finally, the possible research directions in the prospect.
Learning Joint Detection, Equalization and Decoding for Short-Packet Communications

We propose and practically demonstrate a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all “at once”, detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not only in a higher spectral efficiency, but also translates into the possibility of shorter messages compared to using a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline and our simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\!=\!96$ </tex-math></inline-formula> information bits. Finally, we practically evaluate and confirm the improved performance of the proposed system over-the-air (OTA) using a software-defined radio (SDR)-based measurement testbed.
Generating Authorship Embeddings with Transformers

Authorship attribution and profiling tools provide useful instruments with wide areas of application, such as disinformation spreaders detection. Models developed until now to fulfil these tasks usually rely on manually crafted features or on a training process restricted and limited by the number of authors involved. Besides, current methods have limited capacity to generate a broad representation of the author, without considering a great variety of features that can be extracted from their texts. In this paper, we propose a contrastive training method to generate representative embeddings of the authorship of a text and able to generalize to unseen authors. The core of this method is the Transformer architecture, which is known to generate very powerful semantically-aware text representations. Using a pretrained RoBERTa-large model, we evaluate our method on the environment of the standardized Gutenberg corpus, detecting the authorship of literary works. Representations generated by our proposal can be used to extract representative authorship embeddings and to visualize meaningful relationships between authors, genres and books. Furthermore, the embedding method achieves zero-shot 79% accuracy and 94% top-5 accuracy when tasked to distinguish a text piece from a set of 100 authored texts. Our code is readily available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/jahuerta92/authorship-embedding
A Machine Learning Approach to Model HRI Research Trends in 2010~2021

The present study collects a large amount of HRI-related research studies and analyzes the research trends from 2010 to 2021. Through the topic modeling technique, our developed ML model is able to retrieve the dominant research factors. The preliminary results reveal five important topics, handover, privacy, robot tutor, skin de deformation, and trust. Our results show the research in the HRI domain can be divided into two general directions, namely technical and human aspects regarding the use of robotic applications. At this point, we are increasing the research pool to collect more research studies and advance our ML model to strengthen the robustness of the results.