Reviewerly/exHarmony huggingface dataset:
DINER: Disorder-Invariant Implicit Neural Representation

Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problems. However, the capacity of INR is limited by the spectral bias in the network training. In this paper, we find that such a frequency-related problem could be largely solved by re-arranging the coordinates of the input signal, for which we propose the disorder-invariant implicit neural representation (DINER) by augmenting a hash-table to a traditional INR backbone. Given discrete signals sharing the same histogram of attributes and different arrangement orders, the hash-table could project the coordinates into the same distribution for which the mapped signal can be better modeled using the subsequent INR network, leading to significantly alleviated spectral bias. Experiments not only reveal the generalization of the DINER for different INR backbones (MLP vs. SIREN) and various tasks (image/video representation, phase retrieval, and refractive index recovery) but also show the superiority over the state-of-the-art algorithms both in quality and speed. Project page: https://ezio77.github.io/DINER-website/
Context Query Generation using Scene Graph approach

Context-awareness (CA) has become an evolving trend, especially in the domain of Internet of Things (IoT). With the progress of IoT, the necessity for accessing real-time contextual information has become a critical factor for the advancement of IoT applications. Context management platforms (CMPs) have been proposed in the literature to support the needs of such Context-aware IoT applications. However, there are still significant gaps in terms of supporting the increasing needs of Context-aware applications, including the performance analysis of CMPs. In this paper, we propose a scene-graph based approach to generate context queries which primarily intends to support the performance analysis of CMPs and its ability to support plethora of Context-aware IoT application needs. Given the situation driven nature of IoT applications, the ability to generate relevant queries needs to be very realistic. Hence, we propose a novel Situation State Machine based approach to capture and model real-world situations. To demonstrate the potential to generate relevant context queries based on dynamic situations, a bicycle dooring use case is considered. We then present a template-based query generation approach to create realistic queries that represent real-world IoT application environment. The dooring use case is considered to validate the ability to represent complex queries, and the ability to generate complex queries in linear time.
GLeaD: Improving GANs with A Generator-Leading Task

Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition. Towards a fairer game in GANs, we propose a new paradigm for adversarial training, which makes G assign a task to D as well. Specifically, given an image, we expect D to extract representative features that can be adequately decoded by G to reconstruct the input. That way, instead of learning freely, D is urged to align with the view of G for domain classification. Experimental results on various datasets demonstrate the substantial superiority of our approach over the baselines. For instance, we improve the FID of StyleGAN2 from 4.30 to 2.55 on LSUN Bedroom and from 4.04 to 2.82 on LSUN Church. We believe that the pioneering attempt present in this work could inspire the community with better designed generator-leading tasks for GAN improvement. Project page is at https://ezioby.github.io/glead/.
Cybersecurity in Fitness Devices-A Systematic Literature Review

Wearable devices are becoming increasingly popular, with users adopting them for a wide range of purposes. For example, fitness equipment can now perform new functions such as shopping or purchasing train tickets using contactless payments. Furthermore, fitness trackers gather a variety of personal data, including body temperature, pulse rate, eating habits, body weight, steps, distance travelled, calories burned, and sleep stage. Although these devices can be helpful to customers, more and more publications are warning about the cybersecurity threats they pose and the potential for them to be hijacked and used as launching grounds for other systems. Furthermore, because of their wireless broadcasts, these devices might be vulnerable to a malicious attack, exposing the data they gather. In addition, these devices are vulnerable due to a lack of authentication, Bluetooth connection difficulties, location monitoring, and third-party vulnerabilities. The purpose of this article is to offer consumers cybersecurity recommendations so that they may take precautions when using fitness devices.
Smart Contract Service Optimization in Blockchain-Cloud Collaborative Computing

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain.
Federated Neural Compression Under Heterogeneous Data

We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heterogeneous, yet share a common underlying representation. We propose a distributed source model that encompasses both characteristics, and naturally suggests a compressor architecture that uses analysis and synthesis transforms shared by clients. Inspired by personalized federated learning methods, we employ an entropy model that is personalized to each client. This allows for a global latent space to be learned across clients, and personalized entropy models that adapt to the clients’ latent distributions. We show empirically that this strategy outperforms solely local methods, which indicates that learned compression also benefits from a shared global representation in statistically heterogeneous federated settings.
SPIDVerify: A Secure and Privacy-Preserving Decentralised Identity Verification Framework

Traditional identity management (IdM) systems rely on third-party identity providers (IdPs) and are centralised, which can make them vulnerable to data breaches and other security risks. Self-sovereign identity (SSI) is a newer IdM model that allows users to control their own identities by using decentralised technologies like blockchain to store and verify them. However, SSI systems have their own security concerns, such as digital wallet vulnerabilities, blockchain threats and conflicts with general data protection regulation (GDPR). Additionally, the lack of incentives for issuers, verifiers and data owners could limit its acceptance. This paper proposes SPIDVerify, which is a decentralised identity verification framework that utilises an SSI-based architecture to address these issues. The framework uses a mixed method for acquiring a W3C standard verified credentials and to ensure that only a thoroughly verified entity acquires verified credential, and employs secure key cryptographic protocols; Diffie-Hellman (DH) and Extended Triple Diffie-Hellman (X3DH) for forward secrecy secure communication, single-use challenge-response for authentication, and Swarm network for decentralised storage of data. These methods enhance the security of the proposed framework with better resilience against impersonation and credential stealing. To evaluate the proposal, we have outlined the limitations in related works and demonstrated two scenarios to showcase the strength and effectiveness of SPIDVerify in dealing with the threats identified. We have also tested the methods used in SPIDVerify by measuring the time taken to execute certain processes.
AccelIR: Task-aware Image Compression for Accelerating Neural Restoration

Recently, deep neural networks have been successfully applied for image restoration (IR) (e.g., super-resolution, de-noising, de-blurring). Despite their promising performance, running IR networks requires heavy computation. A large body of work has been devoted to addressing this issue by designing novel neural networks or pruning their parameters. However, the common limitation is that while images are saved in a compressed format before being enhanced by IR, prior work does not consider the impact of compression on the IR quality. In this paper, we present AccelIR, a framework that optimizes image compression considering the end-to-end pipeline of IR tasks. AccelIR encodes an image through IR-aware compression that optimizes compression levels across image blocks within an image according to the impact on the IR quality. Then, it runs a lightweight IR network on the compressed image, effectively reducing IR computation, while maintaining the same IR quality and image size. Our extensive evaluation using nine IR networks shows that AccelIR can reduce the computing overhead of super-resolution, de-nosing, and de-blurring by 49%, 29%, and 32% on average, respectively.
TOPLight: Lightweight Neural Networks with Task-Oriented Pretraining for Visible-Infrared Recognition

Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images. Most existing works achieve promising results by transfer learning, such as pretraining on the ImageNet, based on advanced neural architectures like ResNet and ViT. However, such methods ignore the neg-ative influence of the pretrained colour prior knowledge, as well as their heavy computational burden makes them hard to deploy in actual scenarios with limited resources. In this paper, we propose a novel task-oriented pretrained lightweight neural network (TOPLight) for VI recognition. Specifically, the TOPLight method simulates the domain conflict and sample variations with the proposed fake do-main loss in the pretraining stage, which guides the network to learn how to handle those difficulties, such that a more general modality-shared feature representation is learned for the heterogeneous images. Moreover, an effective fine-grained dependency reconstruction module (FDR) is developed to discover substantial pattern dependencies shared in two modalities. Extensive experiments on VI person re-identification and VI face recognition datasets demonstrate the superiority of the proposed TOPLight, which signifi-cantly outperforms the current state of the arts while de-manding fewer computational resources.
Quantum Multi-Model Fitting

Geometric model fitting is a challenging but fundamental computer vision problem. Recently, quantum optimization has been shown to enhance robust fitting for the case of a single model, while leaving the question of multi-model fitting open. In response to this challenge, this paper shows that the latter case can significantly benefit from quantum hardware and proposes the first quantum approach to multimodel fitting (MMF). We formulate MMF as a problem that can be efficiently sampled by modern adiabatic quantum computers without the relaxation of the objective function. We also propose an iterative and decomposed version of our method, which supports real-world-sized problems. The experimental evaluation demonstrates promising results on a variety of datasets. The source code is available at: https://github.com/FarinaMatteo/qmmf.
ReCo: Region-Controlled Text-to-Image Generation

Recently, large-scale text-to-image (T2I) models have shown impressive performance in generating high-fidelity images, but with limited controllability, e.g., precisely specifying the content in a specific region with a free-form text description. In this paper, we propose an effective technique for such regional control in T2I generation. We augment T2I models' inputs with an extra set of position tokens, which represent the quantized spatial coordinates. Each region is specified by four position tokens to represent the top-left and bottom-right corners, followed by an open-ended natural language regional description. Then, we fine-tune a pre-trained T2I model with such new input interface. Our model, dubbed as ReCo (Region-Controlled T2I), enables the region control for arbitrary objects described by open-ended regional texts rather than by object labels from a constrained category set. Empirically, ReCo achieves better image quality than the T2I model strengthened by positional words (FID: 8.82 → 7.36, SceneFID: 15.54 → 6.51 on COCO), together with objects being more accurately placed, amounting to a 20.40% region classification accuracy improvement on COCO. Furthermore, we demonstrate that ReCo can better control the object count, spatial relationship, and region attributes such as color/size, with the free-form regional description. Human evaluation on PaintSkill shows that ReCo is +19.28% and +17.21% more accurate in generating images with correct object count and spatial relationship than the T2I model. Code is available at https://github.com/microsoft/Reeo.
Fading Channel Classification with Walsh-Hadamard Transform and Convolutional Neural Network

Fading channel classification is a useful function in the design of wireless communications because the knowledge of the channel state information can help the subsequent steps in the wireless communication processing including the information symbols extraction from the received signal. This paper proposes the application of the Walsh-Hadamard Transform (WHT) in combination with Convolutional Neural Network (CNN) for the problem of fading channel classification. WHT belongs to the generalized class of Fourier transforms and it is a non-sinusoidal, orthogonal transformation technique that decomposes a signal into a set of Walsh functions. WHT has been used in image processing but less in the wireless communication domain. CNN has been recently used in many wireless communications problems including fading channel classification, where it has shown to outperform ’shallow’ machine learning algorithms. This paper presents the novel combination of WHT with CNN for the problem of channel classification. The approach is applied to a data set of chirp signals derived from the technical specification of the radar altimeter, which is submitted to different fading conditions in a channel emulator implemented with FPGA in a radio frequency laboratory. The results show that the proposed approach is able to significantly outperform (especially in presence of noise) the application of CNN on the original time-based representation of the signal or the spectral domain representation based on the use of the Fourier transform and Wavelet transform.
Majorizing Measures, Codes, and Information

The majorizing measure theorem of Fernique and Talagrand is a fundamental result in the theory of random processes. It relates the boundedness of random processes indexed by elements of a metric space to complexity measures arising from certain multiscale combinatorial structures, such as packing and covering trees. This paper builds on the ideas first outlined in a little-noticed preprint of Andreas Maurer to present an information-theoretic perspective on the majorizing measure theorem, according to which the boundedness of random processes is phrased in terms of the existence of efficient variable-length codes for the elements of the indexing metric space.
Monocular Scene Flow Estimation with Convolution Vision Transformer

The process of artificial intelligence processing is inseparable from the perception of information in the scene, and the monocular scene flow estimation combining the optical flow and depth can reflect the motion information in the scene. In this paper, a monocular scene flow estimation network with convolution vision transformer(CVT) is proposed. Specifically, introducing CVT into the feature extraction part of the monocular scene flow estimation network can effectively capture local information while dynamically paying attention to global context, which solves the problems of large local redundancy and complex global dependencies in computer vision data. In addition, a single rectified linear unit(RELU) activation is used in the CVT to provide pixel-wise modeling capabilities to enrich the detail feature. On the KITTI driving dataset, the experimental results show that the proposed method improves the performance of the estimation of the network and has the competitive advantage.
Attention-based Insulator Defect Detection

With the rapid development of the electric system, the demand for auto-inspection of the electric transmission system, e.g., transmission lines, and insulators, is increasing. To achieve automatic insulator inspection, we propose a two-stage insulator defect inspection framework which consists of the attention-based insulator detection network and the defect inspection network. The attention-based insulator detection network is responsible for the positioning of the insulator and the defect inspection model determines whether the insulator is corrupted. The two-stage detect-then-inspect design avoids the influence of background and enables high-precision inspection. Experimental results on our collected dataset show that our model outperforms previous object detection method.
Context-aware Alignment and Mutual Masking for 3D-Language Pre-training

3D visual language reasoning plays an important role in effective human-computer interaction. The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks. Despite the encouraging progress in vision-language pre-training for image-text data, 3D-language pre-training is still an open issue due to limited 3D-language paired data, highly sparse and irregular structure of point clouds and ambiguities in spatial relations of 3D objects with viewpoint changes. In this paper, we present a generic 3D-language pre-training approach, that tackles multiple facets of 3D-language reasoning by learning universal representations. Our learning objective constitutes two main parts. 1) Context aware spatial-semantic alignment to establish fine-grained correspondence between point clouds and texts. It reduces relational ambiguities by aligning 3D spatial relationships with textual semantic context. 2) Mutual 3D-Language Masked modeling to enable cross-modality information exchange. Instead of reconstructing sparse 3D points for which language can hardly provide cues, we propose masked proposal reasoning to learn semantic class and mask-invariant representations. Our proposed 3D-language pre-training method achieves promising results once adapted to various downstream tasks, including 3D visual grounding, 3D dense captioning and 3D question answering. Our codes are available at https://github.com/leolyj/3D-VLP
Binary convolutional codes with optimal column distances

There exists a large literature of construction of convolutional codes with maximal or near maximal free distance. Much less is known about constructions of convolutional codes having optimal or near optimal column distances. In this paper, a new construction of convolutional codes over the binary field with optimal column distances is presented.
Optimal Uncorrelated Polyphase ZCZ Sequences over an Alphabet of Minimum size

In this paper, we derive a lower bound on the alphabet size of the optimal uncorrelated polyphase ZCZ sequence families, assuming that Mow’s Conjecture is true. We also propose some new optimal uncorrelated polyphase ZCZ sequence families for all the optimal ZCZ parameters. All our proposed families achieve our proposed alphabet size bound so that this bound is the minimum alphabet size (assuming Mow’s conjecture is true). We also derive some interesting facts regardless of the truth of Mow’s Conjecture: an optimal ZCZ family of normalized sequence (not necessarily uncorrelated) is always periodic complementary; therefore, a sequence generated by interleaving all the sequences in an optimal uncorrelated ZCZ family of normalized sequences is always a perfect sequence.
Indoor Navigation Assistance System for Visually Impaired with Semantic Segmentation using EdgeTPU

Recognizing the limited supply and high cost of alternative solutions for visual assistance, such as guides, it is critical to create an affordable and accessible means of recognizing walkable paths for visually impaired individuals. In this paper, we assess deep learning and traditional machine learning models for image segmentation and object detection to identify walkable, indoor paths in real-time, given low-resolution images from a camera. Specifically, we leverage the processing capabilities of Google’s EdgeTPU chip, which accelerates inferences of light-weight TensorFlow models deployed in embedded devices. We retrain the MobileNet computer vision model on the ADE MIT Scene Parsing Benchmark Dataset, and improve the performance accuracy by consolidating 150 categories into just two categories. The segmentation model is post-quantize-aware trained and co-compiled with a light-weight object detection model. The resulting model is capable of simultaneous semantic segmentation and object detection with inference times of 65 milliseconds. Our approach lays the foundation for transforming the level of assistance for the visually impaired to sense the world through a wearable device assistance system for indoor navigation.
A Strong Baseline for Generalized Few-Shot Semantic Segmentation

This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the well-known InfoMax principle, where the Mutual Information (MI) between the learned feature representations and their corresponding predictions is maximized. In addition, the terms derived from our MI-based formulation are coupled with a knowledge distillation term to retain the knowledge on base classes. With a simple training process, our inference model can be applied on top of any segmentation network trained on base classes. The proposed inference yields substantial improvements on the popular few-shot segmentation benchmarks, PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> . Particularly, for novel classes, the improvement gains range from 7% to 26% (PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> ) and from 3% to 12% (COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> ) in the 1-shot and 5-shot scenarios, respectively. Furthermore, we propose a more challenging setting, where performance gaps are further exacerbated. Our code is publicly available at https://github.com/sinahmr/DIaM.