Reviewerly/exHarmony huggingface dataset:
Y-Vector: Multiscale Waveform Encoder for Speaker Embedding

State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies as speech features. Recent studies attempted to extract speaker embeddings directly from raw waveforms and have shown competitive results. In this paper, we propose a novel multi-scale waveform encoder that uses three convolution branches with different time scales to compute speech features from the waveform. These features are then processed by squeeze-and-excitation blocks, a multi-level feature aggregator, and a time delayed neural network (TDNN) to compute speaker embedding. We show that the proposed embeddings outperform existing raw-waveform-based speaker embeddings on speaker verification by a large margin. A further analysis of the learned filters shows that the multi-scale encoder attends to different frequency bands at its different scales while resulting in a more flat overall frequency response than any of the single-scale counterparts.
Synthesizing Five-Body Interaction in a Superconducting Quantum Circuit

Synthesizing many-body interaction Hamiltonians is a central task in quantum simulation. However, it is challenging to synthesize Hamiltonians that have more than two spins in a single term. Here we synthesize m-body spin-exchange Hamiltonians with m up to 5 in a superconducting quantum circuit by simultaneously exciting multiple independent qubits with time-energy correlated photons generated from a qudit. The dynamic evolution of the m-body interaction is governed by the Rabi oscillation between two m-spin states, in which the states of each spin are different. We demonstrate the scalability of our approach by comparing the influence of noises on the three-, four- and five-body interaction and building a many-body Mach-Zehnder interferometer which potentially has a Heisenberg-limit sensitivity. This study paves a way for quantum simulation involving many-body interaction Hamiltonians such as lattice gauge theories in quantum circuits.
DSPCANet: Dual-Channel Scale-Aware Segmentation Network With Position and Channel Attentions for High-Resolution Aerial Images

As a basic research topic in the field of remote sensing, semantic segmentation of high-resolution aerial images has broad application prospects. However, most existing semantic segmentation methods usually extract multiscale features of images in a hierarchical manner and fail to make full use of the surface information from high-resolution remote sensing images. To address the above problem, we propose a novel dual-channel scale-aware segmentation network with position and channel attentions (DSPCANet) for high-resolution aerial images, which contains an Xception branch and a digital surface model-based position and channel attention fusion (DSMPCF) branch to process the near-infrared, red, and green (IRRG) spectral images and DSM images, respectively. First, the inner residual block (R2_Block) module represents the multiscale features at the granularity level and increases the range of the receptive field of each network layer. Furthermore, channel attention module (CAM) and improved position attention module (IPAM) are developed to embed into the DSMPCF branch to learn the geographic feature representation from the DSM images, while the Xception branch is applied to process the IRRG spectral images. Finally, in the fusion part of the proposed model, IPAM and CAM are further utilized to effectively model the fusion features from the spatial and channel dimensions, obtain the class-based correlation, and recalibrate the class-level information. The proposed DSPCANet model is evaluated on the ISPRS Vaihingen and Potsdam datasets, and the extensive experiments demonstrate that it is more accurate and efficient than other state-of-the-art methods.
Integrating vertex and edge features with Graph Convolutional Networks for skeleton-based action recognition

Methods based on Graph Convolutional Networks(GCN) for skeleton-based action recognition have achieved great success due to their ability to exploit graph structural information from skeleton data. Recently, the bone information has attracted considerable attention as an effective modality which complements the more conventional joint information for action recognition. However, most existing GCN-based methods extract the joint and bone features with two separate GCN networks, ignoring the dependencies between them. In this paper, a novel GCN model is proposed to exploit the information across joints, bones and their relationship collaboratively on a single undirected graph instead of two separate networks. We call the proposed model Vertex-Edge Graph Convolutional Network (VE-GCN) since it conducts the graph convolution operation on the sampling area containing the designated vertexes from joints and edges from bones, respectively. In addition to conducting the Vertex-Edge graph convolution based on the physical connections of the skeleton, we further apply the Vertex-Edge graph convolution to the non-physical joint-joint and joint-bone connections to capture the distal dependencies, and then the convolution results on the non-physical connections are incorporated into the VE-GCN. Moreover, the Conditional Random Field (CRF) is adopted as the loss function to achieve the task of action recognition. Experimental results on four challenging benchmarks (NTU RGB+D, NTU RGB+D 120, N-UCLA, SYSU) show that the proposed model achieves state-of-the-art performance.
An Exemplary Template Matching Techniques for Counterfeit Currency Detection

In recent years, a lot of illegal fake notes have been created, and printed without any permission from the government. This leads to great loss for the government by using these fake notes. Currency plays a major role in the development of the society or nation. India has already been cursed with some major problems like black money and corruption. This proposed system describes the method for validating currency notes. The given currency will be validated using image processing methods. The currency image is showed in the dissimilarity space. We use many kinds of pixel levels for detection of the fake currency. In previous days only some places used to print this fake currency, but nowadays anyone can print the fake note using a simple laser printer with accuracy. At present, fake Currency recognition is being a vigorous topic for researchers in many potential applications. Pattern recognition and neural network pattern recognition are some of the techniques for detecting fake note. Here for detecting the fake note we use MATLAB. It is used for recognizing fake currency.
Blockchain for genomics and healthcare: a literature review, current status, classification and open issues

The tremendous boost in the next generation sequencing technologies and in the "omics" technologies resulted in the generation of hundreds of gigabytes of data per day. Nowadays, via integrating -omics data with other data types, such as imaging and electronic health record (EHR) data, panomics studies attempt to identify novel and potentially actionable biomarkers for personalized medicine applications. In this respect, for the accurate analysis of -omics data and EHR, there is a need to establish secure and robust pipelines that take the ethical aspects into consideration, regulate privacy and ownership issues, and data sharing. These days, blockchain technology has picked up significant attention in diverse fields, including genomics, since it offers a new solution for these problems from a different perspective. Blockchain is an immutable transaction ledger, which offers secure and distributed system without a central authority. Within the system, each transaction can be expressed with cryptographically signed blocks, and the verification of transactions is performed by the users of the network. In this review, firstly, we aim to highlight the challenges of EHR and genomic data sharing. Secondly, we attempt to answer "Why" or "Why not" the blockchain technology is suitable for genomics and healthcare applications in detail. Thirdly, we elucidate the general blockchain structure based on the Ethereum, which is a more suitable technology for the genomic data sharing platforms. Fourthly, we review current blockchain-based EHR and genomic data sharing platforms, evaluate the advantages and disadvantages of these applications, and classify these applications using different metrics. Finally, we conclude by discussing the open issues and introducing our suggestion on the topic. In summary, to facilitate the diagnosis, monitoring and therapy of diseases with the effective analysis of -omics data with other available data types, through this review, we put forward the possible implications of the blockchain technology to life sciences and healthcare.
A Blockchain Framework in Post-Quantum Decentralization

The decentralization and transparency have provided wide acceptance of blockchain technology in various sectors through numerous applications.The claimed security services by blockchain have been proved using various cryptographic techniques, mainly public key infrastructure and digital signatures.However, the use of generic cryptographic primitives using large prime numbers or elliptic curves with logarithms is going to be an issue with quantum computers as those techniques are vulnerable in post-quantum era.Therefore, the paradigm shift from pre-quantum to the post-quantum era has necessitated new cryptographic developments which are robust against quantum attacks and applicable in blockchain for post-quantum decentralization.Therefore, we have presented a solution for post-quantum decentralization in the blockchain.It uses lattices with polynomials for identity-based encryption (IBE) and aggregate signatures for the consensus to ensure efficiency and suitability in post-quantum blockchain applications.We experiment the proposed approach based on delay, throughput, energy consumption and complexity.The comparative results prove that the presented work is efficient.
"I'm a Professor, which isn't usually a dangerous job": Internet-facilitated Harassment and Its Impact on Researchers

While the Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars. To understand the impact that these attacks can have on the work of researchers, we perform a series of systematic interviews with researchers including academics, journalists, and activists, who have experienced targeted, Internet-facilitated harassment. We provide a framework for understanding the types of harassers that target researchers, the harassment that ensues, and the personal and professional impact on individuals and academic freedom. We then study preventative and remedial strategies available, and the institutions that prevent some of these strategies from being more effective. Finally, we discuss the ethical structures that could facilitate more equitable access to participating in research without serious personal suffering.
A fine-grained data set and analysis of tangling in bug fixing commits

Abstract Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.
Sentiment Classification for Financial Texts Based on Deep Learning

Sentiment classification for financial texts is of great importance for predicting stock markets and financial crises. At present, with the popularity of applications in the field of natural language processing (NLP) adopting deep learning, the application of automatic text classification and text-based sentiment classification has become more and more extensive. However, in the field of financial text-based sentiment classification, due to a lack of labeled samples, such applications are limited. A domain-adaptation-based financial text sentiment classification method is proposed in this paper, which can adopt source domain (SD) text data with sentiment labels and a large amount of unlabeled target domain (TD) financial text data as training samples for the proposed neural network. The proposed method is a cross-domain transfer-learning-based method. The domain classification subnetwork is added to the original neural network, and the domain classification loss function is also added to the original training loss function. Therefore, the network can simultaneously adapt to the target domain and then accomplish the classification task. The experiment of the proposed sentiment classification transfer learning method is carried out through an open-source dataset. The proposed method in this paper uses the reviews of Amazon Books, DVDs, electronics, and kitchen appliances as the source domain for cross-domain learning, and the classification accuracy rates can reach 65.0%, 61.2%, 61.6%, and 66.3%, respectively. Compared with nontransfer learning, the classification accuracy rate has improved by 11.0%, 7.6%, 11.4%, and 13.4%, respectively.
Point Cloud Projection and Multi-Scale Feature Fusion Network Based Blind Quality Assessment for Colored Point Clouds

With the wide applications of colored point cloud (CPC) in many fields, many attentions have been paid to CPC's distortions caused by its compression and reconstruction. How to effectively evaluate the visual quality of CPC has become an urgent issue to be resolved. In this paper, a Point cloud projection and Multi-scale feature fusion network based Blind Visual Quality Assessment method (denoted as PM-BVQA) is proposed for CPC. CPC in 3D space is first projected into 2D color projection map and geometric projection map, then a multi-scale feature fusion network is designed to blindly evaluate the visual quality of CPC. The proposed PM-BVQA method includes three modules, that is, joint color-geometric feature extractor, two-stage multi-scale feature fusion, and spatial pooling module. Considering the multi-channel characteristics of human visual system (HVS), unimodal features of different scales are obtained by joint color-geometric feature extractor from the color and geometric projection maps. The fusion of the unimodal color and geometric features is carried out to capture the cross-modal complementary information between these two types of information. By integrating cross-modal fused features at different scales, the complementary relationships between different channels of HVS are simulated. The spatial pooling module takes into account the attention mechanism of HVS and realizes the weighted summation of local regional quality to obtain the final global quality score of CPC. A subjective CPC database with coding distortion is used to verify the effectiveness of the proposed method, and the experimental results show that the proposed blind quality assessment method is more consistent with the subjective visual perception than the existing quality assessment methods.
Multi-Modal Neural Feature Fusion for Automatic Driving Through Perception-Aware Path Planning

Path planning is a significant and challenging task in the domain of automatic driving. Many applications, such as autonomous driving, robotic navigation and aircraft object tracking in complex and changing urban road scenes, need accurate and robust path planning by detecting obstacles in the forward direction. The traditional methods only rely on the path search method without considering the environmental factors, the vehicle path planning method cannot deal with the complex and changeable environment. To deal with above problems, we propose a perception-aware based multi-modal feature fusion approach that combines visual-inertial odometer (VIO) poses and semantic obstacles in the forward scene of vehicles to plan driving paths. The proposed method takes environment awareness as the guide and combines path search algorithm to realize path optimization task in complex environment. The proposed approach first uses a long short memory network (LSTM) to build a VIO that fuses visual and inertial data for pose estimation. To detect obstacles, the proposed method uses a segmentation model with a lightweight structure to extract semantic 3D landmarks. Finally, a path search strategy combining an A* algorithm and visual information is proposed to plan driving paths for intelligent vehicles. We estimate the proposed path planning method on assimilated scenes and public datasets (KITTI and Cityscapes) by using a micro controller (Jetson Xavier NX) installed on a small vehicle. We also show comparable results with path planning that only uses the greedy algorithm or heuristic algorithm without using visual information and show that our method is adequate in coping with different complex scenes.
Diagnosis of information scrambling from Hamiltonian evolution under decoherence

We apply a quantum teleportation protocol based on the Hayden-Preskill thought experiment to quantify how scrambling a given quantum evolution is. It has an advantage over the direct measurement of out-of-time ordered correlators when used to diagnose the information scrambling in the presence of decoherence effects stemming from a noisy quantum device. We demonstrate the protocol by applying it to two physical systems: Ising spin chain and SU(2) lattice Yang-Mills theory. To this end, we numerically simulate the time evolution of the two theories in the Hamiltonian formalism. The lattice Yang-Mills theory is implemented with a suitable truncation of Hilbert space on the basis of the Kogut-Susskind formalism. On a two-leg ladder geometry and with the lowest nontrivial spin representations, it can be mapped to a spin chain, which we call it Yang-Mills-Ising model and is also directly applicable to future digital quantum simulations. We find that the Yang-Mills-Ising model shows the signal of information scrambling at late times.
A Systematic Survey of Remote Sensing Image Captioning

Image captioning is a cross-disciplinary task to automatically generate textural descriptions for a given image using computer vision and natural language processing techniques. Remote sensing image captioning refers to the application of this task to remote sensing images taken from high altitude by satellites, aircraft or drones. This interesting and valuable topic has only emerged in recent years and attracted considerable research attention. There has been extensive related work in the literature, with considerable results and an independent body of research, and various issues must be addressed in future work. However, to the best of our knowledge, there has been no review study in this area that can provide researchers with systematic reference information, which is the motivation of this study. To achieve this goal, 30 relevant articles were conditionally filtered and obtained for the review study. We analyzed and summarized the existing work from various perspectives, including technical solutions, data, evaluation metrics, and the experimental results of state-of-the-art methods. Based on this summary, the trends, pros and cons of the existing studies, issues to be addressed and valuable research directions in future work are discussed. The results of this paper can provide valuable reference information for researchers in related fields.
Transitional Interfaces in Mixed and Cross-Reality: A new frontier?

Transitional interfaces (TIs) and related concepts such as cross-reality (XR) or cross-virtuality (XV) are key topics for future HCI and AR/VR research. Future TIs will enable users to freely move between different locations within the reality-virtuality continuum during work, to choose the best technologies for their task at hand and current information need. Our workshop will explore the core advantages and challenges of TIs and related concepts and address them in presentations and workshop activities at ACM ISS 2021.
Cloud to cloud data migration using self sovereign identity for 5G and beyond

The Coronavirus pandemic and the work-from-anywhere has created a shift toward cloud-based services. The pandemic is causing an explosion in cloud migration, expected that by 2025, 95% of workloads will live in the cloud. One of the challenges of the cloud is data security. It is the responsibility of cloud service providers to protect user data from unauthorized access. Historically, a third-party auditor (TPA) is used to provide security services over the cloud. With the tremendous growth of demand for cloud-based services, regulatory requirements, there is a need for a semi to fully automated self sovereign identity (SSI) implementation to reduce cost. It’s critical to manage cloud data strategically and extend the required protection. At each stage of the data migration process, such as data discovery, classification, and cataloguing of the access to the mission-critical data, need to be secured. Cloud storage services are centralized, which requires users must place trust in a TPA. With the SSI, this can become decentralized, reducing the dependency and cost. Our current work involves replacing TPA with SSI. A cryptographic technique for secure data migration to and from the cloud using SSI implemented. SSI facilitate peer-to-peer transactions, meaning that the in-between presence of TPA needs no longer be involved. The C2C migration performance is recorded and found the background or foreground replication scenario is achievable. Mathematically computed encrypted and decrypted ASCII values for a word matched with the output by the algorithm. The keys generated by the algorithm are validated with an online validator to ensure the correctness of the generated keys. RSA based mutual TLS algorithm is a good option for SSI based C2C migration. SSI is beneficial because of the low maintenance cost, and users are more and more using a cloud platform. The result of the implemented algorithm shows that the SSI based implementation can provide a 13.32 Kbps encryption/decryption rate which is significantly higher than the TPA method of 1 Kbps.
Featherweight go

We describe a design for generics in Go inspired by previous work on Featherweight Java by Igarashi, Pierce, and Wadler. Whereas subtyping in Java is nominal, in Go it is structural, and whereas generics in Java are defined via erasure, in Go we use monomorphisation. Although monomorphisation is widely used, we are one of the first to formalise it. Our design also supports a solution to The Expression Problem.