Reviewerly/exHarmony huggingface dataset:
ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds

In this paper, we are concerned with rotation equivariance on 2D point cloud data. We describe a particular set of functions able to approximate any continuous rotation equivariant and permutation invariant function. Based on this result, we propose a novel neural network architecture for processing 2D point clouds and we prove its universality for approximating functions exhibiting these symmetries. We also show how to extend the architecture to accept a set of 2D-2D correspondences as indata, while maintaining similar equivariance properties. Experiments are presented on the estimation of essential matrices in stereo vision.
CNN encryption using XOR Gate for Hardware Optimization

Artificial intelligence networks have been researched in many fields such as computer vision, health care, and military service. Convolutional neural network (CNN) is one of the basic neural networks that uses convolutional operations as the basis to train data and perform desired application. However, important parameters used in CNN applications suffer from security issues. Thus, the need for data protection is increasing. The traditional approach of storing training data and weight parameters encrypted in external memory has the disadvantage of large hardware area. In this paper, the CNN-based encrypting method using XOR gates is proposed to reduce hardware resources by allowing encryption and decryption to operate in a single module. The proposed encrypting method reduces hardware usage by more than 100 times compared to the conventional encrypting method.
Multi-complementary and unlabeled learning for arbitrary losses and models

A weakly-supervised learning framework named as complementary-label learning has been proposed recently, where each sample is equipped with a single complementary label that denotes one of the classes the sample does not belong to. However, the existing complementary-label learning methods cannot learn from the easily accessible unlabeled samples and samples with multiple complementary labels, which are more informative. In this paper, to remove these limitations, we propose the novel multi-complementary and unlabeled learning framework that allows unbiased estimation of classification risk from samples with any number of complementary labels and unlabeled samples, for arbitrary loss functions and models. We first give an unbiased estimator of the classification risk from samples with multiple complementary labels, and then further improve the estimator by incorporating unlabeled samples into the risk formulation. The estimation error bounds show that the proposed methods are in the optimal parametric convergence rate. We also propose a risk correction scheme for alleviating over-fitting caused by negative empirical risk. Finally, the experiments on both linear and deep models show the effectiveness of our proposed methods.
Spectral analysis for a multi-dimensional split-step quantum walk with a defect

This paper studies the spectrum of a multi-dimensional split-step quantum walk with a defect that cannot be analyzed in previous papers by Fuda et al. (Quantum Inf Process 18:203–226, 2017; J Math Phys 59:082201, 2018). To this end, we have developed a new technique which allow us to use a spectral mapping theorem for the one-defect model. We also derive the time-averaged limit measure for one-dimensional case as an application of the spectral analysis.
A Comparison Between External and Internal Cluster Validity Indices

This paper presents a comparison between external and internal cluster validity indices with a similar bounded index range. F-measure (FM) and Fowlkes-Mallows (FMI) of external validity indices, as well as Silhouette (SIL) of internal validity index, were chosen for this comparative analysis. Ten numerical data sets, namely Haberman, BUPA (liver disorder), Wisconsin Diagnostic Breast Cancer (WDBC), Iris, Seeds, Wine, User Knowledge, Cleveland, Segmentation, and Glass, were deployed to benchmark the clustering outcomes based on Fuzzy C-Mean (FCM) algorithm. Mean, minimum, and maximum scores were calculated to determine the similarities and differences among the indices. Pearson correlation was reported as well. As a result, the index scores displayed a slight difference between the external and internal validity indices. A moderate and positive correlation was noted between the external and internal validity index (r=. 66, r=.65 p<0.01) scores. This correlation signifies a similar graph pattern between the cluster validity indices. This comparative analysis revealed that the external and internal cluster validity indices with similar bounded index ranges and slightly different index scores generate a moderate and positive correlation with a similar graph pattern.
Resolvable cluster target tracking based on wavelet coefficients and JPDA

It is difficult to track the cluster target accurately because of the interaction between the clusters. In this paper, the tracking problem of cluster targets is studied. Firstly, the stochastic differential equation is used to model the cooperative motion of clusters, and the movement and measurement model of cluster target is derived. Secondly, the cluster tracking filter in the framework of Bayesian filtering is deduced, and a method of cluster partition using wavelet coefficients is proposed. Finally, in order to achieve measurement correlation in clutter environment, the JPDA target tracking algorithm is derived. The simulation results show the effectiveness of the proposed cluster target tracking algorithm in clutter environment.
Evaluating the ROCKY Countermeasure for Side-Channel Leakage

ROCKY is a recently introduced countermeasure against fault attacks for authenticated encryption algorithms. It is based on the random rotation of the internal state. In this work, we evaluate the effectiveness of ROCKY as a countermeasure against side-channel attacks. We implement four different types of FPGA-oriented architectures of Xoodoo: an unprotected version and three different versions protected with ROCKY. Xoodoo is used as round function of Xoodyak, which is a scheme in the NIST lightweight cryptography standardization competition. For the experimental setup, the SAKURA-G target board with Spartan-6 FPGA is used. The evaluation of the results is done through test vector leakage assessment (TVLA). This is the first work looking into the side-channel security of the ROCKY countermeasure.
Modeling of Industrial and Technological Processes in Complex Systems Based on Neuro-Fuzzy Petri Nets

Abstract For effective functioning within a complex system, industrial and technological processes are supported by appropriate information-analytical processes that ensure the collection and analysis of information, as well as modeling and making control decisions for the industrial and technological process. A variety of neuro-fuzzy Petri nets with temporal fuzzy neurons is proposed. An example of con-structing a model of an industrial and technological process and the corresponding information-analytical processes is considered. The developed specialized software for modeling industrial and technological processes and the implementation of information-analytical processes is considered.
Interpretable Machine Learning: A Case Study of Healthcare

With the evolution of artificial intelligence, Machine Learning (ML) techniques have become more powerful predictors, and accordingly, the use of ML techniques has become a part of our daily life in different application scenarios such as disease diagnosis, movie recommendation, monitoring system, or detection of malicious attacks. Although ML provides high accurate predictions, it suffers from opacity. By behaving like a black box it excluded users about how to reach particular decisions. Interpretable Machine Learning (IML) is a recent technology that offers a promising solution to the opaqueness problem of complex ML techniques. It provides transparency of how the inner workings of ML lead to certain decisions and allows users to be aware of the decision-making process. Especially, in critical scenarios such as healthcare, it may become extremely important to know the reasons that affect the decision as well as the result. In this study, we aim to show the benefits of IML over a healthcare case study. In experiments, we employ SHAP and LIME IML models for the Random Forest (RF) and Gradient Boosting (GB) algorithms for the problem of diagnosing diabetes and its explanations. Overall results exhibit that applying IML models to complex and hard-to-interpret ML techniques ensures detailed interpretability while maintaining accuracy. We also perform experiments for local interpretability by focusing on an instance, which is another advantage of IML.
TOCAB: A Dataset for Chinese Abusive Language Processing

This paper introduced TOCAB, a larger dataset for Chinese abusive language detection and classification. This dataset contains 121,344 real sentences collected from a social media site. Several baseline systems built by machine learning or deep learning were proposed to test this benchmark. BERT is the best baseline system which achieves F1-scores of 0.886 in detection and 0.781 in classification. The bootstrap aggregating BERT model, a state-of-the-art system, outperforms our BERT baseline system, with F1-scores of 0.893 in detection and 0.782 in classification.
Capturing Expert Knowledge for Building Enterprise SME Knowledge Graphs

Whilst Knowledge Graphs (KGs) are increasingly used in business scenarios, the construction of enterprise ontologies and the population of KGs from existing relational data remains a significant challenge. In this paper we report our experience in supporting CSols (an SME operating in the analytical laboratory domain) in transitioning their data from legacy databases to a bespoke KG. We modelled the KG using a streamlined approach based on state of the art ontology engineering methodologies, that addresses the challenges faced by SMEs when transitioning to new technologies: lack of resources to devote to the transition, paucity of comprehensive data governance policies, and resistance within the organisation to accepting new practices and knowledge. Our approach uses a combination of UML diagrams and a controlled language glossary to support stakeholders in reaching consensus during the knowledge capture phase, thus reducing the intervention of the ontology engineer only to cases where no agreement can be found. We present a case study illustrating the generation of the KG from a UML specification of part of the analytical domain and from legacy relational data, and we discuss the benefits and challenges of the approach.
Distributed Nonlinear-Polynomial Computing Based on a Group of Polynomials over a Galois Field in the FPGA Architecture

A technique is proposed for performing distributed nonlinear-polynomial (NP) computing over Galois field. The technique is based on representing this polynomial by a group of similar polynomials over a Galois field, the power of which will be considerably lower than that of the field, over which the initial polynomial was defined. Due to the joint computation of elementary polynomials for a group of polynomials, the estimates of the NP computational complexity were reduced. Moreover, distributed computation of a group of similar polynomials allows adjusting the NP computation process to the predefined hardware basis. This also includes adjusting to the basis of field-programmable gate arrays (FPGAs), which also comprises logic and storage devices.
Broadband Balanced Homodyne Detector for High-Rate (&gt;10 Gb/s) Vacuum-Noise Quantum Random Number Generation

We demonstrate a die-level balanced homodyne detector with a high quantum-to-classical noise clearance of 19.1dB over 3GHz bandwidth. We evaluate this receiver as a high-quality entropy source for random number generation at >10Gb/s and prove the functional integration of a classical 10Gb/s duobinary communication channel.
Investigating the Application of Augmented Reality to Support Wire Harness Activities

In the industrial context, the wire harness represents a process with a high degree of manual work and significant customizability of final products. To reduce the workers' mental demand, the augmented reality (AR) can be an effective tool. The existing industrial applications mainly focus on manual assembly assistance, but few solutions have been developed for the wire harness. This paper tries to enhance such a process by supporting operators with an AR application that shows the work instructions and allows reducing their cognitive workload. The main goals are to propose a user-friendly and versatile tool and carry out a structured and complete evaluation of the user experience. Two experimental sessions were conducted in the laboratory by simulating the wire harness assembly and quality inspection. The use of the AR application is efficient and effective especially when instructions far from the workstation are considered as a reference.
Weight Sparseness for a Feature-Map-Split-CNN Toward Low-Cost Embedded FPGAs

Convolutional neural network (CNN) has a high recognition rate in image recognition and are used in embedded systems such as smartphones, robots and self-driving cars. Low-end FPGAs are candidates for embedded image recognition platforms because they achieve real-time performance at a low cost. However, CNN has significant parameters called weights and internal data called feature maps, which pose a challenge for FPGAs for performance and memory capacity. To solve these problems, we exploit a split-CNN and weight sparseness. The split-CNN reduces the memory footprint by splitting the feature map into smaller patches and allows the feature map to be stored in the FPGA's high-throughput on-chip memory. Weight sparseness reduces computational costs and achieves even higher performance. We designed a dedicated architecture of a sparse CNN and a memory buffering scheduling for a split-CNN and implemented this on the PYNQ-Z1 FPGA board with a low-end FPGA. An experiment on classification using VGG16 shows that our implementation is 3.1 times faster than the GPU, and 5.4 times faster than an existing FPGA implementation.
Blockchain Enabled Electronics Medical System Proposed Framework with Research Directions

The upcoming industry is continuously changing, and the current systems and technologies are not suitable in their present form. They need to upgrade to solve the complex problems that are faced by the different applications. The healthcare industry is one of them. In the current healthcare industries, the patient’s information is stored in the central cloud servers, and the third party (TP) is required to share these details among the service providers. As a result, sharing information without adopting TP in a distributed manner is a challenging task. The distributed ledger technology blockchain has the potential to resolve the problems mentioned above. The blockchain provides the immutability, transparency, and traceability of records where the records are distributed among the many nodes. This article first presents the various problems of the current electronic medical record (EMR) system, discusses the importance of blockchain in EMR systems, and finally summarizes the existing approaches with their future scopes. Based on it, this paper proposed a novel architecture for a blockchain-enabled secure sharing of health records. In addition to it, possible research directions with the relevant security attacks for blockchain systems are also highlighted in the paper.
IFCM clustering segmentation based on genetic algorithm

In the early diagnosis of breast cancer(BC), computer aided design (CAD) is particularly important, and the accurate detection of breast mass in mammography plays an important role Objective: In order to distinguish the mass region from other background regions, an effective segmentation scheme was proposed in Mammographic Image Analysis Society (MIAS) segmentation Methods: First, the initial clustering center of intuitionistic fuzzy C-means (IFCM) is determined by genetic algorithm (GA), and then the image is segmented by IFCM algorithm to turn the random initial clustering center into a purpose-selected one, so as to ensure the optimal result of the final clustering center results: The average segmentation precision of MIAS.I images with noise level of 5%,7% and 9% were 90.15% and 86.85% and 87.31%. Conclusion: This method combines the advantages of the two algorithms to segment the location of mass more accurately and quickly.
A feasible and practically secure quantum bit commitment protocol

As a fundamental cryptographic primitive, bit commitment has lots of important and practical applications in modern cryptography. All previously proposed non-relativistic quantum bit commitment protocols cannot evade the Lo–Chau and Mayers attacks. Furthermore, relativistic quantum bit commitment protocols require rigorous spacetime constraints. In this paper, we present a simple, feasible but practically secure quantum bit commitment protocol without any spacetime constraint. The security of the proposed protocol is based on non-relativistic quantum mechanics, but it can resist all known attacks, including the Lo–Chau and Mayers attacks in practice.
An image classification method based on Echo State Network

As a traditional neural network architecture, Echo State Network(ESN) has achieved good results in time series forecasting. However, in the past ten years, few people have conducted research on its application to images, especially in classification tasks. Echo State Network is a kind of Recurrent Neural Network, it has the advantages of simple structure and convenient training. It only needs to train its output weight <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w_{out}$</tex> , which saves a lot of time and resources. In this article, we input the whole picture into ESN in multi-input mode to classify images, and use the traditional Long Short-Term Memory(LSTM) model to compare experiments with it, and the results found that esn has better stability and accuracy.
A Two Layer Dynamic Load Balancing Algorithm Applied in Cloud Computing

Cloud computing is one of the emergent technologies in the field of computing. It has proven computing as a Service. From individual users to large scale business organizations all are gradually migrating towards cloud. The number of request to the cloud is increasing day by day. It is becoming important to distribute the incoming loads optimally in different virtual machine for proper service delivery. In this paper, a dynamic load balancing is proposed which will distribute the group jobs according the priority to competent group of virtual machines based on performance tier, then at second layer in each group of virtual machine queued jobs are distributed considering network bandwidth, current load, memory capacity, security measure.
The Internet Is Dead: Long Live the Internet

Abstract Social exclusion, data exploitation, surveillance, and economic inequality on the web are mainly technological problems. The current web of centralized social media clouds delivers by design a winner-takes-all digital economy that stifles innovation and exacerbates power asymmetries between citizens, governments, and technology oligopolies. To fix the digital economy, we need a new, decentralized web where citizens are empowered to own their data, participate in disintermediated peer-to-peer marketplaces, and influence policy-making decisions by means of innovative applications of participatory and deliberative democracy. By reimagining “web 3.0” as a cloud commonwealth of networked virtual machines leveraging blockchains and sharing code, it is possible to design new digital business models where all stakeholders and participants, including users, can share the bounty of the Fourth Industrial Revolution fairly.
FMapper: Scalable read mapper based on succinct hash index on SunWay TaihuLight

One of the most important application in bioinformatics is read mapping. With the rapidly increasing number of reads produced by next-generation sequencing (NGS) technology, there is a need for fast and efficient high-throughput read mappers. In this paper, we present FMapper – a highly scalable read mapper on the TaihuLight supercomputer optimized for its fourth-generation ShenWei many-core architecture (SW26010). In order to fully exploit the computational power of the SW26010, we employ dynamic scheduling of tasks, asynchronous I/O and data transfers and implement a vectorized version of the banded Myers algorithm tailored to the 256 bit vector registers of the SW26010. Our performance evaluation demonstrates that FMapper using all 4 compute groups of a single SW26010 processor outperforms S-Aligner on the same hardware as well as RazerS3, Hobbes3, Minimap2 and BWA running on a 4-core Xeon W-2123v3 CPU and achieves speedups of 4.7, 24.8, 2.4, 4.6 and 14.7 respectively. Using several optimizations, we achieve a speedup of 6 compared to the naïve implementation on one compute group of an SW26010 processor and a strong scaling efficiency of 65% on 512 compute groups.
Synchronous development in open-source projects: A higher-level perspective

Abstract Mailing lists are a major communication channel for supporting developer coordination in open-source software projects. In a recent study, researchers explored temporal relationships (e.g., synchronization) between developer activities on source code and on the mailing list, relying on simple heuristics of developer collaboration (e.g., co-editing files) and developer communication (e.g., sending e-mails to the mailing list). We propose two methods for studying synchronization between collaboration and communication activities from a higher-level perspective, which captures the complex activities and views of developers more precisely than the rather technical perspective of previous work. On the one hand, we explore developer collaboration at the level of features (not files), which are higher-level concepts of the domain and not mere technical artifacts. On the other hand, we lift the view of developer communication from a message-based model, which treats each e-mail individually, to a conversation-based model, which is semantically richer due to grouping e-mails that represent conceptually related discussions. By means of an empirical study, we investigate whether the different abstraction levels affect the observed relationship between commit activity and e-mail communication using state-of-the-art time-series analysis. For this purpose, we analyze a combined history of 40 years of data for three highly active and widely deployed open-source projects: QEMU , BusyBox , and OpenSSL . Overall, we found evidence that a higher-level view on the coordination of developers leads to identifying a stronger statistical dependence between the technical activities of developers than a less abstract and rather technical view.
Adaptive Asynchronous Federated Learning for Edge Intelligence

Edge intelligence has received great attention for the rapid development and wild application of edge computing and artificial intelligence. As a key technology in edge intelligence for its ability of privacy protection, federated learning faces many problems when deployed in an edge environment. The staleness effect causes by device heterogeneity make the performance of synchronous federated learning limited by these slower devices. And algorithm may also obtain a worse model for statistical heterogeneity. In this paper, we develop an adaptive asynchronous federated learning for edge intelligence to solve the above problems. To improve accuracy and stability of the algorithm, we realize a balance between synchronous and asynchronous settings and implement an adaptive optimization method. At last, we verify the performance in an experiment with heterogeneous settings.