Reviewerly/exHarmony huggingface dataset:
Workload Prediction of Cloud Workflow Based on Graph Neural Network

With the continuous expansion of cloud computing market, the problem of low utilization rate of cloud computing resource has become increasingly prominent, because cloud computing vendors can not schedule a large number of server cluster effectively as before. Improving the utilization rate of cloud resources can not only improve the net profit of cloud computing manufacturers, but also reduce the time cost and economic cost of cloud computing users. In addition to resource scheduling, the current research on cloud workflow load is still focused on single task or single instance prediction, and even the data sets used are simulation data. This paper aims to predict workload of cloud workflow resources to make the cloud computing resources get better scheduling, and ultimately facilitate all relevant personnel in the cloud computing market. Firstly, compared with task and single instance, cloud workflow can get more context information. Secondly, in order to make this research more practical, this paper selects Alibaba cluster data V2018 released by Alibaba in 2018 as our research object. Thirdly, based on the graph structure characteristics of cloud computing workflow, this paper selects the Graph Neural Network (GNN) architecture which closely fits the graph structure to predict the load of cloud computing workflow, and specifically selects the homogeneous Graph Convolution Neural Network and Graph Attention Neural Network and heterogeneous GCN as our prediction algorithm. And it describes how cloud workflow is modeled as homogeneous graph and heterogeneous graph in detail. Finally, the algorithm in GNN is used to classify and predict Ali data with workflow length ranges from 4 to 12 separately and combined, and predicts the last and penultimate tasks of each length workflow. Besides, all the data from 4 to 12 are combined into one data to predict the last and penultimate tasks.
A Reconfigurable Neural Network Processor With Tile-Grained Multicore Pipeline for Object Detection on FPGA

In order to improve the computational efficiency of convolutional neural networks (CNNs) for object detection on reconfigurable platforms such as field-programmable gate arrays (FPGAs), we propose a CNN processor with hierarchical pipelining and multicore reconfigurable computing based on parallel parameter constraints. First, we propose a pipelined multicore processing architecture that can adapt to computations and on-chip memory requirements of different convolutional layers. We present the design of a CNN processor that can configure the computing units while adjusting the interconnection of multicore to improve the utilization of reconfigurable computing resources. Second, we propose an elastic on-chip buffer and a data access approach by dynamically configuring addresses to better utilize on-chip memory. Meanwhile, we present a cross-layer feature map fusion strategy based on computing near memory (CNM) to reduce off-chip memory accesses. Finally, we propose a scheduling algorithm for pipelined tasks to improve the computational efficiency and throughput of the proposed processor. For evaluation, the well-known object detection methods (RetinaNet-ResNet-50, MobileNetV2-SSDLite, and YOLOv3) performed using the proposed CNN processor on the ZCU102 platform and reached the throughput of 1503, 1066, and 809 GOPS and the computational efficiency of 0.79, 0.62, and 0.36 GOPS/DSP, respectively. The designed processor realized a better tradeoff between computing efficiency and detection accuracy compared with the recently proposed object detection CNNs on FPGA.
Ideologies and Imaginaries in Blockchain Communities: The Case of Ethereum

Background: Academic literature on blockchains has focused on Bitcoin, which is traditionally associated with right-wing libertarianism. This article looks at Ethereum, an alternative that emerged in Canada and is now the second most used blockchain technology after Bitcoin. Analysis: Using participatory observation supplemented with publicly available material, this article examines the ideologies and imaginaries surrounding Ethereum and how they are articulated with its technical design. Conclusion and implications: Ethereum’s design ostensibly widens the ideological spectrum of cryptocurrency while “masking” certain currency ideologies still prominent within it. This complicates the distinction seen in the literature between blockchain as currency and blockchain as media and points to the increasing need to study non-currency-based blockchain technologies.
APAS: Application-Specific Accelerators for RLWE-Based Homomorphic Linear Transformations

Recently, the application of multi-party secure computing schemes based on homomorphic encryption in the field of machine learning attracts attentions across the research fields. Previous studies have demonstrated that secure protocols adopting packed additive homomorphic encryption (PAHE) schemes based on the ring learning with errors (RLWE) problem exhibit significant practical merits, and are particularly promising in enabling efficient secure inference in machine-learning-as-a-service applications. In this work, we introduce a new technique for performing homomorphic linear transformation (HLT) over PAHE ciphertexts. Using the proposed HLT technique, homomorphic convolutions and inner products can be executed without the use of number theoretic transform and the rotate-and-add algorithms that were proposed in existing works. To maximize the efficiency of the HLT technique, we propose APAS, a hardware-software co-design framework consisting of approximate arithmetic units for the hardware acceleration of HLT. In the experiments, we use actual neural network architectures as benchmarks to show that APAS can improve the computational and communicational efficiency of homomorphic convolution by 8× and 3×, respectively, with an energy reduction of up to 26× as compared to the ASIC implementations of existing methods.
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay

We present Gaussian Mixture Replay (GMR), a rehearsal-based approach for continual learning (CL) based on Gaussian Mixture Models (GMM). CL approaches are intended to tackle the problem of catastrophic forgetting (CF), which occurs for Deep Neural Networks (DNNs) when sequentially training them on successive sub-tasks. GMR mitigates CF by generating samples from previous tasks and merging them with current training data. GMMs serve several purposes here: sample generation, density estimation (e.g., for detecting outliers or recognizing task boundaries) and providing a high-level feature representation for classification. GMR has several conceptual advantages over existing replay-based CL approaches. First of all, GMR achieves sample generation, classification and density estimation in a single network structure with strongly reduced memory requirements. Secondly, it can be trained at constant time complexity w.r.t. the number of sub-tasks, making it particularly suitable for life-long learning. Furthermore, GMR minimizes a differentiable loss function and seems to avoid mode collapse. In addition, task boundaries can be detected by applying GMM density estimation. Lastly, GMR does not require access to sub-tasks lying in the future for hyper-parameter tuning, allowing CL under real-world constraints. We evaluate GMR on multiple image datasets, which are divided into class-disjoint sub-tasks.
Understanding Bystanders’ Tendency to Shoulder Surf Smartphones Using 360-degree Videos in Virtual Reality

Shoulder surfing is an omnipresent risk for smartphone users. However, investigating these attacks in the wild is difficult because of either privacy concerns, lack of consent, or the fact that asking for consent would influence people's behavior (e.g., they could try to avoid looking at smartphones). Thus, we propose utilizing 360-degree videos in Virtual Reality (VR), recorded in staged real-life situations on public transport. Despite differences between perceiving videos in VR and experiencing real-world situations, we believe this approach to allow novel insights on observers' tendency to shoulder surf another person's phone authentication and interaction to be gained. By conducting a study (N=16), we demonstrate that a better understanding of shoulder surfers' behavior can be obtained by analyzing gaze data during video watching and comparing it to post-hoc interview responses. On average, participants looked at the phone for about 11% of the time it was visible and could remember half of the applications used.
Membership-Mappings for Data Representation Learning: A Bregman Divergence Based Conditionally Deep Autoencoder

This paper suggests to use membership-mapping as the building block of deep models. An alternative idea of deep autoencoder, referred to as Bregman Divergence Based Conditionally Deep Autoencoder (that consists of layers such that each layer learns data representation at certain abstraction level through a membership-mappings based autoencoder), is presented. A multi-class classifier is presented that employs a parallel composition of conditionally deep autoencoders to learn data representation for each class. Experiments are provided to demonstrate the competitive performance of the proposed framework in classifying high-dimensional feature vectors and in rendering robustness to the classification.
Extreme Learning Machine (ELM) for Fast User Clustering in Downlink Non-Orthogonal Multiple Access (NOMA) 5G Networks

Non-orthogonal multiple access (NOMA) has been envisaged as a successor of orthogonal multiple access (OMA) in the fifth generation (5G) networks and beyond because NOMA has been theoretically and empirically proven to be more bandwidth-efficient than OMA. Nevertheless, user clustering (UC) in NOMA is another prevalent issue. To maximize the throughput and fulfill the successive interference cancellation (SIC) constraints, the UC has been formulated as a clustering optimization problem which has been extensively researched in literature. Recently, an artificial neural network-based UC (ANN-UC) scheme has emerged as a viable solution that can optimally cluster users after exhaustive training. However, the ANN model has an extremely slow learning speed, due to the gradient-based back-propagation (BP) algorithm used by the ANN. To address these issues, this paper proposes a novel fast-learning extreme learning machine-based UC (ELM-UC) scheme. Unlike the ANN-UC technique, the input weights and the bias for the hidden layer nodes of ELM are randomly generated and tuning of parameters is not required, thereby leading to a faster learning rate. In this work, the ELM architecture is adapted to operate in NOMA environments where the optimal cluster formation can be predicted rapidly based on the users’ channel gains and powers. Performance comparisons with the state-of-the-art UC schemes are investigated via extensive simulations. Remarkably, simulation results demonstrate that the proposed ELM-UC can achieve near-optimal performance compared to the brute-force search (BF-S) method and outperforms the existing clustering techniques including ANN-UC and dynamic user clustering (DUC).
Heterogeneous Graph Gated Attention Network

Heterogeneous graph containing different types of nodes or links is one of graph types, which is most relevant to actual problems. However, the research for heterogeneous graph has not been studied adequately. In this paper, we propose a new model named Heterogeneous Graph Gated Attention Network (HGGAN) to process heterogeneous graph, including node feature space unification, center-neighbor nodes (C-N) aggregation and metapath-metapath (M-M) aggregation. Especially, we use multihead attention mechanism in C-N aggregation. Owing to the contribution of each attention head is different, so we use a convolutional sub-network to assign a parameter to reflect the contribution of different attention heads. Experimental results on three real-word heterogeneous datasets show that HGGAN achieves state-of-the-art results on node classification task.
Federated Reservoir Computing Neural Networks

A critical aspect in Federated Learning is the aggregation strategy for the combination of multiple models, trained on the edge, into a single model that incorporates all the knowledge in the federation. Common Federated Learning approaches for Recurrent Neural Networks (RNNs) do not provide guarantees on the predictive performance of the aggregated model. In this paper we show how the use of Echo State Networks (ESNs), which are efficient state-of-the-art RNN models for time-series processing, enables a form of federation that is optimal in the sense that it produces models mathematically equivalent to the corresponding centralized model. Furthermore, the proposed method is compliant with privacy constraints. The proposed method, which we denote as Incremental Federated Learning, is experimentally evaluated against an averaging strategy on two datasets for human state and activity recognition.
Hill Climb Modular Assembler Encoding: Evolving Modular Neural Networks of fixed modular architecture

The paper presents a novel generative Neuro-Evolutionary (NE) method called Hill Climb Modular Assembler Encoding (HCMAE). The target application of the HCMAE is to evolve modular Artificial Neural Networks (ANNs) whose modular structure is known in advance. Different variants of HCMAE were tested on two well-known ANN benchmarks, i.e. the Two-Spiral problem (feed-forward ANNs), and the Inverted-Pendulum problem (recurrent ANNs), for four different modular neural architectures. Particle Swarm Optimization and Differential Evolution were selected as rivals for HCMAE. Both rival methods were tested in two variants, i.e. a classical one-population variant and cooperative co-evolutionary multi-population variant. The paper presents the proposed method and reports all the experiments.
A 3D image encryption algorithm based on chaos and random cross diffusion

3D image is a kind of image closer to life, and 3D image has a stronger visual impact. In the past, researchers mostly protected flat images, but ignored the protection of 3D images. This paper combines the 2D chaotic system and uses random cross diffusion to propose a new encryption algorithm for 3D images which are STL format. Different from the traditional scrambling and diffusion operations, this paper uses only one iterative operation to achieve the effects of scrambling and diffusion, making the algorithm more secure. In the encryption algorithm, each value of the ciphertext is XORed with the value of the plaintext on the same coordinate axis and different from the coordinate where the ciphertext is located, the chaotic sequence, the ciphertext of different coordinate axes significantly reduces the correlation between the adjacent coordinate values. In addition, the proposed 3D image encryption algorithm is tested by statistical analysis. The experimental results show that this 3D image encryption algorithm has high security.
Channel Estimation and User Identification With Deep Learning for Massive Machine-Type Communications

In this paper, we investigate the detection problem for a massive machine-type communication (mMTC) system that has correlated user activities. Two deep learning assisted algorithms are proposed to exploit the user activity correlation to facilitate channel estimation and user identification. Due to the dependency among user activities, conventional element-wise minimum mean square error (MMSE) denoiser used in the orthogonal approximate message passing (OAMP) algorithm cannot achieve satisfying performance during the two-step iterative process. Therefore, we propose a deep learning modified OAMP (DL-mOAMP) algorithm, which iteratively modifies the user activity ratio via exploiting the user activity correlation in the MMSE denoiser based on the estimated sequence during each OAMP iteration. Moreover, given a specific false alarm probability, a constant threshold employed in the conventional user identification is not optimal in the presence of user activity correlation. Thus, we propose a neural network framework that is dedicated to the user identification (DL-mOAMP-UI algorithm), which minimizes the missed detection probability under a pre-determined false alarm probability. Numerical results show that the proposed DL-mOAMP algorithm provides a substantial mean squared error performance gain compared to the conventional OAMP algorithm and the DL-mOAMP-UI algorithm can further improve the user identification accuracy of an mMTC system.
An optimal variable exponent model for Magnetic Resonance Images denoising

This paper investigates a novel PDE-constrained optimization model with discontinuous variable exponent p(x) identification. Since the parameter p is always related to a better approximation of the image gradient, its computation plays a critical role in preserving the image texture. Analytically, we include results on the approximation of this parameter as well as the resolution of the encountered PDE in a well posed framework. In addition, to resolve the PDE-constrained minimization problem, we proposed a modified primal-dual algorithm. Finally, numerical results are provided to compute the parameter p and also to remove high intensity of noise. The proposed algorithm simultaneously keep safe fine details and important features in medical image applications (Magnetic Resonance Images (MRI)) with numerous comparisons to show the performance of the proposed approach.
Modeling Multi-factor and Multi-faceted Preferences over Sequential Networks for Next Item Recommendation

Attributes of items carry useful information for accurate recommendations. Existing methods which tried to use items' attributes relied on either 1) feature-level compression which may introduce much noise information of irrelevant attributes, or 2) item- and attribute- level transition modeling which ignored the mutual effects of multi-factor for users' behaviors. In addition, these methods failed to capture multi-faceted preferences of users, therefore, the prediction for the next behavior may be affected or misled by the irrelevant facets of preferences. To address these problems, we propose a Sequential Network based Recommendation model, named SNR, to extract and utilize users' multi-factor and multi-faceted preferences for next item recommendation. To model users' multi-factor preferences, we organize the item- and attribute- level sequences of users' behaviors as unified sequential networks, and propose an attentional gated Graph Convolutional Network model to explore the mutual effects of the preference factors contained in sequential networks. To capture users' multi-faceted preferences, we propose a multi-faceted preference learning model to simulate the decision-making process of users with the Gumbel sotfmax trick. Finally, we fuse the multi-factor and multi-faceted preferences in a unified latent space for next item recommendation. Extensive experiments on four real-world data sets show that the proposed model SNR consistently outperforms several state-of-the-art methods.
Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming

We consider requirements for cyber-physical systems represented in constrained natural language. We present novel automated techniques for aiding in the development of these requirements so that they are consistent and can withstand perceived failures. We show how cyber-physical systems' requirements can be modeled using the event calculus (EC), a formalism used in AI for representing actions and change. We also show how answer set programming (ASP) and its query-driven implementation s(CASP) can be used to directly realize the event calculus model of the requirements. This event calculus model can be used to automatically validate the requirements. Since ASP is an expressive knowledge representation language, it can also be used to represent contextual knowledge about cyber-physical systems, which, in turn, can be used to find gaps in their requirements specifications. We illustrate our approach through an altitude alerting system from the avionics domain.
End-to-end acoustic modelling for phone recognition of young readers

Automatic recognition systems for child speech are lagging behind those dedicated to adult speech in the race of performance. This phenomenon is due to the high acoustic and linguistic variability present in child speech caused by their body development, as well as the lack of available child speech data. Young readers' speech additionally displays peculiarities, such as slow reading rate and presence of reading mistakes, that hardens the task. This work attempts to tackle the main challenges in phone acoustic modelling for young child speech with limited data and improve understanding of strengths and weaknesses of a wide selection of model architectures in this domain. We find that transfer learning techniques are highly efficient on end-to-end architectures for adult-to-child adaptation with a small amount of child speech data. Through transfer learning, a Transformer model complemented with a Connectionist Temporal Classification (CTC) objective function, reaches a phone error rate of 28.1%, outperforming a state-of-the-art DNN–HMM model by 6.6% relative, as well as other end-to-end architectures by more than 8.5% relative. An analysis of the models' performance on two specific reading tasks (isolated words and sentences) is provided, showing the influence of the utterance length on attention-based and CTC-based models. The Transformer+CTC model displays an ability to better detect reading mistakes made by children, which can be attributed to the CTC objective function effectively constraining the attention mechanisms to be monotonic.
Deep Learning based Automatic Modulation Classification Exploiting the Frequency and Spatiotemporal Domain of Signals

Automatic modulation classification (AMC), which aims to identify the modulation types of unknown signals without any prior knowledge, plays a key role in intelligent wireless communication. In recent years, the outstanding achievements of deep learning in the fields of computer vision and nature language processing have promoted the continuous researches of deep learning in AMC. In view of the fact that the existing deep learning-based AMC ignores the frequency attribute of the modulated signals, this paper proposed an AMC approach based on deep learning which comprehensively considers the contribution of frequency and spatiotemporal characteristics of the modulated signals. Specifically, we utilize the sliding window to divide the raw signals into short-time signal slices. In order to obtain the representation of frequency domain of the signals, each short-time signal slice is decomposed into several modes by variational mode decomposition (VMD), which is an adaptive signal decomposition technique. Finally, all the modes are used as input of the convolutional neural network (CNN) as a tensor to learn high-level features from the frequency and spatiotemporal domain to identify the modulation type. In this paper, we verify the effectiveness of the proposed approach with 10 kinds of modulation types generated by SMW200A Vector Signal Generator. More importantly, the proposed approach achieves 99.2% of the overall classification accuracy at 8dB signal to noise ratio (SNR) and shows better robustness to noise than the existing AMC approaches.