Reviewerly/exHarmony huggingface dataset:
Graph analytics and visualization for cyber situational understanding

This paper describes the Cyber Situational Understanding (Cyber SU) Proof of Concept (CySUP) software system for exploring advanced Cyber SU capabilities. CySUP distills complex interrelationships among cyberspace entities to provide the “so what” of cyber events for tactical operations. It combines a variety of software components to build an end-to-end pipeline for live data ingest that populates a graph knowledge base, with query-driven exploratory analysis and interactive visualizations. CySUP integrates with the core infrastructure environment supporting command posts to provide a cyber overlay onto a common operating picture oriented to tactical commanders. It also supports detailed analysis of cyberspace entities and relationships driven by ad hoc graph queries, including the conversion of natural language inquiries to formal query language. To help assess its Cyber SU capabilities, CySUP leverages automated cyber adversary emulation to carry out controlled cyberattack campaigns that impact elements of tactical missions.
Shipsrdet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation

High-resolution remote sensing images can provide abundant appearance information for ship detection. Although several existing methods use image super-resolution (SR) approaches to improve the detection performance, they consider image SR and ship detection as two separate processes and overlook the internal coherence between these two correlated tasks. In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet. In our method, we not only feed the super-resolved images to the detector but also integrate the intermediate features of the SR network with those of the detection network. In this way, the informative feature representation extracted by the SR network can be fully used for ship detection. Experimental results on the HRSC dataset validate the effectiveness of our method. Our ShipSRDet can recover the missing details from the input image and achieves promising ship detection performance.
Real-Time Driver Distraction Detection Using Lightweight Convolution Neural Network with Cheap Multi-scale Features Fusion Block

Degraded driving performance is closely related to distracted driving behavior. Therefore, in this paper we introduce a fast and accurate convolution neural network (CNN) architecture named CMFFNet, which is designed for the edge devices with limited computational resources and application scenario of detecting driver distraction. CMFFNet is built on a multi-branch architecture that uses CMFF blocks to guarantee efficiency. CMFF block is based on depth-wise separable convolution, which consists of a CMFF module, a multi-branch structure fuses filtered multi-scale spatial information to enhance feature representation capability, followed by a MAPGC bottleneck, utilizing two point-wise group convolutions and a channel shuffle operation to implement efficient channel projection. Compare with recently proposed lightweight CNNs, CMFFNet can achieve better performance on StateFarm’s Dataset, e.g. higher accuracy (absolute 5.5%) and lower latency (about 18 ms) compared with MobileNetV3-Large.
Applying Ethereum Smart Contracts to Blockchain-Based Crowdfunding System to Increase Trust and Information Symmetry

research-article Applying Ethereum Smart Contracts to Blockchain-Based Crowdfunding System to Increase Trust and Information Symmetry Share on Authors: Nik Azlina Nik Ahmad Universiti Kuala Lumpur, Malaysia Universiti Kuala Lumpur, MalaysiaView Profile , Syed Abdul Halim Syed Abdul Rahman PETRONAS, Malaysia PETRONAS, MalaysiaView Profile Authors Info & Claims ICCTA 2021: 2021 7th International Conference on Computer Technology ApplicationsJuly 2021 Pages 53–59https://doi.org/10.1145/3477911.3477920Online:15 October 2021Publication History 0citation72DownloadsMetricsTotal Citations0Total Downloads72Last 12 Months72Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep learning based sentiment analysis during public health emergency

The traditional method of text sentiment analysis mainly uses sentiment dictionary or machine learning, but sentiment dictionary method needs to update the thesaurus in time for microblog, which is noisy, has many new words, uses abbreviations to express emotions and the labor cost is high. Traditional machine learning methods can not extract salient features, resulting in low classification accuracy. In order to solve these problems, this paper proposes a model BERT_RCNN for sentiment analysis of netizens during public health emergency based on deep learning. BERT_RCNN first uses BERT to fine tune the input text, and trains the text into vectors to represent it. Then it takes the trained vectors as the input features of the upstream model and learns the microblog text features through RCNN network. Experiments show that: The accuracy of BERT_RCNN is 91.80%, which is better than 72.74% of SVM, 72.03% of logistic regression and 68.95% of naive Bayesian model. Better than other deep learning models BERT_Bi-LSTM, BERT_FC, BERT_DPCNN, ERNIE_FC with higher accuracy. Because it has faster convergence speed and shorter training time. Therefore, the proposed method achieves higher recognition accuracy with lower computational cost.
Sequential community mode estimation

We consider a population, partitioned into a set of communities, and study the problem of identifying the largest community within the population via sequential, random sampling of individuals. There are multiple sampling domains, referred to as boxes, which also partition the population. Each box may consist of individuals of different communities, and each community may in turn be spread across multiple boxes. The learning agent can, at any time, sample (with replacement) a random individual from any chosen box; when this is done, the agent learns the community the sampled individual belongs to, and also whether or not this individual has been sampled before. The goal of the agent is to minimize the probability of mis-identifying the largest community in a fixed budget setting, by optimizing both the sampling strategy as well as the decision rule. We propose and analyse novel algorithms for this problem, and also establish information theoretic lower bounds on the probability of error under any algorithm. In several cases of interest, the exponential decay rates of the probability of error under our algorithms are shown to be optimal up to constant factors. The proposed algorithms are further validated via simulations on real-world datasets.
LogicENN: A Neural Based Knowledge Graphs Embedding Model With Logical Rules

Knowledge graph embedding models have gained significant attention in AI research. The aim of knowledge graph embedding is to embed the graphs into a vector space in which the structure of the graph is preserved. Recent works have shown that the inclusion of background knowledge, such as logical rules, can improve the performance of embeddings in downstream machine learning tasks. However, so far, most existing models do not allow the inclusion of rules. We address the challenge of including rules and present a new neural based embedding model (LogicENN). We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph. To the best of our knowledge, this has not been proved so far for the neural based family of embedding models. Moreover, we derive formulae for the inclusion of various rules, including (anti-)symmetric, inverse, irreflexive and transitive, implication, composition, equivalence and negation. Our formulation allows to avoid grounding for implication and equivalence relations. Our experiments show that LogicENN outperforms the existing models in link prediction.
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments

We are interested in learning models of non-stationary environments, which can be framed as a multitask learning problem. Model-free reinforcement learning algorithms can achieve good asymptotic performance in multitask learning at a cost of extensive sampling, due to their approach, which requires learning from scratch. While model-based approaches are among the most data efficient learning algorithms, they still struggle with complex tasks and model uncertainties. Meta-reinforcement learning addresses the efficiency and generalization challenges on multi task learning by quickly leveraging the meta-prior policy for a new task. In this paper, we propose a meta-reinforcement learning approach to learn the dynamic model of a non-stationary environment to be used for meta-policy optimization later. Due to the sample efficiency of model-based learning methods, we are able to simultaneously train both the meta-model of the non-stationary environment and the meta-policy until dynamic model convergence. Then, the meta-learned dynamic model of the environment will generate simulated data for meta-policy optimization. Our experiment demonstrates that our proposed method can meta-learn the policy in a non-stationary environment with the data efficiency of model-based learning approaches while achieving the high asymptotic performance of model-free meta-reinforcement learning.
R(2+1)D-based Two-stream CNN for Human Activities Recognition in Videos

Two-stream convolutional neural network (CNN) and 3D CNN are the most popular networks for recognizing human activities, but both have disadvantages. Therefore, the combination of 3D CNN and two-stream CNN has become a new hotspot. In this paper, an end-to-end R(2+1)D-based two-stream CNN, in which (2+1)D Resnet CNN is employed in both spatial and temporal stream network, is proposed for human activities recognition. Specifically, in the temporal stream, PWC-Net is introduced to generate optical flow images from RGB image sequence of videos, which is taken as the input of (2+1)D Resnet of temporal stream network. In the spatial stream, RGB image sequence of videos is set as the input of (2+1)D Resnet. Both spatial and temporal stream CNNs are pre-trained on Kinect 400 to improve the performance. And the prediction results of spatial and temporal streams are combined according to fusion method. In addition, the optimal length of input clip is determined by experiments to improve the recognition accuracy further. Experimental results show that owing to the presented developments the accuracy of the proposed end-to-end R(2+1)D-based two-stream CNN on UCF101 has been increased, which is 94.97%.
Lightweight Fine-Grained Recognition Method Based on Multilevel Feature Weighted Fusion

Fine-grained recognition in remote sensing images has played a critical role in military and civil fields. Recently, with the rapid growth of convolutional neural networks (CNNs), many fine-grained recognition methods have been proposed. However, due to the large amount of parameters and computational complexity, it is difficult to apply these methods in practical applications. To this end, we propose a novel lightweight fine-grained recognition method based on multilevel feature weighted fusion. First, we design a lightweight CNN (LCNN) framework. Second, we propose a multilevel feature weighted fusion method to improve the recognition accuracy. Third, we adopt a feature channel based loss function to train the proposed model end-to-end. Experiments are conducted on the challenging remote sensing dataset MTARSI to evaluate our proposed method. The results show that the proposed method can achieve state-of-the-art performance.
SEER: A Time Prediction Model for CNNs from GPU Kernel's View

With the deepening of research and increasing size of data sets, deep neural networks have become larger and larger. To reduce the training time of large neural networks, researchers propose to optimize neural networks from different levels. When performing optimizations, prior knowledge about execution time of each part of the network can help avoid repeatedly time-consuming testing and profiling process. However it is quite challenging to build an accurate iteration time prediction model, due to opaque underlying implementation of network operators and complex architecture of accelerators. In this paper, we propose SEER, an iteration time prediction model for CNNs, targeting on GPU platforms. We propose to categorize convolution kernels into three different types: Compute-bound, DRAM-bound and Under-utilized, then we build performance model for each type respectively. We combined analytical models and learning-based models to make the performance model accurate and in line with GPU execution model. Experimental results show that, our model achieves 14.71% prediction error on convolution kernels and up to 1.79% prediction error for the overall computation time in one iteration of common CNNs. Besides, when used for selecting the best convolution algorithm, our model shows 7.14% lower error rate than cuDNN's official algorithm picker.
High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating Point

Over the past few years, 2-D convolutional neural networks (CNNs) have demonstrated their great success in a wide range of 2-D computer vision applications, such as image classification and object detection. At the same time, 3-D CNNs, as a variant of 2-D CNNs, have shown their excellent ability to analyze 3-D data, such as video and geometric data. However, the heavy algorithmic complexity of 2-D and 3-D CNNs imposes a substantial overhead over the speed of these networks, which limits their deployment in real-life applications. Although various domain-specific accelerators have been proposed to address this challenge, most of them only focus on accelerating 2-D CNNs, without considering their computational efficiency on 3-D CNNs. In this article, we propose a unified hardware architecture to accelerate both 2-D and 3-D CNNs with high hardware efficiency. Our experiments demonstrate that the proposed accelerator can achieve up to 92.4% and 85.2% multiply-accumulate efficiency on 2-D and 3-D CNNs, respectively. To improve the hardware performance, we propose a hardware-friendly quantization approach called static block floating point (BFP), which eliminates the frequent representation conversions required in traditional dynamic BFP arithmetic. Comparing with the integer linear quantization using zero-point, the static BFP quantization can decrease the logic resource consumption of the convolutional kernel design by nearly 50% on a field-programmable gate array (FPGA). Without time-consuming retraining, the proposed static BFP quantization is able to quantize the precision to 8-bit mantissa with negligible accuracy loss. As different CNNs on our reconfigurable system require different hardware and software parameters to achieve optimal hardware performance and accuracy, we also propose an automatic tool for parameter optimization. Based on our hardware design and optimization, we demonstrate that the proposed accelerator can achieve 3.8-5.6 times higher energy efficiency than graphics processing unit (GPU) implementation. Comparing with the state-of-the-art FPGA-based accelerators, our design achieves higher generality and up to 1.4-2.2 times higher resource efficiency on both 2-D and 3-D CNNs.
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition

Existing few-shot learning (FSL) methods usually assume base classes and novel classes are from the same domain (in-domain setting). However, in practice, it may be infeasible to collect sufficient training samples for some special domains to construct base classes. To solve this problem, cross-domain FSL (CDFSL) is proposed very recently to transfer knowledge from general-domain base classes to special-domain novel classes. Existing CDFSL works mostly focus on transferring between near domains, while rarely consider transferring between distant domains, which is in practical need as any novel classes could appear in real-world applications, and is even more challenging. In this paper, we study a challenging subset of CDFSL where the novel classes are in distant domains from base classes, by revisiting the mid-level features, which are more transferable yet under-explored in main stream FSL work. To boost the discriminability of mid-level features, we propose a residual-prediction task to encourage mid-level features to learn discriminative information of each sample. Notably, such mechanism also benefits the in-domain FSL and CDFSL in near domains. Therefore, we provide two types of features for both cross- and in-domain FSL respectively, under the same training framework. Experiments under both settings on six public datasets, including two challenging medical datasets, validate the our rationale and demonstrate state-of-the-art performance. Code will be released.
Graph2Pix: A Graph-Based Image to Image Translation Framework

In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , where users interpolate multiple GAN-generated images to create artworks. This unique approach of creating new images leads to a tree-like structure where one can track historical data about the creation of a particular image. Inspired by this structure, we propose a novel graph-to-image translation model called Graph2Pix, which takes a graph and corresponding images as input and generates a single image as output. Our experiments show that Graph2Pix is able to outperform several image-to-image translation frameworks on benchmark metrics, including LPIPS (with a 25% improvement) and human perception studies (n = 60), where users preferred the images generated by our method 81.5% of the time. Our source code and dataset are publicly available at https://github.com/catlab-team/graph2pix.
Measuring Utility and Speculation in Blockchain Tokens

A large segment of cryptoassets consists of tokens—simple code running on programmable blockchains such as Ethereum—that serve as a payment or governance mechanism for an underlying digital platform, usually a peer-to-peer marketplace of various services. An ongoing debate about the viability of the token market is centered on whether tokens are used purely for speculation, or have actual utility on their underlying platforms. The objective of this study is to create and validate a set of granular measures of token utility and speculation. Using disaggregated data on 270 million transactions in 891 tokens hosted on the Ethereum blockchain, we construct three intuitive measures of token utility and speculation: utility turnover (UT), speculative turnover (ST), and DeFi ratio (DF). These measures first reveal a striking decline—of about 90%—in average token utility usage since 2017. This decline coincides with an equally pronounced increase in token speculation, with exchange-based speculation peaking at mid-2020. We show that the emergence of DeFi applications, which offer increasingly levered and complex speculative tools, are crowding out both utility and speculation usages and further extending the increase in token speculation. We provide additional economic validation of our measures by relating them to future market outcomes and show that token utility negatively predicts return volatility in the cross section, while both speculative measures positively predict volatility. Additionally, DeFi activities positively predict future market-adjusted returns. These results suggest that our measures embed value-relevant information about the economic activities in the token-linked platforms, and about the intensity of speculative noise trading in these tokens, thus making them useful outcome or explanatory variables in future empirical analyses of blockchain tokens and platforms.
Automatic News Article Generation from Legislative Proceedings: A Phenom-Based Approach

Algorithmic journalism refers to automatic AI-constructed news stories. There have been successful commercial implementations for news stories in sports, weather, financial reporting and similar domains with highly structured, well defined tabular data sources. Other domains such as local reporting have not seen adoption of algorithmic journalism, and thus no automated reporting systems are available in these categories which can have important implications for the industry. In this paper, we demonstrate a novel approach for producing news stories on government legislative activity, an area that has not widely adopted algorithmic journalism. Our data source is state legislative proceedings, primarily the transcribed speeches and dialogue from floor sessions and committee hearings in US State legislatures. Specifically, we create a library of potential events called phenoms. We systematically analyze the transcripts for the presence of phenoms using a custom partial order planner. Each phenom, if present, contributes some natural language text to the generated article: either stating facts, quoting individuals or summarizing some aspect of the discussion. We evaluate two randomly chosen articles with a user study on Amazon Mechanical Turk with mostly Likert scale questions. Our results indicate a high degree of achievement for accuracy of facts and readability of final content with 13 of 22 users in the first article and 19 of 20 subjects of the second article agreeing or strongly agreeing that the articles included the most important facts of the hearings. Other results strengthen this finding in terms of accuracy, focus and writing quality.
Path Planning for Nuclear Emergency Robot in Radiation Environment with Uneven Terrain

In order to protect people from nuclear radiation, nuclear emergency robots are widely used in radiation environment. Due to the performance limitation of the robot, it cannot pass the area where the radiation dose rate is extremely high or the land slope exceeds a threshold. However, current path planning method of nuclear emergency robot does not consider all these factors, which are inevitable in nuclear emergency cases. Thus, in this paper, a path planning algorithm for nuclear emergency robot is proposed, which considers both radiation and uneven terrain. Firstly, the costmap of radiation is calculated from the radiation field distribution map. The three-dimensional point cloud map is projected into a gray image, and the image gradient is calculated to obtain the costmap of traversability. The combination of two costmaps forms a multi-layer costmap as the input of the path planning algorithm. Then the A* algorithm with the heuristic function of distance, radiation, and slope is proposed to search for the best path. The simulation results show that the proposed method can plan a safe and effective path for the robots according to different requirements.
Mapping bf:Work to lrm:Work and lrm:Expression: Towards a set‐theoretical approach

Abstract The two bibliographical models currently informing the design of systems for the representation of bibliographical data – the IFLA Library Reference Model (IFLA‐LRM) and BIBFRAME 2.0 – do not seamlessly map onto one another. In particular, there are conceptual problems in mapping between the bibliographical entity BIBFRAME:Work and its counterparts, IFLA‐LRM:Work and IFLA‐LRM:Expression. To mitigate these difficulties, we argue for applying a set‐theoretical framework to the mapping between BIBFRAME:Work and IFLA‐LRM:Work and IFLA‐LRM:Expression.