Reviewerly/exHarmony huggingface dataset:
Fast Core-based Top-k Frequent Pattern Discovery in Knowledge Graphs

Knowledge graph is a way of structuring information in graph form, by representing entities as nodes and relationships between entities as edges. A knowledge graph often consists of large amount of facts in real-world which can be used in supporting many analytical tasks, e.g., exceptional facts discovery and fact check of claims. In this work, we study a core-based top-k frequent pattern discovery problem which is frequently used as a subroutine in analyzing knowledge graphs. The main challenge of the problem is search space of the candidate patterns is exponential to the combinations of the nodes and edges in the knowledge graph.To reduce the search space, we devise a novel computation framework FastPat with a suite of optimizations. First, we devise a meta-index, which can be used to avoid generating invalid candidate patterns. Second, we propose an upper bound of the frequency score (i.e., MNI) of the candidate pattern that prunes unqualified candidates earlier and prioritize the enumeration order of the patterns. Lastly, we design a join-based approach to compute the MNI of candidate pattern efficiently. We conduct extensive experimental studies in real-world datasets to verify the superiority of our proposed method over the baselines. We also demonstrate the utility of the discovered frequent patterns by a case study in COVID-19 knowledge graph.
Semantic Auto-Encoder with L2-norm Constraint for Zero-Shot Learning

Zero-Shot Learning (ZSL) is an effective paradigm to solve label prediction when some classes have no training samples. In recent years, many ZSL algorithms have been proposed. Among them, semantic autoencoder (SAE) is widely used because of its simplicity and good generalization ability. However, our research found that most of the existing SAE based methods use implicit constraints to guarantee the mapping quality between feature space and semantic space. In fact, the implicit constraints are insufficient in minimizing the structural risk of the model and easy to cause the over-fitting problem. To solve this problem, we propose a novel SAE algorithm with the L2-norm constraint (SAE-L2) in this study. SAE-L2 adds the L2 regularization constraint to the mapping parameters in its optimization objective, which explicitly guarantees the structural risk minimization of the model. Extensive experiments on four benchmark datasets show that our proposed SAE-L2 can achieve better performance than the original SAE model and other ZSL algorithms.
A survey of hardware architectures for generative adversarial networks

Recent years have witnessed a significant interest in the “generative adversarial networks” (GANs) due to their ability to generate high-fidelity data. Many models of GANs have been proposed for a diverse range of domains ranging from natural language processing to image processing. GANs have a high compute and memory requirements. Also, since they involve both convolution and deconvolution operation, they do not map well to the conventional accelerators designed for convolution operations. Evidently, there is a need of customized accelerators for achieving high efficiency with GANs. In this work, we present a survey of techniques and architectures for accelerating GANs. We organize the works on key parameters to bring out their differences and similarities. Finally, we present research challenges that are worthy of attention in near future. More than summarizing the state-of-art, this survey seeks to spark further research in the field of GAN accelerators.
A GRU Neural Network with attention mechanism for detection of risk situations on multimodal lifelog data

Multimedia today is also in multimodality. Working with heterogeneous signals we use multimedia techniques of data fusion and mining. Classification from real world datasets are often challenging. The paper is devoted to the detection of personal risk situations of fragile people from multi-modal sensing real world lifelog data named BIRDS. Using a real-world data is challenging as the risk situations are rare and last just a few seconds compared to the global volume of the dataset. In this paper we propose a GRU architecture with global attention block to recognise semantic risk situations from a limited taxonomy. Attention is also focused on data organisation and pre-processing with imputation and normalisation. The proposed method is applied to a real-world collected multimodal dataset and to the OpenSource dataset UCI-HAR for the sake of comparison with the state-of-the-art.
Research on Image Encryption Technology Based on Hyperchaotic System and DNA Encoding

This paper proposes an image encryption technology based on six-dimensional hyperchaotic system and DNA encoding, in order to solve the problem of low security in existing image encryption algorithms. First of all, the pixel values of the R, G, and B channels are divided into blocks and zero-filled. Secondly, the chaotic sequence generated by the six-dimensional hyperchaotic system and logistic mapping is used for DNA coding and DNA operations. Third, the decoded three-channel pixel values are scrambled through diagonal traversal. Finally, merge the channels to generate a ciphertext image. According to simulation experiments and related performance analysis, the algorithm has high security performance, good encryption and decryption effects, and can effectively resist various common attack methods.
Application of YOLO V2 in Construction Vehicle Detection

Based on the characteristics of YOLO network, high efficiency, high regression rate and less computation, this paper proposes the design of on-line detector for construction vehicles. YOLO V2 is selected as the data training network for this time by conducting vehicle detection comparison training for network methods such as CNN, R-CNN, Fast R-CNN and Faster R-CNN in deep learning, and aiming at the requirements of online vehicle detection for anti-blocking construction. By collecting vehicle data on the construction site and using the Video Labeler tool in MATLAB to mark the data set, the vehicle detection training data set was established. By using Resnet 50 network as the feature extraction network, the detector network structure was constructed, and the anchor frame was generated by the central point clustering method, which realized the training of YOLO V2 detector network. Through simulation verification, the designed detection accuracy reaches over 94.79%, which meets the needs of online detection.
Towards Efficient Motif-based Graph Partitioning: An Adaptive Sampling Approach

In this paper, we study the problem of efficient motif-based graph partitioning (MGP). We observe that existing methods require to enumerate all motif instances to compute the exact edge weights for partitioning. However, the enumeration is prohibitively expensive against large graphs. We thus propose a sampling-based MGP (SMGP) framework that employs an unbiased sampling mechanism to efficiently estimate the edge weights while trying to preserve the partitioning quality. To further improve the effectiveness, we propose a novel adaptive sampling framework called SMGP+. SMGP+ iteratively partitions the input graph based on up-to-date estimated edge weights, and adaptively adjusts the sampling distribution so that edges that are more likely to affect the partitioning outcome will be prioritized for weight estimation. To our best knowledge, this is the first attempt to solve the MGP problem without employing exact edge weight computations, which gives hope for existing MGP methods to perform on complicated motifs in a scalable yet effective manner. Extensive experiments on seven real-world datasets have validated that our framework delivers competitive partitioning quality compared to existing workflows based on exact edge weights, while achieving orders of magnitude speedup.
No Seeing is Also Believing: Electromagnetic-Emission-Based Application Guessing Attacks via Smartphones

Mobile devices have emerged as the most popular platforms to access information. However, they have also become a major concern of privacy violation and previous researches have demonstrated various approaches to infer user privacy based on mobile devices. In this paper, we study the electromagnetic (EM) emission of a laptop that could be harvested by a commercial-off-the-shelf (COTS) mobile device, e.g., a smartphone. We propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> , which exploits the electromagnetic side channel of a laptop to guess user activities, i.e., application launching and application operation. The key insight of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> is that applications are discrepant in essence due to the different compositions of instructions, which can be reflected on the CPU power consumption, and thus the corresponding EM emissions. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> is challenging since that EM signals are noisy due to the dynamics of applications and the limited sampling rate of the built-in magnetometers in COTS mobile devices. We overcome these challenges and convert noisy coarse-grained EM signals to robust fine-grained features. We implement <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> on both an iOS and an Android smartphone without any hardware modification, and evaluate its performance with 30 popular applications, 30 YouTube videos, and 50 top websites in China. The results demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> can recognize aforementioned 30 applications with an average accuracy of 98.6 percent, and identify which video out of the 30 candidates being played with an average accuracy of 97.5 percent and visiting which website among the 50 candidates with an average accuracy of 90.4 percent.
Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various predefined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.
Deformable Model-Based Vehicle Tracking and Recognition Using 3-D Constrained Multiple-Kernels and Kalman Filter

Video-based vehicle tracking and recognition is an important application in the Intelligent Transportation System. High similarity among vehicle types, frequent occlusion and low video resolution in traffic surveillance are the major problems in this research area. In this paper, we proposed a vehicle tracking system by using 3-D constrained multiple-kernels, facilitated with Kalman filtering, to continuously update the location of the moving vehicles. To further robustly and efficiently track vehicles that are partially or even fully occluded, evolutionary optimization is applied to camera calibration for systematically building 3-D vehicle model, from which we can extract the vehicle's features such as the vehicle type, color and license plate. Then, a self-similarity descriptor is further introduced for vehicle re-identification. The proposed system is evaluated on the NVIDIA AI City Datasets and one self-recorded high-resolution video. The experimental results have shown the favorable performance, which not only can successfully track vehicles under occlusion, but also can maintain the knowledge of 3-D vehicle geometry.
Image style transfer for autonomous multi-robot systems

In deep learning, multi-robot systems intelligently cooperate to perform a complex task, and improve the performance of a single robot through communication, collaboration and sharing of information. Intelligent collaborative work makes the research and application of multi-robot systems develop rapidly. The traditional image style transfer algorithm mainly uses mathematical modeling to characterize the texture of the picture, and combines the content image with the style image to achieve the effect of style transfer. This style transfer algorithm ignores the edge distribution of the image, which makes the contour of the generated image blurred, and it takes a long time in the iterative process of style transfer, and the effect is poor. Therefore, this paper proposes a ceramic decoration pattern style migration algorithm based on the ESPCN model. The algorithm uses the Laplace operator to sharpen the image to highlight the edge distribution, and then uses downsampling to generate low-resolution images to reduce the iteration time of image style transfer. The final generated image uses ESPCN super-resolution reconstruction to reconstruct the low-resolution image. Converted to high-resolution images. The experimental results show that multiple image evaluation indicators show that the edge distribution of the generated image is clear, the iteration time is shortened, and the image quality and definition are improved.
Relation between Quantum Walks with Tails and Quantum Walks with Sinks on Finite Graphs

We connect the Grover walk with sinks to the Grover walk with tails. The survival probability of the Grover walk with sinks in the long time limit is characterized by the centered generalized eigenspace of the Grover walk with tails. The centered eigenspace of the Grover walk is the attractor eigenspace of the Grover walk with sinks. It is described by the persistent eigenspace of the underlying random walk whose support has no overlap to the boundaries of the graph and combinatorial flow in graph theory.
Attention Based Facial Expression Manipulation

Facial expression manipulation has two objectives: 1) generating an image with target expression; 2) preserving the identity information of the original image as much as possible. Recently, Generative Adversarial Networks (GANs) have shown the abilities for fine-grained facial expression manipulation. However, current methods are still prone to generate images with poor quality. In this work, we propose a U-Net based generator with multi-attention gate for facial expression manipulation. The multi-level attention mechanism is helpful to manipulate relevant regions and preserve identity features, thus improving the editing ability. Furthermore, we adopt self-attention block to replace direct skip-connection to get long-range dependency in images. To suppress artifacts in generated images, we add a discriminator based loss function in the training process. Extensive experiments on both quantitative and qualitative evaluation show that our proposed method achieves better performance for facial expression manipulation.
Enhanced Day-Ahead PV Power Forecast: Dataset Clustering for an Effective Artificial Neural Network Training

The increasing integration of renewable energy sources into the existing energy supply structure is challenging due to the intermittency typical of these energy sources, which implies problems of reliability and scheduling of grid operation. Concerning solar energy, the solar forecast tool predicts the photovoltaic (PV) power production and therefore permits a more efficient grid management. In this paper, the combination of clustering techniques and ANNs (Artificial Neural Networks) for day-ahead PV power forecast is analyzed. Clustering techniques are exploited to divide a dataset into different classes of days with similar weather conditions. Then, a dedicated ANN is developed for every group. The main goal is to assess the forecast improvement determined by the combination of ANNs and dataset clustering methods. Different combinations are compared on a real case study: a PV facility in SolarTechLAB, in Politecnico di Milano.
Data-Driven Public R&amp;D Project Performance Evaluation: Results from China

In public R&amp;D projects, to improve the decision-making process and ensure the sustainability of public investment, it is indispensable to effectively evaluate the project performance. Currently, public R&amp;D project management departments and various academic databases have accumulated a large number of project-related data. In view of this, we propose a data-driven performance evaluation framework for public R&amp;D projects. In our framework, we collect structured and unstructured data related to completed projects from multiple websites. Then, these data are cleaned and fused to form a unified dataset. We train a project performance evaluation model by extracting the project performance information implicit in the dataset based on multi-classification supervised learning algorithms. When facing a new project that needs to be evaluated, its performance can be automatically predicted by inputting the characteristic information of the project into our performance evaluation model. Our framework is validated based on the project data of the National Natural Science Foundation of China (NSFC) in terms of four performance measures (i.e., Accuracy, Recall, Precision, F1 score). In addition, we provide a case study that applies our framework to evaluate the project performance in the logistics and supply chain area of NSFC. In conclusion, this paper contributes to the body of knowledge in sustainability by developing a data-driven method that equips the decision-maker with an automated project performance evaluation tool to make sustainable project decisions.
Analysis of the Design Requirements for Remote Internet-Based E-Voting Systems

Remote electronic voting refers to the use of the computer and Internet to collocate votes in an election. Remote e-voting is convenient and provides easy access for voters. On other hand, it makes it easier to count the votes and generate reports for election operators. Over time, e-voting systems were developed until it reaches the level that we have today, and it is expected that they will continue to improve in the future. Nevertheless, recently there has been increasing criticism of remote e-voting systems' security and integrity. Also, concerns were raised about whether these systems can defend against cyber attacks. An effective remote e-voting system must meet a set of standards required from regulatory bodies. This paper discusses remote e-voting systems' design requirements as discussed in current literature. Then, we examine whether the current public infrastructure is capable of supporting an effective remote e-voting system that meets the design requirements. We found that the current technology infrastructure is not sufficient to support efficient remote e-voting systems, as the technologies that need to be implemented to meet the design requirements are victims of different cyber attacks.
Multi-Scale Selective Residual Learning for Non-Homogeneous Dehazing

As the particles in hazy medium cause the absorption and scattering of light, the images captured under such environment suffer from quality degradation such as low contrast and color distortion. While numerous single image dehazing methods have been proposed to reconstruct clean images from hazy images, non-homogeneous dehazing has been rarely studied. In this paper, we design an end-to- end network to remove non-homogeneous dense haze. We employ the selective residual blocks to adaptively improve the visibility of resulting images, where the input feature and the residual feature are combined with fully trainable weights. Experimental results including the ablation study demonstrate that the proposed method is a promising tool for non-homogeneous dehazing that enhances the contrast of hazy images effectively while restoring colorful appearance faithfully.
DBSCOUT: A Density-based Method for Scalable Outlier Detection in Very Large Datasets

Recent technological advancements have enabled generating and collecting huge amounts of data in a daily manner. This data is used for different purposes that may impact us on an unprecedented scale. Understanding the data, including detecting its outliers, is a critical step before utilizing it.Outlier detection has been studied well in the literature but the existing approaches fail to scale to these very large settings. In this paper, we propose DBSCOUT, an efficient exact algorithm for outlier detection with a linear complexity that can run in parallel over multiple independent machines, making it a fit for the settings with billions of tuples. Besides the theoretical analysis, our experiment results confirm orders of magnitude improvement over the existing work, proving the efficiency, scalability, and effectiveness of our approach.
FairMM: A Fast and Frontrunning-Resistant Crypto Market-Maker

Frontrunning is a major problem in DeFi applications, such as blockchain-based exchanges. Albeit, existing solutions are not practical and/or they make external trust assumptions. In this work we propose a market-maker-based crypto-token exchange, which is both more efficient than existing solutions and offers provable resistance to frontrunning attack. Our approach combines in a clever way a game theoretic analysis of market-makers with new cryptography and blockchain tools to defend against all three ways by which an exchange might front-run, i.e., (1) reorder trade requests, (2) adaptively drop trade requests, and (3) adaptively insert (its own) trade requests. Concretely, we propose novel light-weight cryptographic tools and smart-contract-enforced incentives to eliminate reordering attacks and ensure that dropping requests have to be oblivious (uninformed) of the actual trade. We then prove that with these attacks eliminated, a so-called monopolistic market-maker has no longer incentives to add or drop trades. We have implemented and benchmarked our exchange and provide concrete evidence of its advantages over existing solutions.
Communication Signal Modulation Mechanism Based on Artificial Feature Engineering Deep Neural Network Modulation Identifier

Based on the characteristics of time domain and frequency domain recognition theory, a recognition scheme is designed to complete the modulation identification of communication signals including 16 analog and digital modulations, involving 10 different eigenvalues in total. In the in-class recognition of FSK signal, feature extraction in frequency domain is carried out, and a statistical algorithm of spectral peak number is proposed. This paper presents a method to calculate the rotation degree of constellation image. By calculating the rotation degree and modifying the clustering radius, the recognition rate of QAM signal is improved significantly. Another commonly used method for calculating the rotation of constellations is based on Radon transform. Compared with the proposed algorithm, the proposed algorithm has lower computational complexity and higher accuracy under certain SNR conditions. In the modulation discriminator of the deep neural network, the spectral features and cumulative features are extracted as inputs, the modified linear elements are used as neuron activation functions, and the cross-entropy is used as loss functions. In the modulation recognitor of deep neural network, deep neural network and cyclic neural network are constructed for modulation recognition of communication signals. The neural network automatic modulation recognizer is implemented on CPU and GPU, which verifies the recognition accuracy of communication signal modulation recognizer based on neural network. The experimental results show that the communication signal modulation recognizer based on artificial neural network has good classification accuracy in both the training set and the test set.