Reviewerly/exHarmony huggingface dataset:
Kronecker Graph Generation with Ground Truth for 4-Cycles and Dense Structure in Bipartite Graphs

We demonstrate nonstochastic Kronecker graph generators produce massive-scale bipartite graphs with ground truth global and local properties and discuss their use for validation of graph analytics. Given two small connected scale-free graphs with adjacency matrices A and B, their Kronecker product graph [1] has adjacency matrix C = A ⊗ B. We first demonstrate that having one factor A non-bipartite (alternatively, adding all self loops to a bipartite A) with other factor B bipartite ensures G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> is bipartite and connected. Formulas for ground truth of many graph properties (including degree, diameter, and eccentricity) carry over directly from the general case presented in previous work [2], [3]. However, the analysis of higher-order structure and dense structure is different in bipartite graphs, as no odd-length cycles exist (including triangles) and the densest possible structures are bicliques. We derive formulas to give ground truth for 4-cycles (a.k.a. squares or butterflies) at every vertex and edge in G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> . Additionally, we demonstrate that bipartite communities (dense vertex subsets) in the factors A, B yield dense bipartite communities in the Kronecker product C. We additionally discuss interesting properties of Kronecker product graphs revealed by the formulas an their impact on using them as benchmarks with ground truth for various complex analytics. For example, for connected A and B of nontrivial size, G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> has 4-cycles at vertices/edges associated with vertices/edges in A and B that have none, making it difficult to generate graphs with ground truth bipartite generalizations of truss decomposition (e.g. the k-wing decomposition of [4]).
Research on the Application of BIM + VR Technology in the Project

Abstract At present, the research of BIM + VR technology is the forefront of BIM technology application research. BIM technology can solve some design problems and provide collaborative design solutions for various professions. Virtual reality technology (VR) technology can make up for some of the shortcomings of BIM technology BIM technology and VR technology can complement each other and be used in actual engineering projects. For this case, the actual application results show that BIM + VR technology can effectively solve the visualized expression of the integrated design of the interior pipe of the building, the decorative design and management.
Multiperspective Automotive Labeling

Supervised machine learning techniques inherently rely on datasets to be trained. With image datasets traditionally being annotated by humans, many advancements in image annotation tools have been made to ensure creation of rich datasets with accurate labels. Nevertheless, users still find it challenging to create and use their own datasets with labels that reflect their problem domain. We propose a streamlined labeling process that aligns multiperspective images and allows a transition from a labeled perspective to other perspectives. The main goal of this work is to reduce the human effort required for labeling vehicle images under favorable conditions where the image perspectives are correlated and one or more perspectives are known. A case study is described and analyzed to show the effectiveness of the process, as well as constraints and limitations when applied to other cases.
A Species Conservation-Based Particle Swarm Optimization with Local Search for Dynamic Optimization Problems

In the optimization of problems in dynamic environments, algorithms need to not only find the global optimal solutions in a specific environment but also to continuously track the moving optimal solutions over dynamic environments. To address this requirement, a species conservation-based particle swarm optimization (PSO), combined with a spatial neighbourhood best searching technique, is proposed. This algorithm employs a species conservation technique to save the found optima distributed in the search space, and these saved optima either transferred into the new population or replaced by the better individual within a certain distance in the subsequent evolution. The particles in the population are attracted by its history best and the optimal solution nearby based on the Euclidean distance other than the index-based. An experimental study is conducted based on the moving peaks benchmark to verify the performance of the proposed algorithm in comparison with several state-of-the-art algorithms widely used in dynamic optimization problems. The experimental results show the effectiveness and efficiency of the proposed algorithm for tracking the moving optima in dynamic environments.
Research on Object Detection of Traffic Scene Based on Deep Learning

The development of intelligent vehicle involves many key technologies. The machine vision technology based on deep learning has become one of the research hotspots because of its good performance.In this paper, aiming at the environment perception technology of smart cars, YOLOv2 deep learning algorithm is improved by combining with experimental data set, and it is applied to real-time detection of traffic scene objects.First, based on PASCAL VOC, the YOLOv2 algorithm with different network structures and loss functions was trained and tested.According to the test results, the network structure and loss function of YOLOv2 algorithm are determined.The improved YOLOv2 algorithm was trained on the images of traffic scene objects in the COCO data set, and the algorithm was tested using the actual traffic scene videos collected in the experiment to verify the detection performance of YOLOv2 algorithm on the traffic scene objects in the video.Experimental results show that YOLOv2 algorithm can obtain high detection accuracy and fast detection speed, which basically meets the technical requirements of intelligent vehicles.
Research on BIM Building Space Design under the Background of Internet of Things

Nowadays, BIM technology has set off a trend of research and practical application of this technology worldwide. Information technology represents one of the emerging models for the construction sector, is affecting the development of the construction industry, coupled with efficient networking technology to collect information is gradually recognized by the public, and BIM technology information through digital simulation building has real information coincide. The combination of these two technologies looks forward to the future development trend of BIM building space in the direction of artificial intelligence design.
A Novel Approach to Human–Computer Interaction Using Hand Gesture Recognition

As computers become progressively inescapable in the public arena, encouraging characteristic human–computer interaction (HCI) will positively affect their utilization. Henceforth, there has been developing enthusiasm for the improvement of new methodologies and innovations for bridging this barrier. In this project, a novel approach has been presented toward hand gesture recognition using smartphone sensor reading, which can be applied to interact with your personal computers. The method presented collects data from smartphone sensors and based on the sensor data (accelerometer and gyroscope), classifies the data using deep learning algorithms. Furthermore, once the gesture has been accurately classified, the gesture is mapped to an action. This paper also provides an analysis of a comparative study done for this area.
Professional Competence Management for University Students Based on Knowledge Graph Technology

Effective competence management is beneficial to improve students’ academic performance. Mining the hidden information in teaching data plays an important role in analyzing and modeling professional competence for students. Therefore, we propose a method for constructing a competence management model based on teaching data. By using knowledge graph technology, the model establishes a semantic network which includes seven competence entities and four kinds of relationships between them. To apply and evaluate the competence management method, the model is instantiated by using teaching data from computer science. In the part of named entity recognition, this paper uses the BiLSTM_CNN_CRF model. Compared with the BiLSTM_CRF model, the precision is improved by 1.50%, the recall rate is improved by 1.47% and the F1-score is improved by 1.48%. In the part of entity similarity calculation, in order to help students manage their competence better, the similarity of competence entities and the degree words are calculated respectively. Finally, the Neo4j graphics database is used to store and display the knowledge graph and a competence retrieval service for students is applied in this paper.
Text Mining-based Research on Aircraft Faults Classification and Retrieval Model

With the development of information technology, a large number of equipment management systems have been used in the aviation field. These systems generally have failure management functions. After the failure occurs, it is necessary to input the text information of equipment failure in time, such as “combination switch damage”, “intake pipe rupture” and other failure phenomena. In addition, the corresponding failure causes and troubleshooting methods also need to be fully recorded. Therefore, as the system has been used, a large number of equipment failure texts will be generated. If we can make full use of these unstructured data and discover the knowledge through text mining, it will be of great significance to fault analysis and maintenance decision-making.
Research on Leakage Prevention Technology of Sensitive Data based on Artificial Intelligence

In recent years, network security incidents happen frequently, and each security incident is inseparable from the threat of data leakage. In order to deal with the sensitive data leakage under the new network threat, this paper proposes the sensitive data leakage prevention method based on artificial intelligence technology by studying the current data leakage prevention technology. By prefabricating a simple security policy, the detection result of the data and the data are submitted to the security manager. According to the judgment of the security inspector, the self-learning security policy is carried out with artificial intelligence algorithm to promote the update of the security policy. Data assets leakage protection system is designed to show full view data assets directory, verify the risk analysis technology in data assets for centralized cataloging and grade of risk distribution data visualization display, based on the classification properties of the data security label of the multi-function scenarios such as the technology application effect, effectively reduce the risk of data leakage.
Local XOR Unification: Definitions, Algorithms and Application to Cryptography

Unification techniques have been proven to be useful for formal analysis of cryptographic systems. In this paper, we introduce a new unification problem called local XOR unification, motivated by formal analysis of security of modes of operation. The goal in local XOR unification is to find a substitution making two terms equivalent modulo the theory of exclusive-or, but each variable is only allowed to be mapped to a term from a given set of terms. We present two versions of the local XOR unification problem, and give algorithms to solve them, proving soundness, completeness and termination.
Association Rule Mining with Differential Privacy

Association analysis is an important task in data analysis to find all co-occurrence relationships (i.e., frequent itemsets or confident association rules) from the transactional dataset. An association rule can help people better discover patterns and develop corresponding strategies. The process of data analysis can be highly summarized as a set of queries, where each query is a real-valued function of the dataset. However, without any restriction and protection, accessing the dataset to answer the queries may lead to the disclosure of individual privacy. In this paper, we propose and implement the association rule mining with differential privacy algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items, and uses random truncation and uniform partition to lower the dimensionality of the dataset. We also stabilize the noise scale by adaptively allocating the privacy budgets, and bound the overall privacy loss. In addition, we prove that the association rule mining with differential privacy algorithm satisfies ex post differential privacy, and verify the utility of our association rule mining with differential privacy algorithm through a series of experiments. To the best of our knowledge, our work is the first differentially private association rule mining algorithm under multiple support thresholds.
Cascaded Region Proposal Networks for Proposal-Based Tracking

There still remains some problems which have not been solved in RPN-based trackers, including data imbalance, inappropriate proposals and poor robustness to spatial rotation even scale variation. We propose a cascaded region proposal network framework for visual tracking based on region proposal networks, spatial transformer networks and proposal selection strategy. We first to extract the features from deep and shallow layers via cascaded region proposal network to ensure the spatial information and semantic cue of the appearance model. Then, the feature extraction model based on spatial transformer networks is performed to calculate the parameters of spatial transformer and obtain the fused features. During the tracking and testing of proposed networks, the proposals are generated and re-ranked by formulating the proposals selection strategy to ensure the localization and scale of the estimated target. We extensively prove the effectiveness of the proposed method though the ablation studies of the tracking benchmark which include OTB2015, VOT2016 and UAV123. The experimental results perform that the accuracy and robustness of the proposed method as the real-time tracker and the long-term tracker as well. In the meantime, the test on the benchmark UAV123 shows that the tracker can be employed to some engineering area.
Evolutionary neural network structure search for DNN pruning and features separation

In deep neural networks (DNNs), by removing unnecessary subnetworks can reduce neural network computing redundancy, or by extracting sub-networks with features can explain how the DNN works. In this paper, we propose a neural network structure search algorithm based on evolutionary algorithm (EA) for DNN pruning and features separation, especially considering the evolutionary efficiency issues and individual evaluation. We get verified on the CIFAR-10 (C10) and CIFAR-100 (C100) datasets. With the classification accuracy with a variation of +/- 1% as the precondition, our method significantly reduces the amount of calculations.
DrlNet: Blind object proposal quality assessment with discriminative response learning

Object proposal quality assessment without ground truth as reference is a challenging task. Some existing methods measure the quality with hand-crafted metrics for subjective metrics, such as objectness and foreground confidence. Recently, deep learning is adopted for direct assessment for quantifiable metric, such as Intersection over Union (IoU). However, we find that IoU, the commonly used quality metric, is far from fully describing the quality of an object proposal. Proposals with the same IoU score may carry totally different amount of discriminative attribute. We introduce a new metric named Discriminative Information Richness (DIR) to characterize the discriminative degree of the given object proposal. DIR is derived from the response intensity of the projected deep feature maps, whose high correlation response indicates the discriminative regions. Besides, we design a convolutional neural network named DrlNet to simultaneously predict IoU scores and perceive the richness of the identification information. DrlNet is defined as a multi-metric joint deep regression network for both spatial covering prediction and discriminative information richness perception. Compared with the solely IoU based models, DrlNet can provide more comprehensive quality assessment. We perform comprehensive experiments on both PASCAL VOC dataset and COCO dataset. The experimental results show that our DrlNet performs well on both proposal selection and object detection tasks. Particularly, experimental results on COCO dataset demonstrate the good generalization ability of the proposed model.
Implementing an Attack Graph Generator in CUDA

Attack graphs can be used in security analysis for both computer networks and cyber physical systems to enumerate all possible attack paths. The main challenge in applying attack graphs is the design of scalable generators, which should construct large scale graphs within practical limits for execution time and storage resources. With the availability of high performance computing hardware, parallel computing units can be employed to accelerate the exploration of new states in attack graph generation. While NVIDIA's GPU devices have significantly improved the performance of other graph-oriented algorithms, there is little research using them on the generation of attack graphs. In this paper, we present a parallel attack graph generator based on NVIDIA's extension of C language: CUDA. To accelerate the exploration, the generator uses a large number of warps in the GPU device. Each warp is assigned with multiple initial states, which are then expanded by 32 in-warp threads. The performance experiments are conducted on a virtual machine instance providing a Tesla P4 GPU. The acceleration achieved demonstrates the effectiveness of using a large number of CUDA cores to accelerate the attack graph generator.
Comparison of Anomaly Detection between Statistical Method and Undercomplete Autoencoder

Anomaly detection is one of the key issues in the domain of unsupervised machine learning gaining importance in diverse research and application areas. In the presented work, we aim to compare a specifically developed statistical method with a generic sparse autoencoder approach for the anomaly detection problem. To compare performance, we apply both methods to real energy consumption data taken from supermarket stores. Firstly, the raw data was analyzed and the feature vectors were constructed. Secondly, Tukey's test was implemented on the constructed feature vectors to determine the outliers. Finally as a second method, we designed a generic unsupervised undercomplete autoencoder for the detection of the outliers in order to compare in several experiments the performance of both approaches. We also provided a discussion on the computational complexity of the techniques since, it is an important issue in real application domains.
3D Virtual Environments to Support New Product Development: A Mobile Platform Based on an Open Innovation Laboratory Applied in Higher Education

Nowadays, it is observed that native digital students demand the use of innovative learning methods, as well as the use of specialized technological equipment that can positively influence their professional training, but at the same time it is observed that there is still a deficit not only in the commitment and interest of students to learn, but also to relate how they can apply the knowledge obtained by proposing solutions to real problems. This paper proposes a mobile platform based on 3D virtual environments to support new product development (NPD) as an alternative to address these issues in the Higher Education sector. This mobile platform is an interactive and learning management system that is used to efficiently gather and manage the tangible/intangible resources necessary in an Open Innovation process for the development of new products in which the co-creation and co-development take place. Therefore, an Open Innovation Laboratory applied in higher education was used as a reference. A case study is presented to demonstrate how the proposed platform can be applied in the higher education context.
GA-lapagos, an open-source c framework including a python-based system for data analysis

In this work, we introduce, GA-lapagos, an open-source genetic algorithm framework written in 'C' for 3 exemplar optimization problems, and an accompanying Python-based data analysis scripts to extract and produce results. We created this system to help researchers implement a fast GA solving system for their problems allowing them to implement and leverage many ideas implemented in the research literature. Additionally, we provide a number of compilation paths to parallel computational systems such as multi-core, GPUs, and FPGAs. By building an executable framework and outputting results in comma separated values (CSV) format, we create a set of Python scripts to read the data and create graphs.
Anonymity and Rewards in Peer Rating Systems

When peers rate each other, they may rate inaccurately to boost their own reputation or unfairly lower another’s. This could be mitigated by having a reputation server incentivise accurate ratings with a reward. However, assigning rewards becomes challenging when ratings are anonymous, since the reputation server cannot tell which peers to reward for rating accurately. To address this, we propose an anonymous peer rating system in which users can be rewarded for accurate ratings, and we formally define its model and security requirements. In our system ratings are rewarded in batches, so that users claiming their rewards only reveal they authored one in this batch of ratings. To ensure the anonymity set of rewarded users is not reduced, we also split the reputation server into two entities, the Rewarder, who knows which ratings are rewarded, and the Reputation Holder, who knows which users were rewarded. We give a provably secure construction satisfying all the security properties required. For our construction we use a modification of a Direct Anonymous Attestation scheme to ensure that peers can prove their own reputation when rating others, and that multiple feedback on the same subject can be detected. We then use Linkable Ring Signatures to enable peers to be rewarded for their accurate ratings, while still ensuring that ratings are anonymous. Our work results in a system which allows accurate ratings to be rewarded, whilst still providing anonymity of ratings with respect to the central entities managing the system.
Fog Removal of Aerial Image Based on Gamma Correction and Guided Filtering

In order to improve the pilots’ perception of the runway and the surrounding things in foggy days and improve the visual effect of aerial images, a combination of Gamma correction and Retinex de-fogging algorithm is proposed for aerial foggy images. First of all, the original image is corrected by Gamma as the guided map, and the light intensity of the aerial image is estimated by the guided filter, and the preliminary fog removal image is obtained by Retinex. In combination with the histogram truncation technique, the output of the image is mapped to between 0 and 255, then a de-fogging enhanced image is achieved. Compared with other de-fogging algorithms, this algorithm has higher contrast and color consistency.