Reviewerly/exHarmony huggingface dataset:
Static Detection of Control-Flow-Related Vulnerabilities Using Graph Embedding

Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors.However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions.This paper presents a new deeplearning-based graph embedding approach to accurate detection of CFR vulnerabilities.Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program.We have conducted our experiments using 8,368 realworld vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-theart machine-learning-based approaches.The experimental results show the effectiveness of our approach in terms of both accuracy and recall.Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges.Index Terms-Static analysis, graph
One-shot categorization of novel object classes in humans

One aspect of human vision unmatched by machines is the capacity to generalize from few samples. Observers tend to know when novel objects are in the same class despite large differences in shape, material or viewpoint. A major challenge in studying such generalization is that participants can see each novel sample only once. To overcome this, we used crowdsourcing to obtain responses from 500 human observers on 20 novel object classes, with each stimulus compared to 1 or 16 related objects. The results reveal that humans generalize from sparse data in highly systematic ways with the number and variance of the samples. We compared human responses to ‘ShapeComp’, an image-computable model based on >100 shape descriptors, and ‘AlexNet’, a convolution neural network that roughly matches humans at recognizing 1000 categories of real-world objects. With 16 samples, the models were consistent with human responses without free parameters. Thus, when there are a sufficient number of samples, observers rely on shallow but efficient processes based on a fixed set of features. With 1 sample, however, the models required different feature weights for each object. This suggests that one-shot categorization involves more sophisticated processes that actively identify the unique characteristics underlying each object class.
Ontology-driven approach for KPI meta-modelling, selection and reasoning

A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business strategies together with domain experts in order to represent domain knowledge. In particular, Key Performance Indicators (KPIs) allow human experts to properly model ambiguous enterprise goals by means of quantitative variables with numeric ranges and clear thresholds. Besides business-related domains, the usefulness of KPIs has been shown in multiple domains, such as: Education, Healthcare and Agriculture. However, finding accurate KPIs for a given strategic goal still remains a complex task, specially due to the discrepancy between domain assumptions and data facts. In this regard, the semantic web emerges as a powerful technology for knowledge representation and data modeling through explicit representation formats and standards such as RDF(S) and OWL. By using this technology, the semantic annotation of indicators of business objectives would enrich the strategic model obtained. With this motivation, an ontology-driven approach is proposed to formally conceptualize essential elements of indicators, covering: performance, results, measures, goals and relationships of a given business strategy. In this way, all the data involved in the selection and analysis of KPIs are then integrated and stored in common repositories, hence enabling sophisticated querying and reasoning for semantic validation. The proposed semantic model is evaluated on a real-world case study on water management. A series of data analysis and reasoning tasks are conducted to show how the ontological model is able to detect semantic conflicts in actual correlations of selected indicators.
Motif Enhanced Recommendation over Heterogeneous Information Network

Heterogeneous Information Networks (HIN) has been widely used in recommender systems (RSs). In previous HIN-based RSs, meta-path is used to compute the similarity between users and items. However, existing meta-path based methods only consider first-order relations, ignoring higher-order relations among the nodes ofsame type, captured bymotifs. In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations. With MEMP-based similarities between users and items, we design a recommending model MoHINRec, and experimental results on two real-world datasets, Epinions and CiaoDVD, demonstrate its superiority over existing HIN-based RS methods.
Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification

In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.
The Seamless Solar Radiation (SESORA) Forecast for Solar Surface Irradiance—Method and Validation

Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on the the optical flow of effective cloud albedo and available for Germany and parts of Europe. After the clouds are shifted by applying cloud motion vectors, solar radiation is calculated with SPECMAGIC NOW (Spectrally Resolved Mesoscale Atmospheric Global Irradiance Code), which computes the global irradiation spectrally resolved from satellite imagery. Due to the high spatial and temporal resolution of satellite measurements, solar radiation can be forecasted from 15 min up to 4 h or more with a spatial resolution of 0.05 ∘ . An extensive validation of this short-term forecast is presented in this study containing two different validations based on either area or stations. The results are very promising as the mean RMSE (Root Mean Square Error) of this study equals 59 W/m 2 (absolute bias = 42 W/m 2 ) after 15 min, reaches its maximum of 142 W/m 2 (absolute bias = 97 W/m 2 ) after 165 min, and slowly decreases after that due to the setting of the sun. After a brief description of the method itself and the method of the validation the results will be presented and discussed.
Updatable Oblivious Key Management for Storage Systems

We introduce Oblivious Key Management Systems (KMS) as a much more secure alternative to traditional wrapping-based KMS that form the backbone of key management in large-scale data storage deployments. The new system, that builds on Oblivious Pseudorandom Functions (OPRF), hides keys and object identifiers from the KMS, offers unconditional security for key transport, provides key verifiability, reduces storage, and more. Further, we show how to provide all these features in a distributed threshold implementation that enhances protection against server compromise.
Fast and accurate single image super-resolution via an energy-aware improved deep residual network

Recently, convolutional neural network (CNN) based single image super-resolution (SISR) solutions have demonstrated significant progress on restoring accurate high-resolution image based on its corresponding low-resolution version. However, most state-of-the-art SISR approaches attempt to achieve higher accuracy by pursuing deeper or more complicated models, which adversely increases computational cost. To achieve a good balance between restoration accuracy and computational speed, we make simple but effective modifications to the structure of residual blocks and skip-connections between stacked layers, and then propose a novel energy-aware training loss to adaptively adjust the restoration of high-frequency and low-frequency image regions. Extensive qualitative and quantitative evaluation results on benchmark datasets verify the effectiveness of the proposed techniques that they significantly improve SISR accuracy while causing no/ignorable extra computational loads.
Singular Values for ReLU Layers

Despite their prevalence in neural networks, we still lack a thorough theoretical characterization of rectified linear unit (ReLU) layers. This article aims to further our understanding of ReLU layers by studying how the activation function ReLU interacts with the linear component of the layer and what role this interaction plays in the success of the neural network in achieving its intended task. To this end, we introduce two new tools: ReLU singular values of operators and the Gaussian mean width of operators. By presenting, on the one hand, theoretical justifications, results, and interpretations of these two concepts and, on the other hand, numerical experiments and results of the ReLU singular values and the Gaussian mean width being applied to trained neural networks, we hope to give a comprehensive, singular-value-centric view of ReLU layers. We find that ReLU singular values and the Gaussian mean width do not only enable theoretical insights but also provide one with metrics that seem promising for practical applications. In particular, these measures can be used to distinguish correctly and incorrectly classified data as it traverses the network. We conclude by introducing two tools based on our findings: double layers and harmonic pruning.
MLNE: Multi-Label Network Embedding

Network embedding aims to preserve topological structures of a network using low-dimensional vectors and has shown to be effective for driving a myriad of graph mining tasks (e.g., link prediction or classification) free of the stressful feature extraction procedure. Many methods have been proposed to integrate node content and/or label information, with nodes sharing similar content/labels being close to each other in the learned latent space. To date, existing methods either consider networked instances with a single label or consider a set of labels as a whole for node representation learning. Therefore, they cannot handle network of instances containing multiple labels (i.e. multi-labels), which are ubiquitous in describing complex concepts of instances. In this article, we formulate a new multi-label network embedding (MLNE) problem to learn feature representation for networked multi-label instances. We argue that the key to MLNE learning is to aggregate node topology structures, node content, and multi-label correlations. We propose a two-layer network embedding framework to couple information for effective learning. To capture higher order label correlations, we use labels to form a high-level label-label network over a low-level node-node network, in which the label network interacts with the node network through multi-labeling relations. The low-level node-node network can be enhanced by latent label-specific features from high-level label network with well-captured high-order correlations between labels. To enable the multi-label informed network embedding, we force both node and label representations being optimized under the same low-dimensional latent space by a unified training objective. Experiments on real-world data sets demonstrate that MLNE achieves better performance compared with methods with or without considering label information.
Infrared Image Segmentation for Photovoltaic Panels Based on Res-UNet

Infrared image segmentation is the basis of error detection for photovoltaic panels. In this work, the infrared image data are collected by infrared thermal imager from the view of unmanned aerial vehicle (UAV). A semantic segmentation neural network named Deep Res-UNet, which combines the strengths of residual learning, transfer learning, and U-Net, is proposed for infrared image segmentation. Residual units are applied in both the encoding and decoding path, which makes the whole deep network ease to train. A modified ResNet-34 with pre-trained weights is utilized to get better feature representation. In the modified ResNet-34, maxpooling layer is removed for reducing the loss in resolution, an additional conv1 stage is added to copy features for the corresponding decoding path and the skip block with dilated residual block is added to generate features with larger resolution and larger receptive field. A new loss function combining Binary Cross-Entropy (BCE) and Dice is proposed to get better results. Additionally, Conditional Random Field (CRF) is integrated into the model as a post-process. The experimental results show that the prediction results of the proposed model are 97.11% and 94.47% respectively on the two evaluation indexes of $$F_1$$ and Jaccard index, which is better than FCN-8s, SegNet, U-Net and two descendants of U-Net and ResNet: ResUnet and ResNet34-Unet.
Distributed Collision Avoidance of Multiple Robots with Probabilistic Buffered Voronoi Cells

This paper introduces Probabilistic Buffered Voronoi Cell (PBVC) collision avoidance for multiple robots using noisy on-board sensing. The work builds upon the previously proposed Buffered Voronoi Cell (BVC) approach. We introduce a probabilistic formulation to construct a family of BVCs with specified safety levels, which take into account uncertainty in sensor measurements among the robots. The safety level of a PBVC represents the probability that the area is contained inside the robot's true (but unknown) BVC. The PBVC provides a set of probabilistically safe positions for the robot to navigate to. Each agent chooses its next position constrained within the PBVC of a desired safety level, while minimizing the deviation from its reference trajectory. We prove a conservative bound on the probability of collision given this reciprocal strategy. We also validate through simulations that the proposed approach achieves safe navigation among multiple robots in challenging scenarios, and provides a significantly lower risk of collision than either the Reciprocal Velocity Obstacles (RVO) method or the Buffered Voronoi Cell (BVC) method when the robots use noisy relative measurements. We also show in experiments with small-scale robotic cars that the algorithm is fast, effective and is useful in real applications.
Secure and Fine-Grained Self-Controlled Outsourced Data Deletion in Cloud-Based IoT

The emerging cloud-based Internet of Things (IoT) paradigm enables IoT devices to directly upload their collected data to the remote cloud and allows data owners (DOs) to conveniently manage those data through cloud APIs, which has greatly reduced infrastructure investment and data management cost in many IoT applications. Considering that the outsourced data are out of the physical control of DOs and the cloud server (CS) cannot always be fully trusted, how to securely delete the unneeded sensitive data stored in cloud to prevent potential data leakage issues is a big challenge. Most of the existing solutions only support coarse-grained deletion and rely on the participation of the CS, so their flexibility and practicability are seriously restricted. In this article, based on an enhanced policy-based puncturable encryption (P-PUN-ENC) primitive, we propose a secure and fine-grained self-controlled outsourced data deletion scheme in cloud-based IoT. The main contribution of our scheme is that it enables DOs to precisely and permanently delete their outsourced IoT-driven data in a policy-based way without relying on the CS. To achieve this, we subtly utilize the logical relationship between the puncture policy and access policy, and design a policy transform method to convert the puncture process based on the puncture policies into the update process of access policies. Then, we utilize a key delegation technique in attribute-based encryption (ABE) to complete the corresponding key update operations. Additionally, to address the issue of growing key storage and decryption cost in P-PUN-ENC, we propose the outsourced policy-based puncturable encryption (OP-PUN-ENC) primitive by combining the key and decryption outsource technique with P-PUN-ENC. Comprehensive comparisons show that our proposed scheme can better meet the data deletion requirements in cloud-based IoT, and formal security proof and extensive simulation results demonstrate the reliability and efficiency of the proposed scheme.
Global Feature Guided Local Pooling

In deep convolutional neural networks (CNNs), local pooling operation is a key building block to effectively downsize feature maps for reducing computation cost as well as increasing robustness against input variation. There are several types of pooling operation, such as average/max-pooling, from which one has to be manually selected for building CNNs. The optimal pooling type would be dependent on characteristics of features in CNNs and classification tasks, making it hard to find out the proper pooling module in advance. In this paper, we propose a flexible pooling method which adaptively tunes the pooling functionality based on input features without manually fixing it beforehand. In the proposed method, the parameterized pooling form is derived from a probabilistic perspective to flexibly represent various types of pooling and then the parameters are estimated by means of global statistics in the input feature map. Thus, the proposed local pooling guided by global features effectively works in the CNNs trained in an end-to-end manner. The experimental results on image classification tasks demonstrate the effectiveness of the proposed pooling method in various deep CNNs.
SAR Image Despeckling Using Multiconnection Network Incorporating Wavelet Features

The coherent imaging method of synthetic aperture radar (SAR) brings SAR images with strong and randomly distributed speckle, which causes great interference to subsequent applications. To deal with the affected images, we propose a multiconnection network incorporating wavelet features (MCN-WF) to despeckle the images and then evaluate the results. On the one hand, simplified dense connections in and among Dense Blocks (DBs) utilize the features extracted from the network at different scales to produce despeckled images with more details. On the other hand, performing feature pre-extraction on images by wavelet transform can not only indirectly control the convergence direction of the network by modifying the loss function but also reduce the size of the feature maps to accelerate the speed of the network processing. The experimental results show that the new method has a better performance in terms of despecking, image texture structure preservation, and processing efficiency.
AD-VAT+: An Asymmetric Dueling Mechanism for Learning and Understanding Visual Active Tracking

Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. To learn a robust tracker for VAT, in this article, we propose a novel adversarial reinforcement learning (RL) method which adopts an Asymmetric Dueling mechanism, referred to as AD-VAT. In the mechanism, the tracker and target, viewed as two learnable agents, are opponents and can mutually enhance each other during the dueling/competition: i.e., the tracker intends to lockup the target, while the target tries to escape from the tracker. The dueling is asymmetric in that the target is additionally fed with the tracker's observation and action, and learns to predict the tracker's reward as an auxiliary task. Such an asymmetric dueling mechanism produces a stronger target, which in turn induces a more robust tracker. To improve the performance of the tracker in the case of challenging scenarios such as obstacles, we employ more advanced environment augmentation technique and two-stage training strategies, termed as AD-VAT+. For a better understanding of the asymmetric dueling mechanism, we also analyze the target's behaviors as the training proceeds and visualize the latent space of the tracker. The experimental results, in both 2D and 3D environments, demonstrate that the proposed method leads to a faster convergence in training and yields more robust tracking behaviors in different testing scenarios. The potential of the active tracker is also shown in real-world videos.
AutoML for Architecting Efficient and Specialized Neural Networks

Efficient deep learning inference requires algorithm and hardware codesign to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the neural architecture design makes the design space much larger: it is not only about designing the hardware architecture but also codesigning the neural architecture to fit the hardware architecture. It is difficult for human engineers to exhaust the design space by heuristics. We propose design automation techniques for architecting efficient neural networks given a target hardware platform. We investigate automatically designing specialized and fast models, auto channel pruning, and auto mixed-precision quantization. We demonstrate that such learning-based, automated design achieves superior performance and efficiency than the rule-based human design. Moreover, we shorten the design cycle by 200× than previous work, so that we can afford to design specialized neural network models for different hardware platforms.
Deep Line Drawing Vectorization via Line Subdivision and Topology Reconstruction

Abstract Vectorizing line drawing is necessary for the digital workflows of 2D animation and engineering design. But it is challenging due to the ambiguity of topology, especially at junctions. Existing vectorization methods either suffer from low accuracy or cannot deal with high‐resolution images. To deal with a variety of challenging containing different kinds of complex junctions, we propose a two‐phase line drawing vectorization method that analyzes the global and local topology. In the first phase, we subdivide the lines into partial curves, and in the second phase, we reconstruct the topology at junctions. With the overall topology estimated in the two phases, we can trace and vectorize the curves. To qualitatively and quantitatively evaluate our method and compare it with the existing methods, we conduct extensive experiments on not only existing datasets but also our newly synthesized dataset which contains different types of complex and ambiguous junctions. Experimental statistics show that our method greatly outperforms existing methods in terms of computational speed and achieves visually better topology reconstruction accuracy.