Reviewerly/exHarmony huggingface dataset:
Deep security analysis of program code

Abstract Due to the continuous digitalization of our society, distributed and web-based applications become omnipresent and making them more secure gains paramount relevance. Deep learning (DL) and its representation learning approach are increasingly been proposed for program code analysis potentially providing a powerful means in making software systems less vulnerable. This systematic literature review (SLR) is aiming for a thorough analysis and comparison of 32 primary studies on DL-based vulnerability analysis of program code. We found a rich variety of proposed analysis approaches, code embeddings and network topologies. We discuss these techniques and alternatives in detail. By compiling commonalities and differences in the approaches, we identify the current state of research in this area and discuss future directions. We also provide an overview of publicly available datasets in order to foster a stronger benchmarking of approaches. This SLR provides an overview and starting point for researchers interested in deep vulnerability analysis on program code.
Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis

Existing deep learning-based approaches for histopathology image analysis require large annotated training sets to achieve good performance; but annotating histopathology images is slow and resource-intensive. Conditional generative adversarial networks have been applied to generate synthetic histopathology images to alleviate this issue, but current approaches fail to generate clear contours for overlapped and touching nuclei. In this study, We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images. The proposed network uses normalized nucleus distance map rather than the binary mask to encode nuclei contour information. The proposed sharpness loss enhances the contrast of nuclei contour pixels. The proposed method is evaluated using four image quality metrics and segmentation results on two public datasets. Both quantitative and qualitative results demonstrate that the proposed approach can generate realistic histopathology images with clear nuclei contours.
Joint Feature Disentanglement and Hallucination for Few-Shot Image Classification

Few-shot learning (FSL) refers to the learning task that generalizes from base to novel concepts with only few examples observed during training. One intuitive FSL approach is to hallucinate additional training samples for novel categories. While this is typically done by learning from a disjoint set of base categories with sufficient amount of training data, most existing works did not fully exploit the intra-class information from base categories, and thus there is no guarantee that the hallucinated data would represent the class of interest accordingly. In this paper, we propose Feature Disentanglement and Hallucination Network (FDH-Net), which jointly performs feature disentanglement and hallucination for FSL purposes. More specifically, our FDH-Net is able to disentangle input visual data into class-specific and appearance-specific features. With both data recovery and classification constraints, hallucination of image features for novel categories using appearance information extracted from base categories can be achieved. We perform extensive experiments on two fine-grained datasets (CUB and FLO) and two coarse-grained ones (mini-ImageNet and CIFAR- 100). The results confirm that our framework performs favorably against state-of-the-art metric-learning and hallucination-based FSL models.
GRIP-GAN: An Attack-Free Defense Through General Robust Inverse Perturbation

Despite of its tremendous popularity and success in computer vision (CV) and natural language processing, deep learning is inherently vulnerable to adversarial attacks in which adversarial examples (AEs) are carefully crafted by imposing imperceptible perturbations on the clean examples to deceive the target deep neural networks (DNNs). Many defense solutions in CV have been proposed. However, most of them, e.g., adversarial training, suffer from a low generality due to the reliance on limited AEs. Moreover, some solutions even have a non-negligible negative impact on the classification accuracy of clean examples. Last but not least, they are impotent against the unconstrained attacks in which the attackers optimize the perturbation direction and size by additionally taking the defense methods into accounts. In this article, we propose GRIP-GAN to learn a general robust inverse perturbation (GRIP), which is not only able to offset any potential adversarial perturbations but also strengthen the target class-related features, purely from the clean images via a generative adversarial network (GAN). By feeding a random noise, GRIP-GAN is able to generate a dynamic GRIP for each input image to defend against unconstrained attacks. To further improve the defense performance, we also enable GRIP-GAN to generate a GRIP tailored to each input image via feeding input image specific noise to GRIP-GAN. Extensive experiments are carried out on MNIST, CIFAR10, and ImageNet datasets against 17 adversarial attacks. The results show that GRIP-GAN outperforms all the baselines. We further share insights on the success of GRIP-GAN and provide visualized proofs.
Incomplete multiview nonnegative representation learning with multiple graphs

Multiview clustering has become an important research topic during the past decade. However, partial views of many data instances are missing in some realistic multiview learning scenarios. To handle this problem, we develop an effective incomplete multiview nonnegative representation learning (IMNRL) framework, which is suitable for incomplete multiview clustering in various situations. The IMNRL framework performs matrix factorization on multiple incomplete graphs and decomposes these incomplete graphs into a consensus nonnegative representation and view-specific spectral representations, which integrates the advantages of multiview nonnegative representation learning and graph learning. The proposed framework has the following merits: (1) it learns a consensus nonnegative embedding and view-specific embeddings simultaneously; (2) the nonnegative embedding satisfies the neighbor constraint on each incomplete view, which directly reveals the multiview clustering results. Experimental results show that the proposed framework outperforms other state-of-the-art incomplete multiview clustering algorithms.
Explaining Graph Neural Networks for Vulnerability Discovery

Graph neural networks (GNNs) have proven to be an effective tool for vulnerability discovery that outperforms learning-based methods working directly on source code. Unfortunately, these neural networks are uninterpretable models, whose decision process is completely opaque to security experts, which obstructs their practical adoption. Recently, several methods have been proposed for explaining models of machine learning. However, it is unclear whether these methods are suitable for GNNs and support the task of vulnerability discovery. In this paper we present a framework for evaluating explanation methods on GNNs. We develop a set of criteria for comparing graph explanations and linking them to properties of source code. Based on these criteria, we conduct an experimental study of nine regular and three graph-specific explanation methods. Our study demonstrates that explaining GNNs is a non-trivial task and all evaluation criteria play a role in assessing their efficacy. We further show that graph-specific explanations relate better to code semantics and provide more information to a security expert than regular methods.
Aspect Sentiment Triplet Extraction Using Reinforcement Learning

Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets of aspect terms, their associated sentiments, and the opinion terms that provide evidence for the expressed sentiments. Previous approaches to ASTE usually simultaneously extract all three components or first identify the aspect and opinion terms, then pair them up to predict their sentiment polarities. In this work, we present a novel paradigm, ASTE-RL, by regarding the aspect and opinion terms as arguments of the expressed sentiment in a hierarchical reinforcement learning (RL) framework. We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment. This takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency. Furthermore, this hierarchical RL setup enables us to deal with multiple and overlapping triplets. In our experiments, we evaluate our model on existing datasets from laptop and restaurant domains and show that it achieves state-of-the-art performance. The implementation of this work is publicly available at https://github.com/declare-lab/ASTE-RL.
My data, my control: A secure data sharing and access scheme over blockchain

With the fast growth of networked devices, the shared data volume keeps increasing over time, i.e., the Internet of Things (IoT) devices may generate zettabytes of data in the coming few years. According to the recent version of General Data Protection Regulation (GDPR), users have the right to fully control their personal data. Many web service providers also provide some options for users to control the data. However, it is still a challenge to investigate how they enforce these actions. There is a need to deploy additional measures to secure the data access. Focused on this challenge, in this work, we design a scheme that uses smart contract and blockchain to provide a secure data sharing and access environment. In our scheme, there are three major parties and each of them has its own key pair for encrypting and signing the data. We also develop four main smart contracts for different parties, and discuss what kinds of data should be immutable and placed on-chain. In the evaluation, we explore the performance of our scheme under different platforms such as Ethereum with EtHash, Ethereum with Clique, and Hyperledger. With the analysis of several potential attacks, our results indicate the viability and effectiveness of our scheme.
On the Popularity of Non-Fungible Tokens: Preliminary Results

Blockchain technology supports digital assets, which can take the form of cryptocurrencies and tokens. Tokens are usually created on top of the blockchain platform, using smart contracts. Two main categories of tokens exist: Fungible Tokens and Non-Fungible Tokens (NFTs). Here, we focus on NFTs and propose a correlation analysis between various NFTs' characteristics and the popularity of the NFTs. The results can have practical implications for both designers and users.
Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization

As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&amp;ID) into digital P&amp;ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&amp;ID. The proposed method consists of three steps. In the first step of preprocessing, the outer border and title box in the diagram are removed. In the second step of detection, continuous lines are detected, and then line signs and flow arrows indicating the flow direction are detected. In the third step of post-processing, using the results of line sign detection, continuous lines that require changing of the line type are determined, and the line types are adjusted accordingly. Then, the recognized lines are merged with flow arrows. For verification of the proposed method, a prototype system was used to conduct an experiment of line recognition. For the nine test P&amp;IDs, the average precision and recall were 96.14% and 89.59%, respectively, showing high recognition performance.
UrbanNet: Leveraging Urban Maps for Long Range 3D Object Detection

Relying on monocular image data for precise 3D object detection remains an open problem, whose solution has broad implications for cost-sensitive applications such as traffic monitoring. We present UrbanNet, a modular architecture for long range monocular 3D object detection with static cameras. Our proposed system combines commonly available urban maps along with a mature 2D object detector and an efficient 3D object descriptor to accomplish accurate detection at long range even when objects are rotated along any of their three axes. We evaluate UrbanNet on a novel challenging synthetic dataset and highlight the advantages of its design for traffic detection in roads with changing slope, where the flat ground approximation does not hold. Data and code are available at https://github.com/TRAILab/UrbanNet
Multi-Target Pan-Class Intrinsic Relevance Driven Model for Improving Semantic Segmentation in Autonomous Driving

At present, most semantic segmentation models rely on the excellent feature extraction capabilities of a deep learning network structure. Although these models can achieve excellent performance on multiple datasets, ways of refining the target main body segmentation and overcoming the performance limitation of deep learning networks are still a research focus. We discovered a pan-class intrinsic relevance phenomenon among targets that can link the targets cross-class. This cross-class strategy is different from the latest semantic segmentation model via context where targets are divided into an intra-class and inter-class. This paper proposes a model for refining the target main body segmentation using multi-target pan-class intrinsic relevance. The main contributions of the proposed model can be summarized as follows: a) The multi-target pan-class intrinsic relevance prior knowledge establishment (RPK-Est) module builds the prior knowledge of the intrinsic relevance to lay the foundation for the following extraction of the pan-class intrinsic relevance feature. b) The multi-target pan-class intrinsic relevance feature extraction (RF-Ext) module is designed to extract the pan-class intrinsic relevance feature based on the proposed multi-target node graph and graph convolution network. c) The multi-target pan-class intrinsic relevance feature integration (RF-Int) module is proposed to integrate the intrinsic relevance features and semantic features by a generative adversarial learning strategy at the gradient level, which can make intrinsic relevance features play a role in semantic segmentation. The proposed model achieved outstanding performance in semantic segmentation testing on four authoritative datasets compared to other state-of-the-art models.
HIFA: Promising Heterogeneous Solar Irradiance Forecasting Approach Based on Kernel Mapping

The rapid employment of photovoltaic (PV) has highlighted the importance of accurate solar irradiance forecasting in grid operation. However, the intermittent nature of solar irradiance represents a big challenge and degrades the accuracy of forecasting techniques, posing towards developing ensemble-based approaches. Most ensemble approaches generate weights based on the performance of individual forecasting models (IFMs) where linear operations are often used to aggregate them. The generalization of such weights could not be practically guaranteed due to the high variability among predictions obtained by IFMs. To tackle these issues, a novel heterogeneous solar irradiance forecasting approach, so-called HIFA, is proposed in this article. Specifically, we propose an effective aggregation strategy based on kernel mapping for aggregating the predictions of accurate deep learning based IFMs. The proposed aggregation strategy can properly map the predictions of IFMs onto a consensus prediction. HIFA utilizes efficient deep recurrent neural networks, which can exploit long-term information from previous computations to model the fluctuated solar irradiance, for building the IFMs. The results reveal that HIFA substantially improves the accuracy of solar irradiance forecasting when compared to ensemble-based approaches, thanks to the generalization capability of the proposed aggregation strategy and the high accuracy of deep IFMs.
On Autonomous Dynamic Software Ecosystems

Software ecosystems are considered the natural evolution of software product lines. A software ecosystem provides a (software) product within a particular business and organizational context that supports the exchange of activities and services within a domain. However, the increasing degree of autonomy demanded by software ecosystems is elevating the system response to end users, while the existing software ecosystem architectures are not well prepared to deal with the dynamicity of context changes and autonomous behavior needs. In order to provide a transition toward an increased level of autonomy, in this article, we introduce the notion of autonomous dynamic ecosystems as representative of those software ecosystems able to support dynamic, smart, and autonomous features demanded by modern software systems. In this work, we further investigate and provide evidence of four industrial examples that have fully embodied the principles of autonomous dynamic ecosystems, and we characterize the main features and technology requirements of this kind of new ecosystems.
A comprehensive and systematic review on classical and deep learning based region proposal algorithms

Development of region proposal algorithms has rapidly become one of the most critical research areas over recent years. The perfect accuracy of region-based recognition techniques has led to the use of proposal algorithms as an imperative core in various recognition problems. The main purpose of these algorithms is to extract effective regions of an image with an appropriate number that will reduce the search space and increase detection accuracy. The early development of these algorithms was based on a set of hand-crafted features. Recently, with advances in deep learning techniques, they have been widely and successfully applied to the region proposals. This paper reviews region proposal algorithms, theory, and evaluation metrics and also addresses the existing challenges. In addition, we present a classification for generating proposals, including classical and advanced methods based on hand-crafted features and deep learning, respectively. Both categories are described in details, and an extensive review of recent works is presented. The proposal improvement methods, including ranking algorithms, are also described. In total, more than 60 different algorithms have been studied and classified, and we also point out several applications based on region proposals.
ServerMore

Serverless computing allows customers to submit their jobs to the cloud for execution, with the resource provisioning being taken care of by the cloud provider. Serverless functions are often short-lived and have modest resource requirements, thereby presenting an opportunity to improve server utilization by colocating with latency-sensitive customer workloads. This paper presents ServerMore, a server-level resource manager that opportunistically colocates customer serverless jobs with serverful customer VMs. ServerMore dynamically regulates the CPU, memory bandwidth, and LLC resources on the server to ensure that the colocation between serverful and serverless workloads does not impact application tail latencies. By selectively admitting serverless functions and inferring the performance of black-box serverful workloads, ServerMore improves resource utilization on average by 35.9% to 245% compared to prior works; while having a minimal impact on the latency of both serverful applications and serverless functions.
Self-Verifiable Attribute-Based Keyword Search Scheme for Distributed Data Storage in Fog Computing With Fast Decryption

Presently many searchable encryption schemes have been proposed for cloud and fog computing, which use fog nodes (or fog servers) to partly undertake some computational tasks. However, these related schemes still retain cloud servers to undertake most computational tasks, which result in large communication costs between edge devices and cloud servers. Therefore, in this paper we propose a self-verifiable attribute-based keyword search scheme for distributed data storage (SV-KSDS) in full fog computing, where each decryption operation on the data required by a user must meet the negotiated decryption rule between fog servers. Our SV-KSDS scheme first provides attribute-based distributed data storage among fog servers through the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(w, \sigma)$ </tex-math></inline-formula> threshold secret-sharing scheme, where fog servers can provide self-verifiable keyword search and data decryption for terminal users. Compared with the data storage in cloud computing, our scheme extends it to the distributed structure while providing fine-grained access control for distributed data storage through attribute-based encryption. The access control policy of our scheme is constructed on linear secret-sharing scheme, whose security is reduced to the decisional bilinear Diffie-Hellman assumption against chosen-keyword attack and the decisional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${q}$ </tex-math></inline-formula> -parallel bilinear Diffie-Hellman assumption against chosen-plaintext attack in the standard model. Based on theoretical analysis and practical testing, our SV-KSDS scheme generates less computation and communication costs, which further unloads some computational tasks from terminal users to fog servers so as to reduce computing costs of terminal users.
Low-dimensional Alignment for Cross-Domain Recommendation

Cold start problem is one of the most challenging and long-standing problems in recommender systems, and cross-domain recommendation (CDR) methods are effective for tackling it. Most cold-start related CDR methods require training a mapping function between high-dimensional embedding space using overlapping user data. However, the overlapping data is scarce in many recommendation tasks, which makes it difficult to train the mapping function. In this paper, we propose a new approach for CDR, which aims to alleviate the training difficulty. The proposed method can be viewed as a special parameterization of the mapping function without hurting expressiveness, which makes use of non-overlapping user data and leads to effective optimization. Extensive experiments on two real-world CDR tasks are performed to evaluate the proposed method. In the case that there are few overlapping data, the proposed method outperforms the existed state-of-the-art method by 14% (relative improvement).
Mining Cross Features for Financial Credit Risk Assessment

For reliability, machine learning models in some areas, e.g., finance and healthcare, require to be both accurate and globally interpretable. Among them, credit risk assessment is a major application of machine learning for financial institutions to evaluate credit of users and detect default or fraud. Simple white-box models, such as Logistic Regression (LR), are usually used for credit risk assessment, but not powerful enough to model complex nonlinear interactions among features. In contrast, complex black-box models are powerful at modeling, but lack of interpretability, especially global interpretability. Fortunately, automatic feature crossing is a promising way to find cross features to make simple classifiers to be more accurate without heavy handcrafted feature engineering. However, existing automatic feature crossing methods have problems in efficiency on credit risk assessment, for corresponding data usually contains hundreds of feature fields.