Reviewerly/exHarmony huggingface dataset:
Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions

In this paper, we study the problem of part-of-speech (POS) tagging for security vulnerability descriptions (SVD). In contrast to newswire articles, SVD often contains a high-level natural language description of the text composed of mixed language studded with codes, domain-specific jargon, vague language, and abbreviations. Moreover, training data dedicated to security vulnerability research is not widely available. Existing neural network-based POS tagging has often relied on manually annotated training data or applying natural language processing (NLP) techniques, suffering from two significant drawbacks. The former is extremely time-consuming and requires labor-intensive feature engineering and expertise. The latter is inadequate to identify linguistically-informed words specific to the SVD domain. In this paper, we propose an automatic approach to assign POS tags to tokens in SVD. Our approach uses the character-level representation to automatically extract orthographic features and unsupervised word embeddings to capture meaningful syntactic and semantic regularities from SVD. The character level representations are then concatenated with the word embedding as a combined feature, which is then learned and used to predict the POS tagging. To deal with the issue of the poor availability of annotated security vulnerability data, we implement a finetuning approach. Our approach provides public access to a POS annotated corpus of ~8M tokens, which serves as a training dataset in this domain. Our evaluation results show a significant improvement in accuracy (17.72%-28.22%) of POS tagging in SVD over the current approaches.
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model

How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an untrusted third party. The state-of-the-art approach for this problem is to build a generative model under differential privacy, which offers a rigorous privacy guarantee. However, the existing method cannot adequately handle high dimensional data. In particular, when the input dataset contains a large number of features, the existing techniques require injecting a prohibitive amount of noise to satisfy differential privacy, which results in the outsourced data analysis meaningless. To address the above issue, this paper proposes privacy-preserving phased generative model (P3GM), which is a differentially private generative model for releasing such sensitive data. P3GM employs the two-phase learning process to make it robust against the noise, and to increase learning efficiency (e.g., easy to converge). We give theoretical analyses about the learning complexity and privacy loss in P3GM. We further experimentally evaluate our proposed method and demonstrate that P3GM significantly outperforms existing solutions. Compared with the state-of-the-art methods, our generated samples look fewer noises and closer to the original data in terms of data diversity. Besides, in several data mining tasks with synthesized data, our model outperforms the competitors in terms of accuracy.
One-shot quantum error correction of classical and quantum information

Quantum error correction (QEC) is one of the central concepts in quantum information science and also has wide applications in fundamental physics. The capacity theorems provide solid foundations of QEC. We here provide a general and highly applicable form of capacity theorem for both classical and quantum information, i.e., hybrid information, with the assistance of a limited resource of entanglement in a one-shot scenario, which covers broader situations than the existing ones. Harnessing the wide applicability of the theorem, we show that a demonstration of QEC by short random quantum circuits is feasible and that QEC is intrinsic in quantum chaotic systems. Our results bridge the progress in quantum information theory, near-future quantum technology, and fundamental physics.
Searching for LIS scholarly publications: A comparison of search results from Google, Google Scholar, EDS, and LISA

The study aims to discover the information sources most suitable for exploratory searching in terms of effectiveness and content coverage in searching for library and information science (LIS) scholarly publications on a specific topic. It is intended to support LIS researchers in the choice of a search system in situations when they start exploring a topic and try to find the necessary literature. Four information sources were selected after a preliminary survey: Google, Google Scholar (GS), Ebsco Discovery Service (EDS), and Library and Information Science Abstracts (LISA). They were compared based on query results in terms of precision, relative recall, coverage, and full-text access. In all these aspects Google turned out to be the best. GS was close behind, followed by LISA and EDS. • In searching for LIS scholarly publications, Google outperformed Google Scholar in search effectiveness and content coverage • Google retrieved the highest number of relevant documents for each examined topic • In full-text document retrieval, Google reached the highest ratio of 80%, leaving far behind GS 54%, LISA 42%, and EDS 33% • EDS service failed to compete not only with web search engines, but even with LISA, a single bibliographic database
Model-Agnostic Counterfactual Explanations of Recommendations

Explanations for algorithmically generated recommendations is an important requirement for transparent and trustworthy recommender systems. When the internal recommendation model is not inherently interpretable (e.g., most contemporary systems are complex and opaque), or when access to the system is not available (e.g., recommendation as a service), explanations have to be generated post-hoc, i.e., after the system is trained. In this common setting, the standard approach is to provide plausible interpretations of the observed outputs of the system, e.g., by building a simple surrogate model that is inherently interpretable, and explaining that model. This however has several drawbacks. First, such explanations are not truthful, as they are rationalizations of the observed inputs and outputs constructed by another system. Second, there are privacy concerns, as to train a surrogate model, one has to know the interactions from users other than the one who seeks an explanation. Third, such explanations may not be scrutable and actionable, as they typically return weights for items or other users that are difficult to comprehend, and hard to act upon so to improve the quality of one’s recommendations.
Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.
Harmonious Semantic Line Detection via Maximal Weight Clique Selection

A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-MWCS.
Expand your Training Limits! Generating Training Data for ML-based Data Management

Machine Learning (ML) is quickly becoming a prominent method in many data management components, including query optimizers which have recently shown very promising results. However, the low availability of training data (i.e., large query workloads with execution time or output cardinality as labels) widely limits further advancement in research and compromises the technology transfer from research to industry. Collecting a labeled query workload has a very high cost in terms of time and money due to the development and execution of thousands of realistic queries/jobs.
Learning to Generate Steganographic Cover for Audio Steganography Using GAN

Audio steganography aims to exploit the human auditory redundancy to embed the secret message into cover audio, without raising suspicion when hearing it.However, recent studies have shown that the existing audio steganography can be easily exposed with the deep learning based steganalyzers by extracting high-dimensional features of stego audio for classification.The existing GAN-based steganography approaches mainly studied in images cover, less work is conducted on audio cover.In addition, though a few GAN-based audio steganography methods have been proposed, they still have room for improvements in perceptual quality and undetectability.In this work, we propose an audio steganography framework that could automatically learn to generate superior steganographic cover audio for message embedding.Specifically, the training framework of the proposed framework consists of three components, namely, generator, discriminator and trained deep learning based steganalyzer.Then the traditional message embedding algorithm LSBM, is employed to embed the secret message into the steganographic cover audio to obtain stego audio, which is delivered to the trained steganalyzer for misclassifying as cover audio.Once the adversarial training is completed among these three parties, one can obtain a well-trained generator, which could generate steganographic cover audio for subsequent message embedding.In the practice of our proposed method, the stego audio is produced by embedding the secret message into the steganographic cover audio using a traditional steganography method.Experimental results demonstrate that our proposed audio steganography can yield steganographic cover audio that preserves a quite high perception quality for message embedding.We have compared the detection accuracies with the existing audio steganography schemes as presented in our experiment, the proposed method exhibits lower detection accuracies against the state-of-the-art deep learning based steganalyzers, under various embedding rates.
Distance Estimation in Thermal Cameras Using Multi-Task Cascaded Convolutional Neural Network

The rapid growth of the current pandemic (COVID-19) requires the use of thermal cameras that can perform fast and automatic body temperature measurement. The accuracy of the temperature measurement is affected by its distance from a person. Conventional distance estimation methods utilize the coordinates of the bounding box provided by several face detection algorithms such as YOLOv3 and SSD. The bounding box output of these methods varies which causes inaccurate distance estimation results. In this study, we propose a distance estimation method for thermal camera applications based on the coordinates of the facial key points extracted using multi-task cascaded convolutional neural network. The result obtained in this study proves that the proposed method exhibits higher accuracy (root mean square error of 2.9695 cm in comparison with an RMSE of 25.26 cm using other methods) and the least CPU and memory consumption in comparison with conventional methods.
LogStore

With the prevalence of cloud computing, more and more enterprises are migrating applications to cloud infrastructures. Logs are the key to helping customers understand the status of their applications running on the cloud. They are vital for various scenarios, such as service stability assessment, root cause analysis and user activity profiling. Therefore, it is essential to manage the massive amount of logs collected on the cloud and tap their value. Although various log storages have been widely used in the past few decades, it is still a non-trivial problem to design a cost-effective log storage for cloud applications. It faces challenges of heavy write throughput of tens of millions of log records per second, retrieval on PB-level logs and massive hundreds of thousands of tenants. Traditional log processing systems cannot satisfy all these requirements. To address these challenges, we propose the cloud-native log database LogStore. It combines shared-nothing and shared-data architecture, and utilizes highly scalable and low-cost cloud object storage, while overcoming the bandwidth limitations and high latency of using remote storage when writing a large number of logs. We also propose a multi-tenant management method that physically isolates tenant data to ensure compliance and flexible data expiration policies, and uses a novel traffic scheduling algorithm to mitigate the impact of traffic skew and hotspots among tenants. In addition, we design an efficient column index structure LogBlock to support queries with full-text search, and combined several query optimization techniques to reduce query latency on cloud object storage. LogStore has been deployed in Alibaba Cloud on a large scale (more than 500 machines), processing logs of more than 100 GB per second, and has been running stably for more than two years.
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph

Fraud detection is extremely critical for e-commerce business platforms. Utilizing graph structure data and identifying unexpected dense subgraphs as suspicious is a category of commonly used fraud detection methods. Among them, spectral methods solve the problem efficiently but hurt the performance due to the relaxed constraints. Heuristic methods cannot be accelerated with parallel computation and fail to control the scope of returned suspicious nodes. These drawbacks affect the real-world applications of existing graph-based methods. In this paper, we propose an Ensemble based Fraud DETection (ENSEMFDET) method to scale up fraud detection in bipartite graphs. By oversampling the graph and solving the subproblems, the ensemble approach further votes suspicious nodes without sacrificing the prediction accuracy. Extensive experiments have been done on real transaction data from JD.com and demonstrate the effectiveness, practicability, and scalability of ENSEMFDET.
A Visual Exploration of Cybersecurity Concepts

Cybersecurity-related concepts can be difficult to explain or summarise. The complexity associated with these concepts is compounded by the impact of rapid technological changes and the contextual nature of the meaning ascribed to the various themes. Since visual imagery is often employed in articulation and explanation, we conducted a study in which we asked participants to sketch their understanding of cybersecurity concepts. Based on an analysis of these sketches and subsequent discussions with participants, we make the case for the use of sketching and visuals as a tool for cybersecurity research. Our collection of sketches and icons can further serve as the seed for a visual vocabulary for cybersecurity-related interfaces and communication.
Comparative Investigation of Learning Algorithms for Image Classification with Small Dataset

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Reliable Recommendation with Review-level Explanations

The quality of user-generated reviews is significant for users to understand recommendation results and make online purchasing decisions correctly. However, the reliability of a review, which captures the likelihood that a review is benign, is ignored by many studies. The low reliability reviews cause a recommendation system's unsatisfying performance. Especially the fake reviews written by fraudulent users mislead the system into generating error recommendation results and explanations, which confuse customers and deprive customers of confidence in the system. In this paper, we propose a model, Reliable Recommendation with Review-level Explanations (RRRE), which detects reliable reviews and improves the performance of the explainable recommendation system as well. Recognizing the textual content of reviews, user-item interactions are valuable features for both rating prediction and reliability prediction. RRRE builds a uniform framework to predict rating scores and reliability scores simultaneously. Firstly, RRRE embeds user preferences and item profiles, which are extracted from textual and interactive features, into the representation of the review. Secondly, the supervised information of two subtasks is jointly combined. It makes the optimization of RRRE faster and better. Finally, the reviews with both high reliability scores and rating scores are given to customers as reliable explanations. To the best of our knowledge, we are the first to consider the reliability of reviews for improving explainable recommender system. And the experimental results confirm this idea and show that our model outperforms other baseline methods on Yelp and Amazon datasets.
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias

It is widely accepted that reasoning about object shape is important for object recognition. However, the most powerful object recognition methods today do not explicitly make use of object shape during learning. In this work, motivated by recent developments in low-shot learning, findings in developmental psychology, and the increased use of synthetic data in computer vision research, we investigate how reasoning about 3D shape can be used to improve low-shot learning methods’ generalization performance. We propose a new way to improve existing low-shot learning approaches by learning a discriminative embedding space using 3D object shape, and using this embedding by learning how to map images into it. Our new approach improves the performance of image-only low-shot learning approaches on multiple datasets. We also introduce Toys4K, a 3D object dataset with the largest number of object categories currently available, which supports low-shot learning. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Generative Compression Framework For Low Bandwidth Video Conference

Video conferences introduce a new scenario for video transmission, which focuses on keeping the fidelity of faces even in the low bandwidth network environment. In this work, we propose VSBNet, one of the frameworks to utilize face landmarks in video compression. Our method utilizes the adversarial learning to reconstruct origin frames from the landmarks. To recover more details and keep the consistency of identity, we propose the concept of visual sensitivity to separate the contour of the face from the fast-moving parts, such as eyes and mouth. Experimental results demonstrate the superiority of our framework with a low bit rate of around 1KB/s.
Understanding Architecture Erosion: The Practitioners’ Perceptive

As software systems evolve, their architecture is meant to adapt accordingly by following the changes in requirements, the environment, and the implementation.However, in practice, the evolving system often deviates from the architecture, causing severe consequences to system maintenance and evolution.This phenomenon of architecture erosion has been studied extensively in research, but not yet been examined from the point of view of developers.In this exploratory study, we look into how developers perceive the notion of architecture erosion, its causes and consequences, as well as tools and practices to identify and control architecture erosion.To this end, we searched through several popular online developer communities for collecting data of discussions related to architecture erosion.Besides, we identified developers involved in these discussions and conducted a survey with 10 participants and held interviews with 4 participants.Our findings show that: (1) developers either focus on the structural manifestation of architecture erosion or on its effect on run-time qualities, maintenance and evolution;(2) alongside technical factors, architecture erosion is caused to a large extent by non-technical factors; (3) despite the lack of dedicated tools for detecting architecture erosion, developers usually identify erosion through a number of symptoms; and (4) there are effective measures that can help to alleviate the impact of architecture erosion.
Signal Temporal Logic Task Decomposition via Convex Optimization

In this letter we focus on the problem of decomposing a global Signal Temporal Logic formula (STL) assigned to a multi-agent system to local STL tasks when the team of agents is a-priori decomposed to disjoint sub-teams. The predicate functions associated to the local tasks are parameterized as hypercubes depending on the states of the agents in a given sub-team. The parameters of the functions are, then, found as part of the solution of a convex program that aims implicitly at maximizing the volume of the zero superlevel set of the corresponding predicate function. Two alternative definitions of the local STL tasks are proposed and the satisfaction of the global STL formula is proven when the conjunction of the local STL tasks is satisfied.