Reviewerly/exHarmony huggingface dataset:
Vulnerable Code Clone Detection for Operating System Through Correlation-Induced Learning

Vulnerable code clones in the operating system (OS) threaten the safety of smart industrial environment, and most vulnerable OS code clone detection approaches neglect correlations between functions that limits the detection effectiveness. In this article, we propose a two-phase framework to find vulnerable OS code clones by learning on correlations between functions. On the training phase, functions as the training set are extracted from the latest code repository and function features are derived by their AST structure. Then, external and internal correlations are explored by graph modeling of functions. Finally, the graph convolutional network for code clone detection (GCN-CC) is trained using function features and correlations. On the detection phase, functions in the to-be-detected OS code repository are extracted and the vulnerable OS code clones are detected by the trained GCN-CC. We conduct experiments on five real OS code repositories, and experimental results show that our framework outperforms the state-of-the-art approaches.
Dataset Mention Extraction and Classification

Datasets are integral artifacts of empirical scientific research. However, due to natural language variation, their recognition can be difficult and even when identified, can often be inconsistently referred across and within publications. We report our approach to the Coleridge Initiative’s Rich Context Competition, which tasks participants with identifying dataset surface forms (dataset mention extraction) and associating the extracted mention to its referred dataset (dataset classification). In this work, we propose various neural baselines and evaluate these model on one-plus and zero-shot classification scenarios. We further explore various joint learning approaches - exploring the synergy between the tasks - and report the issues with such techniques.
A* search algorithm optimization path planning in mobile robots scenarios

Path planning for mobile robotics in unknown environments or with moving obstacles requires re-planning paths based on information gathered from the surroundings. Moving obstacles and real time constraints require fast computing to navigate and make decisions in a mobile robot. This paper addresses an optimization approach to compute, with real time constraints, the optimal path for a mobile robot based on a dynamically simplified A* search algorithm with a commitment on the available time.
Efficient and Scalable Multi-Task Regression on Massive Number of Tasks

Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. However, existing MTL methods still fail to offer both the generalization performance and the scalability for such problems. Scaling up MTL methods to problems with a tremendous number of tasks is a big challenge. Here, we propose a novel algorithm, named Convex Clustering Multi-Task regression Learning (CCMTL), which integrates with convex clustering on the k-nearest neighbor graph of the prediction models. Further, CCMTL efficiently solves the underlying convex problem with a newly proposed optimization method. CCMTL is accurate, efficient to train, and empirically scales linearly in the number of tasks. On both synthetic and real-world datasets, the proposed CCMTL outperforms seven state-of-the-art (SoA) multi-task learning methods in terms of prediction accuracy as well as computational efficiency. On a real-world retail dataset with 23,812 tasks, CCMTL requires only around 30 seconds to train on a single thread, while the SoA methods need up to hours or even days.
Heavy hitters via cluster-preserving clustering

We develop a new algorithm for the turnstile heavy hitters problem in general turnstile streams, the EXPANDERSKETCH, which finds the approximate top- k items in a universe of size n using the same asymptotic O ( k log n ) words of memory and O (log n ) update time as the COUNTMIN and COUNTSKETCH, but requiring only O ( k poly (log n )) time to answer queries instead of the O ( n log n ) time of the other two. The notion of "approximation" is the same l 2 sense as the COUNTSKETCH, which given known lower bounds is the strongest guarantee one can achieve in sublinear memory. Our main innovation is an efficient reduction from the heavy hitters problem to a clustering problem in which each heavy hitter is encoded as some form of noisy spectral cluster in a graph, and the goal is to identify every cluster. Since every heavy hitter must be found, correctness requires that every cluster be found. We thus need a "cluster-preserving clustering" algorithm that partitions the graph into pieces while finding every cluster. To do this we first apply standard spectral graph partitioning, and then we use some novel local search techniques to modify the cuts obtained so as to make sure that the original clusters are sufficiently preserved. Our clustering algorithm may be of broader interest beyond heavy hitters and streaming algorithms.
A semantic event based framework for complex situations modeling and identification in smart environments

Integration of technologies and smart services is an essential need in most pervasive smart automation systems, especially those requiring agile situation management such as a smart home. Ontology reasoning is an efficient technique for identifying the situation of such systems by capturing the context and optimizing connected object lifecycle. It is based on common-sense knowledge for interpreting a huge number of events from different and heterogeneous connected objects. The ontology-driven approach may be used to solve the parallel incoming events using new combination operators in order to achieve high accuracy rates. This paper presents a flexible, modular and hierarchical loosely coupled framework based on open semantic services in order to identify highly complex events to hold the situations with high efficiency. The proposed approach is evaluated on smart domains use cases and compared with other existing methods. Results show interesting ratios of situation identification accuracy with low execution time.
Blameworthiness in Strategic Games

There are multiple notions of coalitional responsibility. The focus of this paper is on the blameworthiness defined through the principle of alternative possibilities: a coalition is blamable for a statement if the statement is true, but the coalition had a strategy to prevent it. The main technical result is a sound and complete bimodal logical system that describes properties of blameworthiness in one-shot games.
Pruning variable selection ensembles

In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering‐based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In particular, a greedy sorting strategy is proposed to rearrange the order by which the members are included into the integration process. Through stopping the fusion process early, a smaller subensemble with higher selection accuracy can be obtained. More importantly, the sequential inclusion criterion reveals the fundamental strength‐diversity trade‐off among ensemble members. By taking stability selection with the base learner lasso (abbreviated as SSLasso) as an example, some experiments are conducted to examine the performance of the novel algorithm. Experimental results demonstrate that pruned SSLasso generally achieves higher selection accuracy and lower false discovery rate than SSLasso and several other benchmark methods.
Incorporating Structural Information for Better Coreference Resolution

Coreference resolution plays an important role in text understanding. In the literature, various neural approaches have been proposed and achieved considerable success. However, structural information, which has been proven useful in coreference resolution, has been largely ignored in previous neural approaches. In this paper, we focus on effectively incorporating structural information to neural coreference resolution from three aspects. Firstly, nodes in the parse trees are employed as a constraint to filter out impossible text spans (i.e., mention candidates) in reducing the computational complexity. Secondly, contextual information is encoded in the traversal node sequence instead of the word sequence to better capture hierarchical information for text span representation. Lastly, additional structural features (e.g., the path, siblings, degrees, category of the current node) are encoded to enhance the mention representation. Experimentation on the data-set of the CoNLL 2012 Shared Task shows the effectiveness of our proposed approach in incorporating structural information into neural coreference resolution.
Deep Pairwise Ranking with Multi-label Information for Cross-Modal Retrieval

Cross-modal retrieval has gained much attention due to the growing demand for enormous multi-modal data in recent years (i.e., image-text or text-image retrieval). In order to alleviate the problem of ignoring the existence of irrelevant information between images and texts, this paper proposes Deep Pairwise Ranking model with multi-label information for Cross-Modal retrieval (DPRCM). DPRCM directly learns a mapping from images and texts to a compact Euclidean space where distances correspond to the similarity measure of images and texts. The bi-triplet loss function in DPRCM reduces the distance between associated images and texts on the common subspace and increases the margin of independent samples. The classification loss function can better utilize the multi-label information to reduce the semantic gap between image features and text descriptions. Experiments on three widely-used datasets show that DPRCM can achieve competitive performance compared to state-of-the-art methods.
Unsupervised Embedding Enhancements of Knowledge Graphs using Textual Associations

Knowledge graph embeddings are instrumental for representing and learning from multi-relational data, with recent embedding models showing high effectiveness for inferring new facts from existing databases. However, such precisely structured data is usually limited in quantity and in scope. Therefore, to fully optimize the embeddings it is important to also consider more widely available sources of information such as text. This paper describes an unsupervised approach to incorporate textual information by augmenting entity embeddings with embeddings of associated words. The approach does not modify the optimization objective for the knowledge graph embedding, which allows it to be integrated with existing embedding models. Two distinct forms of textual data are considered, with different embedding enhancements proposed for each case. In the first case, each entity has an associated text document that describes it. In the second case, a text document is not available, and instead entities occur as words or phrases in an unstructured corpus of text fragments. Experiments show that both methods can offer improvement on the link prediction task when applied to many different knowledge graph embedding models.
A Part Power Set Model for Scale-Free Person Retrieval

Recently, person re-identification (re-ID) has attracted increasing research attention, which has broad application prospects in video surveillance and beyond. To this end, most existing methods highly relied on well-aligned pedestrian images and hand-engineered part-based model on the coarsest feature map. In this paper, to lighten the restriction of such fixed and coarse input alignment, an end-to-end part power set model with multi-scale features is proposed, which captures the discriminative parts of pedestrians from global to local, and from coarse to fine, enabling part-based scale-free person re-ID. In particular, we first factorize the visual appearance by enumerating $k$-combinations for all $k$ of $n$ body parts to exploit rich global and partial information to learn discriminative feature maps. Then, a combination ranking module is introduced to guide the model training with all combinations of body parts, which alternates between ranking combinations and estimating an appearance model. To enable scale-free input, we further exploit the pyramid architecture of deep networks to construct multi-scale feature maps with a feasible amount of extra cost in term of memory and time. Extensive experiments on the mainstream evaluation datasets, including Market-1501, DukeMTMC-reID and CUHK03, validate that our method achieves the state-of-the-art performance.
A Proposed Model for Detecting Facebook News’ Credibility

Social networks are currently one of the main News' sources for most of their users.Moreover, News channels also consider social networks as main channels not only for spreading the news but also for measuring the feedback from their followers.Facebook Followers can comment or react to the news, which represents the follower's feedback about this topic.Therefore, it is a fact that measuring the News' credibility is one of the important tasks that could control the propagation of the fake news as well as the number of News' followers.The proposed model in this research highlights the impact of the News' followers on detecting the News' polarity either it is fake or not.The proposed model focuses on applying an intelligent sentiment analysis using Vector Space Model (VSM) which is one of the most successful techniques on the users' comments and reactions through the emoji.Then the degree of credibility is determined according to the correlation coefficient.An experimental study was applied using Facebook News dataset, which included the News and the followers' feedbacks.
Image Captioning with Compositional Neural Module Networks

In image captioning where fluency is an important factor in evaluation, n-gram metrics, sequential models are commonly used; however, sequential models generally result in overgeneralized expressions that lack the details that may be present in an input image. Inspired by the idea of the compositional neural module networks in the visual question answering task, we introduce a hierarchical framework for image captioning that explores both compositionality and sequentiality of natural language. Our algorithm learns to compose a detail-rich sentence by selectively attending to different modules corresponding to unique aspects of each object detected in an input image to include specific descriptions such as counts and color. In a set of experiments on the MSCOCO dataset, the proposed model outperforms a state-of-the art model across multiple evaluation metrics, more importantly, presenting visually interpretable results. Furthermore, the breakdown of subcategories f-scores of the SPICE metric and human evaluation on Amazon Mechanical Turk show that our compositional module networks effectively generate accurate and detailed captions.
Data-Driven Dynamic Security Assessment and Control of Power Systems: An Online Sequential Learning Method

Intelligent systems (IS) have gained popularity in facilitating very fast dynamic security assessment (DSA). However, conventional IS methods are limited in their ability to be updated with current system operation conditions online due to the excessive training time and complex parameters tuning required for updates. In this paper, an online sequential extreme learning machine (ELM) based method is proposed to enable efficient real-time DSA and online model updating. To enhance the performance of ELMs, feature selection using single-feature estimation is conducted and the results are utilized to design generation shifting as a preventive control. The proposed methods are examined based on the New England 39-bus test system and compared with popular IS methods. The simulation results show that the ELM-based DSA method possesses significant superior computation speed while high, competitive accuracy is maintained. The derived generation shifting is also valid to restore system security.
Social Media Text Data Visualization Modeling: A Timely Topic Score Technique

Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information.The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems.Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions.However, these models are often poor estimators of topic proportions.Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling.This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends.The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems.Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.
Joint Projection and Subspace Learning for Zero-Shot Recognition

Zero-shot learning (ZSL) typically learns a projection function between a feature space and a semantic space (e.g. attribute space) to recognize unseen classes without any training samples. However, when the projection function learned only with seen class training samples is applied to unseen class test samples, most existing ZSL models suffer from projection domain shift. In this paper, we propose a new ZSL model, termed joint inductive learning (JIL) of bidirectional projections and shared subspace, to learn more generalizable projections and thus algin the seen and unseen class domains. By inducing the two tasks of bidirectional projection learning (BPL) and shared subspace learning (SSL), we integrate three forms of projection learning into a unified framework, which makes our JIL model become more generalizable across the two domains. Moreover, since a shared subspace is actually learned between the feature and semantic spaces, our JIL model can be naturally applied to cross-modal retrieval (CMR). Extensive experiments demonstrate that our JIL model generally outperforms the state-of-the-art alternatives in the two challenging tasks (i.e. ZSL and CMR).
Reversible Logic: An Introduction

Reversible logic is one of the alternatives to meet the requirement of power, speed and size in EDA (Electronic Design Automation) industry because these circuits are theoretically proven for providing nearly energy free computation by preventing the loss of information during operations. This chapter describes about theory of reversible logic, basic gates, cost matrices used for synthesis and testing of these circuits and connection of reversible logic with quantum computation.
Getting in Shape: Word Embedding SubSpaces

Many tasks in natural language processing require the alignment of word embeddings. Embedding alignment relies on the geometric properties of the manifold of word vectors. This paper focuses on supervised linear alignment and studies the relationship between the shape of the target embedding. We assess the performance of aligned word vectors on semantic similarity tasks and find that the isotropy of the target embedding is critical to the alignment. Furthermore, aligning with an isotropic noise can deliver satisfactory results. We provide a theoretical framework and guarantees which aid in the understanding of empirical results.
Transfer learning-assisted multi-objective evolutionary clustering framework with decomposition for high-dimensional data

Although multi-objective evolutionary subspace clustering approaches have shown promise in handling high-dimensional datasets, their performance is restricted by two main drawbacks. First, their local search strategies have not been well investigated. Second, while exploring the search space, they neglect the useful knowledge from previously solved problems. To tackle these issues, this paper proposes a transfer learning-assisted multi-objective evolutionary clustering framework with decomposition. Firstly, we provide a decomposition-based local search strategy. To capture a comprehensive data structure, this strategy updates the weights of features by considering both the within-class compactness and between-class separation, and spontaneously balances the two properties. Secondly, we develop a knowledge transfer strategy. By transferring search experience from a previously solved clustering problem, the strategy improves the search efficiency, consequently enhances the clustering accuracy of the current problem. It has a closed-form solution and can transfer knowledge across both homogeneous and heterogeneous problems from either different or the same domains. Finally, we conduct an extensive experimental study on the framework by comparing with six representative subspace clustering approaches on a wide range of benchmarks and real-world applications. Results demonstrate the superiority of our framework.
Variational Graph Embedding and Clustering with Laplacian Eigenmaps

As a fundamental machine learning problem, graph clustering has facilitated various real-world applications, and tremendous efforts had been devoted to it in the past few decades. However, most of the existing methods like spectral clustering suffer from the sparsity, scalability, robustness and handling high dimensional raw information in clustering. To address this issue, we propose a deep probabilistic model, called Variational Graph Embedding and Clustering with Laplacian Eigenmaps (VGECLE), which learns node embeddings and assigns node clusters simultaneously. It represents each node as a Gaussian distribution to disentangle the true embedding position and the uncertainty from the graph. With a Mixture of Gaussian (MoG) prior, VGECLE is capable of learning an interpretable clustering by the variational inference and generative process. In order to learn the pairwise relationships better, we propose a Teacher-Student mechanism encouraging node to learn a better Gaussian from its instant neighbors in the stochastic gradient descent (SGD) training fashion. By optimizing the graph embedding and the graph clustering problem as a whole, our model can fully take the advantages in their correlation. To our best knowledge, we are the first to tackle graph clustering in a deep probabilistic viewpoint. We perform extensive experiments on both synthetic and real-world networks to corroborate the effectiveness and efficiency of the proposed framework.