Reviewerly/exHarmony huggingface dataset:
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees

A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equivalence, based on conditional independence relations among the variables. Therefore, the number of DAGs equivalent to the ground truth DAG is an indicator of the causal complexity of the underlying structure–roughly speaking, it shows how many interventions or how much additional information is further needed to recover the underlying DAG. In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class. Our approach is based on the clique tree representation of chordal graphs. We show that in the case of bounded degree graphs, the proposed algorithm is polynomial time. We further demonstrate that this technique can be utilized for uniform sampling from a Markov equivalence class, which provides a stochastic way to enumerate DAGs in the equivalence class and may be needed for finding the best DAG or for causal inference given the equivalence class as input. We also extend our counting and sampling method to the case where prior knowledge about the underlying DAG is available, and present applications of this extension in causal experiment design and estimating the causal effect of joint interventions.
Using Human Attention to Extract Keyphrase from Microblog Post

This paper studies automatic keyphrase extraction on social media.Previous works have achieved promising results on it, but they neglect human reading behavior during keyphrase annotating.The human attention is a crucial element of human reading behavior.It reveals the relevance of words to the main topics of the target text.Thus, this paper aims to integrate human attention into keyphrase extraction models.First, human attention is represented by the reading duration estimated from eye-tracking corpus.Then, we merge human attention with neural network models by an attention mechanism.In addition, we also integrate human attention into unsupervised models.To the best of our knowledge, we are the first to utilize human attention on keyphrase extraction tasks.The experimental results show that our models have significant improvements on two Twitter datasets.
KS-ABESwET: A Keyword Searchable Attribute-Based Encryption Scheme With Equality Test in the Internet of Things

With the widespread application of the Internet of Things (IoT) technology, it is expected to become a new data management model.But IoT devices have limited resource storage and computing power, they are highly vulnerable to hackers and leak sensitive information to third parties.The traditional attribute-based searchable encryption schemes usually require an intelligent terminal to perform complex calculations while accessing data, therefore IoT devices may not be able to withstand excessive calculation burden.In this paper, we present a keyword searchable attribute-based encryption scheme with equality test (KS-ABESwET) in the IoT by combining the notions of attribute-based searchable encryption (ABSE) with equality test.The proposed scheme adopts a keyword search algorithm based on the inverted index and equality test mechanism.If the keyword token match index is successful, the cloud server sends all ciphertexts that meet the conditions to the data user.Then, data user classifies the ciphertexts by equality test mechanism, which is executed by the authorized cloud server to determine whether the two ciphertexts encrypted by different access policies contain the same plaintext without decrypting.In this way, data user does not need to decrypt all ciphertexts, which decreases storage resource consumption of IoT devices and simplifies the complex operations generated by the traditional ABSE schemes.Using outsourcing technology, most calculations in the scheme are outsourced to the server, and IoT devices only perform a few calculations, which reduces greatly the computing and storage burden.Based on the decisional q -1 assumption and decisional Diffie-Hellman (DDH) assumption, the proposed scheme proves that has chosen-plaintext security and chosen-keyword security.Moreover, through comparative analysis and experimental simulation, our scheme is effective and suitable for IoT environment.
Adaptive Safe Semi-Supervised Extreme Machine Learning

Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent learning framework. However, the performance of SSL heavily depends on the construction of manifold graph and the safety degrees of unlabeled samples. Due to the construction of manifold graph and safety degrees of unlabeled samples are usually pre-construct before classification and fixed during the classification learning process, which results independent with the subsequent classification. Aiming at the above problems, we propose a unified adaptive safe semi-supervised learning (Adap-SaSSL) framework. This framework adaptively constructs a manifold graph while adaptively calculating the safety degrees of unlabeled samples. Specifically, the weights of manifold graph and its parameters, as well as the safety degrees of unlabeled samples will be optimized during the learning process rather than being calculated in advance. Finally, we then develop and implement a adaptive safe classification method based on the Adap-SaSSL framework, which is called adaptive safe semi-supervised extreme learning machine (AdSafe-SSELM). Experimental results on artificial, benchmark and image datasets show that the performance of AdSafe-SSELM is effective and reliable compared to other algorithms.
Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

Abstract Clustering algorithms can be optimized using nature‐inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k‐means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k‐means++ is a hybrid k‐means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature‐inspired techniques using cuckoo and krill herd algorithm are implemented on k‐means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f‐measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.
Computing optimal discrete readout weights in reservoir computing is NP-hard

We show NP-hardness of a generalized quadratic programming problem, which we called Unconstrained N-ary Quadratic Programming (UNQP). This problem has recently become practically relevant in the context of novel memristor-based neuromorphic microchip designs, where solving the UNQP is a key operation for on-chip training of the neural network implemented on the chip. UNQP is the problem of finding a vector $\mathbf{v} \in S^N$ which minimizes $\mathbf{v}^T\,Q\,\mathbf{v} +\mathbf{v}^T \mathbf{c} $, where $S = \{s_1, \ldots, s_n\} \subset \mathbb{Z}$ is a given set of eligible parameters for $\mathbf{v}$, $Q \in \mathbb{Z}^{N \times N}$ is positive semi-definite, and $\mathbf{c} \in \mathbb{Z}^{N}$. In memristor-based neuromorphic hardware, $S$ is physically given by a finite (and small) number of possible memristor states. The proof of NP-hardness is by reduction from the Unconstrained Binary Quadratic Programming problem, which is a special case of UNQP where $S = \{0, 1\}$ and which is known to be NP-hard.
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling

Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity.Dialectal variants of morphologically rich languages suffer more as they tend to be more noisy and have less resources.In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging.We use multitask learning for joint morphological modeling for the features within two dialects, and as a knowledge-transfer scheme for crossdialectal modeling.We use adversarial training to learn dialect invariant features that can help the knowledge-transfer scheme from the high to low-resource variants.We work with two dialectal variants: Modern Standard Arabic (high-resource "dialect" 1 ) and Egyptian Arabic (low-resource dialect) as a case study.Our models achieve state-of-the-art results for both.Furthermore, adversarial training provides more significant improvement when using smaller training datasets in particular.
A Strongly Unforgeable Certificateless Signature Scheme and Its Application in IoT Environments

With the widespread application of the Internet of Things (IoT), ensuring communication security for IoT devices is of considerable importance. Since IoT data are vulnerable to eavesdropping, tampering, forgery, and other attacks during an open network transmission, the integrity and authenticity of data are fundamental security requirements in the IoT. A certificateless signature (CLS) is a viable solution for providing data integrity, data authenticity, and identity identification in resource-constrained IoT devices. Therefore, designing a secure and efficient CLS scheme for IoT environments has become one of the main objectives of IoT security research. However, the existing CLS schemes rarely focus on strong unforgeability and replay attacks. Herein, we design a novel CLS scheme to protect the integrity and authenticity of IoT data. In addition to satisfying the strong unforgeability requirement, the proposed scheme also resists public key replacement attacks, malicious-but-passive key-generation-centre attacks, and replay attacks. Compared with other related CLS schemes without random oracles, our CLS scheme has a shorter private key, stronger security, and lower communication and computational costs.
A Comparative Study on Big Data Analytics Frameworks, Data Resources and Challenges

Recently, huge amount of data has been generated in all over the world; these data are very huge, extremely fast and varies in its type. In order to extract the value from this data and make sense of it, a lot of frameworks and tools are needed to be developed for analyzing it. Until now a lot of tools and frameworks were generated to capture, store, analyze and visualize it. In this study we categorized the existing frameworks which is used for processing the big data into three groups, namely as, Batch processing, Stream analytics and Interactive analytics, we discussed each of them in detailed and made comparison on each of them.
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment

Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation.In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE.Our Generalized Lexical ENtailment model (GLEN) is decoupled from the word embedding model and applicable to any distributional vector space.Yet -unlike existing retrofitting models -it captures a general specialization function allowing for LE-tuning of the entire distributional space and not only the vectors of words seen in lexical constraints.Coupled with a multilingual embedding space, GLEN seamlessly enables cross-lingual LE detection.We demonstrate the effectiveness of GLEN in graded LE and report large improvements (over 20% in accuracy) over state-ofthe-art in cross-lingual LE detection.
From Obstacle-Based Space Partitioning to Corridors and Path Planning. A Convex Lifting Approach

This letter proposes a novel methodology for path generation in known and congested multi-obstacle environments. Our aim is to solve an open problem in navigation within such environments: the feasible space partitioning in accordance with the distribution of obstacles. It is shown that such a partitioning is a key concept toward the generation of a corridor in cluttered environments. Once a corridor between an initial and a final position is generated, the selection of a path is considerably simplified in comparison with the methods which explore the original non-convex feasible regions of the environment. The core of the methodology presented here is the construction of a convex lifting which boils down to a convex optimization. This letter covers both the mathematical foundations and the computational details of the implementation and aims to illustrate the concepts with geometrical examples.
RainBio: Proportional Visualization of Large Sets in Biology

Set visualization is a well-known task in information visualization. In biology, it is used for comparing visually sets of genes or proteins, typically using Venn diagrams. However, limitations of the Venn diagram are well-known: they are limited to 6 sets and difficult to read above 4. Many other set visualization techniques have been proposed, but they have never been widely used in biology. In this paper, we introduce RainBio, a technique for visualizing sets in biology and aimed at providing a global overview showing the size of the main intersections, in a proportional way, and the similarities between sets. We adapt rainbow boxes, a technique for visualizing small datasets, to the visualization of larger sets, using element aggregation and intersection clustering. We present the application of RainBio to three datasets, with 5, 6 and 12 sets. We also describe a small user study comparing RainBio with Venn diagrams, involving 30 students in biology. Results showed that RainBio led to significantly fewer errors on 6-set dataset, and that the majority of students preferred RainBio. RainBio is proposed as a web-based tool for up to 15 sets.
Mobile Money and Socioeconomic Development: A Cross-Country Investigation in Sub-Saharan Africa

Over the past decade, mobile money, as a disruptive financial services innovation, has been widely adopted in Sub-Saharan Africa where the majority of population was financially excluded.Despite the transformative role of mobile money, the macro-level socioeconomic implications have been weakly examined so far.To fill the research gap, this paper explores the theoretical and empirical links between mobile money and socioeconomic development.Drawing on the theory of technology affordances and the emerging mobile money literature, this paper argues that mobile money, when adopted on a massive scale, has the potential to lead to socioeconomic development at the societal level.Results of Difference-in-Differences analysis show that mobile money, when widely adopted, has a positive effect on a country's economic growth.Additional analysis reveals that the positive effect is found to increase as mobile money spreads into a society over time.This paper contributes to the emerging mobile money literature by documenting the positive relationship between mobile money and socioeconomic development at the societal level.
From Coarse to Fine: A Stage-Wise Deraining Net

In this paper, we propose a novel deep learning the based deraining method. The proposed method is motivated by the idea that an effective deraining algorithm should have the ability to remove various remaining rain streaks, which have been processed by the deraining method, in a repeated way. So, we design the deraining network in a coarse-to-fine manner that is multi-stage processing procedure and the parameters are shared in each stage. As the spatial contextual information is important for single image deraining, a densely connected dilation convolution block is proposed to deal with rain streaks with different sizes. Moreover, outer dense connections are used to guide the subsequent deraining procedures by fusing all the previous estimated rain-free images. The quantitative and qualitative experimental results demonstrate the superiority of the proposed method compared with recent state-of-the-art deraining methods on Rain100H, Rain1200, and Rain1400 datasets, while the number of parameters of our proposed method is greatly reduced due to the shared parameters strategy.
Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks

Malicious accounts are one of the biggest threats to the security and privacy of online social networks (OSNs). In this work, we study a new type of OSN, called privacy-centric mobile social network (PC-MSN), such as KakaoTalk and LINE, which has attracted billions of users recently. The design of PC-MSN is inspired to protect their users' privacy from strangers: (1) a stranger is not easy to send a friend request to a user who does not want to make friends with strangers; and (2) strangers cannot view a user's post. Such a design mitigates the security issue of malicious accounts. At the same time, it also brings the battleground between attackers and defenders to an earlier stage, i.e., making friendship, than the one studied in previous works. Also, previous defense proposals mostly rely on certain assumptions on the attacker, which may not be robust in the new PC-MSNs. As a result, previous malicious accounts detection approaches are less effective on a PC-MSN.
Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding

The traditional collaborative filtering recommendation algorithm only uses the item-user rating matrix without considering the semantic information of the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relationship in the knowledge graph, while maintaining the structure and semantic information of the original graph in the vector. By calculating the semantic similarity between items, the semantic information of the item itself is incorporated into the collaborative filtering recommendation. The algorithm makes up for the defect that the collaborative filtering recommendation algorithm does not consider the knowledge information of the item itself, and enhances the effect of collaborative filtering recommendation on the semantic level. The experimental results on the MovieLens dataset show that the algorithm can get higher values on precision, recall and F1 measure.
An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest

Bloggers play a role in individual decision making of users in online social networking platforms. Their capability of addressing a wide audience gives them influence over their audience, which companies seek to exploit. Identification of influential bloggers can be seen as a machine learning (ML) task and different ML techniques can help in classifying the professional blogger. In this paper, we propose a predictive and adaptive model named as Influential Blogger based Case-Based Reasoning (IB-CBR) model for the recognition of unseen influential bloggers. It incorporates self-prediction and self-adaptation (self-management) capabilities which are the essence of an automated system. The integration of Random Forest is found contributing to the efficiency of the IB-CBR model as compared to Nearest-Neighbor, and Artificial Neural Network. The performance of the proposed IB-CBR model is evaluated against other ML techniques by using standard performance measures on a standard blogger's dataset. It is observed that our proposed model exhibits 88-95% Accuracy and 94-97% True Positive Rate in the prediction and adaptation of professional bloggers, respectively, in three iterations of the proposed model. What's more, the IB-CBR model achieved 91-96% (increasing) F-measure, 91-98% (increasing) ROC AUC, and 36-11% (decreasing) False Positive Rate due to adaptivity. The IB-CBR model performed well when it is compared with other ML techniques using different standard datasets.
MULAPI: A Tool for API Method and Usage Location Recommendation

Software is incrementally evolved as various new feature requests are implemented to meet users' requirements. To accelerate the incoming feature implementation, developers often utilize existing third-party APIs that encapsulate featurerelated functionality into simple APIs. However, it is non-trivial for developers to choose which APIs to use and where to use them in a target program since the search space of APIs and their usage locations are usually large. In this paper, we introduce a tool, MULAPI, to facilitate the decision of suitable APIs at potential usage locations for implementing the incoming feature requests. MULAPI combines feature localization and information retrieval techniques to accomplish API recommendation and usage location. Empirical studies demonstrate that MULAPI can effectively recommend correct APIs and their usage locations with higher precision than state-of-the-art approaches. The video of our demo is available at https://youtu.be/s3Cs5ltqdvs.
Executing Multilevel Domain-Specific Models in Maude.

Multilevel modelling (MLM) tackles the limitation in the number of abstraction levels present in traditional modelling approaches within the model-driven software engineering (MDSE) field.One way to specify the behaviour description of MLMs is by means of multilevel model transformations.In this paper, we propose an approach to achieve reusability and flexibility in specifying and executing multilevel model transformations.For this purpose, we rely on code-generation and the efficient rewriting logic mechanisms that Maude provides.As a proof of concept, we have developed an infrastructure which combines our MLM tool MultEcore, that facilitates definition of MLM hierarchies and transformations, with Maude, which performs the execution of the transformations on these hierarchies.
UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution

This paper describes our submission toSemEval-2019 Task 12 on toponym resolution in scientific papers.We train separate NER models for toponym detection over text extracted from tables vs. text from the body of the paper, and train another auxiliary model to eliminate mis-detected toponyms.For toponym disambiguation, we use an SVM classifier with hand-engineered features.Our best model achieved a strict micro-F1 score of 80.92% and overlap micro-F1 score of 86.88% in the toponym detection subtask, ranking 2nd out of 8 teams on F1 score.For toponym disambiguation and end-to-end resolution, we officially ranked 2nd and 3rd, respectively.
University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities

We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for SemEval 2019 task 12.We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for task 1, which utilizes only character, word, prefix and suffix information for the identification of geolocation entities.Despite using just the training data provided by task organizers and not using any lexicon features, we achieved 78.85% strict micro F-score on task 1.We used the unsupervised population heuristics for task 3 and achieved 52.99% strict micro-F1 score in this task.