Reviewerly/exHarmony huggingface dataset:
Self-Attentive Neural Network for Hashtag Recommendation

A hashtag is a type of metadata tag used on social networks and can help people search for specific topics or content.To capture the interactive information between words and understand the content of microblog posts deeply, this study proposed a neural network model based on a word-level self-attention mechanism.Given a microblog post, the weight of each word was calculated through a self-attention mechanism, and then the representation of a microblog post was obtained through the weighted summation of words.Finally, a multi-layer perceptron was adopted on the representation of a microblog post to perform the classification.The effectiveness of the proposed model was verified through experiments of large-scale datasets.Results show that: (1) introducing word-level self-attention mechanism into hashtag recommendation is effective.(2) In comparison with the baseline methods used in previous studies, such as convolutional neural network or long short-term memory network, the proposed self-attentive neural networks can provide a more accurate representation of a microblog post and significantly improve the F-score of hashtag recommendation on the same dataset.This study provides references for the methods and evaluation of short-text hashtag recommendations, such as microblogs.
Performance-enhancing network pruning for crowd counting

The Counting Convolutional Neural Network (CCNN) has been widely used for crowd counting. However, they typically end up with a complicated network model resulting in a challenge for real-time processing. Existing solutions aim to reduce the size of the network model, but unavoidably sacrifice the network accuracy. Different from existing pruning solutions, in this paper, a new pruning strategy is proposed by considering the contributions of various filters to the final result. The filters in the original CCNN model are grouped into positive, negative and irrelevant types. We prune the irrelevant filters of which feature maps contain little information, and the negative filters determined by a mask learned from the training dataset. Our solution improves the results of the counting model without fine-tuning or retraining the pruned model. We demonstrate the advantages of our proposed approach on the problem of crowd counting. Our experimental results on benchmark datasets show that the network model pruned using our approach not only reduces the network size but also improves the counting accuracies by 4% to 17% less MAE than the state of the arts.
Analysis of Standard Security Features for Selected NoSQL Systems

NoSQL solutions have recently been gaining significant attention because they address some of the inefficiencies of traditional database management systems.NoSQL databases offer features such as performant distributed architecture, flexibility and horizontal scaling.Despite these advantages, there is a vast quantity of NoSQL systems available, which differ greatly from each other.The resulting lack of standardization of security features leads to a questionable maturity in terms of security.What is therefore much needed is a systematic lab research of the availability and maturity of the implementation of the most common standard database security features in NoSQL systems, resulting in a NoSQL security map.This paper summarizes the first part of our research project trying to outline such a map.It documents the definition of the standard security features to be investigated based on a literature review in the area of standard database security.After selection of OrientDB, Redis, Cassandra and MongoDB as initial representatives of commonly used NoSQL systems, a description of systematic investigation of standard database security features for each of these four systems is given.All findings are summarized in tables for quick and easy comparison.We conclude that systems investigated need better default configurations and should enable their security features per default.Finally, we provide an outlook to the next steps of researching a security map for NoSQL systems.
Hadoop Storage Big Data layer: meta-modeling of key concepts and features

In the era of information, humanity produces huge quantities of data measured in terms of terabytes or petabytes that is yet growing exponentially with time.This situation led to the emergence of a large number of big data systems and technologies that share similar architectures but with different implementations.The common architecture is composed of Data sources, Ingestion, Visualization, Hadoop Platform management, Hadoop Storage, Hadoop Infrastructure, Security, and Monitoring Layers.In our way for a unified abstract implementation, we proposed in a previous work a meta-model for data sources and ingestion layers.We relied on our previous comparatives studies to define key concepts of storage in Big Data to propose a meta-model for storage layer.Thus, in this paper, we are going to present our meta-model for storage layer.The main goal of this universal meta-modeling is to enable Big Data distribution providers to offer standard and unified solutions for a Big Data system.
Correcting Whitespace Errors in Digitized Historical Texts

Whitespace errors are common to digitized archives.This paper describes a lightweight unsupervised technique for recovering the original whitespace.Our approach is based on count statistics from Google n-grams, which are converted into a likelihood ratio test computed from interpolated trigram and bigram probabilities.To evaluate this approach, we annotate a small corpus of whitespace errors in a digitized corpus of newspapers from the 19th century United States.Our technique identifies and corrects most whitespace errors while introducing a minimal amount of oversegmentation: it achieves 77% recall at a false positive rate of less than 1%, and 91% recall at a false positive rate of less than 3%.
Policy Optimization with Stochastic Mirror Descent

Improving sample efficiency has been a longstanding goal in reinforcement learning. This paper proposes VRMPO algorithm: a sample efficient policy gradient method with stochastic mirror descent. In VRMPO, a novel variance-reduced policy gradient estimator is presented to improve sample efficiency. We prove that the proposed VRMPO needs only O(ε−3) sample trajectories to achieve an ε-approximate first-order stationary point, which matches the best sample complexity for policy optimization. Extensive empirical results demonstrate that VRMP outperforms the state-of-the-art policy gradient methods in various settings.
Reversible Data Hiding Algorithm in Fully Homomorphic Encrypted Domain

This paper proposes a reversible data hiding scheme by exploiting the DGHV fully homomorphic encryption, and analyzes the feasibility of the scheme for data hiding from the perspective of information entropy. In the proposed algorithm, additional data can be embedded directly into a DGHV fully homomorphic encrypted image without any preprocessing. On the sending side, by using two encrypted pixels as a group, a data hider can get the difference of two pixels in a group. Additional data can be embedded into the encrypted image by shifting the histogram of the differences with the fully homomorphic property. On the receiver side, a legal user can extract the additional data by getting the difference histogram, and the original image can be restored by using modular arithmetic. Besides, the additional data can be extracted after decryption while the original image can be restored. Compared with the previous two typical algorithms, the proposed scheme can effectively avoid preprocessing operations before encryption and can successfully embed and extract additional data in the encrypted domain. The extensive testing results on the standard images have certified the effectiveness of the proposed scheme.
Online education recommendation model based on user behavior data analysis

How to recommend learning resources to users accurately to meet the individual needs of users becomes the key issue with the increasing number of online education users. A personalized recommendation system was proposed in this paper based on user preference behavior data analysis to analyze the on line education recommendation model. It determines the criteria set of the recommendation system with the product attribute mining method, and then uses the personalized recommendation algorithm for user preference modeling to explore the user’s preference for each criterion, thereby producing more accurate recommendations. The simulation results of the algorithm proposed in this paper show that the multi-criteria recommendation algorithm using user distance similarity works best. Using this personalized recommendation algorithm based on user preference can effectively improve the recommendation quality.
CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter

In recent years, the use of social media has increased incredibly.Social media permits Inter-net users a friendly platform to express their views and opinions.Along with these nice and distinct communication chances, it also allows bad things like usage of hate speech.Online automatic hate speech detection in various aspects is a significant scientific problem.This paper presents the Instituto Politécnico Nacional (Mexico) approach for the Semeval 2019 Task-5 [Hateval 2019] (Basile et al., 2019) competition for Multilingual Detection of Hate Speech on Twitter.The goal of this paper is to detect (A) Hate speech against immigrants and women, (B) Aggressive behavior and target classification, both for English and Spanish.In the proposed approach, we used a bag of words model with preprocessing (stemming and stop words removal).We submitted two different systems with names: (i) CIC-1 and (ii) CIC-2 for Hateval 2019 shared task.We used TF values in the first system and TF-IDF for the second system.The first system, CIC-1 got 2nd rank in subtask B for both English and Spanish languages with EMR score of 0.568 for English and 0.675 for Spanish.The second system, CIC-2 was ranked 4th in subtask A and 1st in subtask B for Spanish language with a macro-F1 score of 0.727 and EMR score of 0.705 respectively.
Nonparametric Hierarchical Bayesian Models for Positive Data Clustering Based on Inverted Dirichlet-Based Distributions

In this paper, we propose nonparametric hierarchical Bayesian models based on two inverted Dirichlet-based distributions and Pitman-Yor process for positive data features clustering. The choice of the inverted Dirichlet and the generalized inverted Dirichlet distributions is motivated by their flexibility and modeling capabilities when dealing with this kind of data, while deploying the Pitman-Yor process prior is justified by its power-law behavior, which makes it a natural choice in real-life application compared with Dirichlet processes for instance. The inference for the resulting models takes into account the challenging problem of feature weighting/selection and is conducted under a Bayesian setting by means of the recently proposed stochastic variational Bayes technique. The efficacy and merits of the proposed approaches are examined using the synthetic data and a challenging real-life application that concerns video background subtraction.
Comparative Analysis of LSB, LSB2, PVD Methods of Data Steganography

Steganography is the art of hiding data in another covering medium.This process is very important because it is used in various applications concerned deferent life fields.In this paper we will introduce 3 methods of data steganography: LSB, LSB2 and PVD.These methods will be implemented, tested and analyzed.The obtained experimental results will be compared in order to do some judgment regarding the quality, capacity, hiding time and extracting time.
Incremental Local Distribution-Based Clustering Using Bayesian Adaptive Resonance Theory

Most of the existing Bayesian clustering algorithms perform well on the balanced data. When the data are highly imbalanced, these Bayesian clustering algorithms tend to strongly favor the larger clusters, but provide a notably low detection of the smaller clusters. In this paper, we present an incremental local distribution-based clustering algorithm with the Bayesian adaptive resonance theory (ILBART). This algorithm is developed to adapt itself to a changing environment without using any predefined parameters. The algorithm not only accurately finds the clusters, even in data sets with a severely imbalanced distribution, but also efficiently processes the dynamic data according to the evolving relationships among the clusters. We test our proposed algorithm with experiments conducted on several imbalanced data sets. The experimental results show that our proposed algorithm performs well for clustering imbalanced data and can also obtain a better performance than many other relevant clustering algorithms in several performance indices.
Generating a Novel Dataset of Multimodal Referring Expressions

Referring expressions and definite descriptions of objects in space exploit information about both object characteristics and locations.Linguistic referencing strategies can rely on increasingly highlevel abstractions to distinguish an object in a given location from similar ones elsewhere, yet the description of the intended location may still be unnatural or difficult to interpret.Modalities like gesture may communicate spatial information like locations in a more concise manner.When communicating with each other, humans mix language and gesture to reference entities, changing modalities as needed.Recent progress in AI and human-computer interaction has created systems where a human can interact with a computer multimodally, but computers often lack the capacity to intelligently mix modalities when generating referring expressions.We present a novel dataset of referring expressions combining natural language and gesture, describe its creation and evaluation, and its uses to train models for generating and interpreting multimodal referring expressions.
Penggunaan Teknologi Multimedia Pada Pembelajaran Literasi Anak Usia Dini

This study was aimed to describe the use of multimedia technology for literacy learning in early childhood education, including: implementation of multimedia technology utilization in childrenâ€™s literacy learning. This study was a qualitative research. Data collection used observation techniques, interviews and document review. The data validity was obtained through triangulation. The data analysis technique was done using interactive model. The results show that: Implementation of use of multimedia technology in literacy learning was implemented in each step of learning centers with steps:(a) focusing attention, (b) orientation, (c) modeling, (d) child play stage, and (e) summary.
An efficient full dynamic group signature scheme over ring

The group signature scheme is an important primitive in cryptography, it allows members in a group to generate signatures anonymously on behalf of the whole group. In view of the practical application of such schemes, it is necessary to allow users’ registration and revocation when necessary, which makes the construction of dynamic group signature schemes become a significant direction. On the basis of (Ling et al., Lattice-based group signatures: achieving full dynamicity with ease, 2017), we present the first full dynamic group signature scheme over ring, and under the premise of ensuring security, the efficiency of the scheme is improved mainly from the following three aspects: the size of keys, the dynamic construction of a Merkle hash tree that used to record the information of registered users, and the reuse of the leaves in this tree. In addition, the public and secret keys of both group manager and trace manager are generated by a trusted third party, which prevents the situation that the two managers generate their respective public key and secret key maliciously. Compared with the counterpart of the scheme in (Ling et al., Lattice-based group signatures: achieving full dynamicity with ease, 2017) over ring, the expected space complexity of the Merkle tree used in our work down almost by half, and the computational complexity of its update has been reduced by a notch because of the dynamic construction of the hash tree.
Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization

Abstract Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio‐temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.
Designing an Image Encryption Scheme Based on Compressive Sensing and Non-Uniform Quantization for Wireless Visual Sensor Networks

Wireless visual sensor networks (WVSN) have been widely used to capture images in the fields of monitoring, intelligent transportation, and reconnaissance in recent years. Because of the wireless transmission mode and the huge amount of image data, major challenges in this application are frequent information stealing, big data problems, and harsh communication circumstances. Some encryption schemes based on compressive sensing (CS) and chaotic systems have been proposed to cope with these threats, but most of them are vulnerable against the chosen-plaintext attack (CPA). To remedy these defects, this paper designs a novel method based on non-uniform quantization (NQ). Then, in order to evaluate the true compression ratio (CR), our work takes into account limited data precision in cipher images, while most papers ignored this fact and calculated CR with the assumption of infinite data precision. Besides, to eliminate the periodic windows in the bifurcation diagram of the logistic map (LM), an optimized logistic map (OLM) is designed. Furthermore, simulation results prove that the performance of anti-jamming in the proposed cryptosystem is better than that in existing schemes under the condition of strong noise interference or severe data loss. In conclusion, the proposed method could improve the performance of security and anti-jamming for WVSN.
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use

Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their inability to handle new user behavior at deployment has limited their usage in real world. In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems that handles new user behaviors at deployment by transferring the dialog to a human agent intelligently. The proposed method has three goals: 1) maximize user’s task success by transferring to human agents, 2) minimize the load on the human agents by transferring to them only when it is essential, and 3) learn online from the human agent’s responses to reduce human agents’ load further. We evaluate our proposed method on a modified-bAbI dialog task, 1 which simulates the scenario of new user behaviors occurring at test time. Experimental results show that our proposed method is effective in achieving the desired goals.
Fast Registration for Liver Motion Compensation in Ultrasound-Guided Navigation

In recent years, image-guided thermal ablations have become a considerable treatment method for cancer patients, including support through navigational systems. One of the most critical challenges in these systems is the registration between the intraoperative images and the preoperative volume. The motion secondary to inspiration makes registration even more difficult. In this work, we propose a coarse-fine fast patient registration technique to solve the problem of motion compensation. In contrast to other state-of-the-art methods, we focus on improving the convergence range of registration. To this end, we make use of a Deep Learning 2D U-Net framework to extract the vessels and liver borders from intraoperative ultrasound images and employ the segmentation results as regions of interest in the registration. After an initial 3D-3D registration during breath hold, the following motion compensation is achieved using a 2D-3D registration. Our approach yields a convergence rate of over 70% with an accuracy of $1.97 \pm 1.07$ mm regarding the target registration error. The 2D-3D registration is GPU-accelerated with a time cost of less than 200 ms.
Market Capitalization and Financial Variables: Evidence from Italian Listed Companies

In the company-financial markets relationship, a crucial role is played by economic-financial information, because the financial variables deriving from the balance sheet analysis reduce the information asymmetries between management and potential investors and improve market efficiency.The present study is conducted to examine the effect of some financial variables on market capitalization.The market capitalization is considered as a dependent variable, while six are the independent variables: Return on Equity, Return on Assets, Price/Earnings Ratio, Operating Income/Turnover per share, Earnings Yield and Working Capital per Share.307 Italian listed companies are considered.The study period is 10 years: from 2008 to 2017.This research, while noting that the variables considered are not decisive drivers of the market capitalization, outlines some important relationships: positive relationship between market capitalization and Price/Earnings Ratio, Operating income/Turnover per share and Working Capital per Share and negative relationship between market capitalization and ROE, ROA and Earnings Yield.
Speaker Recognition Based on Long-Term Acoustic Features With Analysis Sparse Representation

The performance of a speaker recognition system depends highly on which acoustic features are used. Most speaker recognition systems use short-term acoustic features extracted from a single speech frame, and the most popular short-term acoustic features are the Mel-frequency cepstral coefficients (MFCCs). The short-term features are generally static features no dynamic information in the speech signal is included in either cepstral coefficients or an MFCCs frame. Using an analysis sparse representation model, in this paper, we introduce the long-term acoustic (LTA) feature for text-independent speaker recognition, which is a sparse presentation of the static features and dynamic information for the speaker's speech. First, the speech signal is segmented into frames which are overlapping with each other, and then the MFCCs frame features can be extracted to construct some super MFCCs frames by stacking some following frames of the current frame to capture the dynamic information of the speech signal. The super MFCCs frames can be combined into a 2-D MFCCs features map (MFCCsmap). Finally, the speaker model can be built based on the analysis sparse model and the sparse representations of the MFCCsmap are used as the LTA features. A state-of-the-art deep neural network (DNN) is employed as a classifier for speaker recognition. The experimental results illustrate the effectiveness and robustness of the proposed system.