Reviewerly/exHarmony huggingface dataset:
Dispreferred speech acts in virtual reality: Analysis of tone choices and hesitations

This study compared participants' dispreferred speech acts between two oral discourse completion tasks (oral DCT): computer-based and virtual reality (VR)-based. Both tasks involved a series of scenarios eliciting speech acts of refusals and complaints (i.e., dispreferred acts). While the computer-based oral DCT elicited participants' speech directed to their imaginary interlocutor, in the VR-based task participants produced their speech to an actual interlocutor appearing in 360° visualizations. Sixty-two native and nonnative speakers of English completed both tasks. Using mixed effects models, the impact of task conditions (computer-based or VR-based) and language background (native or nonnative speakers) was assessed on two linguistic features: tone choices (rising, falling, and level tone) and hesitations (e.g., occurrences of filled and unfilled pauses). Both participant groups produced more level tones and hesitations in the VR-based than the computer-based DCT. Although native speakers used more falling tones, nonnative speakers used more rising tones, and their speech involved more hesitations.
Prototype based granular neuro-fuzzy system for regression task

Artificial intelligence is often inspired by biological solutions. Prototypes are among these inspirations. Humans often describe complex entities by comparing them to previously known items (prototypes) instead of providing a detailed description. In this study, we apply this approach to neuro-fuzzy systems. Neuro-fuzzy systems operate using fuzzy IF-THEN rules. In our approach, the premises of rules are represented by prototypes. A new item is compared to the prototypes (as wholes) in the rule premises and its similarities to the prototypes in the rules define the firing strengths of the rules. The similarity is assessed based on the Minkowski metric. Prototypes are elaborated by granulation of the input domain with clustering and by applying the principle of justifiable granularity. We tested the proposed method in numerical experiments based on the regression task for benchmark data sets in public data repositories.
The use of blockchain by international organizations: effectiveness and legitimacy

Abstract Blockchain is a new general-purpose technology that poses significant challenges to policymaking, law, and society. Blockchain is even more distinctive than other transformative technologies, as it is by nature a global technology; moreover, it operates based on a set of rules and principles that have a law-like quality—the lex cryptographia. The global nature of blockchain has led to its adoption by international organizations such as the United Nations and the World Bank. However, the law-like nature of the technology makes some of its uses by international organizations questionable from an international law and foreign affairs perspective. In this light, the article examines the effectiveness and legitimacy of the use of blockchain for international policymaking.
Suboptimal Local Minima Exist for Wide Neural Networks with Smooth Activations

Does a large width eliminate all suboptimal local minima for neural nets? An affirmative answer was given by a classic result published in 1995 for one-hidden-layer wide neural nets with a sigmoid activation function, but this result has not been extended to the multilayer case. Recently, it was shown that, with piecewise linear activations, suboptimal local minima exist even for wide nets. Given the classic positive result on smooth activation and the negative result on nonsmooth activations, an interesting open question is: Does a large width eliminate all suboptimal local minima for deep neural nets with smooth activation? In this paper, we give a largely negative answer to this question. Specifically, we prove that, for neural networks with generic input data and smooth nonlinear activation functions, suboptimal local minima can exist no matter how wide the network is (as long as the last hidden layer has at least two neurons). Therefore, the classic result of no suboptimal local minimum for a one-hidden-layer network does not hold. Whereas this classic result assumes sigmoid activation, our counterexample covers a large set of activation functions (dense in the set of continuous functions), indicating that the limitation is not a result of the specific activation. Together with recent progress on piecewise linear activations, our result indicates that suboptimal local minima are common for wide neural nets.
Hiding Message Using a Cycle Generative Adversarial Network

Training an image steganography is an unsupervised problem, because it is impossible to obtain an ideal supervised steganographic image corresponding to the cover image and secret message. Inspired by the success of cycle generative adversarial networks in unsupervised tasks such as style transfer, this article proposes to use a cycle generative adversarial network to solve the problem of unsupervised image steganography. Specifically, this article jointly trains five networks, i.e., a steganographic network, an inverse steganographic network, a hidden message reconstruction network, and two discriminative networks, which together constitute a hidden message cycle generative adversarial network (HCGAN). Compared with the recent image steganography based on generative adversative network, HCGAN provides more accurate supervised information, which makes the training process of HCGAN converge faster and the performance of the trained image steganography network is better. In addition, this article introduces an image steganographic network based on residual learning and shows that residual learning can effectively improve the performance of steganography. Furthermore, to the best of our knowledge, we are the first to propose an inverse steganographic network for eliminating steganographic message from steganographic images, which can be used to avoid steganographic message being discovered or acquired by a third party. The experimental results show that compared with the steganography based on generative adversarial network, the proposed HCGAN has a higher correct decoding rate, better visual quality of steganographic image, and higher secrecy.
Machine Learning-based Approach on Dealing with Binary Classification Problem in Imbalanced Financial Data

Imbalanced data are known to be notably difficult to deal with, as it needs a thorough understanding of the data to know how it should be done. However, it occurs in many fields, especially in finance, like banking. Many problems that use financial data can be summarized as binary problems. Some of them are fatal if not identified correctly. This research aims to find how to utilize machine learning models to deal with imbalanced data, specifically one that contains binary classification problems. In this paper, we use imbalanced insurance and credit card datasets. The research is conducted by starting from doing feature selection in the datasets by removing irrelevant columns, followed by using SMOTE algorithm variants (K-Means SMOTE and Borderline SMOTE) and pure SMOTE algorithm as the oversampling methods, and Near-Miss and All-KNN for undersampling methods. The algorithms are implemented by using scikit libraries. Lastly, PCA is used for dimensionality reduction and Logistic Regression as the machine learning model with cross-validations for deciding the best hyperparameter. The procedure produces five different Logistic Regression models that differ in how it handles the imbalances, which will be compared. The result shows that the oversampling methods work better than undersampling methods, with K-Means SMOTE and Borderline SMOTE performing better than the pure SMOTE, meaning that machine learning can be used as a solution to deal with binary classification problems in imbalanced financial data.
Affective Interaction: Attentive Representation Learning for Multi-Modal Sentiment Classification

The recent booming of artificial intelligence (AI) applications, e.g., affective robots, human-machine interfaces, autonomous vehicles, and so on, has produced a great number of multi-modal records of human communication. Such data often carry latent subjective users’ attitudes and opinions, which provides a practical and feasible path to realize the connection between human emotion and intelligence services. Sentiment and emotion analysis of multi-modal records is of great value to improve the intelligence level of affective services. However, how to find an optimal manner to learn people’s sentiments and emotional representations has been a difficult problem, since both of them involve subtle mind activity. To solve this problem, a lot of approaches have been published, but most of them are insufficient to mine sentiment and emotion, since they have treated sentiment analysis and emotion recognition as two separate tasks. The interaction between them has been neglected, which limits the efficiency of sentiment and emotion representation learning. In this work, emotion is seen as the external expression of sentiment, while sentiment is the essential nature of emotion. We thus argue that they are strongly related to each other where one’s judgment helps the decision of the other. The key challenges are multi-modal fused representation and the interaction between sentiment and emotion. To solve such issues, we design an external knowledge enhanced multi-task representation learning network, termed KAMT. The major elements contain two attention mechanisms, which are inter-modal and inter-task attentions and an external knowledge augmentation layer. The external knowledge augmentation layer is used to extract the vector of the participant’s gender, age, occupation, and of overall color or shape. The main use of inter-modal attention is to capture effective multi-modal fused features. Inter-task attention is designed to model the correlation between sentiment analysis and emotion classification. We perform experiments on three widely used datasets, and the experimental performance proves the effectiveness of the KAMT model.
Parallel Stylometric Document Embeddings with Deep Learning Based Language Models in Literary Authorship Attribution

This paper explores the effectiveness of parallel stylometric document embeddings in solving the authorship attribution task by testing a novel approach on literary texts in 7 different languages, totaling in 7051 unique 10,000-token chunks from 700 PoS and lemma annotated documents. We used these documents to produce four document embedding models using Stylo R package (word-based, lemma-based, PoS-trigrams-based, and PoS-mask-based) and one document embedding model using mBERT for each of the seven languages. We created further derivations of these embeddings in the form of average, product, minimum, maximum, and l2 norm of these document embedding matrices and tested them both including and excluding the mBERT-based document embeddings for each language. Finally, we trained several perceptrons on the portions of the dataset in order to procure adequate weights for a weighted combination approach. We tested standalone (two baselines) and composite embeddings for classification accuracy, precision, recall, weighted-average, and macro-averaged F1-score, compared them with one another and have found that for each language most of our composition methods outperform the baselines (with a couple of methods outperforming all baselines for all languages), with or without mBERT inputs, which are found to have no significant positive impact on the results of our methods.
Detecting incongruent news headlines with auxiliary textual information

News readers are left with their initial impression gained from its headline, whereas a headline’s purpose is to attract the reader’s attention to the news content. Incongruent news headlines can easily mislead readers with their clickbait headlines, which have become pervasive in online. Thus, considerable attention has been paid to detect incongruent news headlines before they reach to the readers, however there is still a lack of large-scale dataset which is restricted to title and body text. Accordingly, in this study, we released Incongruent News Headline Dataset, which has been collected and written by one of the largest news media outlets in South Korea. The generated dataset contains additional textual information, such as subtitles, image captions, and other auxiliary information. We proposed a method that effectively detects incongruent news headlines by capturing the complex lexical and contextual textual relationships between a headline and its body using an attention mechanism. The proposed model outperforms the existing models, and we investigated how it fully utilizes all the possible features.
MDD-Net: A generalized network for speckle removal with structure protection and shape preservation for various kinds of ESPI fringe patterns

In the paper, we propose a generalized deep nonlinear CNN model, named as MDD-Net, for filtering various kinds of ESPI fringe patterns with structure protection and shape preservation. We firstly design the multi dilated dense module (MDD-Module) via merging five 1-, 2-, 5- dilated convolutions with abundant skip connections. Then, we construct the MDD-Net via combing three MDD-Modules and five common convolutions in a staggered series manner with skip connection. In this way, the MDD-Net enhances the reuse of original features, improves the flow of deep features, promotes the fusion of different deep features, finally obtains the rich deep features in the filtering process. We also propose a shape-structure-consideration loss function by combining the MAE, MSE and SSIM loss functions, and construct an available pertinent dataset for ESPI fringe filtering. With the proposed dataset and the proposed loss function, we train the MDD-Net successfully. With the trained network, we directly get the results in batch without any parameter fine-turning and any pre-process or post-process. We test our method on many simulated and experimental ESPI fringes with different densities, and compare it with the existing SOOPDE, WFF, BL-Hilbert-L2, and FFD-Net methods. The performance is also evaluated quantitatively and qualitatively in terms of speckle reduction, structure protection, shape preservation, parameter finetuning and running time. Results demonstrate that our method can simultaneously reduce speckles, protect structures and preserve shapes in the fringe filtering and get the better results than the compared methods in all cases. Moreover, it has full advantages on excellent generalization and batch performance, and can be used to process a great number of ESPI fringe patterns rapidly. In fact, it has been successfully applied to the dynamic measurement in the paper.
A cost-driven online auto-scaling algorithm for web applications in cloud environments

Today, many web application service providers rely on clouds to deploy applications to serve users. Generally, request arrivals faced by web applications are dynamic and uncertain. When a service provider deploys web applications in clouds, for saving costs, it needs to flexibly rent cloud VM (Virtual Machine) instances based on dynamic request arrivals. However, renting an instance too early may incur more rental fees for the new instance being added incorrectly due to few future requests, and renting an instance too late may incur more penalty fees for SLA (service-level agreement) violations due to too many future requests, which indicates that an arbitrary instance scaling decision will incur more costs. For making optimal instance scaling decisions, future request arrival rate curves are needed, but it is generally very hard to predict them precisely. To solve this problem, in this paper, we propose a cost-driven online auto-scaling algorithm which can make optimized instance rental decisions without requiring future knowledge. We show theoretically that the proposed algorithm can achieve a guaranteed competitive ratio which is less than 2. Eventually, we verify the effectiveness of our online auto-scaling algorithm via extensive experiments using workload data which can simulate real end users.
Identifying the outlier in tunnel monitoring data: An integration model

Structural health monitoring (SHM) system based on the Internet of Things is an important method to evaluate the safety of tunnel operation through the real-time monitoring data analysis. Identifying the outlier in SHM data is a non-trivial task but it is challenging for the tunnel engineers because the measurements are quite complicated with the characteristics of time series, unlabeled, high-dimensional, inter-correlations between variables, etc. To detect the outliers, an integration model is developed based on the independent data analysis from the Probabilistic, Proximity-Based (global), Proximity-Based (local), Linear Model and Outlier Ensembles. The model is examined with the shuttle data set in the University of California Irvine (UCI) database and its precision rate is up to 94.5%, highlighting the favorable performance in identifying the outliers. This method is thus applied to the outlier detection of the SHM data in the Nanjing Yangtze River tunnel. 6698 data sets collected from SHM are evaluated and 270 groups of outliers are identified effectively. By eliminating these outliers, comparisons between the proposed integrated model and the single model (i.e. IForest, ABOD, KNN, LOF) are further conducted to discuss the model performance based on the regression analysis. Results show that the integrated model is better than the single model and it possesses the great potential to detect the outliers in SHM system.
A deterministic approach for protecting privacy in sensitive personal data

Abstract Background Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. Methods In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm. Results We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort. Conclusions The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.
Optimized trajectory tracking control of the clearance manipulator based on improved PSO algorithm

Aiming at the problems of low joint trajectory tracking accuracy and poor anti-interference ability caused by parameter uncertainty, nonlinearity, and external interference in the hydraulic servo system of the clearance manipulator, an active disturbance rejection servo control method optimized based on the improved Particle Swarm Optimization algorithm is proposed. First, through the kinematic and dynamic analysis of the clearance manipulator, the general solutions of its forward and inverse kinematics are derived, and the relationship expression between each joint angle and the extension of the piston rod of the driving cylinder is obtained according to the relationship between the joint space and the driving mechanism space; Second, the state space differential equations of the hydraulic system of the working device are derived using mechanistic modeling, and this is used to design an active disturbance rejection servo controller for each joint, and the parameters of the active disturbance rejection controller are optimized using an improved PSO algorithm to improve the control performance. Simulation and experimental results show that compared with classical PID control and conventional Active Disturbance Rejection Control, the improved ADRC in this paper has higher trajectory tracking control accuracy and the ability to suppress external disturbances.
A recommender system fused with implicit social information through network representation learning

Traditional recommender systems have always suffered from cold start and data sparsity problems by leveraging only a rating matrix. Social recommender systems try to address such problems by leveraging the user's social information, such as trust relationships, as auxiliary information. However, such explicit social information is not always available. In this paper, an implicit interest relationship mining approach based on network representation learning for recommendation, called INRec, is proposed to deeply mine implicit social information and fuse both explicit and implicit social information into a matrix factorization (MF) model and thus improve the effectiveness of the recommendations is proposed. Specifically, a modified one-mode projection method is first designed to construct an interest-connected user interaction network, and a network representation learning method is then employed to generate low-dimensional user representations. Abundant user interest relationships can be later obtained from user representations through a similarity calculation. Finally, a multi-source information fusion approach is applied to incorporate explicit and implicit social relationships into the MF model. The experimental results demonstrated that the average MAE and RMSE by INRec achieves 0.7415 and 0.9633 on three datasets (FilmTrust, CiaoDVD, Epinions), and it remarkably outperforms the state-of-the-art by a large margin.
VILS: A Verifiable Image Licensing System

Image licensing regulates the scope, type, and limitations of using an image through an agreement. However, it is challenging to verify whether an agreement has been fulfilled honestly. Existing techniques, such as watermarking and perceptual hashing, help check image originality and editing operations specified in the agreement, but fail to achieve editor designation. In this paper, we propose a verifiable image licensing system (VILS) which provides an effective solution to verify if a received image is used legally according to its licensing agreement. The core building block of our design is a new kind of cryptographic primitive, called accumulator with a designated entity. The new accumulator helps achieve not only editing restriction, but also editor designation in image authentication. Our VILS has the following two appealing features: (1) Authorization: Only an authorized licensee who edits an image with operations declared in a licensing agreement can produce valid images; (2) Efficiency: The verification of VILS is efficient and independent of the number of operations or image size. Compared with the most relevant schemes from the state-of-the-art, the new design enriches the functionality of image authentication but reduces the verification time by 40%.