Reviewerly/exHarmony huggingface dataset:
From experiment to real-life data

Abstract We examine unintentional spelling errors on verb homophones in informal online chat conversations of Flemish adolescents. In experiments, these verb forms yielded an effect of homophone dominance, i.e., most errors occurred on the lower-frequency form ( Sandra et al., 1999 ). Verb homophones are argued to require the conscious application of a spelling rule, which may cause a temporary overload of working memory resources and trigger automatic retrieval of the higher-frequency spelling from the mental lexicon. Unlike most previous research, we investigate homophone intrusions in a natural writing context. Thus, we test the ‘ecological validity’ of psycholinguistic experiments. Importantly, this study relates these psycholinguistic constructs to different social variables in social media writing to test a prediction that directly follows from Sandra et al.’s account. Whereas social factors likely affect the error rates, they should not affect the error pattern : the number of working memory failures occurs at another processing level than the homophone intrusions. Hence, the focus is on the interaction between homophone dominance and the social variables. The errors for two types of verb homophones reveal (a) an impact of all social variables, (b) an effect of homophone dominance, and (c) no interaction between this effect and the social factors.
Remote Reinforcement Learning over a Noisy Channel

A collaborative multi-agent reinforcement learning (RL) problem is considered, where agents communicate over a noisy communication channel towards achieving a common goal. In particular, we consider a remote-controlled version of a single-agent RL problem, in which the system state is observed by a guide agent, while the actions are taken by a scout. The guide can communicate to the scout over a noisy communication link, reminiscent of a remote-controlled version of the single-agent RL problem. This transformation turns the original single-agent Markov decision process (MDP) into a two-agent partially observable MDP (POMDP). In conventional systems, communication and learning tasks are taken care of separately. We show the suboptimality of this approach, and propose a deep Q-learning solution that aims at learning the optimal policy taking into account the channel impairments.
Neural Network Models for the Analysis and Visualization of Latent Dependencies: Examples of Psycho Diagnostic Data Processing

Analysis and visualization of causal and latent dependencies between statistical inhomogeneous data are important tasks for different fields. The quite simple neural network algorithms are proposed for the analysis and interpretation of psycho-diagnostic data in the case when traditional statistical methods do not give adequate results. Examples of the analysis of multidimensional psycho-diagnostic data based on the use of neural network models are presented. Artificial neural network (ANN) has a simple architecture. Neural network models are able to evaluate non-linear and indirect (latent) relationships between data of psychological testing. It is shown that, with a sufficiently high quality of ANN training, computational models describing the relationship between the input data and the target function are not sufficiently informative for practical psychology. This is a consequence of the fact that such dependencies (approximation), are very different for different respondents. For visualizing latent relationships of psychological characteristics, using the range of variation of these dependencies is proposed. This range, as a measure (parameter) of connection of psycho-diagnostic data for each Respondent, can act as an independent sign, allowing to interpret the obtained psycho-diagnostic results. The proposed algorithms can be implemented by psychologists using standard, widespread software and do not require special mathematical skills.
Communication-Efficient Federated Learning in UAV-enabled IoV: A Joint Auction-Coalition Approach

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning. However, the performance of the FL suffers from the failure of communication links and missing nodes. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework. The joint auctioncoalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profitmaximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer not to form any coalition.
Graph Structure Reasoning Network for Face Alignment and Reconstruction

In the unconstrained scene, a large amount of self-occlusion and large poses occurs in the face. These occluded points lead to a huge challenge in face alignment and reconstruction. When performing 3DMM parameter regression, existing face alignment and reconstruction methods only consider reducing the error between the predicted point and the real point, which learn the relevant apparent information but ignore the face geometric contour structure information. Therefore, this paper proposes a graph structure reasoning network for face alignment and reconstruction. By constructing a contour map of the face structure with nose tip as the anchor, the errors between the predictably and actually established maps are optimized. When there is a large pose or occlusion, the neural network can infer the position of the invisible landmark through the contextual information of the visible landmark. Thus, the neural network effectively extracts the structural information of the human face and makes inference on the invisible points. Our GSRN has achieved significant results in both the face alignment and face reconstruction on the AFLW2000-3D and AFLW datasets.
An improved feature extraction algorithm for insider threat using hidden Markov model on user behavior detection

Purpose By using a new feature extraction method on the Cert data set and using a hidden Markov model (HMM) to model and analyze the behavior of users to distinguish whether the behavior is normal within a continuous period. Design/methodology/approach Feature extraction of five parts of the time series by rules and sorting in chronological order. Use the obtained features to calculate the probability parameters required by the HMM model and establish a behavior model for each user. When the user has abnormal behavior, the model will return a very low probability value to distinguish between normal and abnormal information. Findings Generally, HMM parameters are obtained by supervised learning and unsupervised learning, but the hidden state cannot be clearly defined. When the hidden state is determined according to the data set, the accuracy of the model will be improved. Originality/value This paper proposes a new feature extraction method and analysis mode, which determines the shape of the hidden state according to the situation of the data set, making subsequent HMM modeling simple and efficient and in turn improving the accuracy of user behavior detection.
Multi-aquifer susceptibility analyses for supporting groundwater management in urban areas

In the densely urbanised Milan Metropolitan area (northern Italy), the long history of anthropogenic activities still exerts a significant pressure on groundwater resource. One of the most serious threats to the water quality of urban aquifers is attributed to diffuse contamination, which is caused by a series of unknown small sources (i.e., multiple point sources) distributed over large areas. In the study area and in many industrialised regions of the world, tetrachloroethylene [PCE], trichloroethylene [TCE] and hexavalent chromium [Cr(VI)] represent the common example of long-standing and persistent pollution in groundwater. In the Milan Metropolitan area, high levels of PCE + TCE and Cr(VI) were detected in the shallow aquifer as well as in the deep aquifer. To assess and map the shallow and deep aquifers susceptibility to PCE + TCE and Cr(VI) contamination at a regional scale, the Weights of Evidence modelling technique has been applied. This method has been used to objectively evaluate the spatial correlation between the high presence of these pollutants in each aquifer and hydrogeological and land use factors that can potentially influence the contamination. Moreover, the results allowed us to quantify on a large scale the effect that preferential flowpaths, due to both thickness variation in the aquitard and the areal density of multi aquifer wells, have in reducing the protection of the underlying deep aquifer. The end-products of the study constitute a key tool to be used by water-resource managers and decision-makers for the improvement of groundwater management and protection strategies.
A Generalization of Spatial Monte Carlo Integration

Spatial Monte Carlo integration (SMCI) is an extension of standard Monte Carlo integration and can approximate expectations on Markov random fields with high accuracy. SMCI was applied to pairwise Boltzmann machine (PBM) learning, achieving superior results over those of some existing methods. The approximation level of SMCI can be altered, and it was proved that a higher-order approximation of SMCI is statistically more accurate than a lower-order approximation. However, SMCI as proposed in previous studies suffers from a limitation that prevents the application of a higher-order method to dense systems. This study makes two contributions. First, a generalization of SMCI (called generalized SMCI (GSMCI)) is proposed, which allows a relaxation of the above-mentioned limitation; moreover, a statistical accuracy bound of GSMCI is proved. Second, a new PBM learning method based on SMCI is proposed, which is obtained by combining SMCI and persistent contrastive divergence. The proposed learning method significantly improves learning accuracy.
Realization of entanglement-assisted weak-value amplification in a photonic system

Weak-value amplification has been used to enhance the sensitivity of a linear detector response to small interaction parameters. However, the generation of a large weak value gives rise to reduction in postselection probability, which restricts the feasibility of the weak-value amplification technique. Recently, theoretical works have shown that the use of entanglement can increase the efficiency of the weak-value amplification method. Specifically, it is proven that by entangling $n$ particles of a system, the maximum postselection probability scales quadratically with $n$ [Pang, Dressel, and Brun, Phys. Rev. Lett. 113, 030401 (2014)]. In this work, we demonstrate an experimental realization of entanglement-assisted weak-value amplification in a photonic system, which shows that importing quantum entanglement can indeed improve the efficiency of weak-value amplification.
A new approach to crowd journalism using a blockchain-based infrastructure

The significant evolution of smartphones has given ordinary people the power to create good-quality content which can then be spread, by the press, over multiple platforms. Citizens are almost always the first ones to arrive at a breaking news location and can provide the initial images of the scene. However, existing crowdsourced tools and platforms are predominantly centralized and are usually fed with unreliable and untrustworthy information.
A Hierarchical Graph Convolution Network for Representation Learning of Gene Expression Data

The curse of dimensionality, which is caused by high-dimensionality and low-sample-size, is a major challenge in gene expression data analysis. However, the real situation is even worse: labelling data is laborious and time-consuming, so only a small part of the limited samples will be labelled. Having such few labelled samples further increases the difficulty of training deep learning models. Interpretability is an important requirement in biomedicine. Many existing deep learning methods are trying to provide interpretability, but rarely apply to gene expression data. Recent semi-supervised graph convolution network methods try to address these problems by smoothing the label information over a graph. However, to the best of our knowledge, these methods only utilize graphs in either the feature space or sample space, which restrict their performance. We propose a transductive semi-supervised representation learning method called a hierarchical graph convolution network (HiGCN) to aggregate the information of gene expression data in both feature and sample spaces. HiGCN first utilizes external knowledge to construct a feature graph and a similarity kernel to construct a sample graph. Then, two spatial-based GCNs are used to aggregate information on these graphs. To validate the model's performance, synthetic and real datasets are provided to lend empirical support. Compared with two recent models and three traditional models, HiGCN learns better representations of gene expression data, and these representations improve the performance of downstream tasks, especially when the model is trained on a few labelled samples. Important features can be extracted from our model to provide reliable interpretability.
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment

Performance monitoring of object detection is crucial for safety-critical applications such as autonomous vehicles that operate under varying and complex environmental conditions. Currently, object detectors are evaluated using summary metrics based on a single dataset that is assumed to be representative of all future deployment conditions. In practice, this assumption does not hold, and the performance fluctuates as a function of the deployment conditions. To address this issue, we propose an introspection approach to performance monitoring during deployment without the need for ground truth data. We do so by predicting when the per-frame mean average precision drops below a critical threshold using the detector's internal features. We quantitatively evaluate and demonstrate our method's ability to reduce risk by trading off making an incorrect decision by raising the alarm and absenting from detection.
A deep multitask learning approach for requirements discovery and annotation from open forum

The ability in rapidly learning and adapting to evolving user needs is key to modern business successes. Existing methods are based on text mining and machine learning techniques to analyze user comments and feedback, and often constrained by heavy reliance on manually codified rules or insufficient training data. Multitask learning (MTL) is an effective approach with many successful applications, with the potential to address these limitations associated with requirements analysis tasks. In this paper, we propose a deep MTL-based approach, DEMAR, to address these limitations when discovering requirements from massive issue reports and annotating the sentences in support of automated requirements analysis. DEMAR consists of three main phases: (1) data augmentation phase, for data preparation and allowing data sharing beyond single task learning; (2) model construction phase, for constructing the MTL-based model for requirements discovery and requirements annotation tasks; and (3) model training phase, enabling eavesdropping by shared loss function between the two related tasks. Evaluation results from eight open-source projects show that, the proposed multitask learning approach outperforms two state-of-the-art approaches (CNC and FRA) and six common machine learning algorithms, with the precision of 91% and the recall of 83% for requirements discovery task, and the overall accuracy of 83% for requirements annotation task. The proposed approach provides a novel and effective way to jointly learn two related requirements analysis tasks. We believe that it also sheds light on further directions of exploring multitask learning in solving other software engineering problems.
The Openness Hypothesis in the Context of Economic Development in Sub-Saharan Africa: The Moderating Role of Trade Dynamics on FDI

This study investigates the simultaneous openness hypothesis by assessing the importance of trade openness in modulating the effect of foreign direct investment (FDI) on the economic dynamics of gross domestic product (GDP) growth, real GDP, and GDP per capita. The focus of the study is on 25 countries in Sub-Saharan Africa over the period spanning from 1980 to 2014. Trade imports modulate FDI to induce net positive effects on GDP growth and GDP per capita. Trade exports moderate FDI to generate overall positive impacts on GDP growth, real GDP, and GDP per capita. Implications of the study are discussed.
The potential impacts and risks of global stablecoins

This paper aims to study the implications of the potential large-scale usage of global stablecoins. We start with describing the origin and the evolution of stablecoins, and then analyze the operational mechanism and application scenarios. An analysis on whether global stablecoins will lead to dollarization of a country's monetary system is then followed, based on the monetary quantity theory and Fisher's equation. We find that large-scale usage of global stablecoins may have an impact on both large and small open economies, and with a greater impact on the sovereign monetary system of small economies. Further, global stablecoins may affect financial stability through influencing monetary policy, holder confidence and financial system. The potential risks are embodied in technology development, payment security and illegal transactions. To deal with these potential impacts and risks, we propose to strengthen judicial research and plan arrangements for global cooperation and coordination.
Functional analysis of generalized linear models under non-linear constraints with applications to identifying highly-cited papers

This article introduces a versatile functional form for Generalized Linear Models (GLMs) through a simple, yet effective, transformation of the current framework. The models are applied through a new hierarchical bayesian estimation procedure for logistic regression to highly-cited papers in the Management Information Systems (MIS) field. The results are uniformly better, in regards to model fit and inference for in-sample and out-of-sample data, for simulation studies and real-world data applications, requiring very little time to convergence to true population parameters. In simulation studies, I show that the method contains the true parameters nearly three times as often as widely used existing GLMs, and does so while having confidence intervals that are 54.50% smaller, while requiring around two-thirds the number of MCMC iterations as existing bayesian methods. In Scientometric applications the methodology is shown to be highly robust with predictive/classification accuracy, either equaling or exceeding existing methods for identifying highly-cited articles including Artificial Neural Networks (ANN). Thus, the method is shown to be robust to the amount of asymmetry (or symmetry) of the probability of success (or failure) and robust to unbalanced samples and varying Data Generating Processes. Further, the methodology is equivalent to current methods if the data support them and is therefore complementary to existing methods, without loss of interpretability of model parameters. For the MIS field it finds that Popularity Parameter (PP) of an article Keywords can predict whether a paper will be highly-cited (top 25% of highly-cited articles) between two to three years after publication and beyond. Furthermore, given the small number of iterations needed for convergence, the methodology can also be used as a baseline method in Big Data (BD) settings for both Artificial Intelligence (AI) and Machine Learning (ML) contexts as well.
Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.
The effect of android-based earthquake game toward Bengkulu City elementary school student’s knowledge about earthquake disaster preparedness

Abstract Earthquake is one of the frequent natural disasters which is still unpredictable. Nevertheless, we can always be able to reduce and prevent its negative impacts by implementing earthquake disaster preparedness. This study aimed to examine the effect of an android-based earthquake game on Bengkulu City elementary school student’s knowledge about earthquake disaster preparedness. The subjects were fifth-grade elementary school students in Bengkulu City. The quasi-experiment research was conducted using Matching – Only Pre-test Post-test Control Group Design. Android-based earthquake game software used as a treatment in the experiment group. Data were collected using a multiple-choice test sheet instrument. The result found that the experiment group means knowledge about earthquake disaster preparedness was higher than the control group. It concluded that there was a significant difference between experiment group and control group. The advantages and limitations of the android-based earthquake game and its implication to earthquake disaster preparedness education discussed