Reviewerly/exHarmony huggingface dataset:
Events in Semantics

Event Semantics (ES) says that clauses in natural languages are descriptions of events. Why believe this? The answer cannot be that we use clauses to talk about events, or that events are important in ontology or psychology. Other sorts of things have the same properties, but no special role in semantics. The answer must be that this view helps to explain the semantics of natural languages. But then, what is it to explain the semantics of natural languages? Here there are many approaches, differing on, among other issues, whether natural languages are social and objective or individual and mental; whether the semantics delivers truth-values at contexts or just constraints on truth-evaluable thoughts; which inferences it should explain as formally provable, if any; and which if any grammatical patterns it should explain directly. The argument for ES will differ accordingly, as will the consequences, for ontology, psychology, or linguistics, of its endorsement.
A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD

Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps). It has an important impact on subsequent anomaly location and root cause analysis. Variational auto-encoder (VAE) is a symmetry network structure composed of encoder and decoder, which has attracted extensive attention because of its ability to capture complex KPI data features and better detection results. However, VAE is not well applied to the modeling of KPI time series data and it is often necessary to set the threshold to obtain more accurate results. In response to these problems, this paper proposes a novel hybrid method for KPI anomaly detection based on VAE and support vector data description (SVDD). This method consists of two modules: a VAE reconstructor and SVDD anomaly detector. In the VAE reconstruction module, firstly, bi-directional long short-term memory (BiLSTM) is used to replace the traditional feedforward neural network in VAE to capture the time correlation of sequences; then, batch normalization is used at the output of the encoder to prevent the disappearance of KL (Kullback–Leibler) divergence, which prevents ignoring latent variables to reconstruct data directly. Finally, exponentially weighted moving average (EWMA) is used to smooth the reconstruction error, which reduces false positives and false negatives during the detection process. In the SVDD anomaly detection module, smoothed reconstruction errors are introduced into the SVDD for training to determine the threshold of adaptively anomaly detection. Experimental results on the public dataset show that this method has a better detection effect than baseline methods.
BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning

Backchannel (BC), a short reaction signal of a listener to a speaker's utterances, helps to improve the quality of the conversation.Several studies have been conducted to predict BC in conversation; however, the utilization of advanced natural language processing techniques using lexical information presented in the utterances of a speaker has been less considered.To address this limitation, we present a BC prediction model called BPM_MT (Backchannel prediction model with multitask learning), which utilizes KoBERT, a pre-trained language model.The BPM_MT simultaneously carries out two tasks at learning: 1) BC category prediction using acoustic and lexical features, and 2) sentiment score prediction based on sentiment cues.BPM_MT exhibited 14.24% performance improvement compared to the existing baseline in the four BC categories: continuer, understanding, empathic response, and No BC.In particular, for empathic response category, a performance improvement of 17.14% was achieved.
Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.
ADQDA

Affinity diagramming is widely applied to analyze qualitative data such as interview transcripts. It involves multiple analytic processes and is often performed collaboratively. Drawing on interviews with three practitioners and upon our own experience, we show how practitioners combine multiple analytic processes and adopt different artifacts to help them analyze their data. Current tools, however, fail to adequately support mixing analytic processes, devices, and collaboration styles. We present a vision and prototype ADQDA, a cross-device, collaborative affinity diagramming tool for qualitative data analysis, implemented using distributed web technologies. We show how this approach enables analysts to appropriate available pertinent digital devices as they fluidly migrate between analytic phases or adopt different methods and representations, all while preserving consistent analysis artifacts. We validate this approach through a set of application scenarios that explore how it enables new ways of analyzing qualitative data that better align with identified analytic practices.
Applicable Image Security Based on New Hyperchaotic System

Hyperchaotic systems are widely applied in the cryptography domain on account of their more complex dynamical behavior. In view of this, the greatest contribution of this paper is that a two-dimensional Sine coupling Logistic modulated Sine (2D-SCLMS) system is proposed based on Logistic map and Sine map. By a series of analyses, including Lyapunov index (LE), 0–1 test, two complexity analysis methods, and two entropy analysis methods, it is concluded that the new 2D-SCLMS map is hyperchaotic with a wider range of chaos and more complex randomness. The new system combined with two-dimensional Logistic-Sine Coupling Mapping (2D-LSCM) is further applied to an image encryption application. SHA-384 is used to generate the initial values and parameters of the two chaotic systems. Symmetric keys are generated during this operation, which can be applied to the proposed image encryption and decryption algorithms. The encryption process and the decryption process of the new image encryption approaches mainly include pixel scrambling, exclusive NOR (Xnor), and diffusion operations. Multiple experiments illustrate that this scheme has higher security and lower time complexity.
Evaluation of Naïve Bayes Classification in Arabic Short Text Classification

In the last few years, and due to the vast widespread of social media applications, texts have become more important and get more attention. Since texts, in general, are carrying a lot of information that can be extracted and analyzed. Several researchers have done significant works in text classification. Within different scripts such as English and other western languages, several challenges and obstacles have been addressed with such a field of research. Regarding the Arabic language, the process is different from other vital languages since Arabic is considered an orthographic language that depends on the word shape. It is not easy to apply the standard text preprocessing techniques since it affects the word meaning. This paper evaluates Arabic short text classification using three standard Naïve Bayes classifiers. In our method, we classify the thesis and dissertations using their titles to perform the classification process. The collected dataset is collected from different repositories by using standard scrapping techniques. Our method classifies the document based on their titles to be placed in the desired specialization. Several preprocessing techniques have been applied, such as (punctuation removal, stop words removal, and space vectorization). For feature extraction, we adopt the TF-IDF method. We implemented three types of Naïve Bayes classifiers which are (Multinomial Naïve Bayes, Complemented Naïve Bayes, and Gaussian Naïve Bayes). The study results showed that Complemented Naïve Bayes Classifier proposed the best performance with (0.84) of accuracy for the testing phase. The results of the study are promising to be applied with different short text classifications.
News Verification using Ethereum Smart Contract and Inter Planetary File System (IPFS)

News is a form of information sharing that tells the people about current event that is happening. With the advancement of technologies, the rate of spreading of the news is also increasing. One of the most popular places to read news is through the Internet. However, most of the people that read news from the Internet is not aware about the news sources. This leads to the spread of some fake news in society. In this paper, we suggest a way to verify a news using Ethereum smart contract and IPFS. There are four entities that involved in the system, such as Journalist as the one that provides the news, Validator as the one that will rate the news, Ethereum Smart Contract that will store the data inside the blockchain, and IPFS that will store the news and giving hash code to be stored inside the blockchain. This paper also analyzed the cost required to run the function of the smart contract deployed. The result is that all of the Rating Functions cost the same. However, the News Submit Function is not. The difference is the bigger the news stored in the IPFS, the more expensive the cost required to run the function.
Heuristic Search Comes of Age

In looking back on the last five to ten years of work in heuristic search a few trends emerge. First, there has been a broadening of research topics studied. Second, there has been a deepened understanding of the theoretical foundations of search. Third, and finally, there have been increased connections with work in other fields. This paper, corresponding to a AAAI 2012 invited talk on recent work in heuristic search, highlights these trends in a number of areas of heuristic search. It is our opinion that the sum of these trends reflects the growth in the field and the fact that heuristic search has come of age.
Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments

Objective This article presents two studies (one simulation and one pilot) that assess a custom computer algorithm designed to predict motion sickness in real-time. Background Virtual reality has a wide range of applications; however, many users experience visually induced motion sickness. Previous research has demonstrated that changes in kinematic (behavioral) parameters are predictive of motion sickness. However, there has not been research demonstrating that these measures can be utilized in real-time applications. Method Two studies were performed to assess an algorithm designed to predict motion sickness in real-time. Study 1 was a simulation study that used data from Smart et al. (2014). Study 2 employed the algorithm on 28 new participants’ motion while exposed to virtual motion. Results Study 1 revealed that the algorithm was able to classify motion sick participants with 100% accuracy. Study 2 revealed that the algorithm could predict if a participant would become motion sick with 57% accuracy. Conclusion The results of the present study suggest that the motion sickness prediction algorithm can predict if an individual will experience motion sickness but needs further refinement to improve performance. Application The algorithm could be used for a wide array of VR devices to predict likelihood of motion sickness with enough time to intervene.
Model and Verification of Medical English Machine Translation Based on Optimized Generalized Likelihood Ratio Algorithm

Phrase identification plays an important role in medical English machine translation. However, the phrases in medical English are complicated in internal structure and semantic relationship, which hinders the identification of machine translation and thus affects the accuracy of translation results. With the aim of breaking through the bottleneck of machine translation in medical field, this paper designed a machine translation model based on the optimized generalized likelihood ratio (GLR) algorithm. Specifically, the model in question established a medical phrase corpus of 250,000 English and 280,000 Chinese words, applied the symbol mapping function to the identification of the phrase’s part of speech, and employed the syntactic function of the multioutput analysis table structure to correct the structural ambiguity in the identification of the part of speech, eventually obtaining the final identification result. According to the comprehensive verification, the translation model employing the optimized GLR algorithm was seen to improve the speed, accuracy, and update performance of machine translation and was seen to be more suitable for machine translation in medical field, therefore providing a new perspective for the employment of medical machine translation.
Defining Online Hating and Online Haters

According to a view widely held in the media and in public discourse more generally, online hating is a social problem on a global scale. However, thus far there has been little scientific literature on the subject, and, to our best knowledge, there is even no established scholarly definition of online hating and online haters in the first place. The purpose of this manuscript is to provide a new perspective on online hating by, first, distinguishing online hating from the phenomena it is often confused with, such as trolling, cyberstalking, and online hate speech, and, second, by proposing an operational definition of online hating and online haters based on ethnographic interviews and surveys of the existing scholarly literature.
Effect of orientation and tilt angles of solar collectors on their performance: Analysis of the relevance of general recommendations in the West and Central African context

Installing solar panels or collectors with optimum orientation and tilt angles to maximise energy generation over a specific period is important to improve the economics of solar systems, and hence, their large-scale utilisation. As a general rule, for installations aiming at maximum annual solar production in the intertropical region, it is recommended to point the panel towards the Equator at a tilt angle equal to the local latitude. However, there are several situations where it is difficult or even impossible to follow these general recommendations, for example in case of the presence of an obstacle creating shading, inadequate orientation or slope of the roofs, or when roofs are inaccessible. This raises the question of the impact of non-compliance with the general recommendations on solar systems’ performance. We have used RETScreen software for the calculation of annual average irradiation on tilted panels in various locations in West and Central Africa, namely in the capital cities (latitudes between 0° N and 15° N). For each site, the maximum annual solar irradiation is simulated and taken as a reference value. The annual irradiations for various orientations and tilt angles were then recorded and compared to the reference. It appears that in West and Central Africa, a moderate deviation (up to 20°) from the optimal orientation and inclination does not significantly influence the incident solar radiation (reduction in irradiation < 5%). Construction of structures dedicated to solar installations or architectural modification to comply with general recommendations is therefore generally not justified. A set of diagrams and tables, which allow us to quickly determine the percentage of incident solar radiation on a solar panel based on its orientation and tilt angle compared to the optimal tilt and orientation for any capital city in West and Central Africa, is provided.
Graph Neural Networks in Data Mining

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Reconfigurable and Agile Legged-Wheeled Robot Navigation in Cluttered Environments With Movable Obstacles

Legged and wheeled locomotion are two standard methods used by robots to perform navigation. Combining them to create a hybrid legged-wheeled locomotion results in increased speed, agility, and reconfigurability for the robot, allowing it to traverse a multitude of environments. The CENTAURO robot has these advantages, but they are accompanied by a higher-dimensional search space for formulating autonomous economical motion plans, especially in cluttered environments. In this article, we first review our previously presented legged-wheeled footprint reconfiguring global planner. We describe the two incremental prototypes, where the primary goal of the algorithms is to reduce the search space of possible footprints such that plans that expand the robot over the low-lying wide obstacles or narrow into passages can be computed with speed and efficiency. The planner also considers the cost of avoiding obstacles versus negotiating them by expanding over them. The second part of this article presents our new work on local obstacle pushing, which further increases the number of tight scenarios the planner can solve. The goal of the new local push-planner is to place any movable obstacle of unknown mass and inertial properties, obstructing the previously planned trajectory from our global planner, to a location devoid of obstruction. This is done while minimising the distance traveled by the robot, the distance the object is pushed, and its rotation caused by the push. Together, the local and global planners form a major part of the agile reconfigurable navigation suite for the legged-wheeled hybrid CENTAURO robot.
Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method

Background Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome (“attributed”) or state the simple treatment and outcome without an association (“unattributed”). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, “transfusion” and “time-based.” Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians’ documentation of attributed AEs. Objective We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs. Methods We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs. Results Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination. Conclusions The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.
Collaborative semi-global stereo matching

Efficiency and accuracy of semi-global matching (SGM) make it outperform many stereo matching algorithms and is widely used under challenging occasions. However, SGM only incorporates information along a scanline in each pass and lacks interaction between scanlines, resulting in streak artifacts in the disparity image. We introduce a local edge-aware filtering method to SGM to enhance the interaction of neighboring scanlines, since streak artifacts can be avoided. We use bilateral weights based on intensity similarity and spatial affinity between pixels to build connections among scanlines. In each pass, we recursively estimate the aggregated cost of SGM and compute the weighted average of aggregated costs for pixels in the orthogonal direction to obtain the output of our method along each scanline. As one-dimensional bilateral filtering is used in our method, the extra computation is linear to image resolution and label space, which is a small fraction of that needed by SGM. We present ablation studies using stereo pairs under both constrained and natural conditions to verify the effectiveness of our method. Extensive experiments on Middlebury and Karlsruhe Institute of Technology and Toyota Technology Institute datasets demonstrate that our method removes all streak artifacts, improves the quality of the disparity image, and outperforms many other non-local cost aggregation approaches.
An Immunity Passport Scheme Based on the Dual-Blockchain Architecture for International Travel

The implementation of immunity passport has been hampered by the controversies over vaccines in various countries, the privacy of vaccinators, and the forgery of passports. While some existing schemes have been devoted to accelerating this effort, the problems above are not well solved in existing schemes. In this paper, we present an immunity passport scheme based on the dual-blockchain architecture, which frees people from the cumbersome epidemic prevention process while traveling abroad. Specially, the dual-blockchain architecture is established to fit with the scenarios of immunity passport. Searchable encryption and anonymous authentication are utilized to ensure users’ privacy. In addition, the performance and security evaluations show that our scheme achieves the proposed security goals and surpasses other authentication schemes in communicational and computational overheads.