Reviewerly/exHarmony huggingface dataset:
Role-Exchange Playing: An Exploration of Role-Playing Effects for Anti-Bullying in Immersive Virtual Environments

Role-playing is widely used in many areas, such as psychotherapy and behavior change. However, few studies have explored the possible effects of playing multiple roles in a single role-playing process. We propose a new role-playing paradigm, called role-exchange playing, in which a user plays two opposite roles successively in the same simulated event for better cognitive enhancement. We designed an experiment with this novel role-exchange playing strategy in the immersive virtual environments; and school bullying was chosen as a scenario in this case. A total of 234 middle/high school students were enrolled in the mixed-design experiment. From the user study, we found that through role-exchange, students developed more morally correct opinions about bullying, as well as increased empathy and willingness to engage in supportive behavior. They also showed increased commitment to stopping bullying others. Our role-exchange paradigm could achieve a better effect than traditional role-playing methods in situations where participants have no prior experience associated with the roles they play. Therefore, using role-exchange playing in the immersive virtual environments to educate minors can help prevent them from bullying others in the real world. Our study indicates a positive significance in moral education of teenagers. Our role-exchange playing may have the potential to be extended to such applications as counseling, therapy, and crime prevention.
Axiomatic Foundations of Explainability

Improving trust in decisions made by classification models is becoming crucial for the acceptance of automated systems, and an important way of doing that is by providing explanations for the behaviour of the models. Different explainers have been proposed in the recent literature for that purpose, however their formal properties are under-studied. This paper investigates theoretically explainers that provide reasons behind decisions independently of instances. Its contributions are fourfold. The first is to lay the foundations of such explainers by proposing key axioms, i.e., desirable properties they would satisfy. Two axioms are incompatible leading to two subsets. The second contribution consists of demonstrating that the first subset of axioms characterizes a family of explainers that return sufficient reasons while the second characterizes a family that provides necessary reasons. This sheds light on the axioms which distinguish the two types of reasons. As a third contribution, the paper introduces various explainers of both families, and fully characterizes some of them. Those explainers make use of the whole feature space. The fourth contribution is a family of explainers that generate explanations from finite datasets (subsets of the feature space). This family, seen as an abstraction of Anchors and LIME, violates some axioms including one which prevents incorrect explanations.
Exploring Attitudes Towards Increasing User Awareness of Reality From Within Virtual Reality

The occlusive nature of VR headsets introduces significant barriers to a user's awareness of their surrounding reality. While recent research has explored systems to facilitate a VR user's interactions with nearby people, objects, etc, we lack a fundamental understanding of user attitudes towards and expectations of these systems. We present the results of a card sorting study (N=14) which investigated attitudes towards increasing a VR user's reality awareness (awareness of people, objects, audio, pets, and systems to manage and moderate personal usage) whilst in VR. Our results confirm VR headsets should be equipped with systems to increase a user's awareness of reality. However, opinions vary on how increased awareness should be achieved as our results also highlight differing expectations regarding: persistent vs temporary notification design, notification content and when, why and how awareness should be increased.
Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text

(1) Background: the ability to use social media to communicate without revealing one’s real identity has created an attractive setting for cyberbullying. Several studies targeted social media to collect their datasets with the aim of automatically detecting offensive language. However, the majority of the datasets were in English, not in Arabic. Even the few Arabic datasets that were collected, none focused on Instagram despite being a major social media platform in the Arab world. (2) Methods: we use the official Instagram APIs to collect our dataset. To consider the dataset as a benchmark, we use SPSS (Kappa statistic) to evaluate the inter-annotator agreement (IAA), as well as examine and evaluate the performance of various learning models (LR, SVM, RFC, and MNB). (3) Results: in this research, we present the first Instagram Arabic corpus (sub-class categorization (multi-class)) focusing on cyberbullying. The dataset is primarily designed for the purpose of detecting offensive language in texts. We end up with 200,000 comments, of which 46,898 comments were annotated by three human annotators. The results show that the SVM classifier outperforms the other classifiers, with an F1 score of 69% for bullying comments and 85 percent for positive comments.
Towards generalizable person re-identification with a bi-stream generative model

Generalizable person re-identification (re-ID) has attracted growing attention due to its powerful adaptation capability in the unseen data domain. However, existing solutions often neglect either crossing cameras (e.g., illumination and resolution differences) or pedestrian misalignments (e.g., viewpoint and pose discrepancies), which easily leads to poor generalization capability when adapted to the new domain. In this paper, we formulate these difficulties as: 1) Camera-Camera (CC) problem, which denotes the various human appearance changes caused by different cameras; 2) Camera-Person (CP) problem, which indicates the pedestrian misalignments caused by the same identity person under different camera viewpoints or changing pose. To solve the above issues, we propose a Bi-stream Generative Model (BGM) to learn the fine-grained representations fused with camera-invariant global feature and pedestrian-aligned local feature, which contains an encoding network and two stream decoding sub-network. Guided by original pedestrian images, one stream is employed to learn a camera-invariant global feature for the CC problem via filtering cross-camera interference factors. For the CP problem, another stream learns a pedestrian-aligned local feature for pedestrian alignment using information-complete densely semantically aligned part maps. Moreover, a part-weighted loss function is presented to reduce the influence of missing parts on pedestrian alignment. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on the large-scale generalizable re-ID benchmarks, involving domain generalization setting and cross-domain setting.
Classification of volcano-seismic events using waveforms in the method of k-means clustering and dynamic time warping

In most volcano observatories classification of seismic events is made as a routine work to get some information on eruptive activities. Automating the classification with artificial intelligence (AI) techniques is a current issue but many AI techniques are not directly applicable to waveforms because they consist of individually different data lengths. This study develops a simple method in which the distance between waveforms with different lengths is evaluated by dynamic time warping and waveforms are classified by k-means clustering. In this method each of the classified groups is specified by a prototype, one of the waveforms that may represent the group best and good classifications are selected considering high generality and good fitting to the members. The seismic events of Sakurajima volcano are classified in this method and waveforms, spectra, time sequences and source locations are compared among groups for some good classifications. It is revealed that one of the groups consisting of tremor-like waveforms has an intimate connection with a magma ascent event beneath the active crater of the volcano.
Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach

With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs). At the same time, this computational finance model is solved with a nonlinear activated Beetle Antennae Search (NABAS) algorithm, which is a variant of the meta-heuristic optimization algorithm named Beetle Antennae Search (BAS) algorithm. Firstly, the fraud detection model is transformed into an optimization problem of minimizing loss function and using the NABAS algorithm to find the optimal solution. NABAS has only one search particle and explores the space under a given gradient estimation until it is less than an “Activated Threshold” and the algorithm is efficient in computation. Then, the random under-sampling with AdaBoost (RUSBoost) algorithm is employed to comprehensively evaluate the performance of NABAS. In addition, to reflect the superiority of NABAS in the fraud detection problem, it is compared with some popular methods in recent years, such as the logistic regression model and Support Vector Machine with Financial Kernel (SVM-FK) algorithm. Finally, the experimental results show that the NABAS algorithm has higher accuracy and efficiency than other methods in the fraud detection of public datasets.
A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure

The adoptability of the heart to external and internal stimuli is reflected by heart rate variability (HRV). Reduced HRV can be a predictor of post-infarction mortality. In this study, we propose an automated system to predict and diagnose congestive heart failure using short-term heart rate variability analysis. Based on the nonlinear, nonstationary, and highly complex dynamics of congestive heart failure, we extracted multimodal features to capture the temporal, spectral, and complex dynamics. Recently, the Bayesian inference approach has been recognized as an attractive option for the deeper analysis of static features, in order to perform a comprehensive analysis of extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from congestive heart failure signals and then ranked them based on ROC methods. This study focused on utilizing the dissimilarity feature, which is ranked as highly important, as a target node for the empirical analysis of dynamic profiling and optimization, in order to explain the nonlinear dynamics of GLCM features extracted from heart failure signals, and distinguishing CHF from NSR. We applied Bayesian inference and Pearson&rsquo;s correlation (PC). The association, in terms of node force and mapping, was computed. The higher-ranking target node was used to compute the posterior probability, total effect, arc contribution, network profile, and compression. The highest value of ROC was obtained for dissimilarity, at 0.3589. Based on the information-gain algorithm, the highest strength of the relationship was obtained between nodes &ldquo;dissimilarity&rdquo; and &ldquo;cluster performance&rdquo; (1.0146), relative to mutual information (81.33%). Moreover, the highest relative binary significance was yielded for dissimilarity for 1/3rd (80.19%), 2/3rd (74.95%) and 3/3rd (100%). The results revealed that the proposed methodology can provide further in-depth insights for the early diagnosis and prognosis of congestive heart failure.
Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Analytics over social graphs allows to extract valuable knowledge and insights for many fields like community detection, fraud detection, and interest mining. In practice, decentralized social graphs frequently arise, where the social graph is not available to a single entity and is decentralized among a large number of users, each holding only a limited local view about the whole graph. Collecting the local views for analytics of decentralized social graphs raises critical privacy concerns, as they encode private information about the social interactions among individuals. In this paper, we design, implement, and evaluate PrivGED, a new system aimed at privacy-preserving analytics over decentralized social graphs. PrivGED focuses on the support for eigendecomposition, one popular and fundamental graph analytics task producing eigenvalues/eigenvectors over the adjacency matrix of a social graph and benefits various practical applications. PrivGED is built from a delicate synergy of insights on graph analytics, lightweight cryptography, and differential privacy, allowing users to securely contribute their local views on a decentralized social graph for a cloud-based eigendecomposition analytics service while gaining strong privacy protection. Extensive experiments over real-world social graph datasets demonstrate that PrivGED achieves accuracy comparable to the plaintext domain, with practically affordable performance superior to prior art.
Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps

This work applies emergent self-organizing map (ESOM) techniques, a form of machine learning, in the multidimensional interpretation and prediction of rare earth element (REE) abundance in produced and geothermal waters in the United States. Visualization of the variables in the ESOM trained using the input data shows that each REE, with the exception of Eu, follows the same distribution patterns and that no single parameter appears to control their distribution. Cross-validation, using a random subsample of the starting data and only using major ions, shows that predictions are generally accurate to within an order of magnitude. Using the same approach, an abridged version of the U.S. Geological Survey Produced Waters Database, Version 2.3 (which includes both data from produced and geothermal waters) was mapped to the ESOM and predicted values were generated for samples that contained enough variables to be effectively mapped. Results show that in general, produced and geothermal waters are predicted to be enriched in REEs by an order of magnitude or more relative to seawater, with maximum predicted enrichments in excess of 1000-fold. Cartographic mapping of the resulting predictions indicates that maximum REE concentrations exceed values in seawater across the majority of geologic basins investigated and that REEs are typically spatially co-associated. The factors causing this co-association were not determined from ESOM analysis, but based on the information currently available, REE content in produced and geothermal waters is not directly controlled by lithology, reservoir temperature, or salinity.
Digital synchronization for continuous-variable quantum key distribution

Abstract Continuous variable quantum key distribution (CV-QKD) is a promising emerging technology for the distribution of secure keys for symmetric encryption. It can be readily implemented using commercial off-the-shelf optical telecommunications components. A key requirement of the CV-QKD receiver is the ability to measure the quantum states at the correct time instance and rate using the correct orthogonal observables, referred to as synchronization. We propose a digital synchronization procedure for a modern CV-QKD system with locally generated local oscillator for coherent reception. Our proposed method is modulation format independent allowing it to be used in a variety of CV-QKD systems. We experimentally investigate its performance with a Gaussian modulated CV-QKD system operating over a 10–20 km span of standard single mode fibre. Since the procedure does not require hardware modifications it paves the way for cost-effective QKD solutions that can adapt rapidly to changing environmental conditions.
A feature selection method via relevant-redundant weight

Feature selection is a crucial preprocessing technique in data mining and machine learning and has attracted increasing attentions. However, the relevance of existing methods only contains limited information. To enhance the classification ability of filter and information theory-based feature selection methods and reduce the redundancy of the selected subset, a relevant-redundant weight-based feature criterion (FSRRW) is proposed. In this paper, a feature relevant-redundant weight (RRW) is constructed to extract the important relevant and redundant information. Then, a novel feature relevance is defined based on the weight, which contains more comprehensive information from the dynamically changing features. Additionally, a feature evaluation criterion is presented via maximizing the feature relevance and minimizing the feature redundancy. The proposed algorithm and seven compared methods are tested on 20 benchmark datasets. Extensive experiments demonstrate that the proposed criterion exhibits better feature screening abilities, effectively facilitates classification, and has preferable applicability and robustness.
Context-aware reinforcement learning for course recommendation

Online course recommendation is an extremely relevant ingredient for the efficiency of e-learning. The current recommendation methods cannot guarantee the effectiveness and accuracy of course recommendation, especially when a user has enrolled in many different courses. Because these methods fail to distinguish the most relevant historical courses, which can contribute to predicting the target course that indeed reflects the user’s interests from her sequential learning behaviors. In this paper, we propose a context-aware reinforcement learning method, named Hierarchical and Recurrent Reinforcement Learning (HRRL), to efficiently reconstruct user profiles for course recommendation. The key ingredient of our scheme is the novel interaction between an attention-based recommendation model and a profile reviser with Recurrent Reinforcement Learning (RRL) that exploits temporal context. To this aim, a contextual policy gradient with approximation is proposed for RRL. By employing RRL in hierarchical tasks of revising user profiles, the proposed HRRL model enables reliable convergence in revising policy learning and improves the recommendation accuracy. We demonstrate the effectiveness of our proposed method by experiments on two open online courses datasets. Empirical results show that HRRL significantly outperforms state-of-the-art baselines.
Shedding light on images: Multi-level image brightness enhancement guided by arbitrary references

The non-linearity between human perception and image brightness levels results in different definitions of NORMAL-light. Thus, most existing low-light image enhancement methods which produce one-to-one mapping can not meet the aesthetic demand. Other pioneers enhance low-light images guided by a given value. However, the inherent problem of non-linearity will cause poor usability. To this end, we propose a user-friendly neural network for multi-level low-light image enhancement. Inspired by style transfer, our method decomposes an image into content component feature and luminance component feature in the latent space. Then we enhance the image brightness to different levels by concatenating the content components from low-light images and the luminance components from reference images. The network meets various user requirements by selecting different brightness references. Moreover, information except for brightness is preserved to alleviate color distortion. Extensive experiments demonstrate the superiority of our network against existing methods.
Named Entity Recognition Using Conditional Random Fields

Named entity recognition (NER) is an important task in natural language processing, as it is widely featured as a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection in Western and Asian languages. However, little effort has been made to develop techniques for the Urdu language, which is a prominent South Asian language with hundreds of millions of speakers across the globe. NER in Urdu is considered a hard problem owing to several reasons, including the paucity of large, annotated datasets; an inaccurate tokenizer; and the absence of capitalization in the Urdu language. To this end, this study proposed a conditional-random-field-based technique with both language-dependent and language-independent features, such as part-of-speech tags and context windows of words, respectively. As a second contribution, we developed an Urdu NER dataset (UNER-I) in which a large number of NE types were manually annotated. To evaluate the effectiveness of the proposed approach, as well as the usefulness of the dataset, experiments were performed using the dataset we developed and an existing dataset. The results of the experiments showed that our proposed technique outperformed the baseline technique for both datasets by improving the F1 scores by 1.5% to 3%. Furthermore, the results demonstrated that the enhanced dataset was useful for learning and prediction in a supervised learning approach.
A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing

Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance r is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of r are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value μ and variance value σ2 of that set, we define the decomposition stability w as μ/σ2. Then, the wavelet that maximizes w for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.