Reviewerly/exHarmony huggingface dataset:
Environmental Characteristics of the Mining Area of Ni–Cu–Fe Paleoproterozoic PGE Monchepluton Intrusion (NE Scandinavia)

The problem of dump recultivation associated with sulfide ore mining is always a challenge for ecologists. A special case is the Arctic, where the specific climate and short vegetative period mean that any traces of such activities can persist for many years. The Monchepluton massif is a Paleoproterozoic, layered Platinum Group Element (PGE) intrusion, which is located in NE Scandinavia, beyond the Arctic Circle. This intrusion is mainly composed of ultramafic and alkaline rocks, represented by dunites, harzburgites, orthopyroxenites, norites, and gabbronorites. In these rocks, there is mineralization of the oxide ores Cr, Fe, and Ti, as well as the sulfide ores Cu, Ni, Fe, and PGE. The massifs of this intrusion were mined in the 20th century. The traces of intensive mining in the area are still visible today. The purpose of this study was to demonstrate the state of the environment and propose its rehabilitation. The authors carried out an inventory of the mining facilities and studies of the area’s rocks, soils, plants, and waters. The results of these studies unequivocally show that the current condition of the site indicates the conditions for an ecological disaster. The most important polluting factor is the presence of metallurgical plants, which are responsible for acid rain and soil pollution. Another threat is uncontrolled mine water outflows. Due to the specific climatic conditions and the vegetation found in the area, the process of rehabilitating the area may be costly and lengthy.
Extractive Text Summarization on Large-Scale Dataset Using K-Means Clustering and Word Embedding

Automatic text summarization tasks play an important role in natural language processing. In this work, we introduce the single-document extractive summarization model based on clustering and word embedding. In the model, we use K-Means clustering to create the clusters on the large-scale dataset by using word embedding as the feature vector, then use these clusters to extract the most relevant sentences on the document to summarize. At first, we collected the articles on the Vietnamese online newspapers, cleaned them and built up the dataset with a total of 1,101,101 articles. After that, we applied our summarization model for the experimentation. The average time cost for summarizing one document in the test set is 6.22 ms, and the best F-Score of this model based on ROUGE-1, ROUGE-2, and ROUGE-L are 51.40, 16.15, and 29.18%.
Learning Behavior Based on Data Mining Technology

With the release of the Education Informatization 2.0 Action Plan and the rapid development of learning analysis technology, educational data mining becomes a new research direction. Data mining can improve teachers’ teaching methods and students’ learning skills by acquiring information hidden in the educational data. Based on the learning behavior data of college students, this paper uses BP neural network, a data mining method, to predict their comprehensive evaluation results. The results show that there is a close relationship between students’ learning behavior and their comprehensive scores. In addition, models of naive Bayes, logistic regression, and decision tree are established for verification and comparison. Compared with other models, BP neural network model has higher prediction accuracy and better performance. It can serve as an important basis to improve students’ learning methods and teachers’ teaching methods.
N-Gram Based Amharic Grammar Checker

Objective: To investigate the applicability of all possible sentence level grammatical features along with grammatical agreement rules for grammatical error detection and correction of textual documents without language restriction. Methods: This work used sentence level tagged n-gram and word n-gram with grammatical agreement rules of a language to develop a system. To demonstrate the language independency of the model we used Amharic language corpus taken from HaBit (Harvesting big text data for under-resourced languages). Findings: This work indicates the relevance of sentence level tag and word features on detection and correction of grammatical errors. The result of this model outperformed the existing fixed N-gram (i.e. Bigram and Trigram) and this will be used for researchers to investigate further. Novelty: This work indicates the weight of all possible sentence level grammatical features to boost the effectiveness of grammar proofing systems, particularly at multilingual textual document setting.
Modeling interventions in multi-level causal systems: supervenience, exclusion and underdetermination

This paper explores some issues concerning how we should think about interventions (in the sense of unconfounded manipulations) of "upper-level" variables in contexts in which these supervene on but are not identical with lower-level realizers. It is argued that we should reject the demand that interventions on upper-level variables must leave their lower-level realizers unchanged– a requirement that within an interventionist framework would imply that upper-level variables are causally inert. Instead an intervention on an upper-level variable at the same time changes its lower-level realizer in a way that is consistent with the change in the upper- level variable. The lower-level realizer should not be regarded as a potential "confounder" of upper-level causal relations, as in "exclusionist" arguments. Several proposals for making this precise are considered and several pro-exclusionist arguments are criticized.
Ask a Further Question or Give a List? How Should Conversational Agents Reply to Users’ Uncertain Queries

Conversational agents (CAs) have recently become ubiquitous. Smart speakers, mobile phone voice assistants, and in-car voice assistants have entered our lives. Studies have examined some factors influencing the user experience (UX) of CAs. However, there is little research on CAs’ reply design, especially when the information the users need is uncertain, which is regarded as an uncertain information scenario. The current research mainly focuses on measuring the UX with CAs’ replies in uncertain information scenarios. We designed two reply strategies of CAs, namely, a further inquiry strategy and a list-style reply strategy, to improve UX in this kind of uncertain information scenario. Two studies were designed and conducted based on the E-prime platform to verify the effect of the two reply strategies on UX. In Study 1, we verified the influence of inquiry strategy with different address terms for users on UX. In Study 2, we verified the influence of the list-style reply strategy (explicit and implicit) on UX. The gender differences in the evaluation of the two reply strategies were also examined. The results showed that, in the uncertain information scenarios, the reply strategy of further inquiries received a higher UX evaluation than direct replies in Study 1. Moreover, male participants preferred the “master” address to the “nin” address. However, male participants had no significant preference for further inquiries. For male participants, there was no significant difference in the UX evaluations for the “master” address and “nin” address. List-style replies with ranking information received the highest UX ratings, followed by list-style replies without ranking information and direct replies in Study 2, indicating that users preferred the explicit reply design of CAs. In addition, we found that male participants tended to have a higher rating of CA replies for all reply methods than female participants in our studies, suggesting that women have higher expectations for the reply design of CAs. In general, these results may contribute to the design of CA replies and highlight the importance of personalizing CA language.
An Approach for Gesture Recognition Based on a Lightweight Convolutional Neural Network

Gesture recognition, which plays an important role to understand meaningful movements of human bodies, is one of the most effective approaches for humans to interact. Sign language is a fundamental and innate means of communication for hearing-impaired individuals. Though significant progress has been made, the state-of-the-art gesture recognition methods yield week performance for conditions with dynamic gestures in videos. Thus, robust gesture recognition remains a challenging issue because of many barriers of gesture-irrelevant factors. The key to robust gesture recognition is to learn effective and concise spatiotemporal information. Inspired by the great promise of the convolutional neural network (CNN) and its breakthroughs, we introduce an approach for identifying static alphabet gestures in the American Sign Language (ASL). The proposed CNN-based approach has been developed to classify letters of the alphabet from A to Z. It composes of three phases: a preprocessing phase for extracted of the region of interest, a feature extraction, and a classification phase. The performance of proposed gesture recognition approach is evaluated on the common ASL dataset and it achieves 94.83% accuracy, which is good enough to develop a strong translator from gesture-based ASL to spoken language as it is capable to handle a variety of 24 hand gestures.
A hybrid method based on undecimated discrete wavelet transform and autoregressive model to<i>S</i>-wave automatic picking

SUMMARY Automatic S-wave arrival time estimation is, due to the complex characteristic of most of the S onsets, a topic of ongoing research. Manual as well as automated S-wave picking is more difficult than P-wave picking, as S wave is usually buried in the preceding P-coda. In addition, S-wave splitting, due to possible seismic anisotropy, and the presence of Sp-converted precursors, due to shallow strong velocity discontinuities, increase the complexity of S-wave onset time picking. The goal of this study is to develop an automatic S-wave onset time picking algorithm, using undecimated discrete wavelet transform (UDWT) and autoregressive (AR) model. The novelty of this research is the application of UDWT to define a characteristic function based on the seismogram envelope that leads to accurate S-wave detection. First, an initial arrival time is estimated using the signal envelope. Then S-wave onset is improved with an AR model regarding the fact that a short time after S waves arrival the amplitude is maximized. The robustness of the proposed method under different SNR’s has been tested on synthetic seismograms, contaminated with noise. It has also been applied to 180 local and regional events with magnitude greater than 4 and epicentral distance from 100 to 1000 km, recorded by the permanent seismic networks within Iran. We also applied our method to a data set from Japan; the data set contains 30 events with a magnitude range greater than 3. The results of our proposed algorithm are compared with a traditional reference method, novel deep learning methods and manually picked phases. The tested data set contains 1160 manual picks from Iran earthquakes data set and 518 manual picks from Japan earthquakes data set. The results show that the proposed method appears to be promising to replace manual phase picking. The automatic picking algorithm described in this study is applicable in many seismological studies that require S onset detection and picking.
Intermediate prototype network for few-shot segmentation

Few-shot segmentation aims to learn a model that can quickly adapt to new classes with limited labeled images. It remains challenging due to the large discrepancy of the targets between the support and query image, which hinders the label propagation from the support to query image. In this work, from a perspective of data distribution, we are committed to explicitly modeling an appropriate intermediate representation space to alleviate the target discrepancy between the support and query image. Specifically, we first propose the Intermediate Representation Module (IRM) that integrates the target information in the support and query image into an intermediate representation space, where the targets in the support and query image are represented as a Gaussian distribution respectively and the targets' distributional representations in the support and query image are probabilistically mixed to extend the target distribution. Second, we take one step further by incorporating an Intermediate Prototype Module (IPM) that replaces the target feature statistics in the support image with that in the intermediate representation space and then generates a new intermediate prototype to re-locate the target region in the query image to mitigate the target discrepancy. Extensive experiments on PASCAL-5i, COCO- 20i and FSS-1000 demonstrate that our approach outperforms state-of-the-art methods.
Robust Losses for Learning Value Functions

Most value function learning algorithms in reinforcement learning are based on the mean squared (projected) Bellman error. However, squared errors are known to be sensitive to outliers, both skewing the solution of the objective and resulting in high-magnitude and high-variance gradients. To control these high-magnitude updates, typical strategies in RL involve clipping gradients, clipping rewards, rescaling rewards, or clipping errors. While these strategies appear to be related to robust losses-like the Huber loss-they are built on semi-gradient update rules which do not minimize a known loss. In this work, we build on recent insights reformulating squared Bellman errors as a saddlepoint optimization problem and propose a saddlepoint reformulation for a Huber Bellman error and Absolute Bellman error. We start from a formalization of robust losses, then derive sound gradient-based approaches to minimize these losses in both the online off-policy prediction and control settings. We characterize the solutions of the robust losses, providing insight into the problem settings where the robust losses define notably better solutions than the mean squared Bellman error. Finally, we show that the resulting gradient-based algorithms are more stable, for both prediction and control, with less sensitivity to meta-parameters.
Virtual Reality Laboratory as a Factor in Increasing Engineering Students’ Motivation

Globally, young people have a decline in interest in obtaining engineering specialties. Therefore, it is necessary to create a learning environment that will motivate students to learn engineering professions. It is necessary to use all possible resources, as well as stimulate and encourage the creativity of both teachers and students. Therefore, the purpose of this study is to create a method for increasing motivation based on the use of new tools and approaches in training future engineers. Since virtual reality technologies allow a deeper understanding of real processes’ essence, demonstrate the main steps of the process and possible critical situations use of these technologies are very relevant. We put forward the assumption that the process of developing educational content itself can be used as an incentive motivating force in the educational process. We have developed virtual reality laboratory, with the help of which students are able to study the work of stands used in vehicle repair. They can master structure and operation principles of the balancing machine, study and practice of the technological process of balancing vehicle tires and disks. In order to maximize usage of students’ and teachers’ creative, it was proposed to attract computer students to develop the content of a virtual reality laboratory for the study of vehicle repair processes. A survey conducted among students showed that this approach allows to get a higher motivation level compared to the use of traditional teaching.
Integration of Business and Education in the Training of Engineers for Industry 4.0

The fourth industrial revolution (Industry 4.0 - IR 4.0) led to a paradigm shift and changes in production and related processes, increased requirements for the competence of engineers, and therefore the business is experiencing difficulties in recruiting. Dynamic changes in the field of engineering and technology have radically changed the requirements for the engineering education system, since its inertia does not allow satisfying the needs of business for personnel with creative thinking, eco-consciousness and skills to work in the context of digital transformation. At the same time, there is a decline in interest in the profession of an engineer in the world. All this requires a paradigm shift and an increase in the sustainability of the engineering education system in a dynamically changing environment, the goal of which will be to prepare an engineer “for the future”, that will meet the real needs of the business. The internationalization and digitalization of the economy and new work functions make digital competencies critical. At the same time, new and promising technologies should be adapted and included in the educational process in order to facilitate the adaptation of university graduates in production. The purpose of the article is to consider topical issues of engineering education related to interdisciplinarity, new standards and technologies for engineering education, digital educational environment and online technologies, the interaction of engineering education with high-tech business and industry.
Creating ‘a Simple Conversation’: Designing a Conversational User Interface to Improve the Experience of Accessing Support for Study

Administrative processes are ubiquitous in modern life and have been identified as a particular burden to those with accessibility needs. Students who have accessibility needs often have to understand guidance, fill in complex forms, and communicate with multiple parties to disclose disabilities and access appropriate support. Conversational user interfaces (CUIs) could allow us to reimagine such processes, yet there is currently limited understanding of how to design these to be accessible, or whether such an approach would be preferred. In the ADMINS (Assistants for the Disclosure and Management of Information about Needs and Support) project, we implemented a virtual assistant (VA) which is designed to enable students to disclose disabilities and to provide guidance and suggestions about appropriate support. ADMINS explores the potential of CUIs to reduce administrative burden and improve the experience of arranging support by replacing a static form with written or spoken dialogue. This article reports the results of two trials conducted during the project. A beta trial using an early version of the VA provided understanding of accessibility challenges and issues in user experience. The beta trial sample included 22 students who had already disclosed disabilities and 3 disability support advisors. After improvements to the design, a larger main trial was conducted with 134 students who disclosed their disabilities to the university using both the VA and the existing form-based process. The results show that the VA was preferred by most participants to completing the form (64.9% vs 23.9%). Qualitative and quantitative feedback from the trials also identified accessibility and user experience barriers for improving CUI design, and an understanding of benefits and preferences that can inform further development of accessible CUIs for this design space.
Blockchain-based multi-hop permission delegation scheme with controllable delegation depth for electronic health record sharing

Permission delegation has become a new way for data sharing by delegating the authorized permission to other users. A flexible authorization model with strict access control policies is promising for electronic health record (EHR) sharing with security. In this paper, a blockchain-based multi-hop permission delegation scheme with controllable delegation depth for EHR sharing has been presented. We use the interplanetary file system (IPFS) for storing the original EHRs. Smart contracts and proxy re-encryption technology are implemented for permission delegation. In order to ensure data security, we use attribute-based encryption to provide fine-grained access control. Additionally, blockchain is used to achieve traceability and immutability. We deploy smart contracts so that the delegation depth can be set by delegators. Security analysis of the proposed protocol shows that our solution meets the designed goals. Finally, we evaluate the proposed algorithm and implement the scheme on the Ethereum test chain. Our scheme outperforms the competition in terms of performance, according to the results of our experiments.
Fault identification model using IIoT for industrial application

To prevent substantial economic losses brought on by problems in rolling element bearings, Industrial revolution 4.0 detection methods have been revitalized by Artificial Intelligence (AI) and anIndustrialized Internet of Things (IIoT). Strategies in final devices have been challenged because diagnostic systems receive a range of inputs that provide variances in the input space. Normally, a two-way cross-domain training strategy was used to solve this problem. For the final devices, the researchers provide a soft real-time defect diagnosis technology that utilizes a training strategy to adapt the domain. Deep Learning (DL) patterns develop concepts independently of the input dimension used in the survey. A comparative study was done on a dataset accessible to determine the effectiveness of the proposed methodology with an average accuracy of 88.08%.Experimental results showed in an IIoT ecosystem and our proposed system using a short-term memory system provides the most accurate bearing detecting results.
Everyday Information Behavior of Marginalized Communities in the Global South: Informal Transportation as Example

Abstract Information science is increasingly focused on giving voice to marginalized communities, improving their daily lives, and contributing to Sustainable Development Goals (SDGs) and equity, diversity, and inclusion (EDI). However, challenges facing marginalized communities in the Global South are less frequently investigated, yet of great importance. Millions of people depend on information for essential everyday life activities such as transportation. Successful, timely daily commuting between home and work influences financial stability, family time, and safety. Everyday life information behavior as a research lens can reveal information activities, influencing factors, information sources, and contexts applying to informal transportation use by poorer socio‐economic groups. Theories of information behavior such as information poverty and information horizons can shed light. Understanding information behavior contexts, and how to bridge the digital divide and promotion of networking, sharing, and learning for marginalized populations through community‐led digital literacy training can help tailor interventions.
Censorship Is Not a Panacea: Access To Information in a Resilient Society

Abstract Information resiliency is a difficult idea to conceptualize, though it seems to include resistance to mis‐ and dis‐information, algorithmic injustices, and filter bubbles. A possible solution to these information challenges and the current “infodemic” is to restrict access to and circulation of certain information. Social media platforms, in particular, have an extraordinarily wide reach in contemporary society. While compelling these platforms to restrict information may be appealing, at first glance, there are inherent problems of power and control bound up in the suggestion of censorship (or, as it is sometimes framed, content moderation). Rules about permitted speech can be wielded in ways that increase harm to already marginalized populations. In this panel, we discuss these ideas and debate the merits of restricting access to information via social media content moderation. We anticipate a robust discussion with attendees