Reviewerly/exHarmony huggingface dataset:
Reducing environmental impact caused by mining activities in limestone mines

Mining and its related activities have always resulted in changes in the environment and these changes can vary from one area to another. As a result of mining, several types of changes can be distinguished: destruction of land and existing vegetation, changes in terrain topography, modification in air quality, changes in surface and groundwater quality, as well as change geotechnical conditions of the rock. Environmental impact of mining has been a public concern. There is widespread global interest in mining and its sustainability, and it is focused on the need to shift mining industry to a more sustainable framework. This article describes the possibilities of how to reduce the environmental impact of limestone excavation. Successful closure must consider medium to long term post mining land use and land capability, as well as minimizing environmental impact.
Learning Advisor Networks for Noisy Image Classification

In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy labels during the training, completely removing their contribution. This discarding process prevents DNNs from learning wrong associations between images and their correct labels but reduces the amount of data used, especially when most of the samples have noisy labels. Differently, our method weighs the feature extracted directly from the classifier without altering the loss value of each data. The advisor helps to focus only on some part of the information present in mislabeled examples, allowing the classifier to leverage that data as well. We trained it with a meta-learning strategy so that it can adapt throughout the training of the main model. We tested our method on CIFAR10 and CIFAR100 with synthetic noise, and on Clothing1M that contains real-world noise, reporting state-of-the-art results.
Automatic Detection of Thyroid Nodule Characteristics from 2D Ultrasound Images

Thyroid cancer is one of the most common types of cancer in the female population worldwide. Ultrasound (US) imaging is a non-invasive modality successfully used for cancer diagnostics. However, US image is adversely affected by artefacts and speckle noises that make the automatic detection of lesion characteristics a challenging task. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) is a guideline commonly used by doctors to identify and classify US image characteristics of malignancy for thyroid nodules. In this paper, we present new methods for detecting such characteristic descriptors derived from TIRADS using both traditional computer vision and deep learning techniques. In particular, our methods return descriptions about the nodule irregularity, smoothness, echogenicity and calcification. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labelled by multiple experts. Our methods achieved overall accuracies of 100.00%, 93.18%, 89.13%, 90.60% and 88.00% in classifying nodule shape, irregularity, smoothness, echogenicity and calcification, respectively. This study provides a comprehensive and effective translation of thyroid nodule characteristics from 2D ultrasound images. Correctly detected characteristics provide not only evidence but also explanations to assist medical specialists in making diagnostic decisions.
A Study on Learning Motivation of Newly Enrolled Adult Students in Higher Vocational Colleges

China has been vigorously developing vocational education and actively promotes the enrollment expansion of higher vocational education.Some adult candidates can improve their cognition and skills by receiving higher vocational education, which contributes to the iteration of labor force.Existing literature shows that adult learners are different from full-time students from common colleges and universities, and it is of great significance to explore the learning motivation of newly-enrolled adult students.This study takes newly-enrolled adult students who had participated in the expanded enrollment of higher vocational colleges as the research object, and applies the method of questionnaire to quantitatively analyze their learning motivation.Descriptive analysis shows the fundamentals among this group of students that women generally have higher learning motivation than men; the group of age 26 to 35 displays the highest motivation; married people have higher motivation than the unmarried; there are also higher learning motivations revealed in learners with children.The variance analysis presents that different genders lead to the differences in cognitive interest, interpersonal relationship and social service; while childbearing results in differences in career development, external influence and social service.Cluster analysis provides two types of learners, one of which has low average scores and high motivations, the other has high average scores and low motivations.The scores fluctuate sharply in different sub-dimensions, targeted stimulation of learning motivation can be carried out.In-depth research could be taken in further studies, and an enriched sample size may contributes to more accurate results.
The Effect of a Blended Learning Course of Visual Literacy for In-service Teachers

Aim/Purpose The purpose of this study was to examine the effects of a 20-hour blended learning visual literacy course applied to in-service teachers. For this purpose, we designed the course to train the educators and the instrument to measure the level of visual literacy of participants before and after the intervention. Then, we found the differences. Background Visual literacy is essential for improving visual communication skills on in-service teachers because they use and construct visual material permanently. Hence, they need to be trained for developing visual literacy taking into account their pace of life and specific needs. Methodology We employed a quasi-experimental one-group pretest-posttest design. The area of study is Social Science, specifically Education. The population was in-service teachers who work in private schools in the north zone of Quito, Ecuador. The convenience sampling method was used to conduct this pilot study of 51 teachers at one private school. The visual literacy course was designed based on the ACRL Visual Literacy Competency Standards. The differences in the level of visual literacy were measured through a visual literacy test of 45 items made for this specific purpose. Differences between pretest and posttest were found after performing paired samples t-test on collected data. Contribution This research contributes to visual literacy research focused on in-service teacher’s instruction. This practical study was based on a complete proposal for training and evaluated the visual literacy level of in-service teachers. Findings Findings show that there are statistically significant differences in pretest and posttest scores, so teachers improved their level of visual literacy after the 20-hour blended learning visual literacy course. Recommendations for Practitioners Practitioners should adapt the length of the training course to the teachers’ schedules. They should review the course themes and the items in the visual literacy test to know about the specific content to be taught along the course. Recommendation for Researchers Researchers who want to replicate a similar study should have a bigger group of participants and, if possible, they should have a control group. Impact on Society This study indicates that teachers could improve their level of visual literacy after attending a well-structured training course. Thus, it is crucial to offer in-service teachers the opportunity to improve their visual communication skills through a concrete learning process adapted to their schedules and life. Future Research Future research should focus on evaluating before and after the treatment, through practical projects, the previous and acquired knowledge of in-service teachers.
High Risk Bank Loan Recognition Based on Machine Learning

This paper aims to compare the performance of C5.0 decision tree and random forest in high-risk bank loans' recognition.The author mine 1000 loan information data containing 16 variables and find our model's accuracy is around 0.71.Then the author try to do some change on model parameters to improve the model.The author finds that increasing the cost of false negative in error matrix can help bank better avoid the high risk loan.Compared different trials in decision tree, the author find that the model perform the best when trials equal to 45.Compared different mtry in random forest, the author find that the model performs the best when the mtry equal to 11.Then the author compares the random forest and decision tree according to ROC, sensitivity and specificity.The results show that random forest has strength in ROC and sensitivity, while decision tree has strength in specificity.And due to the random forest's bigger AUC value, the author conclude that random forest model slightly outperformed the tree model.
Transformer based Model for Coherence Evaluation of Scientific Abstracts: Second Fine-tuned BERT

Coherence evaluation is a problem related to the area of natural language processing whose complexity lies mainly in the analysis of the semantics and context of the words in the text.Fortunately, the Bidirectional Encoder Representation from Transformers (BERT) architecture can capture the aforementioned variables and represent them as embeddings to perform Fine-tunings.The present study proposes a Second Fine-Tuned model based on BERT to detect inconsistent sentences (coherence evaluation) in scientific abstracts written in English/Spanish.For this purpose, 2 formal methods for the generation of inconsistent abstracts have been proposed: Random Manipulation (RM) and K-means Random Manipulation (KRM).Six experiments were performed; showing that performing Second Fine-Tuned improves the detection of inconsistent sentences with an accuracy of 71%.This happens even if the new retraining data are of different language or different domain.It was also shown that using several methods for generating inconsistent abstracts and mixing them when performing Second Fine-Tuned does not provide better results than using a single technique.
Review of Smart Contracts for Cloud-Based Manufacturing

Cloud-based manufacturing is taking shape, and many industries seem interested to make the transition to it. Developing blockchain solutions for trusted computing is also taking its roots. Developing a blockchain-based solution for cloud-based manufacturing systems is a field that is new but also faces limitations and a lack of case studies. Smart contracts are one part of the solution which deals with making blockchain successful in cloud-based manufacturing. As we move towards smart contracts design and development for cloud-based manufacturing, there is no complete survey of smart contract and cloud manufacturing that can highlight critical, challenging issues and limitations. Most of the work found in smart contracts is mostly financial and notary-centric applications. On the cloud manufacturing side, most of the literature deals with Internet of Things (IoT) and cloud computing systems. Therefore, there is a need to study the best practices to start manufacturing supported by blockchain smart contracts. We conducted a scoping review for smart contracts for cloud manufacturing to address the problem mentioned above. We studied the latest case studies and concepts in data extracted from digital libraries and online repositories. Furthermore, we follow the relevance and acceptance criteria of research articles for inclusion and exclusion from this work. This paper focuses on blockchain systems, smart contracts and architecture, smart contracts in the cloud, and the IoT environment. Furthermore, we tried to bridge design and implementation details for readers to understand the patterns that can replicate for cloud-based manufacturing systems.
RUTA:MED – Dual Workflow Medical Speech Transcription Pipeline and Editor

In the medical domain various approaches are used to produce examination reports and other medical records. Depending on the language-specific technology support, the type of examination, the size of the hospital or clinic, and other aspects, the reporting workflow can range from completely manual to (semi-)automated. A manual workflow may completely depend on the doctor itself or may include a transcriptionist centre in the loop. In an automated workflow, the transcriptionist centre is typically replaced by an automatic speech recognition (ASR) system. While the latter approach is well suited for high resource languages where word error rate (WER) is as low as 5–10%, for less resourced languages a dual approach combining automated transcription with the support from a transcriptionist centre may be more suited. In this paper, we present a platform that supports both workflows simultaneously. The RUTA:MED platform currently includes an ASR pipeline for the less resourced Latvian language, and it is being deployed and tested at several hospitals and clinics in Latvia. The platform can be adopted for any other language, and it emphasizes that WER is only one of the performance indicators in case of medical transcription.
Aspect Based Fine-Grained Sentiment Analysis for Public Policy Opinion Mining

Fine-grained mining has substantial practical significance for understanding public opinion toward public policies and optimizing relevant decision-making processes. In this study, an aspect-based sentiment analysis model integrated with both a sentiment lexicon for the policy domain and mutual information (MI) is established from the perspective of attitudinal orientations toward several elements of public policies, including subjects, objects, and tools to analyze comments. First, terms related to the aspects of comments on different policy elements are extracted using the association rules in conjunction with the word2vec algorithm. In addition, the contextual neighbor principle is employed to extract terms that express evaluative opinions and to extricate aspect-based two-tuples from comments. Subsequently, the sentiment lexicon for the policy domain is expanded using the sentiment orientation pointwise mutual information (SO-PMI) algorithm. Four machine learning models (i.e., the support vector machine, logistic regression, naive Bayes, and k-nearest neighbors models) and two deep learning models (i.e., the convolutional neural network (CNN) and long short-term memory models) are compared in terms of their classification performance based on 87,304 comments on public policies crawled from the Weibo platform. The results show that the CNN model integrated with the domain-specific sentiment lexicon and mutual information is effective at facilitating policy element-oriented fine-grained sentiment analysis of public opinion toward policies and therefore has practical decision-making value for the government to better understand the policy demands of the public.
Personnel identification and distribution density analysis of subway station based on convolution neural network

In this paper, a method based on convolution neural network and multi-camera fusion is proposed to improve the recognition accuracy of crowd and then the personnel distribution of subway station platform is analyzed. In this method, tensorflow is used as the deep learning training framework and the yolov4 neural network algorithm is used to identify the subway station platform area using three videos synchronously. Through affine transformation and time average statistics, the passenger density of each sub-area is calculated and the distribution of personnel density in the whole area is analyzed. The results show that the number of people recognized by multiple cameras is 58% higher than that by single camera. The new recognition method has high recognition rate for the actual scene with large crowd and more obstacles. Finally some areas with high risk of personnel aggregation have been found, which should be the focus of safety monitoring.
Systemic mechanisms for improving the quality of professional growth models for heads of educational organizations

The article is devoted to consideration of mechanisms for improving professional growth and enhancing the effectiveness of educational organizations leaders, among which are certification, monitoring effectiveness of educational organizations leaders, system of in-service training aimed at solving professional problems and needs of this contingent, including a set of effective models. The necessity of introducing the target model for certification of leaders, the goals and objectives of monitoring and its potential in ensuring the quality and efficiency of management personnel are substantiated. The role and possibilities of in-service training system in improving the &amp;quot;innovative potential&amp;quot; an educational organization leaders are analyzed. Specific examples of models for the development of leaders&amp;apos; qualifications (including a personalized model, cascade or facet models, a horizontal learning model) are given.
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and Inclusion

Social media platforms have been provoking masses of people.The individual comments affect a prevalent way of thinking by moving away from preoccupation with discrimination, loneliness, or influence in building confidence, support, and good qualities.This paper aims to identify hope in these social media posts.Hope significantly impacts the well-being of people, as suggested by health professionals.It reflects the belief to achieve an objective, discovers a new path, or become motivated to formulate pathways.In this paper we classify given a social media post, hope speech or not hope speech, using ensembled voting of BERT, ERNIE 2.0 and RoBERTa for English language with 0.54 macro F1-score (2 st rank).For non-English languages Malayalam, Spanish and Tamil we utilized XLM RoBERTA with 0.50, 0.81, 0.3 macro F1 score (1 st , 1 st ,3 rd rank) respectively.For Kannada, we use Multilingual BERT with 0.32 F1 score(5 th )position.We release our code-base here https: //github.com/Muskaan-Singh/Hate-Speech-detection.git
Smart Blended Learning Framework based on Artificial Intelligence using MobileNet Single Shot Detector and Centroid Tracking Algorithm

The Covid-19 pandemic has affected all aspects of human life and has even forced humans to shift their life habits, including in the world of education.The learning model must shift from the traditional face-to-face pattern to a modern faceto-face pattern or an asynchronous pattern with information technology-based applications.Blended learning is one of the appropriate solutions to adjust the limited face-to-face learning conditions.Blended learning can be done, for example, by scheduling learning by dividing the number of participants by 50% and entering on a scheduled basis.However, the problem is that the time and effort used are less efficient.Blended learning can also be done by conducting learning simultaneously with 50% of students in class and the remaining 50% through conferences.This concept will streamline the time and effort used.However, the problem is that there is a gap in the learning experience between students in class and students who do learning via conference.This innovative blended learning system framework is proposed to overcome these problems.The system built seeks to present an online learning experience atmosphere so that it is expected to be able to resemble an offline learning atmosphere.We created a system using camera technology and object detection that will track the movement of the teacher so that the teacher can move freely in the room without having to be stuck in front of the computer holding the conference.The algorithms used are MobileNet Single Shot Detector and Centroid Tracking.This research produces an accurate model for detecting teacher movement at a distance of 2, 4, and 6 meters with a camera installation height of 1.5 and 3 meters.
An Integrative Access Control Scheme Supporting Ciphertext Sharing and Aggregation in the Social Internet-of-Things

In the large-scaled, heterogeneous and scalable social Internet of things, there are proposed an edge cloud system model and an integrative fine-grained access control scheme. The scheme supports ciphertext sharing and aggregation successively. By using features of Hash function and CDH assumption(Computational Diffie-Hellman assumption), A part of encryption operation is transferred onto a device controller to light the computational cost on source-limited end-devices. The expensive Attribute Based Encryption algorithm providing fine-grained access control is run on the device controller. Efficient data storage and pushing mechanisms were achieved by using cloud services and social network pushing mechanisms respectively. Ciphertext aggregation is outsourced onto cloud servers under the continuity of authorization. That is, the new ciphertext formed by ciphertext aggregation should still support fine-grained permission control of authorized readers or authorized aggregators. In addition, the read permission keys could be derived from the root key of the controller for the ciphertext sharing permission. The balance between security and efficiency was able to be controlled by adjusting the time interval of key derivation. Security proof and performance analysis show that the scheme is feasible.
Inter-target Occlusion Handling in Multi-vehicle Corner Tracking Based on Labeled Multi-Bernoulli Filter with Detection Probability Optimization Model

Multi-vehicle tracking (MVT) system is a prerequisite for intelligent vehicles. Due to the high angular resolution and high range accuracy, LiDAR has become the most commonly used sensor in MVT system. However, the inter-target occlusion (ITO) which is often observed in MVT system using LiDAR, causes estimated track break and vehicle target loss. To address this issue, this paper proposed a multi-vehicle corner tracking algorithm based on the labeled multi-Bernoulli (LMB) filter with detection probability optimization model (DPOM). The DPOM takes the inter-target occlusion probability of tracked vehicle corners into consideration and achieves better modeling of the likelihood of gaining vehicle-generated corner measurements during the occlusion stage. We designed MVT simulation and experiment with challenging occlusions to verify the effectiveness of our proposed method. Both simulation and experiment results show that our proposed method can greatly improve the ability of the MVT algorithm to deal with the ITO problem and outperform conventional algorithms.
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?

Skeleton-based motion capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on tenfold cross-Validation and 82% on leave-one-out cross-validation by using the proposed ensemble model.