Reviewerly/exHarmony huggingface dataset:
SUSTAINABLE DEVELOPMENT OF GEM PRODUCTION: AN APPLICATION OF THE ANALYTICAL HIERARCHY PROCESS

The gems, jewelry, and precious metals sector is essential for developing regions with this activity and there is a high degree of informality in the mining and trading gemstone sectors. The present study analyzed data from the production process of precious stones in the Brazilian national market. We identified the importance of the factors contributing to the quality of the gemstone production process concerning mining, cutting, and marketing, under the scope of sustainable chain development. A list of references was used to select thirteen significant factors in the precious stone production process that contributed to the sustainable development of the chain related to mining, cutting, and marketing. We used a multicriteria decision model applying the Analytical Hierarchy Process (AHP) to consider the degree of importance of the production stage process of gemstones and the factors that impact the sustainable development of the production chain. In applying the model to the production stages, the criteria and sub-criteria factors were considered. The results suggest that sustainable development is highly related to gem mining (44.7%, rank 1), moderately related to gem cutting (32.5%, rank 2), and there is little relationship to gem marketing (22 .8%, rank 3), considering the criteria, sub-criteria, and alternatives. The requirement related to the technical qualification and skill of the lapidary is the most critical factor in the production process. The second most important factor is commercialization, followed by the preservation of the mining area and the valuable life of the mine.
Zero-Aware Low-Precision RNS Scaling Scheme

Scaling is one of the complex operations in the Residue Number System (RNS). This operation is necessary for RNS-based implementations of deep neural networks (DNNs) to prevent overflow. However, the state-of-the-art RNS scalers for special moduli sets consider the 2k modulo as the scaling factor, which results in a high-precision output with a high area and delay. Therefore, low-precision scaling based on multi-moduli scaling factors should be used to improve performance. However, low-precision scaling for numbers less than the scale factor results in zero output, which makes the subsequent operation result faulty. This paper first presents the formulation and hardware architecture of low-precision RNS scaling for four-moduli sets using new Chinese remainder theorem 2 (New CRT-II) based on a two-moduli scaling factor. Next, the low-precision scaler circuits are reused to achieve a high-precision scaler with the minimum overhead. Therefore, the proposed scaler can detect the zero output after low-precision scaling and then transform low-precision scaled residues to high precision to prevent zero output when the input number is not zero.
A Low-Cost Wearable Hand Gesture Detecting System Based on IMU and Convolutional Neural Network

In this paper, a low-cost wearable hand gesture detecting system based on distributed multi-node inertial measurement units (IMUs) and central node microcontroller is presented. It can obtain hand kinematic information and transmit data to the remote processing terminal wirelessly. To have a comprehensive understanding of hand kinematics, a convolutional neural network (CNN) model on the terminal is proposed to recognize and classify gestures and the modified Denavit-Hartenberg notation is used to acquire finger spatial locations. The experiment has not only completed a variety of gesture recognitions, but also captured and displayed the orientation and posture of a single finger. The prototype can be used in various occasions such as hand rehabilitation evaluation and human-computer interaction.
Data Driven for Early Breast Cancer Staging using Integrated Mammography and Biopsy

Objective: Breast cancer patients who have a rapid diagnosis have been better prognosis than late diagnosis. The popular screening is mammogram or ultrasound. In recent years, researchers try to develop data driven models to predict early cancer staging from the first screening. However, data elements are not complete such as lymph node status. Therefore, the Integrated dataset approach will be challenging. Methods: Because the data elements are not collected from the same source, joining between mammography and biopsy data were performed using latent variables that determine by tumor severity. The datasets consist of 445 mammography reports and 183 pathological reports. The latent variables of the mammogram dataset were determined by the severity of mass, while latent variables of the pathological dataset were determined by TNM Staging. The latent variables were used to join between two datasets. Then, the prediction models were built using the machine learning technique. The modeling is divided into three steps; staging prediction, lymph node prediction, and prognosis. Results: Integrated dataset from mammography and biopsy extend more factors and built the models to predict breast cancer staging in the mammography process. The staging prediction is 100% accuracy. The lymph node prediction are 72.47% accuracy, 73.94% specificity, and 72.5% sensitivity. An area under ROC curve is 0.74. The prognosis model prediction are 72.72% accuracy, 80% specificity, and 77% sensitivity. An area under ROC curve is 0.87. There are also built the rule for early staging, diagnosis, and prognosis. Conclusion: This study aims to build the models for early staging, diagnosis, and prognosis using the less aggressive method. The advantages are (1) predict staging from the first screening (2) estimate the lymph node metastases for planning to ALND or SLNB (3) evaluate overall survival time. These advantages help the physician planning the best treatment for cancer patients.
Factors Influencing Organizational Performance in Hotel Sector of Nepal

The present study is designed to identify the factors influencing organizational performance based on native research work carried out through field study. Adopting descriptive cum analytical research design, this study is based on primary data obtained from the hotels located inside the Kathmandu valley of Nepal through the use of a five-point Likert-type questionnaire. With the help of the random sampling technique, 130 employees working in 23 hotels in the position of executive, managerial and officer’s level are taken as sample respondents. Four different variables affecting hotels' performance i.e., qualification, training and experience, administrative efficiency and location are taken as the independent variables and organizational performance as the dependent variable. Descriptive and inferential statistics have been used in this study. All the variables obtained Cronbach alpha value greater than .65, which shows the consistency on items taken for the study. The correlation analysis showed a strong positive correlation between organization performance and performance factors in the Nepalese hotel sector. Moreover, the regression analysis indicated that qualification of the employee, administrative efficiency of hotel management and training and experience provided to the employee are found strong predictors of organizational performance but the location factor was not found the determinant of organization performance in the Nepalese hotel sector. This study can further expand by adopting other variables affecting organizational performance in the hospitality sectors of Nepal.
Exploring Prison Rehabilitation Programs and the Factors That Contributed to Former Prisoners' Career Success

This study aims to explore the rehabilitation program of prisoners in Prisons and the causes that cause ex-prisoners to be stuck with the phenomenon of homelessness after their release from prison.This study was conducted based on two main research questions, namely: (1) Whether the vocational training program implemented is the main thrust in the approach to treating and rehabilitating prisoners in prisons; (2) What are the factors that contribute towards successful ex-prisoners in their careers.This study was conducted with a qualitative approach in which the data of this study were obtained through in-depth individual interviews from two different individuals and document analysis.The findings of this study show that the rehabilitation program of prisoners in this country is implemented in an orderly and systematic manner under a grand design known as the Human Development Plan (PPI).The PPI conducted in four phases emphasizes the development and strengthening of personality.In the third phase of this program, prisoners are given vocational skills and knowledge in preparation for a career after release.The vocational knowledge and skills imparted are accredited and certified with similar programs conducted outside the prison walls.However, somewhat sadly, after these prisoners were released, they were caught up in the phenomenon of homelessness.Questions about the causes of their involvement can be found in this study.Not only that, but this study also seeks to recommend appropriate actions to prevent the occurrence of similar issues in the future.This study would like to suggest that more in-depth studies related to the formulation of more structured and comprehensive prerelease programs can be made, so that released prisoners are better prepared to face life after release from prison.
Summarization of Text Based on Deep Neural Network

Abstract: To automatically summarise a piece of content, the length of the original text must be reduced while keeping the crucial informative characteristics and meaning of the content. As a result, the automation of manual text summarization, which is a time-consuming and labor-intensive technique, is gaining popularity and is therefore a great stimulant for academic research. In today's world of information overload, abstracting and summarising large texts is crucial. Various techniques to text summarization have emerged throughout time. Traditional ways create a summary directly as a result of the document summary connection's duplication and omission. Deep learning systems have been shown to be effective in producing summaries. We focus on deep learning-based text summarization algorithms that have been developed throughout time. Keywords: Summarize text, Deep Learning Techniques, Effective, Automatization
Fine-Grained Semantic Image Synthesis with Object-Attention Generative Adversarial Network

Semantic image synthesis is a new rising and challenging vision problem accompanied by the recent promising advances in generative adversarial networks. The existing semantic image synthesis methods only consider the global information provided by the semantic segmentation mask, such as class label, global layout, and location, so the generative models cannot capture the rich local fine-grained information of the images (e.g., object structure, contour, and texture). To address this issue, we adopt a multi-scale feature fusion algorithm to refine the generated images by learning the fine-grained information of the local objects. We propose OA-GAN, a novel object-attention generative adversarial network that allows attention-driven, multi-fusion refinement for fine-grained semantic image synthesis. Specifically, the proposed model first generates multi-scale global image features and local object features, respectively, then the local object features are fused into the global image features to improve the correlation between the local and the global. In the process of feature fusion, the global image features and the local object features are fused through the channel-spatial-wise fusion block to learn ‘what’ and ‘where’ to attend in the channel and spatial axes, respectively. The fused features are used to construct correlation filters to obtain feature response maps to determine the locations, contours, and textures of the objects. Extensive quantitative and qualitative experiments on COCO-Stuff, ADE20K and Cityscapes datasets demonstrate that our OA-GAN significantly outperforms the state-of-the-art methods.
Assessing the quality of TTS audio in the LARA learning-by-reading platform

A popular idea in Computer Assisted Language Learning (CALL) is to use multimodal annotated texts, with annotations typically including embedded audio and translations, to support L2 learning through reading. An important question is how to create the audio, which can be done either through human recording or by a Text-To-Speech (TTS) synthesis engine. We may reasonably expect TTS to be quicker and easier, but humans to be of higher quality. Here, we report a study using the open-source LARA platform and ten languages. Samples of LARA audio totaling about three and a half minutes were provided for each language in both human and TTS form; subjects used a web form to compare different versions of the same item and rate the voices as a whole. Although human voice was more often preferred, TTS achieved higher ratings in some languages and was close in others.
Extending Referring Expression Generation through shared knowledge about past Human-Robot collaborative activity

Being able to refer to an object, a person, or a place in a non-ambiguous manner is a need when one has to achieve collaborative activities with a partner. This is the so-called Referring Expression Generation (REG) problem. While widely used for Human-Robot Interaction, state of the art approaches restrict its use to the current environment. We propose a novel extension to the REG which takes full advantage of the Human-Robot shared knowledge about past actions as additional information to generate Referring Expressions. We show that our approach is usable with a domain-independent ontology as a knowledge base and that it can also use a semantic representation of past activity to generate RE. We illustrate our method through simulated situations and discuss its efficiency and pertinence.
Tensions Ahead: How Do We Build Feasible Digital Health Systems That Are Person-Centred and Usable?

Globally, the term person-centred care has become all-pervasive across healthcare delivery. It has been suggested that it has become synonymous with good quality care. The term is used frequently in the aims and objectives for healthcare service and provision, although what lies behind the rhetoric in terms of practice may be questionable. Further, in an environment which is becoming increasingly dependent on technology, the interaction needs the user experience of both consumer and healthcare provider. There is a challenge as national surveys of health professionals continue to criticize the usability of digital health interfaces. This panel discussion aims to highlight the factors, which should be considered when designing health information interfaces as person-centred for all users. Drawing on our panel's expertise, we suggest interfaces need to consider two main factors: personalization and user acceptance.
Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

Abstract To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes.
Multi-label Classification for Hate Speech and Abusive Language in Indonesian-Local Languages

Each hate speech has a target, category, and level that needs to be detected to help the authorities prioritize hate speech cases that need to be solved first. Various studies have been conducted in Indonesia on abusive speech and hate speech and their targets, categories, and levels, but only in Indonesian and English. On the other hand, various local languages in Indonesia open up opportunities for hate speech to occur using the local language. This study aims to compare some of the best machine learning algorithms, transformation methods, and feature extraction techniques in classifying abusive language and hate speech and their targets, categories, and levels using Twitter data in Indonesian and local languages. This study uses five local languages in Indonesia with the most speakers: Javanese, Sundanese, Madurese, Minangkabau, and Musi (Palembang). The algorithms used are Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), and Random Forest Decision Tree (RFDT) with Binary Relevance (BR), Classifier Chains (CC), and Label Powerset (LP) as transformation methods. The term weighting used in this study is TF-IDF with word n-gram and char n-gram features. The results showed that the SVM algorithm with the CC transformation method and unigram feature extraction gave the highest F1-score results, 66.33% for Javanese and 65.68% for Sundanese. In Madurese, Minangkabau, and Musi language data, the best F1-score was obtained using the RFDT algorithm with the CC transformation method and unigram feature extraction with F1-score 76.37%, 80.75%, and 77.34%.
A Data-Driven Method for Online Fault Diagnosis in Single-Phase PWM Rectifier

In power electronical traction transformer, the failure of the single-phase PWM rectifier will lead to irreparable degradation of the system. Thus, this article proposes a feasible data-driven method to diagnose both the current sensor faults and insulated gate bipolar transistors (IGBTs) open-circuit faults of single-phase PWM rectifier online. The principle of the method is to construct a signal predictor by combining nonlinear autoregressive exogenous (NARX) model and an advanced learning algorithm, Extreme Learning Machine (ELM). Then the faults are detected based on the residual between the signal output of the predictor and the sensor. Furthermore, the faults are identified by logical judgment based on the system fault performance. Several hardware-in-loop tests are implemented to verify the applicability and effectiveness of the proposed diagnosis method. Test results show that this method has a very fast speed to detect the faults within 1 ms and a high accuracy to classify different faults.
Retraction notice regarding several articles published in the Journal of Intelligent &amp; Fuzzy Systems

The Publisher and Editor-in-Chief of the Journal of Intelligent &amp; Fuzzy Systems retract a total of 49 articles from the journal’s online catalogue. The articles were published in different issues of the journal in the period July 2019 –April 2021. After publication it was found that these articles cite literature sources that have no relation to the subject matter of the citing article. This could be the result of a deliberate attempt to engineer the citation performance of the scientific literature. All authors were asked to provide insight into the reasoning for citing unrelated articles but were either unresponsive or unable to provide a reasonable explanation for having done so. It was decided to remove these articles from the published literature completely. This retraction is carried out in accordance with the recommendations of the Committee on Publication Ethics (COPE).
Multi-Scale Relation Network for Person Re-identification

Person re-identification (reID) has received extensive study and achieved great progress in recent years. Extensive research has proved that combining global and local features is an effective solution to improve the performance of person reidentification tasks. While many existing reID approaches are still suffering from occlusions, body part missing, different lighting, and background clutter, where the learned features may achieve a sub-optimal solution. In this paper, we propose an efficient network structure, Multi-Scale Relation Network (MSRN), which can not only extract robust regional features and global features but also integrate the asymptotic cues and relations between them. In addition, we introduce a dynamic loss weight as supplementary components to improve learning efficiency and the representation capacity of our model. Extensive experiments are conducted on three widely used datasets, including Market-I501, DukeMTMC-ReID and CUHK03-NP. The experimental results indicate that our proposed method achieves the state-of-the-art results on three datasets.
Image Segmentation Using Genetic Algorithms

Image segmentation is a crucial step in the image processing and analysis process. Image segmentation is the process of splitting one image into many segments. Image segmentation divides images into segments that are more representative and easier to examine. Individual surfaces or items can be used as such pieces. The process of image segmentation is used to locate objects and their boundaries.Genetic algorithms are stochastic search methods, the work of which is taken from the genetic laws, natural selection, and evolution of organisms. Their main attractive feature is the ability to solve complex problems of combinatorial search effectively, because the parallel study of solutions, largely eliminates the possibility of staying on the local optimal solution rather than finding a global one.The point of using genetic algorithms is that each pixel is grouped with other pixels using a distance function based on both local and global already calculated segments. Almost every image segmentation algorithm contains parameters that are used to control the segmentation results; the genetic system can dynamically change parameters to achieve the best performance.Similarly to image sequencing, to optimize several parameters in the process, multi-targeted genetic algorithms were used, which enabled finding a diverse collection of solutions with more variables. Multi- targeted Genetic Algorithm (MTGA) is a guided random search method that consists of optimization techniques. It can solve multi-targeted optimization problems and explore different parts of the solution space. As a result, a diversified collection of solutions can be found, with more variables that can be optimized at the same time. In this article several MTGA were used and compared.Genetic algorithms are a good tool for image processing in the absence of a high-quality labeled data set, which is either a result of the long work of many researchers or the contribution of large sums of money to obtain an array of data from external sources.In this article, we will use genetic algorithms to solve the problem of image segmentation.
ARCH-COMP21 Category Report: Hybrid Systems Theorem Proving

This paper reports on the Hybrid Systems Theorem Proving (HSTP) category in the ARCH-COMP Friendly Competition 2021. The characteristic features of the HSTP cate- gory remain as in the previous editions [MST+18, MST+19, MMJ+20], it focuses on flexi- bility of programming languages as structuring principles for hybrid systems, unambiguity and precision of program semantics, and mathematical rigor of logical reasoning principles. The benchmark set includes nonlinear and parametric continuous and hybrid systems and hybrid games, each in three modes: fully automatic verification, semi-automatic verifica- tion from proof hints, proof checking from scripted tactics. This instance of the competition introduces extensions to the scripting language, a comparison of the influence of arithmetic backend versions on verification performance in KeYmaera X, as well as improvements in the HHL Prover.