Reviewerly/exHarmony huggingface dataset:
A new AI assisted medical molecular image diagnostic model

In recent times, ML algorithms that plays a significant role right from drug discovery to clinical decision making. The recent advances in DL technologies contribute towards improved performance for carrying out computer aided medical image analysis and disease diagnosis. The key benefit of AI in processing of medical big data offers spectacular insights into the hierarchal relationships that exist among data which can be algorithmically explored thus replacing the tedious manual processes to extract and localize specific areas of interests in medical images thus considerably changing the way medicine has been practiced so far. In bio medical related clinical applications, there is a constant demand pertaining the research and development with respect to deploying AI as a mainstream tool to perform several medical imaging activities like analysis, diagnosis, segmentation as well as classification. The increased usage of electronic health records and medical images being its integral component the need for appropriate and efficient AI assisted medical image analysis system that takes care of accurate and automated decision making could be of great help to radiologists and medical practitioners. Molecular image analysis is a dynamic field that makes use of ML and DL algorithms that utilizes labeled and structured information which also proves to be helpful to the patients as they serve as an initial interface before further diagnosis and treatments. Thus our research aims to offer a novel and efficient AI based medical analysis system that can assist clinical practitioners to focus on enhancing the disease diagnosis through DL based medical image analysis and decision making. In addition, we also address specific challenges related to disease diagnosis and propose novel GAN model for improved diagnosis and implementation. Our proposed technique can also be generalized to generate synthetic data for further issues related to molecular image analysis in the field of medicine and help towards building a better disease diagnosis model.
Multi-scale attention and boundary enhancement with long-range dependency for salient object detection

Existing saliency detection methods have achieved great progress in extracting multi-level features, however it is a challenging problem to catch accurate long-range dependencies that can enhance the accuracy of semantic information. To address this, a Transformer-based multi-scale attention and boundary enhancement with long-range dependency (MSBE) network is proposed in this paper. A multi-scale attention enhancement module (MSAEM) is designed to reduce the redundant or noisy features and generate a high-quality feature representation by integrating multiple attentional features with diverse perspectives. The high-quality features are then fed into the triple Transformer encoder embedding module (TEM) to enhance high-level semantic features by learning long-range dependencies across layers. In the decoder part, a cross-layer feature fusion module (CLFFM) and boundary enhancement module (BEM) are designed to improve the effect of feature fusion and get accurate prediction results. Extensive experiments on six challenging public datasets demonstrate that the proposed method achieves competitive performance.
An Ensemble Learning Method Based on One-Class and Binary Classification for Credit Scoring

It is crucial to correctly assess whether a potential borrower can repay the loan in the credit scoring model. The credit loan data has a serious data imbalance because the number of defaulters is far less than the nondefaulters. However, most current methods for dealing with data imbalance are designed to improve the classification performance of minority data, which will reduce the performance of majority data. For a financial institution, the economic loss caused by the decrease in the classification performance of nondefaulters (majority data) cannot be ignored. This paper proposes an ensemble learning method based on one-class and binary classification (EMOBC) for credit scoring. The purpose is to improve the classification accuracy of the minority class while mitigating the loss of classification accuracy of the majority class as much as possible. EMOBC uses undersampling for the majority class (nondefault samples in credit scoring) and perform binary-class learning on the balanced data to improve the classification accuracy of the minority. To alleviate the decline in classification performance of the majority class, EMOBC uses one-class and binary collaborative classification to train classifiers. The classification result is determined by the average of one-class and binary-class classifiers. The experimental results show that EMOBC has good comprehensive performance compared with the existing methods.
Complex Queries for Querying Linked Data

Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax of query languages, such as SPARQL. However, because users are accustomed to natural language-based search, novice users may find it challenging to use these features. As a result, new approaches for querying Linked Data sources on the web with NL queries must be defined. In this paper, we propose a novel system for converting natural language queries into SPARQL queries to query linked and heterogeneous semantic data on the web. While most existing methods have focused on simple queries and have ignored complex queries, the method described in this work aims to handle various types of NL queries, particularly complex queries containing negation, numbers, superlatives, and comparative adjectives. Three complementary strategies are used in this context: (1) identifying the semantic relations between query terms in order to understand the user’s needs; (2) mapping the NL terms to semantic entities; and (3) constructing the query’s valid triples based on the different links used to describe the identified entities in order to generate correct SPARQL queries. The empirical evaluations show that the proposed system is effective.
Adolescents' perceptions and user experiences with a virtual reality-based alcohol prevention tool in Germany: A focus group study

Background Excessive alcohol consumption is a major public health problem, with substance use early in life contributing to higher levels of use later in life. Virtual reality (VR) is an innovative technology for alcohol prevention among adolescents that could solve the problem of insufficient outreach to the target group of young people. The co-created German Virtual LimitLab simulation is one of the few examples of VR-based alcohol prevention tools and consists of a virtual house party simulation. The aims of Virtual LimitLab are to increase the users' awareness of how social pressure can influence their own decision-making as well as to enable various actions and communication strategies in order to train competencies when dealing with alcohol. The present study thus aims to explore adolescents' content- and technique-specific perceptions of Virtual LimitLab in order to gain insights into user experiences and to test the prototype with the German target group. Methods Four semi-structured focus groups with adolescents aged 15–18 years ( n = 13) were conducted and analyzed using thematic analyses. A user experience questionnaire (UEQ–S) was applied in order to quantitatively assess adolescents' satisfaction with Virtual LimitLab . Results Three main themes were identified ( VR experience, content , and technical aspects ). Participants positively assessed both the content and the technical aspects of Virtual LimitLab . This trend was also seen by the UEQ–S data, which yielded positive ratings for both pragmatic and hedonic quality. The broad variety of options in the simulation that allow the user to try new behaviors was perceived particularly positively. In general, Virtual LimitLab was regarded as an innovative tool that encourages adolescents to think critically about their personal alcohol consumption. Technical errors in the simulation and users' difficulties in identifying with the simulation were the main points of criticism. Conclusions Feedback from adolescent users revealed positive and therefore promising results when using Virtual LimitLab as a gaming alcohol-prevention tool. Some technical aspects still need to be improved in order to further refine the prototype, and suggestions for expanding the content of the application have already been made.
The named entity recognition of vessel power equipment fault using the multi-details embedding model

The fault diagnosis of vessel power equipment is established by the manual work with low efficiency. The knowledge graph(KG) usually is applied to extract the experience and operation logic of controllers into knowledge, which can enrich the means of fault judgment and recovery decision. As an important part of KG building, the performance of named entity recognition (NER) is critical to the following tasks. Due to the challenges of information insufficiency and polysemous words in the entities of vessel power equipment fault, this study adopts the fusion model of Bidirectional Encoder Representations from Transformers (BERT), revised Convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and conditional random field (CRF). Firstly, the adjusted BERT and revised CNN are respectively adopted to acquire the multiple embeddings including semantic information and contextual glyph features. Secondly, the local context features are effectively extracted by adopting the channel-wised fusion structures. Finally, BiLSTM and CRF are respectively adopted to obtain the semantic information of the long sequences and the prediction sequence labels. The experimental results show that the performance of NER by the proposed model outperforms other mainstream models. Furthermore, this work provides the foundation of the tasks of intelligent diagnosis and NER in other fields.
SAC-GAN: Face Image Inpainting with Spatial-Aware Attribute Controllable GAN

The objective of image inpainting is refilling the masked area with semantically appropriate pixels and producing visually realistic images as an output. After the introduction of generative adversarial networks (GAN), many inpainting approaches are showing promising development. Several attempts have been recently made to control reconstructed output with the desired attribute on face images using exemplar images and style vectors. Nevertheless, conventional style vector has the limitation that to project style attribute representation onto linear vector without preserving dimensional information. We introduce spatial-aware attribute controllable GAN (SAC-GAN) for face image inpainting, which is effective for reconstructing masked images with desired controllable facial attributes with advantage of utilizing style tensors as spatial forms. Various experiments to control over facial characteristics demonstrate the superiority of our method compared with previous image inpainting methods.
The Prediction of COVID-19 Pandemic Situation in Indonesia Using SVR and SIR Algorithm

The COVID-19 epidemic, which initially surfaced at the end of 2019, has since expanded to every corner of the globe and has profoundly impacted all facets of human existence. This case started to emerge in Indonesia at the end of February 2020, and until this point, there has been a spike in new patients. Researchers have run several models and projections for COVID-19 cases in Indonesia, but the results are not yet entirely reliable. Predictions produced at the national level must take into account these variations in patterns because this is likely related to the distinct patterns in each region. In this study, the prediction process will be conducted for cases of COVID-19 by using the SVR algorithm and mathematical models to predict reproduction numbers. SVR analysis to overcome the problem of nonlinearity of data in model formation. The modeling is done based on the SIR model, whose parameters are estimated based on the data. Testing result by using 3 kernels is different on each test, prediction of data cases and the level of mistake room are by using Kernel ’ RBF ‘ with a value of C = 1E3, and gamma = 0.1 with the value of MAPE and MSE respectively are 4.5% and 4.2.
Experimental examination of the possibilities of geophysical methods in wooded and rugged terrain: case study of the defunct medieval glasswork Vysoká Jedle in the Ore Mountains

Rugged and wooded terrains of mountains limit possibilities of archaeological prospection. Various production or exploitation areas are typical for Czech mountain regions. Magnetometer survey of medieval glass works has been standard geophysical method for decades. The other geophysical methods could be also successful in glass furnaces identification or study. Magnetic susceptibility survey with a Multi Kappa instrument observes the state of production feature.
MENTORING CHILDREN TO LEARN AT HOME IN DISTANCE LEARNING DURNG THE COVID-19 PANDEMIC IN TASIKMALAYA CITY

Learning activities during the pandemic are carried out online at home, of course it is the responsibility of the family, especially parents, to accompany the child during home study. Parents should be prepared to accompany and guide their children learning from home. People play a role in accompanying and guiding children during online learning, parents must also understand the applications used during online learning. This research is qualitative phenomenology research, it aims to analyze children's learning assistance during distance learning during the Covid-19 pandemic in Tasikmalaya City. The data collection techniques used in this study are interviews, observations and documentation. The subjects of this study were parents of children who did online learning. The results showed that during the pandemic almost the entire primary school (SD) in Tasikmalaya city learning was conducted online. Learning carried out online has several problems, namely the limitations of teachers in delivering materials that make the child do not understand the material in its entirety, parents should increase the cost of spending on internet costs during online learning. Learning at home accompanied by parents makes the child closer to the parents, the child feels safe and comfortable being close to the parents so that the child becomes more confident.
Pengaruh Penggunaan Media TV Sekolah dan Metode Belajar Contextual Teaching And Learning (CTL) Terhadap Kemampuan Kognitif Anak

This study aims to reveal the effect of using school television media and Contextual Teaching and Learning (Ctl) learning methods on children's cognitive abilities. This research is a quantitative research. The sampling technique used was Cluster Random Sampling. The research sample amounted to 22 students in group B. The test results of the research instrument showed that the reliability of the cognitive ability instrument was 0.921. The data analysis technique used is simple regression analysis. The conclusions of this study are 1) the cognitive ability of students who are given school TV has a positive effect on the cognitive abilities of students who are given the CTL method; 2) The cognitive ability of students given the school TV affects the cognitive abilities of students given the CTL method; 3) the coefficient of determination R Square = 0.150, which means that 15.0% of the variation of the variable cognitive ability of students given school TV can be influenced by the variable of cognitive ability of students given the CTL method.Keywords: Cognitive Ability, School TV, Contextual Teaching Learning
Enhancing Entrepreneurship Intentions through Communication Activities

At the Language Centre NDUM, the students have initiated an entrepreneurial business company which offers English language related services to the public and the name of the company is The English Language House or TELaH. An example of these services is the online public speaking course which has been offered for more than a year now. This study analyses communication apprehension (CA) in students who run TELaH and investigate if CA influences their entrepreneurship intentions (EI) in becoming entrepreneurs once they have graduated. The study also sees whether demographic factors such as age, gender, education level and grades, work and business experience, and family background affect EI amongst university students, and whether there is any relationship between CA and EI. This is a quantitative study where an adapted questionnaire which tests samples’ CA and EI is used in data collection. It also uses convenient sampling and data are analysed using Pearson Correlation using SPSS version 23.0. Samples are 65 Language Centre’s undergraduate students who are involved with TELaH’s activities such as trainings and courses which involved group discussion, meeting skills, interpersonal, and public speaking. This study supports that good communication skills can contribute to an increase in entrepreneurial intentions amongst graduates. In a way, it implies that people with good communication skills will be inclined to start up own business and succeed at it. Therefore, if universities in Malaysia want to facilitate in promoting entrepreneurship or producing future entrepreneurs, students’ communication skills should be cultivated and enhanced.
Inferring Strings from Position Heaps in Linear Time

Position heaps are index structures of text strings used for the string matching problem. They are rooted trees whose edges and nodes are labeled and numbered, respectively. This paper is concerned with variants of the inverse problem of position heap construction and gives linear-time algorithms for those problems. The basic problem is to restore a text string from a rooted tree with labeled edges and numbered nodes. In the variant problems, the input trees may miss edge labels or node numbers which we must restore as well.
On the robustness of vision transformers for in-flight monocular depth estimation

Abstract Monocular depth estimation (MDE) has shown impressive performance recently, even in zero-shot or few-shot scenarios. In this paper, we consider the use of MDE on board low-altitude drone flights, which is required in a number of safety-critical and monitoring operations. In particular, we evaluate a state-of-the-art vision transformer (ViT) variant, pre-trained on a massive MDE dataset. We test it both in a zero-shot scenario and after fine-tuning on a dataset of flight records, and compare its performance to that of a classical fully convolutional network. In addition, we evaluate for the first time whether these models are susceptible to adversarial attacks, by optimizing a small adversarial patch that generalizes across scenarios. We investigate several variants of losses for this task, including weighted error losses in which we can customize the design of the patch to selectively decrease the performance of the model on a desired depth range. Overall, our results highlight that (a) ViTs can outperform convolutive models in this context after a proper fine-tuning, and (b) they appear to be more robust to adversarial attacks designed in the form of patches, which is a crucial property for this family of tasks.
Innovative approaches to teaching students in the modern educational information environment in the USA and Great Britain

The trend of development of priorities in education and training of students, secondary and higher schools in the USA and Great Britain in retrospect (XIX – early XXI centuries) is clarified. In particular, classical education is briefly described, which had a reproductive character and considered the student as a passive object of learning, which must be filled with knowledge in accordance with certain programs and methods. Classical education was represented by the following approaches: secular, theological, biographical, introspectionist, logical and methodological. American scientists noted that traditional types of Education (classical education and non-classical education) are based on the causal mechanism of the development of thought, the basis of their thinking is considered either the object of learning (classical education) or the subject of learning (non-classical education). Postnonclassical Education combines the positive aspects of traditional education in a single syzygy process of educational activity, which means constant interchangeability, on the one hand, of those who study and on the other – of those who teach. The implementation of postnonclassical education is characterized by the following modern approaches: competence-based, continuous professional improvement of teachers, situational, cultural, systematic, interdisciplinary, axiological, synergetic.
Research on Mobile Robot Autonomous Following System based on ROS

We constructed an autonomous pedestrian target following system based on the ROS (Robot Operating System) platform, including building a mobile robot simulation environment, deep image processing, and differential motion control. And we proposed a deep learning-based re-detection tracking method for pedestrian target tracking, based on loading the YOLOv3-Tiny model and screening the target frame with the highest DIoU value to re-initialize KCF and update the target frame when the KCF algorithm fails to track. We tested the accuracy of the tracking algorithm and the feasibility of implementing it on a mobile robot experimentally. Finally, we designed a pedestrian following an experimental scenario and compared the angular and linear velocity values of the robot at different distances, which demonstrated the stability of the autonomous pedestrian following.
Hybrid Optimization Algorithm Based on Arithmetic and Golden Sine Algorithms for Constrained Engineering Problem

Arithmetic Optimization Algorithm (AOA) is a newly proposed meta-heuristics (MAs) inspired by the major arithmetic operators. Although it provides satisfactory solutions to several real-world problems, when facing complex problems, AOA also easily suck into local optimal solution and slow convergence speed. Golden Sine Algorithm (Gold-SA) is another MAs and has an acceptable local search capability with fewer parameters. Considering these situations, this paper we propose a novel hybrid optimization algorithm (HAGA) that integrated both AOA and Gold-SA to achieve superior optimization ability. In HAGA, the search agents are divided into two consistent subgroups and optimized by AOA and Gold-SA, respectively. By this operator, we can exchange useful information and utilize their advantages to find the optimal or near-optimal regions throughout the iterative process. In addition, Lens Opposition- Based Learning (LOBL) is employed to accelerate the convergence speed. We test the proposed method using thirteen benchmark functions and a constrained engineering problem. The results prove the significant superiority of HAGA to those of four counterpart methods in terms of optimization accuracy and convergence speed.
Glauber P-representations for fermions

Abstract The Glauber-Sudarshan P-representation for bosons is well-known within quantum optics, and is widely applied to problems involving photon statistics. Less familiar, perhaps, is its fermionic counterpart, introduced by Cahill and Glauber. We present a derivation of both the bosonic and fermionic distributions and, in doing so, demonstrate the reason for the existence of two distinct fermionic forms and the relationship between these. We consider both single mode systems and also multiparticle systems with many modes. Expressions for the moments involving products of mode annihilation and creation operators are obtained. For simplicity only one type of boson or fermion will be considered, but generalising to more types is straightforward.