Reviewerly/exHarmony huggingface dataset:
A Study on The Teaching Media used by The English Teacher at SMP Muhammadiyah 2 Malang

Teaching media are commonly used by English teacher in teaching English in schools of Indonesia. The teacher uses media to encourage students’ motivation because it is one of important parts of success in learning process. Moreover, use of teaching media helps the teacher to connect learners with events that are culturally relevant in order to achieve the teaching and learning goal. SMP Muhammadiyah 2 Malang is a Junior High School with two inclusive classes. In this descriptive qualitative research design, the researcher limited the problem to the media used by English teacher in seventh grade inclusive classes at SMP Muhammadiyah 2 Malang. The subject of this study was one of the English teachers at SMP Muhammadiyah 2 Malang. Therefore, the purposes of this research were to know the media used by the English teacher, and to know the strengths and weaknesses the media used by the teacher in teaching English at SMP Muhammadiyah 2 Malang. In collecting the data, the researcher used three kinds of instruments. They were observation, interview and documentation. Based on the data, the researcher found that the teacher utilized mobile phone, laptop, internet, digital projector, Microsoft Power Point, book, poster, real object, speaker active, video and Youtube as the teaching media used in teaching English. Additionally, the researcher also found that there were several strengths of using teaching media in teaching English. Those strengths were the media could make students active in in learning process and the students were focus to understand the material. However, the researcher also found that the weakness of using teaching media in teaching English. The weakness was it was difficult for the teacher to get students’ attention.Keywords: Teaching Media, SMP Muhammadiyah 2 Malang.
Helicopter visual signaling simulation: Integrating VR and ML into a low-cost solution to optimize Brazilian Navy training

Landing Signalman (LS) is the military in charge of visually signaling the helicopter pilot when landing and taking off from a moving ship, ensuring general safety conditions of the flight deck area. It requires self-confidence, knowledge, skills, team coordination, and appropriate reaction capacity only achieved with intensive training. After studying the theory, Brazilian Navy LS trainees go directly to the practice stage at sea. This hands-on training phase straight after classroom reveals a series of limitations on LS trainee's performance. To better prepare the LS trainee, reduce training costs and provide a safe environment to perform unlimited training opportunities, we developed a lightweight, portable, and low-cost VR simulator for training optimization, denominated Helicopter Visual Signal Simulator (HVSS). Due to its specificities, a Machine Learning gesture recognition method was integrated into the virtual environment, and two datasets were created. This study details the process of achieving the design goals, developing the VR simulator prototype, and analyzing the tests we performed With 15 experienced instructors. The results indicate that HVSS meets all requirements to provide a reliable training solution.
Personalized Predictive Models for Identifying Clinical Deterioration Using LSTM in Emergency Departments

Early detection of deterioration at hospitals could be beneficial in terms of reducing mortality and morbidity rates and costs. In this paper, we present a model based on Long Short-Term Memory (LSTM) neural network used in deep learning to predict the illness severity of patients in advance. Hence, by predicting health severity, this model can be used to identify deteriorating patients. Our proposed model utilizes continuous monitored vital signs, including heart rate, respiratory rate, oxygen saturation, and blood pressure automatically collected from patients during hospitalization. In this study, a short-time prediction using a sliding window approach is applied. The performance of the proposed model was compared with the Multi-Layer Perceptron (MLP) neural network, a feedforward class of neural network, based on R2 score and Root Mean Square Error (RMSE) metrics. The results showed that the LSTM has a better performance and could predict the illness severity of patients more accurately.
DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets

One of the significant challenges in machine learning is the classification of imbalanced data. In many situations, standard classifiers cannot learn how to distinguish minority class examples from the others. Since many real problems are unbalanced, this problem has become very relevant and deeply studied today. This paper presents a new preprocessing method based on Delaunay tessellation and the preprocessing algorithm SMOTE (Synthetic Minority Over-sampling Technique), which we call DTO-SMOTE (Delaunay Tessellation Oversampling SMOTE). DTO-SMOTE constructs a mesh of simplices (in this paper, we use tetrahedrons) for creating synthetic examples. We compare results with five preprocessing algorithms (GEOMETRIC-SMOTE, SVM-SMOTE, SMOTE-BORDERLINE-1, SMOTE-BORDERLINE-2, and SMOTE), eight classification algorithms, and 61 binary-class data sets. For some classifiers, DTO-SMOTE has higher performance than others in terms of Area Under the ROC curve (AUC), Geometric Mean (GEO), and Generalized Index of Balanced Accuracy (IBA).
An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods

This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images.Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder.Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory.Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost.So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome.In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques.First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others.Second, we randomly selected samples and features to establish a basic learning model.Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task.Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%.The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process.
Analysis of Influencing Factors of Academic Entrepreneurship Based on Blockchain

Under the background of mass entrepreneurship, academic entrepreneurship activities in universities are booming. DEA model is used to analyze the input-output data of academic entrepreneurship in colleges and universities in 2012 and 2016. According to the validity of the input-output data of academic entrepreneurship in different regions, the differences in efficiency between regions are compared and the reasons are analyzed. The research shows that the economic service function of colleges and universities to regional economic and social development is becoming increasingly prominent, resulting in a certain scale effect; the overall development of academic entrepreneurship efficiency, pure technical efficiency, and scale of colleges and universities in various regions is good, showing an upward trend; academic entrepreneurship activities of colleges and universities in different regions show different development trends. The scale efficiency of each region is at a higher level, but we should also actively pay attention to the utilization efficiency of academic entrepreneurship resources in colleges and universities, improve the allocation of resources, and prevent excessive investment in human and financial resources to produce redundancy, so as to achieve scale expansion while improving efficiency.
Level of administrative empowerment at private institution and its impact on institutional performance: a case study

The study aims to scrutinize the level of administrative empowerment involving its dimensions (delegating powers/devolution of authorities, independence, learning, and training) on institutional performance (commitment to quality standards, achieving the goals, and staff performance) of Al-Ahliyya Amman University (AAU).The researchers applied the descriptive-analytical method by developing a questionnaire that was distributed among a simple random sample consisting of (200) members, and the data were analyzed, and the assumptions were tested using the statistical program (SPSS v20).The findings of the study revealed that there was a statistically significant impact of the level of administrative empowerment including its combined dimensions on the institutional performance at AAU.Furthermore, there was a statistically significant impact of the level of administrative empowerment involving its combined dimensions on the commitment to quality standards at AAU.The researchers recommend adopting a strategy that achieves the implementation of modern management concepts at AAU such as career empowerment, Total Quality Management (TQM), and teamwork, as well as focusing on the qualification of AAU staff.Such training will help workers practice career empowerment through the implementation of specific action plans for developmental purposes, the development of annual training plans, the use of effective means such as lectures, and motivating employees to participate in training courses and activities.
A Neural Network Approach for Online Nonlinear Neyman-Pearson Classification

We propose a novel Neyman-Pearson (NP) classifier that is both online and nonlinear as the first time in the literature. The proposed classifier operates on a binary labeled data stream in an online manner, and maximizes the detection power about a user-specified and controllable false positive rate. Our NP classifier is a single hidden layer feedforward neural network (SLFN), which is initialized with random Fourier features (RFFs) to construct the kernel space of the radial basis function at its hidden layer with sinusoidal activation. Not only does this use of RFFs provide an excellent initialization with great nonlinear modeling capability, but it also exponentially reduces the parameter complexity and compactifies the network to mitigate overfitting while improving the processing efficiency substantially. We sequentially learn the SLFN with stochastic gradient descent updates based on a Lagrangian NP objective. As a result, we obtain an expedited online adaptation and powerful nonlinear Neyman-Pearson modeling. Our algorithm is appropriate for large scale data applications and provides a decent false positive rate controllability with real time processing since it only has $O(N)$ computational and $O(1)$ space complexity ($N$ : number of data instances). In our extensive set of experiments on several real datasets, our algorithm is highly superior over the competing state-of-the-art techniques, either by outperforming in terms of the NP classification objective with a comparable computational as well as space complexity or by achieving a comparable performance with significantly lower complexity.
Selection of The Most Proper Unmanned Aerial Vehicle for Transportation in Emergency Operations by Using Analytic Hierarchy Process

Today rapid development on Unmanned Aerial Vehicle (UAV) technologies continues to evolve and expand its use as a tool that can be used in many fields. The most communal areas for the use of UAV are mapping, assessing, and supporting damage assessments, and search and rescue activities, respectively. However, the application of transporting light and important cargoes that have become widespread recently accessing to the difficult areas is also becoming a new sector in the usage areas of UAV. Lightweight, user-friendly designs of UAVs and flight systems managed by automated workflows giving a convenience for this field application. In this study, two different flight platforms (3 fixed-wing and 3 multi-rotor) of flight parameters evaluated by selecting the sales catalogue. Brand knowledge of the UAVs has not given in consideration of the belief that it may adversely affect commercial competition. The sample modelled as a multi-criteria decision-making problem consisting of quantitative and qualitative criteria, and the analytic hierarchy process (AHS), which clearly reveals the decision-makers' views for such problems. Priorities for the criteria of each alternative for the payload, UAV weight, maximum altitude, maximum speed, flight time and controller range criteria with numerical performance values in order to maintain the consistency problem in the AHS method were figured out by direct assignment. The impact of each criterion in the choice and coherence analysis to determine the effect of each alternative criteria were performed using Expert Choice v2000 software. Because of the analysis made, it is seen that multi-rotor UAVs are the predominant choice for fixed-winged UAVs, among which the UAV_4 flight platform is the most appropriate vehicle for emergency transport with an overall weight of 0.2530. When UAV_4 is compared with other alternative flight platforms, it is seen to be in the foreground according to the criteria of maximum altitude, maximum ground speed, landing field and ease of use. As seen in the analysis of 2-dimensional alternatives at the end of the example, analysis of UAV to be used in emergency transport according to landing field and ease of use criteria revealed that multi-rotor UAVs (UAV_4, UAV_5, UAV_6) have more dominant results than fixed-wing UAVs (UAV_1, UAV_2, UAV_3). Although most of the UAV models being developed are still in prototypes, with the rapid development in the field of technology and the industrial knowledge in these applications, further progress can be expected in the future projects.
The Effect of Applying Blockchain to The Accounting and Auditing

Blockchain provides a extreme brand-brand new method towards document, procedure, as well as keep monetary info and deals. It has actually the prospective towards essentially alter the yard of the bookkeeping occupation as well as improve business community. Within this particular short post, our team talk about the ramifications of Blockchain in the business of bookkeeping as well as bookkeeping. Using a blockchain can help provide clarity on asset ownership and the existence of liabilities, and can dramatically increase efficiency for an accountant. Blockchain offers the potential for accounting and auditing. Blockchain helps auditors to track an account. Blockchain technology for auditors has conducted audit traces that they follow. For an accountant, Blockchain helps reconcile, trace the ownership of assets and authenticate transactions through features found in the technology. Blockchain can be used as a source of verification for data reported by accountants to users of the information generated by accounting
Zero-shot Singing Voice Conversion

In this paper, we propose the use of speaker embedding networks to perform zero-shot singing voice conversion, and suggest two architectures for its realization. The use of speaker embedding networks not only enables the capability to adapt to new voices on-the-fly, but also allows for model training on unlabeled data. This not only facilitates the collection of suitable singing voice data, but also allows networks to be pretrained on large speech corpora before being refined on singing voice datasets, improving network generalization. We illustrate the effectiveness of the proposed zero-shot singing voice conversion algorithms by both qualitative and quantitative means.
Value Partitioning: A Lightweight Approach to Relational Static Analysis for JavaScript

In static analysis of modern JavaScript libraries, relational analysis at key locations is critical to provide sound and useful results. Prior work addresses this challenge by the use of various forms of trace partitioning and syntactic patterns, which is fragile and does not scale well, or by incorporating complex backwards analysis. In this paper, we propose a new lightweight variant of trace partitioning named value partitioning that refines individual abstract values instead of entire abstract states. We describe how this approach can effectively capture important relational properties involving dynamic property accesses, functions with free variables, and predicate functions. Furthermore, we extend an existing JavaScript analyzer with value partitioning and demonstrate experimentally that it is a simple, precise, and efficient alternative to the existing approaches for analyzing widely used JavaScript libraries.
AI-enhanced constellation design for NOMA system: A model driven method

Grant-free Non-orthogonal Multiple Access (GF-NOMA) is a promising technology for massive access users and sporadic small-packet transmission for Beyond the 5th Generation mobile communication system(B5G) / the 6th Generation mobile communication system (6G). One of the key aspects in GF-NOMA system is the signature/constellation design. However, due to the channel variation and random activation of users, conventional optimization approaches seem unsuitable for such complex models. In this paper, as an initial attempt, we propose a human intelligence (HI)-guided artificial intelligence (AI)-enhanced signature/constellation design method. By separate design of modulation and power allocation inspired by prior knowledge, the proposed deep neuron network (DNN) for NOMA signature/constellation design not only has smaller size of DNN and less training data, but also has stronger interpret-ability. In the last section, via simulations we demonstrate that in terms of bit error rate, the proposed scheme can achieve significant performance gain over the conventional NOMA schemes.
Sequential Pattern Mining for e-Commerce Recommender System

Recommender system is one of the strategies carried out by e-commerce to increase their users' satisfaction. In this paper, we implement sequence pattern mining for recommender system in e-commerce domain. We perform the PrefixSpan algorithm to mine the frequent patterns. The frequent patterns generate the suitable rules for the recommender system. The experiments reported in this paper utilize the datasets from two different marketplaces in Indonesia. The best performance of recommendation model is obtained when applying the rules derived from the product category with minimum confidence of 0.5.
Understanding the Effect of Distance Learning vs. Face-to-Face Learning Experiences on Students' Engagement in Higher Education

The traditional face-to-face lecture method of teaching approach wherein students absorb information from the instructor that trickles down from top to bottom to them has been abruptly unused in all school systems due to the escalation of the COVID-19 pandemic globally. The current implementation of hybrid classes and distance learning by most institutions of higher learning nationwide is critical but effective out of necessity, assuring safety measures in every school site domestically and globally. According to Rajabalee, Santaly, and Rennie (2019), based on literature, one of the critical components of quality online education is to ensure learner engagement. that is, the need to effectively measure learner engagement is imperative to determine the performance and the successful achievements of learners. Also, Rajabalee et al.'s (2019) research study aimed to understand the relationship between students' engagement in an online module with their overall performances by analyzing students' learning activities in an online module. In this perspective, this inquiry seeks to highlight the effect of distance learning experiences on students' engagement via determining their performances and successful achievements after undergoing distance learning in comparison to face-to-face learning. In doing so, this paper: defines and explains face-to-face learning, distance learning , and student engagement, i,e, students' performances and successful achievements via reviews of extensive relevant literature on the topic, and finally, concludes this writing.
Better Understanding: Stylized Image Captioning with Style Attention and Adversarial Training

Compared with traditional image captioning technology, stylized image captioning has broader application scenarios, such as a better understanding of images. However, stylized image captioning faces many challenges, the most important of which is how to make the model take into account both the image meta information and the style factor of the generated captions. In this paper, we propose a novel end-to-end stylized image captioning framework (ST-BR). Specifically, we first use a style transformer to model the factual information of images, and the style attention module learns style factor form a multi-style corpus, it is a symmetric structure on the whole. At the same time, we use back-reinforcement to evaluate the degree of consistency between the generated stylized captions with the image knowledge and specified style, respectively. These two parts further enhance the learning ability of the model through adversarial learning. Our experiment has achieved effective performance on the benchmark dataset.
Critical analysis of classical scenario-based decision rules for pure strategy searching

Purpose:The contribution briefly presents the essence and applications of the well-known classical decision rules designed for decision making under uncertainty with unknown probabilities and based on scenario planning.We concentrate on games against nature, pure strategy searching and one-criterion optimization.Design/methodology/approach: The main goal of this work is to analyse numerous case studies and formulate conclusions concerning the properties (in particular drawbacks and limitations) of the aforementioned strategic procedures.Findings: The paper focuses on the limited usefulness of classical decision rules in real economic decision problems.It is advised to apply them very carefully.Research limitations/implications: A similar analysis for mixed strategy searching and multicriteria optimization should be performed within a future research.Originality/value: The work offers numerous practical guidelines modifying existing procedures and allowing decision makers to obtain logic recommendations.