Reviewerly/exHarmony huggingface dataset:
Narrative Inquiry of an English as a Foreign Language (EFL) Teacher’s Experiences in Working with Special Needs Students

This study is intended to unravel how an EFL teacher working in a special needs school teaches her students. The central issues of this study are challenges and strategies employed by the teacher, government concern, readiness, and future expectations of the teacher. Narrative inquiry is carried out as the method of the study. This study involves a participant who has been teaching as an EFL teacher for special needs students for more than seven years. The in-depth narrative interview is conducted to collect the data. To ensure the trustworthiness of the results, the researcher confirms the interpretation of the data with the participant. From the story told by the participant, it is concluded that the biggest challenge faced by the teacher in teaching special needs students is growing their motivation. Thus, the teacher shall be wise in planning the lessons based on each students’ characteristics to enhance their learning motivation. Furthermore, in relation to the government, supports in forms of training and material availability have been given by the Indonesian government. In terms of readiness, most of the English teachers working with special needs students have limited knowledge and experiences at the initial stage of their careers. However, the teachers learn along the way through trials and errors and from a series of training provided to develop their professional skills. In the future, it is expected that the English pre-service teachers can be prepared to teach special needs students as a part of their training and education. Furthermore, the existing teachers are expected to continue learning from different media so that they can keep improving the quality of their teaching.
Analysis of Students’ Problems in Writing Procedure Text at The Tenth Grade Students of SMA Negeri 1 Sepang

Writing is one of the basic English language abilities that should be mastered by English Learners. However, many Indonesian students, especially at the High School level, face difficulties in writing. Evidently, in the context of this study was conducted to find out students’ abilities and to explore the main problems faced by students in writing procedure texts. The method of this research was descriptive qualitative research. The sample of this research was 29 students of class X IPA2 at SMA Negeri 1 Sepang. Form of writing procedure text was used as the instrument to collect the data and a documentation technique was applied to collect the data. Afterward, the data were analyzed with a rubric analytical scale by brown (2007). The result showed that 13,8% of students were in a very good level, 17.2% of students in a good level, 17.2% of students in a fair level, and 51.6% of students were in a poor level. Therefore, 24.1% of students are classified in the poor level of language features category which means, language features are the most difficult area faced by the tenth-grade students of SMA Negeri 1 Sepang with the number frequency 24.1% students in a poor level.
Harnessing the Potential of Nigerian Pidgin to Serve as a Tool for National Integration in Nigeria

This paper critically examined the issues surrounding the choice of an indegenous language(s) to serve as Nigeria’s national language, and resolved that provided focus remains on the indegenous languages, the national language question will continue to linger in the Nigerian linguistic space in spite of the provisions in the language policy. It argued that Nigerian Pidgin is a language that possesses all the linguistic requirements and is capable of not only functioning as the national language for Nigeria, but has all the potential to serve as a unifying factor and a major tool for enhancing national integration in a nation so divided along ethnic lines. It concluded that seeing as Nigeria's national language issue may never be peacefully resolved in favour of an indigenous mother tongue due to the multiplicity of languages and the deep affinity that their speakers have with them, the Nigerian government should consider recognizing, standardizing and adopting Nigerian Pidgin as Nigeria's national language and as the language for instruction and research for the overall good of the nation.
Improving Student Interests and learning outcomes for English Subject by using Mind Mapping through Online Zoom Application

This study aims to Improve Interest and Learning Outcomes in English Using the Mind Mapping Method assisted by the Zoom Meeting Application in Class X IPS 2 SMA Negeri 3 Pangkalan Bun. This is a class action research (PTK) study to create better English learning. This PTK uses the Arikunto stage. Each cycle consists of four stages, namely: (1) the action planning stage, (2) the action implementation stage, (3) the observation and inner achievement stage, and (4) the analysis and reflection stage. The research subject consisted of 32 students. Research Instruments use student learning outcomes. Data Analysis uses percentage techniques. The results showed that students’ English learning outcomes increased, namely from the first cycle of 32 students of Class X IPS 2 SMAN 3 Pangkalan Bun, there were 23 students, or 72%, who had achieved a KKM score of 73 with an average class score of 75.15. In cycle II, of the 32 students of Class X IPS 2 SMAN 3 Pangkalan Bun, 30 students, or 94%, had achieved a KKM score of 73 with an average score of 81.87. Based on the actions taken, it can be concluded that the Mind Mapping Method assisted by the Zoom Meeting Application can improve the quality of the English learning process.
Developing Example of Narrative Texts in Local Content Theme for Senior High School

Education is one of the basic needs of society and the state in improving the quality of society. Teaching learning material with local content can help student to improve knowledge about local wisdom. But, there are no local content about Palangka Raya contained in teaching learning material used.This research used ADDIE Model as its research method. The data for this research were obtained from expert, English teacher grade X and students’ SMA Negeri 5 Palangka Raya. By using research and development with ADDIE model, the researcher develop a teaching material with local content that concentrate writing skill of the examplary of narrative texts according to the syllabus from the Ministry of Education and Culture of Indonesia. The product from this study are helped the teacher to provide good narrative text with local content of Palangka Raya.The results of this study showed that a good teaching learning material has fulfilled through validation results at an average of 98% by teacher and 97,5% (very valid) by expert. It supported by assessment results at an average 10 by teacher and 10 (A) by expert. Then, the effectiveness based on the results of first trial task at an average 80,56 (Good) and the second trial task get avarage 83,27 (Good). Also, the products received the responses from students in the first meeting with avarge of 84% (Valid) and the second meeting 82% (Valid). From these result, the examplary narrative text can be use and effective to be teaching learning material.
DESIGN AND IMPLEMENTATION OF A RULE BASED AFAAN OROMOO CONVERSATIONAL CHATBOTS

Chatbot technologies are very popular one in the field of NLP and AI to process basic expressions of interaction between humans (end users) and devices as Question Answering System (QAS). They are used in many ranges like in industry to provide customer services based on their need to reach on their desires, transportation, health, entertainment, education, psychology to monitor human emotions and in many. Messaging service is a popular means of communication as text based messaging (SMS) or through messaging applications. Chatbots are virtual assistant technologies that are designed and developed based on the user’s query and to their language. The aim of this paper is to develop and implement a rule based conversational chatbot for Afaan Oromo Language. The approach gets a simple Afaan Oromoo query as input and retrieves answers from a set of predefined rules or responses. The performance of our model is evaluated through human judgment.
The role of noise in denoising models for anomaly detection in medical images

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research.
Open-Set Likelihood Maximization for Few-Shot Learning

We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore the popular transductive setting, which leverages the unlabelled query instances at inference. Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle, in which latent scores down-weighing the influence of potential outliers are introduced alongside the usual parametric model. Our formulation embeds supervision constraints from the support set and additional penalties discouraging overconfident predictions on the query set. We proceed with a block-coordinate descent, with the latent scores and parametric model co-optimized alternately, thereby benefiting from each other. We call our resulting formulation \textit{Open-Set Likelihood Optimization} (OSLO). OSLO is interpretable and fully modular; it can be applied on top of any pre-trained model seamlessly. Through extensive experiments, we show that our method surpasses existing inductive and transductive methods on both aspects of open-set recognition, namely inlier classification and outlier detection.
Fair Credit Scorer through Bayesian Approach

Machine learning currently plays an increasingly important role in people's lives in areas such as credit scoring, auto-driving, disease diagnosing, and insurance quoting. However, in many of these areas, machine learning models have performed unfair behaviors against some sub-populations, such as some particular groups of race, sex, and age. These unfair behaviors can be on account of the pre-existing bias in the training dataset due to historical and social factors. In this paper, we focus on a real-world application of credit scoring and construct a fair prediction model by introducing latent variables to remove the correlation between protected attributes, such as sex and age, with the observable feature inputs, including house and job. For detailed implementation, we apply Bayesian approaches, including the Markov Chain Monte Carlo simulation, to estimate our proposed fair model.
Performance of Joint XR and Best Effort eMBB Traffic in 5G-Advanced Networks

In this paper, we address the joint performance of eXtended reality (XR) and best effort enhanced mobile broadband (eMBB) traffic for a 5G-Advanced system. Although XR users require stringent throughput and latency performance, operators do not lose significant additional network capacity when adding XR users to an eMBB dominated network. For instance, adding an XR service at 45 Mbps with 10 ms packet delay budget, yields close to a 45 Mbps drop in eMBB capacity. In an XR only network layer, we show how the capacity in number of supported XR users depends significantly on the rate but also the latency budget. We show also how the XR service capacity is significantly reduced in the mixed service setting as the system goes into full load and other-cell interference becomes significant. The presented results can be used by cellular service providers to assess their networks performance of XR traffic based on their current eMBB performance, or as input to dimensioning to be able to serve certain XR traffic loads.
PREDICTION OF NEUROLOGICAL DISEASE USING MACHINE LEARNING

In the modern era, neurological diseases are a growing concern. Finding neurological disease in its early stages is a challenging task for a doctor. However, accurate disease diagnosis at an early stage is crucial because treatments are more likely to enhance patients' quality of life at that point. We suggest a statistical approach using the most prevalent symptoms of neurological diseases, which are gait, tremors, and micrographic, since there is no established test to identify the symptoms. In order to find the classification algorithm that provides the highest accuracy in diagnosing the patients, this involves analyzing the correlation between both the symptoms and classifying the obtained data using various classification algorithms. Greater medical care and higher standards of care may result from earlier detection. Keywords : Neurological Disease, micrographic, diagnosis.
The necessity of depth for artificial neural networks to approximate certain classes of smooth and bounded functions without the curse of dimensionality

In this article we study high-dimensional approximation capacities of shallow and deep artificial neural networks (ANNs) with the rectified linear unit (ReLU) activation. In particular, it is a key contribution of this work to reveal that for all $a,b\in\mathbb{R}$ with $b-a\geq 7$ we have that the functions $[a,b]^d\ni x=(x_1,\dots,x_d)\mapsto\prod_{i=1}^d x_i\in\mathbb{R}$ for $d\in\mathbb{N}$ as well as the functions $[a,b]^d\ni x =(x_1,\dots, x_d)\mapsto\sin(\prod_{i=1}^d x_i) \in \mathbb{R} $ for $ d \in \mathbb{N} $ can neither be approximated without the curse of dimensionality by means of shallow ANNs nor insufficiently deep ANNs with ReLU activation but can be approximated without the curse of dimensionality by sufficiently deep ANNs with ReLU activation. We show that the product functions and the sine of the product functions are polynomially tractable approximation problems among the approximating class of deep ReLU ANNs with the number of hidden layers being allowed to grow in the dimension $ d \in \mathbb{N} $. We establish the above outlined statements not only for the product functions and the sine of the product functions but also for other classes of target functions, in particular, for classes of uniformly globally bounded $ C^{ \infty } $-functions with compact support on any $[a,b]^d$ with $a\in\mathbb{R}$, $b\in(a,\infty)$. Roughly speaking, in this work we lay open that simple approximation problems such as approximating the sine or cosine of products cannot be solved in standard implementation frameworks by shallow or insufficiently deep ANNs with ReLU activation in polynomial time, but can be approximated by sufficiently deep ReLU ANNs with the number of parameters growing at most polynomially.
Cohere: Managing Differential Privacy in Large Scale Systems

The need for a privacy management layer in today's systems started to manifest with the emergence of new systems for privacy-preserving analytics and privacy compliance. As a result, many independent efforts have emerged that try to provide system support for privacy. Recently, the scope of privacy solutions used in systems has expanded to encompass more complex techniques such as Differential Privacy (DP). The use of these solutions in large-scale systems imposes new challenges and requirements. Careful planning and coordination are necessary to ensure that privacy guarantees are maintained across a wide range of heterogeneous applications and data systems. This requires new solutions for managing and allocating scarce and non-replenishable privacy resources. In this paper, we introduce Cohere, a new system that simplifies the use of DP in large-scale systems. Cohere implements a unified interface that allows heterogeneous applications to operate on a unified view of users' data. In this work, we further address two pressing system challenges that arise in the context of real-world deployments: ensuring the continuity of privacy-based applications (i.e., preventing privacy budget depletion) and effectively allocating scarce shared privacy resources (i.e., budget) under complex preferences. Our experiments show that Cohere achieves a 6.4--28x improvement in utility compared to the state-of-the-art across a range of complex workloads.
Multi-Armed Bandits and Quantum Channel Oracles

Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible when the arms and the randomness of the rewards of the arms can be queried in superposition. Here we introduce further bandit models where we only have limited access to the randomness of the rewards, but we can still query the arms in superposition. We show that then the query complexity is the same as for classical algorithms. This generalizes the prior result that no speed-up is possible for unstructured search when the oracle has positive failure probability.
Hybrid Open-set Segmentation with Synthetic Negative Data

Open-set segmentation is often conceived by complementing closed-set classification with anomaly detection. Existing dense anomaly detectors operate either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches optimize different objectives and therefore exhibit different failure modes. Consequently, we propose the first dense hybrid anomaly score that fuses generative and discriminative cues. The proposed score can be efficiently implemented by upgrading any semantic segmentation model with dense estimates of data likelihood and dataset posterior. Our design is a remarkably good fit for efficient inference on large images due to negligible computational overhead over the closed-set baseline. The resulting dense hybrid open-set models require negative training images that can be sampled from an auxiliary negative dataset, from a jointly trained generative model, or from a mixture of both sources. We evaluate our contributions on benchmarks for dense anomaly detection and open-set segmentation. The experiments reveal strong open-set performance in spite of negligible computational overhead.
Image Memorability Prediction with Vision Transformers

Behavioral studies have shown that the memorability of images is similar across groups of people, suggesting that memorability is a function of the intrinsic properties of images, and is unrelated to people's individual experiences and traits. Deep learning networks can be trained on such properties and be used to predict memorability in new data sets. Convolutional neural networks (CNN) have pioneered image memorability prediction, but more recently developed vision transformer (ViT) models may have the potential to yield even better predictions. In this paper, we present the ViTMem, a new memorability model based on ViT, and evaluate memorability predictions obtained by it with state-of-the-art CNN-derived models. Results showed that ViTMem performed equal to or better than state-of-the-art models on all data sets. Additional semantic level analyses revealed that ViTMem is particularly sensitive to the semantic content that drives memorability in images. We conclude that ViTMem provides a new step forward, and propose that ViT-derived models can replace CNNs for computational prediction of image memorability. Researchers, educators, advertisers, visual designers and other interested parties can leverage the model to improve the memorability of their image material.
Upaya Meningkatkan Hasil Belajar Bahasa Indonesia Pada Materi Keterampilan Menulis Karangan Narasi Menggunakan Teknik Meneruskan Cerita Bagi Siswa Kelas XI SMK Negeri 2 Garut

The skills of writing narrative essays for Class XI students of SMK Negeri 2 Garut are still low. Therefore, it is necessary to use learning techniques that can increase student motivation and achievement, namely by telling stories. The purpose of this study was to describe the improvement in writing narrative essay skills and changes in the behavior of Class XI students at SMK Negeri 2 Garut in writing narrative essays using the storytelling method. This type of research is classroom action research (CAR). Classroom action research was carried out in four stages, namely: (1) planning, (2) action, (3) observation or observation, and (4) reflection. This study uses two variables, namely the skill of writing narrative essays and the technique of telling stories. Data collection was divided into two stages, namely cycle I and cycle II using test and non-test techniques with the application of learning techniques. The tests used were action tests in the form of assignments to write narrative essays, while the non-test techniques used were observations, journals, interviews, and photo documentation. This study uses qualitative and quantitative data analysis techniques. The results of this study indicate an increase in narrative essay writing skills with storytelling techniques for Class XI students of SMKN 2 Garut. The average value in the first cycle stage is 67.8. in cycle II to 79.43. The results of non-test data analysis showed an increase in the behavior of Class XI students at SMKN 2 Garut in a positive direction. Students became more enthusiastic about learning to write news texts.
RANCANG BANGUN SISTEM INFORMASI TRANSAKSI PEMBAYARAN TAGIHAN AIR CSR AQUA BERBASIS ANDROID (STUDI KASUS DESA CIHERANG PONDOK)

CSR Aqua Desa Ciherang Pondok is an Aqua company dedicated to providing clean water for the residents of Ciherang Pondok Village. Manual systems (notebooks and applications in Microsoft Excel) are still used to collect meter recording and water payments, so there are often errors in the recording process and do not respond to the excel documents created. In addition, the payment process takes a lot of time so it is less efficient. One of the functions that can be utilized is to create an online water payment system. Based on the existing problems, an application for information system for paying water bills CSR Aqua Desa Ciherang Pondok Barbasis Android was created. The research method uses the SDLC (System Development Life Cycle) waterfall approach, namely planning, implementation and testing. Applications are created using the Dart programming language and the Flutter framework. The results of the application were tested on several smartphone devices. Based on the test results, the application works well at least with the android 9.0 (pie) operating system.