Reviewerly/exHarmony huggingface dataset:
Image Description Generator using Deep Learning

Abstract: To recognise the context of an image and describe it in a natural language like English, the fundamental task of creating image captions uses computer vision and natural language processing techniques. To create a natural language description from an input image, image caption generation is used. Convolutional Neural Network (CNN) model and Long Short-Term Memory (LSTM) model are the two parts of this Python project that are used to implement it. The CNN-LSTM architecture combines a Convolutional Neural Network (CNN), which creates features that describe the images, with a Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), which precisely structures meaningful sentences out of the generated data. The ability to automatically describe an image's content has a variety of uses, including helping visually impaired people better understand the content of images and providing more precise and condensed image information for social media
Leaf Disease Detection and Prevention Application

Abstract: Agriculture is the backbone of an economy. Farmers play a vital role in strengthening the economy of any country. Due to the diseases a crop undergoes due to various reasons, Food security is compromised and affects the crop production. A crop disease not only affects a single crop but tends to spread to the neighboring crops as well. Detection and cure of such diseases becomes very difficult for a farmer. This problem can be fixed by using an application that uses a Machine Learning system, which takes leaf image as an input and detects the disease suffered by any particular crop and shows the possible cure. Also considering the farmer’s location, Crops that are suitable to grow in the particular location can be displayed.
The use of Different Filters and their effects on Convolution with Grayscale Images

Abstract: Computer vision is ubiquitous in the world today, starting from unlocking phones to robots performing complex surgeries in hospitals and managing the assembly line in factories. In this paper, the focus has been placed on the use of filters or kernels or masks and their effects when convolved with grayscale images.
A faster dynamic convergency approach for self-organizing maps

Abstract This paper proposes a novel variable learning rate to address two main challenges of the conventional Self-Organizing Maps (SOM) termed VLRSOM: high accuracy with fast convergence and low topological error. We empirically showed that the proposed method exhibits faster convergence behavior. It is also more robust in topology preservation as it maintains an optimal topology until the end of the maximum iterations. Since the learning rate adaption and the misadjustment parameter depends on the calculated error, the VLRSOM will avoid the undesired results by exploiting the error response during the weight updation. Then the learning rate is updated adaptively after the random initialization at the beginning of the training process. Experimental results show that it eliminates the tradeoff between the rate of convergence and accuracy and maintains the data's topological relationship. Extensive experiments were conducted on different types of datasets to evaluate the performance of the proposed method. First, we experimented with synthetic data and handwritten digits. For each data set, two experiments with a different number of iterations (200 and 500) were performed to test the stability of the network. The proposed method was further evaluated using four benchmark data sets. These datasets include Balance, Wisconsin Breast, Dermatology, and Ionosphere. In addition, a comprehensive comparative analysis was performed between the proposed method and three other SOM techniques: conventional SOM, parameter-less self-organizing map (PLSOM2), and RA-SOM in terms of accuracy, quantization error (QE), and topology error (TE). The results indicated the proposed approach produced superior results to the other three methods.
The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks

We present an empirical dataset surveying the deep learning phenomenon on fully-connected feed-forward multilayer perceptron neural networks. The dataset, which is now freely available online, records the per-epoch training and generalization performance of 483 thousand distinct hyperparameter choices of architectures, tasks, depths, network sizes (number of parameters), learning rates, batch sizes, and regularization penalties. Repeating each experiment an average of 24 times resulted in 11 million total training runs and 40 billion epochs recorded. Accumulating this 1.7 TB dataset utilized 11 thousand CPU core-years, 72.3 GPU-years, and 163 node-years. In surveying the dataset, we observe durable patterns persisting across tasks and topologies. We aim to spark scientific study of machine learning techniques as a catalyst for the theoretical discoveries needed to progress the field beyond energy-intensive and heuristic practices.
NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras

We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low-power (1.1 mW) but only outputs grey-scale, low resolution, and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods. Open-source code: https://github.com/vb000/NeuriCam.
End-User Puppeteering of Expressive Movements

The end-user programming of social robot behavior is usually limited by a predefined set of movements. We are proposing a puppeteering robotic interface that provides a more intuitive method of programming robot expressive movements. As the user manipulates the puppet of a robot, the actual robot replicates the movements, providing real-time visual feedback. Through this proposed interface, even with limited training, a novice user can design and program expressive movements efficiently. We present our preliminary user study results in this extended abstract.
3D Shape Sequence of Human Comparison and Classification using Current and Varifolds

In this paper we address the task of the comparison and the classification of 3D shape sequences of human. The non-linear dynamics of the human motion and the changing of the surface parametrization over the time make this task very challenging. To tackle this issue, we propose to embed the 3D shape sequences in an infinite dimensional space, the space of varifolds, endowed with an inner product that comes from a given positive definite kernel. More specifically, our approach involves two steps: 1) the surfaces are represented as varifolds, this representation induces metrics equivariant to rigid motions and invariant to parametrization; 2) the sequences of 3D shapes are represented by Gram matrices derived from their infinite dimensional Hankel matrices. The problem of comparison of two 3D sequences of human is formulated as a comparison of two Gram-Hankel matrices. Extensive experiments on CVSSP3D and Dyna datasets show that our method is competitive with state-of-the-art in 3D human sequence motion retrieval. Code for the experiments is available at https://github.com/CRISTAL-3DSAM/HumanComparisonVarifolds.
ANALISIS USER EXPERIENCE MAHASISWA TERHADAP SISTEM INFORMASI SIASAT MENGGUNAKAN USER EXPERIENCE QUESTIONNAIRE

The current development of information systems needs to be reviewed whether the quality of the information system is able to answer the needs of its users. One of the information system technologies is the Satya Wacana Academic Information System (SIASAT), which until now there has been no research that discusses the level of user experience satisfaction when using the SIASAT. Therefore, an analysis of the student SIASAT user experience was carried out using the User Experience Questionnaire (UEQ) method. UEQ is a measurement measured from aspects of its use and aspects of experience including compressive impressions with a questionnaire scale. UEQ has 26 questions covering 6 scales, including the attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty scales. The final result of the study concluded that for the attractiveness scale, the evaluation value was normal and the benchmark was Bad. The perspicuity scale gets a positive evaluation value and the benchmark is Above Average. The efficiency scale gets a normal evaluation value and a Bad benchmark. The dependability scale gets normal evaluation values and Bad benchmark. The stimulation scale gets a normal evaluation value and a Bad benchmark. The novelty scale gets a negative evaluation score and the benchmark is Bad.
ANALISIS DAN PERANCANGAN SISTEM INVENTARISASI ASET PADA PT. BANGUNINDO TEKNUSA JAYA

Asset inventory is an effort to collect data, record, report the results of asset data collection and then document it. PT. Bangunindo Teknusa Jaya is a technology company. Previously PT. Bangunindo Teknusa Jaya already has a website, but the website does not yet have a company asset inventory system. With the creation of an asset inventory system, it is hoped that this system can assist in recording asset loans and collecting company asset data. In making an asset inventory system, the method used is the Waterfall Method. The Waterfall Method has 5 steps: (1) Requirements, (2) Design, (3) Implementation, (4) Verification, and (5) Maintenance. Asset Inventory Information System PT. Bangunindo Teknusa Jaya is designed using Unified Modeling Language (UML) diagrams, namely Use Case Diagrams, Class Diagrams, and Activity Diagrams. The UML diagram of the asset inventory system uses StarUML tools, while the design of the asset inventory system uses MarvelApp. From the research that has been done, it produces a system that can help admins in saving time in data collection and transactions for borrowing company assets so that the admin's work becomes more effective and efficient.
PERANCANGAN MEDIA BELAJAR INSTRUCTIONAL GAMES UNTUK PENINGKATAN MINAT BELAJAR SISWA SMA

than studying. Submission of teaching materials in a conventional way will be boring to be followed by students in class. It is necessary to improve teaching methods that are able to attract students' interest in learning to be motivated in participating in teaching and learning activities. The design of interactive learning media with the instructional games model aims to increase the interest of students so that they can receive teaching materials in a good and fun way. The method used in this research is Research and Development (R&amp;D). To analyze students' learning motivation, a field survey questionnaire was used which was given to 33 students of Class XI SMA Negeri 1 Plered. There has been an increase of 8.22%, where the initial motivation of students is at the percentage of 62.88% increasing to a percentage of 71.10%, which can also be interpreted that the use of interactive instructional games learning media is able to increase student learning motivation.
THE IMPACT OF DIRECT AND TRADITIONAL GRAMMAR TRANSLATION METHODS ON THE STUDENT’S PERFORMANCE AT SECONDARY LEVEL: AN EMPIRICAL EVIDENCE FROM BALOCHISTAN

The impact of teaching techniques on secondary school students' performance is investigated in this study. The study also investigates the interaction between teaching approach and medium of instruction on students' language proficiency. Students' data is collected for this purpose from both Urdu and English medium schools. The Direct Method (DM) and the conventional Grammar-Translation Method (GTM) are used to teach the students. To examine the differential influence of teaching styles on student academic progress, statistical analysis employs the General Linear Model and Hierarchical Regression Analysis. The results are stunning. The post-test mean scores of two comparison groups in the Multiple-Choice test, for example, showed significantly difference. The findings show that the language of communication has a big influence on how well pupils do in school. Furthermore, the language of education and the methods of instruction have a significant interaction effect on students' performance. Finally, the students who were taught using the traditional grammatical method had higher mean scores than those who were taught using the direct method. As a result of the findings, it is safe to conclude that the Grammar Translation Technique is both more accurate and more practicable in Asian educational contexts than the direct method.
PENERAPAN DATA MINING UNTUK REKOMENDASI PAKET PERNIKAHAN MENGGUNAKAN METODE ALGORITMA APRIORI

SM Wedding Decoration is a place that provides services to take care of everything related to weddings. For example, wedding decorations, wedding organizers, and wedding planners. SM Wedding Decoration has several wedding packages that can be offered to customers. The many packages available make the bride and groom or customers confused to determine which wedding package is suitable for their wedding. The a priori algorithm method is used in this study to find recommendations for wedding packages based on existing transaction data and to improve the company's strategy and sales of other wedding packages. The Apriori algorithm is used to help computers learn patterns of association rules. This algorithm looks for a set of things that match the given criteria or sequence and has a certain frequency value. From this research, customers tend to order Photographer &amp; Documentation and MUA → Deluxe packages more often, and these orders account for 44% of all package order transaction data. Package order transaction data for MUA→Deluxe package is 41.3%. Transaction data for the Photographer &amp; Documentation package → Deluxe Package is 41.2%. And the transaction data for ordering the MUA → Premium Deluxe Package package is 41.3%.
BACK-END SYSTEM DESIGN BASED ON REST API

Back-End is a logical space with the functionality and operation of a software application or information system. Currently, SMA Xaverius 1 Palembang does not yet have a rest server-based application whose background can be accessed by a multi-platform rest client. Therefore, an application based on the RESTful rest api based back-end was built. This RESTful application or REST Server provides data to be accessed by the REST Client using data exchange in JSON format using the HTTP protocol built using the Laravel Framework. The Laravel framework provides a mechanism for building REST Servers via the Rest Server library that supports a full RESTful server implementation. The results of this research have been successful. A Laravel 7-based REST Server application has been built which provides endpoints in the form of json and status codes which can later be accessed by multi-platform rest clients. Through the application based on the back-end rest api, the results from the endpoint get a response from the server in the form of json that supports interoperability.
PENERAPAN DATA MINING PEMASARAN PRODUK MENGGUNAKAN METODE CLUSTERING

This study aims to build a data mining application that is able to classify products marketed by companies to find out the products needed for the following month. This application groups non-hierarchical data which is designed to divide existing data into two or more groups so that the same data can be entered into other groups. In this study, the data used is data from PT Cipta Niaga Semesta Mayora Group and the method used is clustering. Data mining is computer-based data processing to gain knowledge. By using data mining, processing sales data at PT Cipta Niaga Semesta Mayora Group becomes easier and gains useful knowledge to take steps to face competition. For this reason, the company's management foresight is needed in choosing technology to help work so that the costs incurred are proportional to the company's opinion. The development of this framework is completed through several stages starting with the first data collection, followed by the second stage of application design, the third stage of the four stages of program development and program implementation. Using data mining applications can help PT Cipta Niaga Semesta categorize the products it sells so that the company can predict the product inventory needed for the following month. So with the new information the company can find out what products customers want.
IDENTIFIKASI PENGENALAN WAJAH UNTUK SISTEM PRESENSI MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR)

Attendance is an activity that is so important and cannot be separated from a teaching and learning activity to calculate and see student attendance. In this Final Project, research on the automatic attendance system is carried out through facial recognition identification (face recognition) using a webcam as a system input, then the resulting image capture results from each image will be processed through feature extraction using the LBPH (Local Binary Pattern Histogram) method and classification with the KNN (K-Nearest Neighbor) method and the help of OpenCV library-based Python software. The research in this Final Project obtained an average accuracy value in facial recognition using LBPH (Local Binary Pattern Histogram) of 93.9%, with an average FAR value of 4.66% and an average FRR value of 1.33%. For the classification of KNN (K-Nearest Neighbor) using Euclidean Distance when k = 1 obtained an accuracy of 100% with a computation time of 34 ms, at the time of k = 3 an accuracy of 98% with a computation time of 37 ms was obtained and at the time of k = 5 an accuracy of 88% with a computation time of 42 ms.
PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST

With so many airlines competing with each other, airlines are competing to become the consumer/market's main choice, but to achieve this, there is no airline strategy that can predict the price of airline tickets according to market needs. To meet the needs of airlines, we need a way to determine the price of airline tickets according to market needs with the help of the influence of technology and information. This research method was carried out using Google Collaboratory as a media to create a data model automated machine learning (AutoML) with the Random Forest, Logistic Regression and Gradient Boosting Regressor algorithms. In this study, the model that produced the highest R2 value and the lowest RMSE was a random forest with an R2 value of 83.91% and an RMSE of $175.9. However, from the three models, Random Forest got a change in accuracy of 1.96% to 85.87. To assist in predicting the determination of flight fares, airline companies can more easily and be alert to determine flight fares that are in accordance with the market. Therefore, Random Forest can be declared better than Logistic Regression and Gradient Boosting models. The Random Forest model that has been created can be used to predict in real-time using Machine Learning.
Academic Purposes Digital Reading

Digital reading is part of English as a foreign language (EFL) students' academic activity in this digital era.Exploring the practices of academic digital reading and the challenges the students face are the purposes of this qualitative study.The participants were the undergraduate EFL students in a public university in Jambi who were in the process of developing the ideas for their thesis.The data were taken through informal interview in WhatsApp group, questionnaire through google form, and focus group discussion through zoom meeting.Qualitative analysis on the data shows that the students practiced reading digitally for their academic assignment and task.They applied certain strategies such as skimming, rereading the text, highlighting and note taking, and generating keywords.During their reading practice, students faced some challenges that can be grouped into three intervening issues.The first is reading process itself, the second is reading in digital environment, and the last is reading in EFL context.In consequence, these issues influenced the students' comprehension toward the text, such as determining the main ideas and understanding the details.Based on the challenges found, it is important to equip students with digital reading strategies, and information literacy skills.
Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU

We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers. We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics. Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domain-specific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations

Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis. However, evaluation on word sense disambiguation (WSD) in prior work shows that using contextualized word representations does not outperform the state-of-the-art approach that makes use of non-contextualized word embeddings. In this paper, we explore different strategies of integrating pre-trained contextualized word representations and our best strategy achieves accuracies exceeding the best prior published accuracies by significant margins on multiple benchmark WSD datasets. We make the source code available at https://github.com/nusnlp/contextemb-wsd.