Reviewerly/exHarmony huggingface dataset:
Design and Evaluation of a Cross-Lingual ML-based Automatic Speech Recognition System Fine-tuned for the Galician Language

In recent years Machine Learning (ML) strategies have proven to be useful to automate numerous classification and pattern detection tasks in diverse fields thanks to the increase of computational power in hardware. One of such fields is the Automatic Speech Recog- nition (ASR), which can use ML architectures to transcribe human speech into readable text. The Word Error Rate (WER) obtained with ML strategies can become relatively low while providing quick responses, reaching accuracy levels that approach human tran- scription accuracy. However, one of the main drawbacks in traditional architectures is the high demand of transcribed data to obtain a low WER in training. This kind of data is particularly hard to be achieved due to the high dependency on human processing. Luckily, a new framework proposed in 2020 (wav2vec2), considerably reduces the need for audio labelling thanks to the use of a Convolutional Neural Network (CNN) with self-supervised training on cross-lingual unlabelled audios of multiple languages and the ability to fine- tune the obtained results with labelled audios of a specific language. Thus, the framework can obtain results that outperform previous architectures by using much smaller audio datasets with transcriptions. This paper presents an ASR system based on wav2vec 2.0 that is fine-tuned for Galician, a language which currently only has small audio datasets available. Such a system is evaluated with a spontaneous speech dataset of approximately 1 hour from the Galicia Parliament, showing a relatively low WER (18.61%).
Rancang Bangun Website Penjualan Frozen Food Berbasis Framework CodeIgniter

The COVID-19 pandemic requires the government to impose large-scale social restrictions in the territory of Indonesia, which has an impact on various areas of life including trade, where there has been a significant decline in sales figures in retail stores. COVID-19 also causes fear to do activities outside the house, including buying groceries every day. On the other hand, online sales have increased. By seeing the increase in the use of technology during the pandemic, selling frozen food through the website is an alternative, where ready-to-cook food can be easily and safely obtained by consumers. This website can also help frozen food business owners to manage product data and transaction data for producing monthly sales reports quickly and efficiently, as well as a promotional tool with the Customer Relationship Management feature. This website mas made with the Modified Waterfall development model, and was built with the CodeIgniter framework to handle application workflows and Boostraps to handle the appearance of the website.
Neural Scanning: Rendering and determining geometry of household objects using Neural Radiance Fields

In this paper we present a hardware and software framework for Neural Scanning of household objects using Neural Radiance Fields (NeRF). The NeRF technique tries to learn a probabilistic representation of radiance and density, that can be used to render objects and to export objects' geometry. Our framework allows for easy scanning of the objects by rotating the object while using cameras in a static position. The objects we scan are mostly taken from the Yale-CMU-Berkeley (YCB) object set, and we release our scans as part of a public dataset. Supplementary URL: https: //robocip-aist.github.io/sii_nerf_scans.
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering

Creating artificial social intelligence – algorithms that can understand the nuances of multi-person interactions – is an exciting and emerging challenge in processing facial expressions and gestures from multimodal videos. Recent multimodal methods have set the state of the art on many tasks, but have difficulty modeling the complex face-to-face conversational dynamics across speaking turns in social interaction, particularly in a self-supervised setup. In this paper, we propose Face-to-Face Contrastive Learning (F2F-CL), a graph neural network designed to model social interactions using factorization nodes to contextualize the multimodal face-to-face interaction along the boundaries of the speaking turn. With the F2F-CL model, we propose to perform contrastive learning between the factorization nodes of different speaking turns within the same video. We experimentally evaluate our method on the challenging Social-IQ dataset and show state-of-the-art results.
Robustness quantification method for network intrusion detection models

Deep learning techniques have been successfully applied to network intrusion detection tasks, but as in the case of autonomous driving and face recognition, the reliability of the system itself has become a pressing issue. Robustness is a key attribute to determine whether a deep learning system is secure and reliable, and we also choose to explore the security of intrusion detection models from a new perspective of robustness quantification. In this paper, we focus on the intrusion detection model based on long and short-term memory, and use a fine-grained linear approximation method to derive a more accurate robustness bound on the nonlinear activation function with tighter linear constraints. We can use this bound to quantitatively measure the robustness of the detection model and determine whether the model is susceptible to the influence of adversarial samples. In our experiments, we test networks with various structures on the MNIST dataset, and the results show that our proposed method can effectively deduce the robustness bounds of output elements, and has good scalability and applicability.
Hybrid Learning in Times of Pandemic Covid-19: An Experience in a Lima University

Hybrid learning became more important in the context of the Covid-19 pandemic. In this sense, the University of Sciences and Humanities implemented adequate strategies to guarantee the continuity of the teaching and learning process. At the beginning, the virtual modality was chosen in its entirety and then moved to a hybrid modality; however, teachers were not trained; students had problems of connectivity and access to technological resources, which led more than 35% of them to abandon their studies. Therefore, the purpose of the study is to know the students' perception of the hybrid modality, through a questionnaire applied to 142 students of the 2021-1 cycle of the Faculty of Accounting, Economic and Financial Sciences, Faculty of Health Sciences, Faculty of Humanities and Social Sciences, Faculty of Engineering Sciences, considering 19 questions integrated in 4 dimensions: teaching strategy, didactic resources, evaluation system and tutoring; all this in order to evaluate the strategies of the hybrid model proposed by the university. The results obtained in the dimensions show the highest score in the evaluation dimension with an average of 4.51, specifically in the planning of the syllabus; however, the lowest average is found in the tutoring dimension with an average of 3.22, which corresponds to the participation in psycho-pedagogical workshops to improve academic performance. It is concluded that the teaching strategy in the hybrid modality has been satisfactorily approved by the students, thanks to the periodic training of teachers. However, tutoring should be strengthened through academic counseling, so that the beneficiaries of this research are the students, teachers and parents; and, therefore, the Peruvian educational system.
Sentiment Analysis Of E-Commerce Product Reviews For Content Interaction Using Machine Learning

With the growth of various e-commerce applications, it has become increasingly important to understand consumer spending patterns and provide products that best respond to their needs. One way of doing this is by utilizing the data available in consumer product reviews to improve the level of content interaction. I&#x0D; n order to tap into this data, companies can utilize a process called sentiment analysis to identify consumers’ sentiments or reactions towards certain products. This study proposes to compare different methods of sentiment analysis focused on one specific product, utilizing a machine learning approach with Naïve Bayes and Support Vector Machine classifiers with the aim of finding which method produces the best results in terms of accuracy, precision, recall and F-measure. A specific focus has been made to analyze the consumer reviews of wireless earphone products from the Indonesian e-commerce company Tokopedia. The results of this study show that utilizing the Naïve Bayes classifier, enhanced with hyperparameter tuning produced the best results in terms of accuracy, recall, and F-measure with a value of 77.03%, 73.03%, and 74.41% respectively. Whereas the best precision was obtained by utilizing an SVM classifier enhanced with hyperparameter tuning, producing a value of 76.05%.
واقع ممارسة مديري المدارس الحكومية الأردنية لمعايير القيادة التكنولوجية وعلاقتها بدافعية الإنجاز الوظيفي من وجهة نظرهم

The study aims to identify the reality of Jordanian public schools principals practice of technological leadership standards and their relationship to job achievement motivation from their point of view. To achieve the objectives of the study, the descriptive correlative approach was adopted, and two questionnaires were developed. The first, measures the technological leadership standards, consisting of (27) items, and the second measures the elements of career achievement motivation, consisting of (20) items. They were intentionally distributed to a sample of (63) principals in the second semester of the year 2021/2022. The results of the study conclude that the reality of public schools principals practice of technological leadership standards, the degree of availability of elements for revealing their job achievement motivation came within the average level. Furthermore, the relationship between the reality of schools principals practice of technological leadership standards and their job achievement motivation was a positive and direct relationship. The results of the study also reveal that there were no statistically significant differences at the significance level (α ≤ 0.05) between the estimates of the sample members about the practice of technological leadership standards among schools principals due to the gender variable, educational qualification, years of experience, The study recommends the need to spread the culture of practicing the dimensions of technological leadership in education and educational institutions, and to make optimal use of it, because it has a positive impact on the quality of educational leadership work.
MENINGKATKAN PRESTASI BELAJAR SISWA MATA PELAJARAN IPA DENGAN METODE DISCOVERY DI KELAS VII-5 SMP NEGERI 1 PATUMBAK

Abstract: Improving Student Learning Achievement in Science Subjects with Discovery Method in Class VII-5 Middle School 1 Patumbak. Cycle I the average value of student learning achievement is 70.00 and mastery learning reaches 67.57% or there are 25 students out of 35 students who have finished learning. Cycle II the average value of student achievement is 77.03 and mastery learning reaches 78.38% or there are 29 students out of 35 students who have finished learning Cycle III the average value of formative tests is 83.24 and of 35 students who have completed 33 students and 2 students have not reached mastery learning. Through the results of this study indicate that discovery learning has a positive impact in improving student achievement. This can be seen from the increased learning completeness of the first, second and third cycles, namely 67.57%, 78.38%, and 89.19%, respectively.Keywords: Learning achievement, Discovery Method, Natural Science LearningAbstrak: Meningkatkan Prestasi Belajar Siswa Pada Mata Pelajaran IPA Dengan Metode Discovery Di Kelas VII-5 SMP Negeri 1 Patumbak. Siklus I nilai rata-rata prestasi belajar siswa adalah 70,00 dan ketuntasan belajar mencapai 67,57% atau ada 25 siswa dari 35 siswa sudah tuntas belajar. Siklus II nilai rata-rata prestasi belajar siswa adalah 77,03 dan ketuntasan belajar mencapai 78,38% atau ada 29 siswa dari 35 siswa sudah tuntas belajar Siklus III nilai rata-rata tes formatif sebesar 83,24 dan dari 35 siswa yang telah tuntas sebanyak 33 siswa dan 2 siswa belum mencapai ketuntasan belajar. Melalui hasil peneilitian ini menunjukkan bahwa pembelajaran penemuan (discovery) memiliki dampak positif dalam meningkatkan prestasi belajar siswa. Hal ini dapat dilihat dari ketuntasan belaja rmeningkat dari sklus I, II, dan III yaitu masing-masing 67,57%, 78,38%, dan 89,19%.Kata kunci: Prestasi belajar, Metode Discovery, Pembelajaran IPA
Selective region medical image encryption algorithm based on cascade chaos and two-dimensional Joseph traversal

Abstract Aiming at the problem that medical image information is easy to be stolen, tampered and maliciously destroyed in the transmission process, a selective region medical image encryption algorithm based on cascade chaos is proposed. The encryption algorithm consists of two stages: region of interest (ROI) encryption and full image encryption. First, the Cubic map is cascaded with an improved Cosine map. Through the analysis of dynamic characteristics, the cascade map has better chaotic performance than the traditional chaotic systems. The key sequence is generated by the cascade map and the image. Then edge detection is performed on the image to determine ROI. ROI is divided into multiple image blocks, and a new gradient algorithm is used to label the sub-blocks containing important information. A modified two-dimensional Joseph traversal method is used to scramble the positions of pixels in each sub-block, and further modify their pixel values with a round of diffusion. Finally, we use a block V-shape scrambling method to scramble the entire image. The final encrypted image is obtained through a round of bidirectional diffusion of all pixels using chaotic sequences. Simulation results and security analysis show that the algorithm has large key space and sufficient security. It can effectively resist various malicious attacks.
Training Data Improvement for Image Forgery Detection using Comprint

Manipulated images are a threat to consumers worldwide, when they are used to spread disinformation. Therefore, Comprint enables forgery detection by utilizing JPEG-compression fingerprints. This paper evaluates the impact of the training set on Comprint's performance. Most interestingly, we found that including images compressed with low quality factors during training does not have a significant effect on the accuracy, whereas incorporating recompression boosts the robustness. As such, consumers can use Comprint on their smartphones to verify the authenticity of images.
Automatic Grading of Invasive Breast Cancer Patients for the Decision of Therapeutic Plan

This chapter discusses an automatic system designed using whole slide imaging (WSI) and artificial intelligence (AI) technology to grade invasive breast cancer patients enabling therapeutic decisions. This chapter presents an automatic Human epidermal receptor growth factor receptor 2 (HER2) grading system for selecting invasive breast cancer patients for giving HER2 therapy. The system is expected to determine HER2 status automatically and provide useful information to decide on the therapeutic plan, thus improve patient care with optimal therapies. WSI system provides the platform for automated image analysis and diagnosis utilizing AI for digital pathology. The automated cancer grading system can be described in three parts: Specimen preparation, WSI acquisition, and automated HER2 scoring. While the quality evaluation and HER2 quantification requires a high-resolution image. The quality of automated analysis highly depends on the quality of the image used. Using a low-resolution WSI for artifact detection enables faster detection with optimal accuracy.
Angstrom-Prescott Type Models for Predicting Solar Irradiation for Different Locations in Zimbabwe

Adequate assessment of solar radiation data is crucial for planning and designing solar energy systems. However, a major challenge facing solar energy technologies is the availability of solar radiation data at the specific area of interest. In this paper solar radiation and sunshine duration data from 29 stations in Zimbabwe were used to generate both monthly and annual Angstrom-Prescott (A-P) type coefficients, a and b, that are location based. The coefficients were developed using linear correlation between the clearness index and sunshine duration. The adaptation relationship between satellite and ground-measured irradiation had an R2 of 0.6738. The correlation between the clearness index and the sunshine duration in most of the stations was fairly high with the highest coefficient of determination, R2, of 0.9030. The A-Pregression coefficient, a, generated using the data from each station ranged between 0.2252 and 0.3976, whereas the regression coefficient, b, ranged between 0.3218 and 0.6265. The estimated and measured values of global solar radiation, He, and Hm, respectively from each station were compared using the mean absolute percentage error (MAPE), the root mean square error (RMSE), the mean absolute error (MAE) and the relative standard error (RSE). The MAE values for the models ranged from 0.5438 MJ/m2 to 2.2845 MJ/m2. The MAPE indicated a range between 2.5642 % and 10.334 %. The RSE ranged between 0.0346 % and 0.1537% while the RMSE for the models ranged from 0.7360 MJ/m2 to 2.9454 MJ/m2. The statistical indicators showed results that were within the recommended range for solar radiation predicting models from similar studies.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI &amp; AIM 2022 Challenge: Report

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.
Teachable Machine: Real-Time Attendance of Students Based on Open Source System

The utilization of open source-based services will be very useful, simplifying and accelerating the process of object recognition and complex computational processes, one of them uses the Teachable Machine service. Identification of student faces in real-time attendance is a case study that will be applied to students to recognize and identify accurately and clearly the presence of students during online / offline lectures, by applying Teachable Machine services that have good algorithms with a machine learning approach that utilizes the Tensorflow.js library where the training data testing uses Convolutional Neural Network (CNN). Of the objects identified, the average accuracy of all classes ranged from 91-100%, with the number of samples for each object class being 23 objects or more. Number of sample images in one class. Clothing, object background and lighting intensity around the image object are also very influential in determining the accuracy value of student face recognition later, so that the use of the tensorflow.js library that implements Convolutional Neural Network (CNN) will be very helpful in facial recognition and influencing factors so that the data entered later needs to be further corrected and improved again, so that the results obtained in implementing the online attendance system have been very helpful in detecting student faces with an average accuracy rate of 91.8%
A New Guess-and-Determine Method for Cryptanalysis of the GSM Encryption

Cryptanalysis is the process of finding flaws or oversights in an encryption algorithm. Nearly, all encryption algorithms are carefully examined through cryptanalysis to determine the security of the system in which the encryption algorithm has been employed. A5/1 is a well-known encryption algorithm which is inbuilt in mobile phone for securing GSM communication, and therefore, cryptanalysis of this algorithm is also important. A5/1 consists of three linear feedback registers of lengths 23, 22, and 19 bits. Due to the nonlinear clocking mechanism of A5/1, cryptanalytic attacks of guess-and-determine (GD) nature are efficient and more successful. In this paper, we propose a new low keystream GD attack on GSM encryption algorithm A5/1. The basic idea of GD attack is guessing some portion of the registers of A5/1 and determining remaining portion of the registers via the relationship between the register’s state and the known intercepted keystream. The guessed and determined register’s state is validated by running the cipher forward from that state. If the intercepted keystream matches the generated keystream, we accept it. Otherwise, we discard and try the attack again. The computational complexity and the success rate of the proposed attack are O <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mn>52</mn> </mrow> </msup> </mrow> </mfenced> </math> and 96.6%, respectively.
Metaverse and Health Care System

The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment with reduced cost.The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners.The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored.