Reviewerly/exHarmony huggingface dataset:
Improving Multi-label Classification Using Feature Reconstruction Methods

Multi-label classification (MLC) is a supervised classification method that allows for a data instance with more than one class label (or target). Solving MLC is still a challenging task. MLC can potentially generate complex decision boundaries as the method is a non-mutual exclusive classification method. Recently, many techniques have been proposed to cope with the complexity of MLC problems, such as the Problem transform method (PTM), the Adaptation method (AM), and the Ensemble method (EM). These techniques can generally produce good results with certain datasets. However, they have poor classification performance when the number of possible class-labels is larger, even if the dataset is well-presented (high density). The aim of this work was to solve the MLC problems by performing a feature reconstruction process on the original data features. The proposed feature reconstruction method generates a set of compact features from the original data instances. AutoEncoder is deployed to learn and encode the features of the data (as the constructed feature steps) before they are classified by learning algorithms (or classifiers). We conducted experiments using different multi-label classifiers based on and around PTM, AM, and EM, on the set of the standard dataset. The results from the experiments demonstrated that the proposed feature reconstruction technique provides promising classification results, especially with high-density data.
Using Online Platforms to Improve Writing

Due to the difficulties that Iraqi students face when writing in the English language, this preliminary study aimed to improve students' writing skills by using online platforms remotely. Sixty first-year students from Al-Furat Al–Awsat Technical University participated in this study. Through these platforms, the researchers relied on stimuli, such as images, icons, and short titles to allow for deeper and more accurate participations. Data were collected through corrections, observations, and feedback from the researchers and peers. In addition, two pre and post-tests were conducted. The quantitative data were analysed by SPSS statistical Editor, whereas the qualitative data were analyzed using the Piot table, an Excel sheet. The results have shown improvement of more than (0.5). This indicates that the study is effective and beneficial for the Iraqi students. Moreover, students were generally satisfied with the experience, as it allowed them to easily communicate, research and rely on themselves when learning to write in English as a foreign language. In addition, semiotic awareness and learning via e-learning platforms can be greatly adopted in teaching English in Iraq. The study has further concluded that there is an improvement in the performance of the Iraqi learners, and that their writing skills were enhanced after the improvement of their semiotic awareness. The is, the improvement of their semiotic awareness has deepened their understanding and enhanced their ability to analyze and criticize. It also developed their communication skills and motivate them to write better and search for details
From “welcome to my channel” to “please like, share, &amp; subscribe”: A conversational analysis of the opening and closing strategies of Filipino Youtube vlogs

In the field of communications much is still to be known about Filipino vlogs. And yet vlogs are subject to much public discourse. Anchored in Jessica De Boeck’s (2015) work, this study determines the purposes that vlogs serve in this computer-mediated communication by examining the opening and closing strategies in 10 Filipino YouTube vlogs. Generally, the results of the study are constitutive of De Boeck’s findings. However, Filipino vlogs have interesting additions to these identified strategies. For the opening strategies, some Filipino vloggers identify their viewers by denoting names to their fan base. They also include short clips or video previews, which I argue as “digital markers.” And as for the closing strategies, some Filipino vloggers operate the discourse marker “so” as a final closing strategy. Lastly, the topic shading forecasts vloggers’ arrangement and framing. By and large, the vlogs’ employed strategies are deemed to provide a profound sense of involvement, engagement, and belongingness.
From individual rights to community obligations

research-article Open Access Share on From individual rights to community obligations: a Jewish approach to speech Authors: Jessica Hammer Carnegie Mellon University Carnegie Mellon UniversitySearch about this author , Samantha Reig Carnegie Mellon University Carnegie Mellon UniversitySearch about this author Authors Info & Claims InteractionsVolume 29Issue 4July - August 2022 pp 30–34https://doi.org/10.1145/3535271Online:29 June 2022Publication History 0citation630DownloadsMetricsTotal Citations0Total Downloads630Last 12 Months630Last 6 weeks72 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Analisis Kemampuan Pemahaman Konsep Matematika Ditinjau Dari Penggunaan Multimedia Game Petualangan Dalam Limas Berbasis Mobile Learning di SMP Negeri 1 Tilango

This study aims to describe the student’s ability to understand the mathematical concept using adventure game multimedia mobile learning based on the pyramid (PDL) in Grade VIII. This study is qualitative descriptive. The subjects are 40 students of grade VIII which is reduced to 6 students to take the preliminary test of the ability to understand the concept and the interview process seen from the results of the students’ preliminary test. Data collection technique includes preliminary tests, interviews, documentation, and observation. The data analysis applies reduction, data presentation, and conclusion. The results based on interviews conducted with students and teachers show that students’ conceptual abilities vary widely, where some achieve high abilities, medium abilities, and low abilities. It is also seen from the learning process that the use of adventure game multimedia in the pyramid helps to give its vibes to a more interesting learning process.
Target Detection and Network Optimization: Deep Learning in Face Expression Feature Recognition

Facial expression recognition technology has been more and more in demand in security, entertainment, education, medical, and other domains as artificial intelligence has advanced, and face expression recognition technology based on deep learning has become one of the research hotspots. However, there are still some issues with the existing deep learning convolutional neural network; the feature extraction technique has to be improved, and the design of the detailed network structure needs to be optimized. It is critical to do more research on the deep learning convolutional neural network model in order to increase the accuracy of face facial expression detection. In this paper, a deep learning convolutional neural network structure combining VGG16 convolutional neural network and long and short-term memory networks is designed to address the shortcomings of existing deep learning methods in face expression recognition, which are prone to overfitting and gradient disappearance, resulting in low test accuracy. This structure easily and effectively collects facial expression information and then classifies the retrieved features using a support vector machine to detect face expressions. Finally, the fer2013 dataset is used to train face expression recognition, and the results demonstrate that the built deep convolutional neural network model can effectively increase face expression identification accuracy.
Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis

A common method in training neural networks is to initialize all the weights to be independent Gaussian vectors. We observe that by instead initializing the weights into independent pairs, where each pair consists of two identical Gaussian vectors, we can significantly improve the convergence analysis. While a similar technique has been studied for random inputs [Daniely, NeurIPS 2020], it has not been analyzed with arbitrary inputs. Using this technique, we show how to significantly reduce the number of neurons required for two-layer ReLU networks, both in the under-parameterized setting with logistic loss, from roughly $\gamma^{-8}$ [Ji and Telgarsky, ICLR 2020] to $\gamma^{-2}$, where $\gamma$ denotes the separation margin with a Neural Tangent Kernel, as well as in the over-parameterized setting with squared loss, from roughly $n^4$ [Song and Yang, 2019] to $n^2$, implicitly also improving the recent running time bound of [Brand, Peng, Song and Weinstein, ITCS 2021]. For the under-parameterized setting we also prove new lower bounds that improve upon prior work, and that under certain assumptions, are best possible.
Construct a Sentence with Multiple Specified Words

This paper demonstrates a task to finetune a BART model so it can construct a sentence from an arbitrary set of words, which used to be a difficult NLP task. The training task is making sentences with four words, but the trained model can generate sentences when fewer or more words are provided. The output sentences have high quality in general. The model can have some real-world applications, and this task can be used as an evaluation mechanism for any language model as well.
Repository-Level Prompt Generation for Large Language Models of Code

With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge in the prompt design process become important. In this work, we propose a framework called Repo-Level Prompt Generator that learns to generate example-specific prompts using prompt proposals. The prompt proposals take context from the entire repository, thereby incorporating both the structure of the repository and the context from other relevant files (e.g. imports, parent class files). Our technique doesn't require any access to the weights of the LLM, making it applicable in cases where we only have black-box access to the LLM. We conduct experiments on the task of single-line code-autocompletion using code repositories taken from Google Code archives. We demonstrate that an oracle constructed from our prompt proposals gives a remarkably high relative improvement of 36% over Codex, showing the quality of these proposals. Further, we show that when we train a model to predict a prompt proposal, we can achieve significant performance gains over Codex and other baselines. We release our code, data, and trained checkpoints at: \url{https://github.com/shrivastavadisha/repo_level_prompt_generation}.
Decomposition and Recognition of English Speech Features Based on Neutrosophic Set Fuzzy Control and Random Matrix Theory

In order to improve the effect of special decomposition and recognition of English speech, based on the idea of neutrosophic set fuzzy control, this paper uses Bayesian method as the basic algorithm of speech recognition to improve the algorithm in combination with English waveform characteristics. Moreover, this paper uses a semi-supervised learning method to process English speech waveform data, collects relevant data through the English speech input system, and then labels the data and obtains a new English speech data set through training and learning. In addition, this paper uses multiple iterations of labeling to obtain the ideal output data, uses neutrosophic set fuzzy control algorithms and machine learning algorithms to perform English speech feature decomposition and recognition, and uses feature parameter extraction methods to perform signal feature extraction. Finally, this article combines the needs of English speech recognition to build a system model and uses simulation tests to perform performance analysis of English speech feature decomposition and recognition model. The results of the research show that the improved algorithm and system model proposed in this paper have relatively good effects.
Eximious Sandstorm Image Improvement Using Image Adaptive Ratio and Brightness-Adaptive Dark Channel Prior

Sandstorm images have a color cast by sand particles. Hazy images have similar features to sandstorm images due to these images having a common obtaining process. To improve hazy images, various dehazing methods are being studied. However, not all methods are appropriate for enhancing sandstorm images as they experience color degradation via an imbalanced color channel and degraded color distributed around the image. Therefore, this paper proposes two steps to improve sandstorm images. The first is a color-balancing step using the mean ratio of the color channel between red and other colors. The sandstorm image has a degraded color channel, and therefore, the attenuated color channel has different average values for each color channel; the red channel’s average value is the highest, and that of the blue channel is the lowest. Using this property, this paper balances the color of images via the ratio of color channels. Although the image is enhanced, if the red channel is still the most abundant, the enhanced image may have a reddish color. Therefore, to enhance the image naturally, the red channel is adjusted by the average ratio of the color channel; those measures (as with the average ratio of color channels) are called image adaptive ratio (IAR). Because color-balanced sandstorm images have the same characteristics as hazy images, to enhance them, a dehazing method is applied. Ordinary dehazing methods often use dark channel prior (DCP). Though DCP estimates the dark region of an image, because the intensity of brightness is too high, the estimated DCP is not sufficiently dark. Additionally, DCP is able to show the artificial color shift in the enhanced image. To compensate for this point, this paper proposes a brightness-adaptive dark channel prior (BADCP) using a normalized color channel. The image improved using the proposed method has no color distortion or artificial color. The experimental results show the superior performance of the proposed method in comparison with state-of-the-art dehazing methods, both subjectively and objectively.
General Characteristics of the Didactic Experiment for Developing Certain Educational Competence – Statistical Approach

This article discusses consistent systemic aspects of the conduct of research aimed at a given teaching methodology through a didactic experiment, dedicated to certain educational competence development. Key details on the use of traditional didactic tests as a measurement tool are considered. The content is tied to a statistical approach.
On the Design of a Blockchain-based Fraud-prevention Performance Appraisal System

The job recruitment process takes a lot of process and number of documents. It is very well known for applicants to exaggerated and falsify their work history data. It may put a company at legal risk and significant commercial losses. Generally, company use third-party to verify applicant’s work history data which is time-consuming and costly. It also makes companies relies on third-party which may not trustworthy and cause several other risks. Generally, experience letters is used as a proof of work history documents of employee. However, the process of publishing an experience letter may contain conflict of interest between company and employee. Yet, publishing an experience letter is not mandatory in several places. In this research, we propose a system to verify applicant’s work history data by using performance appraisal as proof of work history and utilizing Blockchain to provide secure system, tampered-proof and real-time verification. The proposed approach also minimizes trust issues and privacy of data sharing by adding encryption and digital signature schema using Elliptic Curve Cryptography (ECC) algorithm. Furthermore, we have implemented a prototype to demonstrate how the proposed system work using a Quorum-based consortium blockchain.
47‐1: Display Outdoor Visibility Enhancement Using Adaptive Tone Mapping

In this document, we propose an image tone mapping method enhancing the image visibility in outdoor environment. The proposed method improves the outdoor visibility of the displayed image in two aspects: brightness enhancement and content adaptive contrast enhancement. From the experiment results, it is confirmed that the proposed method improves the brightness and the contrast at the same time and enhances outdoor visibility effectively even with the limited dynamic range of the display device. Compared to conventional methods, proposed method has higher flexibility and effectiveness. This method is already utilized on the mobile devices for outdoor visibility enhancement and we expect to apply it to the automotive platforms.
Tifinagh handwritten character recognition using optimized convolutional neural network

&lt;p&gt;&lt;span&gt;Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feed-forward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.&lt;/span&gt;&lt;/p&gt;
Aesthetics Of Psychological Analysis And The World Of Creators

This article is devoted to the philosophical observations of the fine arts. The method of mental analysis in medicine, founded by Sigmund Freud, is widely analyzed in the imagination and creativity of the creators of the scientific-theoretical current “Philosophy of Psychological Analysis”. The essence of the features of consciousness and unconsciousness in the personality of the creator is widely analyzed.
Effects of Adversarial Training on the Safety of Classification Models

Artificial intelligence (AI) is one of the most important topics that implements symmetry in computer science. As like humans, most AI also learns by trial-and-error approach which requires appropriate adversarial examples. In this study, we prove that adversarial training can be useful to verify the safety of classification model in early stage of development. We experimented with various amount of adversarial data and found that the safety can be significantly improved by appropriate ratio of adversarial training.
Neural Neural Textures Make Sim2Real Consistent

Unpaired image translation algorithms can be used for sim2real tasks, but many fail to generate temporally consistent results. We present a new approach that combines differentiable rendering with image translation to achieve temporal consistency over indefinite timescales, using surface consistency losses and \emph{neural neural textures}. We call this algorithm TRITON (Texture Recovering Image Translation Network): an unsupervised, end-to-end, stateless sim2real algorithm that leverages the underlying 3D geometry of input scenes by generating realistic-looking learnable neural textures. By settling on a particular texture for the objects in a scene, we ensure consistency between frames statelessly. Unlike previous algorithms, TRITON is not limited to camera movements -- it can handle the movement of objects as well, making it useful for downstream tasks such as robotic manipulation.
Forecasting Straight Line Methodin The Les Monitoring System Read The Great Children (AHE) in Kudus

The existence of the Covid-19 virus has an impact on almost all fields, one of which is in the field of education. With the Covid-19 virus, the learning process has changed to distance learning or online. Online learning requires parents to be able to accompany their children in learning. However, not all parents can accompany their children to study because of their busy schedule at work, or the parents' low level of education. Therefore, the existence of tutoring or tutoring places is sought to assist parents in educating their children in the midst of a pandemic. Les Baca Anak Hebat (AHE) is a special community tutoring for reading and writing. Its development in Indonesia is increasing from village to village. In Kudus City itself there are many villages that have units. With this significant number of students, the number of existing students also affects the continuation of the unit. To avoid a spike in the number of students, we need a system that can monitor or predict the number of students in the future so that no units are closed. To estimate the number of students can use one of the fields of science such as forecasting. One of the forecasting methods is the Straight Line Method. In developing the system the author uses the waterfall method with the PHP programming language and MySQL database. This web-based system is used to monitor AHE units in Kudus.
User Experience in Efforts to Create a Digital Library during a Pandemic COVID-19

Background of the study: The digital library is one of the places where it is used to find information related to the services provided by the library during the COVID-19 pandemic. However, many users feel that they complain about the performance of the digital library, which is difficult for users to learn, and feel that they have not paid attention to what users want. Purpose: This research was conducted to see the development of digital library publications with the analysis of user experience (UX) and to see the impact of user experience (UX) on digital libraries before and during the pandemic. Method: This study uses the Systematic Literature Review (SLR) method by collecting literature related to the topics discussed and then making selections to produce an overview of research on User Experience in digital libraries from 2011 to 2021. Findings: The results of this study note that users' experience in digital libraries is quite good in terms of user appearance and convenience, and ease of access. However, some users are not yet comfortable with the new form of digital library. Users want comprehensive improvements to make it easier for users to find information and take advantage of online services on digital library webs. Conclusion: It is expected that user experience or user experience in digital libraries at an agency or organization is important for reliable development. End users want high quality services So that when in the current Covid-19 pandemic situation we are better prepared and continue to improve the quality of digital libraries