Reviewerly/exHarmony huggingface dataset:
Socio-­Political Environment and Corruption in Nigeria: An Empirical Analysis

No country is an exception when it comes to the issue of corruption, but the severity and structure of this phenomenon differ from one place to another. This research investigated context-­specific relationships between the socio-­political environment and corruption in Nigeria. The independent variable, i. e., socio-­political environment is proxied by the following government effectiveness indices, such as the rule of law, human development, political stability, and political rights. The dependent variable, i. e., corruption is proxied by the corruption perception index. The study covers the period from 1999 to 2019 using Auto-regressive Distributed Lags (ARDL) for the analysis of the data and some diagnostic tests, such as serial correlation, heteroscedasticity test, and normality test, which were carried out to test the findings. The result revealed that only political stability significantly influences corruption in Nigeria, underscoring the importance of uninterrupted democracy since 1999 in corruption perception of the country. Contrarily, the government effectiveness, the rule of law, human development, and the political right indices failed to have a significant impact on corruption perception and control in Nigeria. This implies that, with the current socio-­political environment in Nigeria, corruption cases in the country have been on the rise. Hence, Nigeria’s socio-­political environment is too weak to have any significant effect on corruption control. The study recommends, among other things, that the rule of law should be followed effectively so that corruption can be curtailed in Nigeria.
Study and innovation of effective classification of XML documents using an advanced deep learning approach

&lt;span lang="EN-US"&gt;In the digital world, classifying real sensed data in huge volumes derived from numerical problems is a challenging task due to the computational complexity of the metaheuristic searching process. The deep learning approach includes convolutional neural network (CNN), long short-term memory (LSTM), and Bidirectional (BI)-LSTM, suitable for an optimistic processing time of analyzing XML datasets (i.e., social media, trade center, and surveillance data exchanged in the internet world). However, it faces process deviation when datasets extend their range beyond the expected volume. This paper proposes a novel deep learning formwork referred to as archimed improved numerical optimization deep learning (AINODL) to improve the classification of XML datasets. The proposed AINODL framework first extracts feature from XML documents using the vector space model. Secondly, it classifies the XML data using the inbuilt function of the AINODL framework. The experiments demonstrate that the performance parameters accuracy (90%), sensitivity (93%), and specificity (94%) of the proposed AINODL framework are significantly enhanced compared with the existing approaches CNN, LSTM, and BI-LSTM.&lt;/span&gt;
The Role of Kelompok Informasi Masyarakat (KIM) in Developing Public Information Literacy in Pandeglang Regency

The establishment of KelompokInformasiMasyarakat (KIM) in Pandeglang Regency by the Pandeglang Regency Communication, Code and Statistics Office is expected to be a mouthpiece between the Pandeglang Regency Government and disadvantaged rural communities in developing information literacy. Through a qualitative approach with descriptive research methods, this study focuses on the role of KIM in developing information literacy in underdeveloped villages. This study seeks to find out the role of KIM in conducting information literacy about the Pandeglang Regency government program. The results of the study show the role of KIM in developing information literacy for the people of Pandeglang Regency, collecting, informing and managing information about programs that are being and will be run by the Pandeglang Regency government in 16 underdeveloped villages that are still difficult to access the internet, such as educating about COVID-19, conducting program socialization " JakaMantul". The recommendation of this research is for KIM to carry out its role more massively by utilizing various media in disseminating information in underdeveloped villages.
Z+ SECURE PATTERN RECOGNITION

Graphical password is one of technique for authentication of System security.Now days digital/computer security is most important things for protected user.And Shoulder-surfing is a one of the threats where a criminal can steal a password by direct observation or by recording the authentication session.There are several techniques available for this authentication, the most prevalent and simple of which is the Graphical password technique.So, we suggest a new technique to combat this problem.There should be a number of color-based watchwords used in color-based authentication, and according on the color, the stoner must flash back the word sequence.It works similarly to three-factor authentication.
Design of Automatic Room Door Lock System Based on Face Recognition

Abstract Face recognition is a method for recognizing human faces using a camera. There are various applications of facial recognition technology, one of which is Face Unlock technology on smartphones which functions as a security feature to open smartphone access through the user’s face. In this study, the use of facial recognition technology is used to automatically open the door of the room according to the registered face. The research method used is the waterfall method which has 5 stages. The research stages consist of requirements analysis, design, implementation &amp; unit testing, integration &amp; system testing, and operation &amp; maintenance. This study uses a Raspberry Pi 4 to perform an automation system. The face detection process is based on the YuNet detection model, as well as the face recognition process using the SFace facial recognition model.
Application of Fuzzy PID in Walking along the Wall Strategy of Mobile Robot

Abstract Aiming at the walk-along-the-wall strategy of the mobile machine, a guidance method based on two analogue laser ranging sensors and a fuzzy PID control algorithm is proposed in this paper, which solves the shortcoming that traditional PID control algorithm does not apply to the nonlinear movement of mobile machine. The fuzzy controller and fuzzy rules were first designed, and the control effect was simulated in MATLAB to verify the superiority of fuzzy PID control in the walking along the wall strategy, making the movement smoother and more stable.
The Theory of Suprematism and the Event: The Search for the Possibility of Transformation

This article proposes to revise the established research approach to Suprematism as a utopian futuristic project. The conceptual analysis of the texts created by K. Malevich (1879–1935) in the period from 1915 to 1922 and devoted to pointlessness has shown that the theory of Suprematism can be viewed as a kind of synthesis of ideas of the fourth dimension (Ch. H. Hinton) and the evolution of human consciousness (P. Ouspensky). It makes it possible to regard the principles of the theory of suprematism through the prism of the concept of the Event. Though the term itself was never used by K. Malevich, the idea of a new way of seeing and a flash of the reason that leads to the possibility of subjective transformation are in line with the meaning of the Event, as seen in philosophical and esoteric thought. The article puts forward the thesis according to which K. Malevich was exposed to these ideas in the atmosphere of spiritual quests of the 1910s and early 1920s. K. Malevich, M. Matyushin, and A. Kruchenykh took part in the production of the opera Victory over the Sun which served as a venue of creative interaction and an outlet for creative approaches and conceptual reinterpretation. Here one can clearly see the logical isomorphism of the artistic practice of the opera creators, based on a specific interpretation of time and the idea of the evolution of consciousness, and those ideas that underlay the esotericism of their time. Another indirect argument in favor of the thesis about the presumed parallelism of the theory of K. Malevich and the concept of the Event is the performative nature of the texts written by the author of the Black Square. According to K. Malevich, through overcoming meaning as the victory over reason and objectivity, the gap between the modus of postulating the possibility of transformation and the real experience of the sense of immortality. This also points to the similarity of his ideas and the esoteric project of irrational cognition and control over the mysterious. Thus, Suprematism can be interpreted as the Event of subjective transformation, and the theory of Suprematism can be viewed as a tool that allows one to experience a sense of immortality, accessible to both the creator and the viewer, who develop the creative will for the future.
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features

While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power. Training dynamics, i.e., the traces left by iterations of optimization algorithms, have recently been proved to be effective to localize mislabeled samples with hand-crafted features. In this paper, beyond manually designed features, we introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network, which learns to predict whether a sample was mislabeled using the raw training dynamics as input. Specifically, the proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises and can adapt to various datasets (either naturally or synthesized label-noised) without retraining. We conduct extensive experiments to evaluate the proposed method. We train the noise detector based on the synthesized label-noised CIFAR dataset and test such noise detector on Tiny ImageNet, CUB-200, Caltech-256, WebVision and Clothing1M. Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation, and outperforms state-of-the-art methods. Besides, more experiments demonstrate that the mislabel identification can guide a label correction, namely data debugging, providing orthogonal improvements of algorithm-centric state-of-the-art techniques from the data aspect.
Conceptualization for intended action: A dynamic model

Concepts are the building blocks of higher-order cognition and consciousness. Building on Conceptual Spaces Theory (CST) and proceeding from the assumption that concepts are inherently dynamic, this paper provides historical context to and significantly elaborates the previously offered Iterative Subdivision Model (ISDM) with the goal of pushing it toward empirical testability. The paper describes how agents in continuous interaction with their environment adopt an intentional orientation, estimate the utility of the concept(s) applicable to action in the current context, engage in practical action, and adopt any new concepts that emerge: a largely pre-intellectual cycle that repeats essentially without interruption over the conceptual agent's lifetime. This paper elaborates utility optimization by establishing three constraints on concept formation/evaluation—non-redundancy, distinctiveness and proportionality—embedding them in a quasi-mathematical model intended for development into a formal logic. The notion of a distinctor—a quality dimension of the conceptual space in focus at any given time, used for making what we call a difference distinction—is key. The primary contribution of the revised ISDM is the way it relates concepts to action via utility optimization/actualization and the way it describes the emergence of quality dimensions through trial-by-action (trial and error), something previous presentations of CST have failed to address.
Probabilistic Reasoning with Transformer Networks: An Application to Predicate Judgment

Transformer networks that are trained on participant-generated feature norms have been shown to replicate several behavioral patterns observed in prior research on human semantic cognition. In this paper, I compare the predictions of these networks with participant evaluations of sentences sampled from a vast domain of discourse, with thousands of naturalistic predicates. I find that existing transformer networks make human-like predictions when given atomic sentences. However, they fail to capture participant evaluations of compound sentences with conjunctions, disjunctions, negations, and conditionals. Instead, participant responses are best captured by a Probabilistic Reasoner, which aggregates transformer judgments according to the rules of probability theory. These results suggest that human semantic cognition may also rely on a combination of associative processes and deliberative processes when judging complex statements about the world. In doing so they show how probabilistic cognitive modeling and neural network modeling can be integrated to study naturalistic high-level cognition.
A Bayesian algorithm for sample selection bias correction

In this paper we present a technique to couple non-traditional data with statistics based on survey data, in order to partially correct for the bias produced by non-random sample selections. All major social media platforms represent huge samples of the general population, generated by a self-selection process. This implies that they are not representative of the larger public, and there are problems in extrapolating conclusions drawn from these samples to the whole population. We present an algorithm to integrate these massive data with ones coming from traditional sources, with the properties of being less extensive but more reliable. This integration allows to exploit the best of both worlds and reach the detail of typical "big data" sources and the representativeness of a carefully designed sample survey.
Penggunaan Media E-Book Creator Untuk Meningkatkan Kemampuan Menulis Cerita Inspiratif Peserta Didik Kelas IX F SMP Negeri 1 Dayeuhkolot Kabupaten Bandung Semester Genap Tahun Pelajaran 2021-2022

This research was conducted based on observations with fellow Indonesian language teachers for class IX at SMP Negeri 1 Dayeuhkolot, it turned out that the students' ability to write inspirational stories was still low. This condition is found especially in Class IX F. The problems of students in writing inspirational stories the writer wants to improve by using e-book creator learning media in the learning process of writing inspirational stories. The purpose of this study was to improve the quality of the learning process of writing inspirational stories for class IX F students of SMP Negeri 1 Dayeuhkolot using e-book creator media and to improve the quality of learning outcomes for writing inspirational stories for students of class IX F at SMP Negeri 1 Dayeuhkolot. This research took place at Dayeuhkolot 1 Public Middle School, Bandung Regency. The form of research is Classroom Action Research, namely action research conducted with the aim of improving the quality of learning practices in the classroom. Data collection techniques with observation/observations, tests, and questionnaires. The data that has been collected is analyzed by comparing the results of the actions in each cycle with predetermined indicators of success. The results of the study obtained that the use of e-book creator media can improve students' ability to write inspirational stories. This increase can be seen, at the beginning of the study the percentage of students completing 38%, Cycle I became 67%, and in Cycle II became 86%.
Improving educational practice using the methodological approach known as “lesson study

This project is designed for the improvement of educational practice using the methodological approach known as “lesson study” in the teaching-learning process of mathematics in the first grade of elementary education. The research, which took place in elementary schools of the city of Puebla, was quasi-experimental with a mixed approach and a descriptive correlational study. It showed that there is indeed a relation between reflective teaching and achievement in students. The process of interpretation of the results is described at the end of the article in a descriptive and explanatory way.
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model

The recently released NLLB-200 is a set of multilingual Neural Machine Translation models that cover 202 languages. The largest model is based on a Mixture of Experts architecture and achieves SoTA results across many language pairs. It contains 54.5B parameters and requires at least four 32GB GPUs just for inference. In this work, we propose a pruning method that enables the removal of up to 80% of experts without further finetuning and with a negligible loss in translation quality, which makes it feasible to run the model on a single 32GB GPU. Further analysis suggests that our pruning metrics can identify language-specific experts.
Cultural Problems and Challenges Encountering Translator of Children’s Literature: Snow White as a Case Study

This study aims to discuss the cultural norms in the translation of children’s literature that involves the concept of culture as well as translation and Culture; it also involves the identification of other concepts that include Translation and Culture as well as Deletions, Retention, and Replacement as well as Addition. This study also involves the description of the Children’s literature of Snow White as a case study and Hyper Cultural Sensitivity.This study relied on the analytical methods of many prior studies, as well as the method of text analysis, to achieve the most accurate results for the research problem i.e. cultural problems and challenges encountered by children's literature translators. The study found that the hypersensitivity of the culture may cause the decisiveness of the influence associated with the images of childhood in the translated texts. Misinterpretations or conflicts may primarily occur when people are not aware of their behavioural rules and project them onto others. Intercultural awareness is essential in the case of communication with people from other cultures as they observe and interpret as well as evaluate things in a variety of ways.
Synthetic Pre-Training Tasks for Neural Machine Translation

Pre-training models with large crawled corpora can lead to issues such as toxicity and bias, as well as copyright and privacy concerns. A promising way of alleviating such concerns is to conduct pre-training with synthetic tasks and data, since no real-world information is ingested by the model. Our goal in this paper is to understand the factors that contribute to the effectiveness of pre-training models when using synthetic resources, particularly in the context of neural machine translation. We propose several novel approaches to pre-training translation models that involve different levels of lexical and structural knowledge, including: 1) generating obfuscated data from a large parallel corpus 2) concatenating phrase pairs extracted from a small word-aligned corpus, and 3) generating synthetic parallel data without real human language corpora. Our experiments on multiple language pairs reveal that pre-training benefits can be realized even with high levels of obfuscation or purely synthetic parallel data. We hope the findings from our comprehensive empirical analysis will shed light on understanding what matters for NMT pre-training, as well as pave the way for the development of more efficient and less toxic models.
A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models

There is growing interest in incorporating eye-tracking data and other implicit measures of human language processing into natural language processing (NLP) pipelines. The data from human language processing contain unique insight into human linguistic understanding that could be exploited by language models. However, many unanswered questions remain about the nature of this data and how it can best be utilized in downstream NLP tasks. In this paper, we present eyeStyliency, an eye-tracking dataset for human processing of stylistic text (e.g., politeness). We develop a variety of methods to derive style saliency scores over text using the collected eye dataset. We further investigate how this saliency data compares to both human annotation methods and model-based interpretability metrics. We find that while eye-tracking data is unique, it also intersects with both human annotations and model-based importance scores, providing a possible bridge between human- and machine-based perspectives. We propose utilizing this type of data to evaluate the cognitive plausibility of models that interpret style. Our eye-tracking data and processing code are publicly available.
Future Sight: Dynamic Story Generation with Large Pretrained Language Models

Recent advances in deep learning research, such as transformers, have bolstered the ability for automated agents to generate creative texts similar to those that a human would write. By default, transformer decoders can only generate new text with respect to previously generated text. The output distribution of candidate tokens at any position is conditioned on previously selected tokens using a self-attention mechanism to emulate the property of autoregression. This is inherently limiting for tasks such as controllable story generation where it may be necessary to condition on future plot events when writing a story. In this work, we propose Future Sight, a method for finetuning a pretrained generative transformer on the task of future conditioning. Transformer decoders are typically pretrained on the task of completing a context, one token at a time, by means of self-attention. Future Sight additionally enables a decoder to attend to an encoded future plot event. This motivates the decoder to expand on the context in a way that logically concludes with the provided future. During inference, the future plot event can be written by a human author to steer the narrative being generated in a certain direction. We evaluate the efficacy of our approach on a story generation task with human evaluators.
Classification of Online Course Teaching Cases Based on an Improved Clustering Method

The classification and sharing of online course teaching cases based on students’ individual learning needs can help teachers save time and cost in preparation of lessons and enrich their teaching content, and what is more, it is in line with the digitalization trend of the modern data-driven education. Currently, there has been few research on the combination of new information technology with teaching case classification, and the research is not adequate on the existing algorithms for classification of online course teaching cases. Therefore, this paper takes the teaching cases of online open courses of finance as the object, and studies the classification method for online course teaching cases based on an improved clustering method. First, the text clustering problem of finance teaching cases was described, and the conversion process of the abstracts of the case text and the evaluation method for the importance of words to teaching cases were given. Then, the key sentences were extracted from the text of the finance teaching cases, and the related algorithm was introduced. After that, the clustering flow chart for long texts of finance teaching cases was shown, and the principle of the combined clustering algorithm for finance teaching cases with uneven distribution of long texts and short texts was described in detail. The experimental results verified the effectiveness of the proposed algorithm.