Reviewerly/exHarmony huggingface dataset:
RANCANG BANGUN SISTEM INFORMASI PENJUALAN BERBASIS WEB PADA CV MULTI MANDIRI

A system is a network of interconnected procedures, gathered together to perform an activity or to accomplish a particular goal. Information is data that has been processed into a form that is more useful and more meaningful for those who receive it. Information system is a system within an organization that brings daily transaction processing needs, support operations, managerial and strategic activities of an organization and provide certain outside parties with the necessary reports. The work done so far only uses manuals where sales are still using the records in a book, so the buildup of files / records becomes a very inefficient thing. In support of sales system activities, required a more computerized system in order to facilitate and simplify the process of selling, recording of incoming goods and out goods. The system design method used in this Final Report is to use a linear sequential methodology of waterfall approach, This model proposes an approach to software development. Modeled after conventional engineering cycles, the linear sequential model encompasses activities. Based on the results of this Final research, expected by the Sales Information System can help CV Multi Mandiri in making sales transactions more computerized, efficient and accurate.
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection

Detecting out-of-distribution (OOD) samples is vital for developing machine learning based models for critical safety systems. Common approaches for OOD detection assume access to some OOD samples during training which may not be available in a real-life scenario. Instead, we utilize the {\em predictive normalized maximum likelihood} (pNML) learner, in which no assumptions are made on the tested input. We derive an explicit expression of the pNML and its generalization error, denoted as the {\em regret}, for a single layer neural network (NN). We show that this learner generalizes well when (i) the test vector resides in a subspace spanned by the eigenvectors associated with the large eigenvalues of the empirical correlation matrix of the training data, or (ii) the test sample is far from the decision boundary. Furthermore, we describe how to efficiently apply the derived pNML regret to any pretrained deep NN, by employing the explicit pNML for the last layer, followed by the softmax function. Applying the derived regret to deep NN requires neither additional tunable parameters nor extra data. We extensively evaluate our approach on 74 OOD detection benchmarks using DenseNet-100, ResNet-34, and WideResNet-40 models trained with CIFAR-100, CIFAR-10, SVHN, and ImageNet-30 showing a significant improvement of up to 15.6\% over recent leading methods.
온･오프라인 일본어 수업 동시 진행 실천 사례 - 관광일본어 수업을 중심으로 -

This article examines the design and practice of a Japanese class that conducted classroom class and Google Meet online class simultaneously under the COVID􋹲19. It intends to use the results of intermediate and end􋹲of􋹲semester surveys conducted on the learners as data for developing the class into a better class. Quizlet, Google Meet, and Kakao Talk were used as edutech applications in this simultaneous online & offline class. As a result of the intermediate survey, Google Meet learners were found to have a lower level of overall understanding, speed and participation in the class than classroom learners, so individual guidance was added after the intermediate survey. As a result of the end􋹲of􋹲the􋹲semester survey, classroom learners were found to have a higher level of satisfaction with the use of Quizlet than Google Meet learners. In this aspect, the role of a teacher as an edutech user and the support of the school are required. In the end􋹲of􋹲the􋹲semester survey, Google Meet learners showed a high level of satisfaction with receiving individual guidance through Kakao Talk. Therefore, in the case of an increase in the number of online learners, a teacher seems to need the support of a teaching assistant.
Contrastively Disentangled Sequential Variational Autoencoder

Self-supervised disentangled representation learning is a critical task in sequence modeling. The learnt representations contribute to better model interpretability as well as the data generation, and improve the sample efficiency for downstream tasks. We propose a novel sequence representation learning method, named Contrastively Disentangled Sequential Variational Autoencoder (C-DSVAE), to extract and separate the static (time-invariant) and dynamic (time-variant) factors in the latent space. Different from previous sequential variational autoencoder methods, we use a novel evidence lower bound which maximizes the mutual information between the input and the latent factors, while penalizes the mutual information between the static and dynamic factors. We leverage contrastive estimations of the mutual information terms in training, together with simple yet effective augmentation techniques, to introduce additional inductive biases. Our experiments show that C-DSVAE significantly outperforms the previous state-of-the-art methods on multiple metrics.
Investigations Oncloud, Wireless Networking And Block Chain For High Encrypted Communications - A Detailed Survey

The easy usage of cell phones, IoT (Internet of Things), social media, analytics, and cloud technology that develop models for smarter decisions has provided the digital world new inventive goods, efficiencies, and fantastic consumer interactions all around the world. In this paper, Certain investigations on Cloud, Wireless Networking and Block Chain Technology for high encrypted communications are surveyed. In case of cloud, generic secure data storage model, A randomized client-side deduplication scheme, Bayesian Attack Graphs, cloud data deduplication scheme is been discussed along with its performance. And encryption techniques include GROSE approach, MCBE Scheme in the public key setting, dynamic Boolean SSE Scheme is discussed. In wireless networking, cluster-related routing protocol for WSNs, a dynamic spectrum access scheme, Mixed Integer Programming (MIP), In WMSN approaches, energy-efficient distributed adaptive cooperative routing is discussed. Bloch chain technologies like DV – PoA (designated – verifier proof of assets) and other networks likeVehicular and hoc networks – (HMAC), FASUS mechanism, PV/WEC Particle Swarm Optimization algorithm, SimpleStab, Online supervised method, UIPA, DNN algorithm, Snort and Suricata and finally USM sharpening detection algorithm for JPEG image performances are surveyed.
Automatic seismic waveform identification using a Convolutional Neural Network

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Automatic seismic waveform identification using a Convolutional Neural NetworkAuthorsJorge AlejandroGarciaiDLaurenWaszekiDBenoitTauziniDNicholas CharlesSchmerriDSee all authors Jorge Alejandro GarciaiDCorresponding Author• Submitting AuthorNew Mexico State UniversityiDhttps://orcid.org/0000-0001-5746-9094view email addressThe email was not providedcopy email addressLauren WaszekiDNew Mexico State UniversityiDhttps://orcid.org/0000-0002-9960-1484view email addressThe email was not providedcopy email addressBenoit TauziniDUniversite de LyoniDhttps://orcid.org/0000-0002-9589-4304view email addressThe email was not providedcopy email addressNicholas Charles SchmerriDUniversity of Maryland, College ParkiDhttps://orcid.org/0000-0002-3256-1262view email addressThe email was not providedcopy email address
Pruning Randomly Initialized Neural Networks with Iterative  Randomization

Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis. Ramanujan et al. (2020) empirically showed that only pruning the weights can achieve remarkable performance instead of optimizing the weight values. However, to achieve the same level of performance as the weight optimization, the pruning approach requires more parameters in the networks before pruning and thus more memory space. To overcome this parameter inefficiency, we introduce a novel framework to prune randomly initialized neural networks with iteratively randomizing weight values (IteRand). Theoretically, we prove an approximation theorem in our framework, which indicates that the randomizing operations are provably effective to reduce the required number of the parameters. We also empirically demonstrate the parameter efficiency in multiple experiments on CIFAR-10 and ImageNet. The code is available at: https://github.com/dchiji-ntt/iterand
Conceptualizing Artefact Influence: A Grounded Theory Exploration of an ICT4D Study in Uganda

In Sub-Saharan Africa (SSA), ICT for Development (ICT4D) and Information Systems in Developing Countries (ISDC) studies postulate ICTs augmenting development conditions in multiple socioeconomic sectors. Building upon the multi-disciplinary nature of Grounded Theory (GT) methodology, this paper examines and theorises participant perspectives regarding the developmental influence of the Water Supply Database (WSDB), which is an ICT artefact implemented by Uganda’s Ministry of Water and Environment (MWE) to improve information flow, data sharing and decision making in the water sector. This exploratory study examines the WSDB, and the experience of adopting Glaserian Classic Grounded Theory (CGT) methodology to collect and analyse qualitative data to develop theory. Findings led to the conceptualisation of the theory Optimising Perspectives which categorises development influence of the WSDB and reasons for its continued success. Finally, the paper highlights relevance of using CGT in ICT4D or ISDC research, and implication of adopting CGT in studies that contribute to understanding developmental influence of ICTs in SSA.
A STUDY ON EFFECTS OF CORPORATE TRANSPARENCY – LITERATURE REVIEW AND RESEARCH AGENDA

Purpose – The main objective of this paper is to perform a literature review on corporate transparency and its effects as well as to identify gaps in the current body of knowledge for justifying future research directions. This paper performs a bibliometric analysis and network visualisation with page rank analysis to appraise journals citation quality and quantity. Methodology – The study reviews 121 articles and research papers from the referred journal and international conferences published on the topic of corporate transparency and its effects.  This review process was achieved by using bibliometric and network analysis with the help of a tool – Gephi, that provides an in-depth insight on this topic through different visualization models. Findings - The detailed analysis has identified that most of the work on corporate transparency has been conducted in the developed economies and lesser amount of work is done on the developing economies like India. With reference to the comprehensive analysis after synthesizing the findings, it is clear that the currently available research has not covered all the multidimensional facets of corporate transparency. Through network analysis and page rank analysis the authors identify the prestigious journals in the current field of research. Thus, by considering all the identified gaps and various open opportunities mentioned, we strongly believe that the creation of a new transparency index model with the additional set of attributes, i.e., Financial as well as Non-Financial determinants, stakeholders will be able to take a conscious and timely decision.  Implications - The current study will be helpful for researchers who are willing to perform research in the field of corporate transparency.  In spite of fewer works of literature on transparency, none of the studies have focussed on corporate transparency and its effects. Hence, this paper presents the main findings of the academic literature by carrying out an in-depth analysis.  it Originality/Value - This paper gives a detailed analysis of a list of publications on the effects of corporate transparency and provides a structured format for its various traits. This paper will be helpful for research scholars, professionals in the field of finance, and investors who make decisions related to investments in the financial markets. To the best of our knowledge, there is no literature review papers using network analysis on this topic in any of the academic journals.
Lista ilustrada de tipos documentais: identificação da atividade produtora e construção conceitos tipos de documentos inseridos no modelo de implantação do Sistema de Eletrônico de Informação (SEI) da Fundação Casa de Rui Barbosa

This research aims to identify the document type as an element of representation of the institution that produced it and the construction of concepts for the document types defined in the institutional project that implemented the electronic process at Fundacao Casa de Rui Barbosa, known as PenSei Digital. In our assumption, when we identify the type of document, we can see in it and in the set of documents to which the representation of the producing institution belongs. The identification of a documental type is constituted by two factors, the recognition of what its species is and its production activity. This research has a qualitative approach and will use the analysis of legislation of the Brazilian public administration, which the Foundation is subject to compliance for the execution of its activities, the Foundation's regulations and entries in legal, administrative and legal dictionaries. types of documents, in order to verify the archival representation, among the requirements of the legislation that govern the procedures of the public administration, the Foundation's activities and the archival documents produced, and also assign definitions to them.
Techniques for Symbol Grounding with SATNet

Many experts argue that the future of artificial intelligence is limited by the field's ability to integrate symbolic logical reasoning into deep learning architectures. The recently proposed differentiable MAXSAT solver, SATNet, was a breakthrough in its capacity to integrate with a traditional neural network and solve visual reasoning problems. For instance, it can learn the rules of Sudoku purely from image examples. Despite its success, SATNet was shown to succumb to a key challenge in neurosymbolic systems known as the Symbol Grounding Problem: the inability to map visual inputs to symbolic variables without explicit supervision (label leakage). In this work, we present a self-supervised pre-training pipeline that enables SATNet to overcome this limitation, thus broadening the class of problems that SATNet architectures can solve to include datasets where no intermediary labels are available at all. We demonstrate that our method allows SATNet to attain full accuracy even with a harder problem setup that prevents any label leakage. We additionally introduce a proofreading method that further improves the performance of SATNet architectures, beating the state-of-the-art on Visual Sudoku.
Linear Stochastic Feature Embedding based Regressive MIL Boost Data Classification for Streaming Data

With the extensive growth of the Internet and rapid development, a large volume of data appears in different formats that are not able to process by conventional technologies. Since the dimensionality of the data stream is higher and the distribution of the data changes over time leads to improve accuracy in classification. Data Stream classification is the process of identifying the useful patterns from the continuous data records. In order to improve the classification accuracy of data stream, a novel technique called Linear Stochastic Feature Embedding Based Regressive Multiple Instance Learning (MIL) Boost Classification (LSFE-RMILBC) is introduced. The LSFE-RMILBC technique comprises of two major processes like dimensionality reduction and data classification. The dimensionality of the dataset is minimized by selecting the relevant features and removes the other features using the Modified Locally-Linear Stochastic Embedding technique. With the selected feature subset, the data stream classification is performed to improve the data stream classification. During the classification, the proposed MIL Boost Classification technique resolves the problem of concept drift with increase in classification accuracy and minimizes the false positive rate. MIL Boost Classification uses the Moving average regressive quadratic discriminant analysis as a weak learner for identifying the concept drift and for classifying the data with higher accuracy.   The extensive experimentation is conducted using weather data streams shows that the novel LSFE-RMILBC technique outperforms the recent methods. Specifically, the method yields higher classification accuracy, lesser time as well as lower false positive rates.
Least Significant Bit (LSB) Modification Analysis With Alternate Insertion Message Technique On Image Media

One aspect of success in data communication is security. Data security can be done by using steganography. Steganography is a way to hide messages in the media in such a way that other people do not realize there is a message in the media. Many of the algorithms used in steganography one of which is the Least Significant Bit. In this research, the authors modify Least Significant Bit algorithms with Alternate Insertion method. Modification is doing of embedding and extracting messages process by changing the RGB values ??at each pixel of image data with confidential file by inserting message alternately and insertionly until the all message inserted. This steganography applications have input password that is used as the key of a message can't be opened in addition to the addressee so that the data will be kept confidential. The parameters used to measure the performance of the Least Significant Bit and Alternate Insertion are using runtime program and analysist algorithms of Big . The message insertion applications built by using C# (C Sharp) programming language. From the results of research that extracting process messages work faster than the embedding process. More larger of image in used it will be spend longer time to process the inserting message. Time complexity of algorithms (Big ) embedding and extracting processes obtained in testing the system is T (n) = (xy). From the results of research that extracting process messages work faster than the embedding process. More larger of image in used it will be spend longer time to process the inserting message. Time complexity of algorithms (Big ) embedding and extracting processes obtained in testing the system is T (n) = (xy). From the results of research that extracting process messages work faster than the embedding process. More larger of image in used it will be spend longer time to process the inserting message. Time complexity of algorithms (Big ) embedding and extracting processes obtained in testing the system is T (n) = (xy).
The Low-Dimensional Linear Geometry of Contextualized Word Representations.

Black-box probing models can reliably extract linguistic features like tense, number, and syntactic role from pretrained word representations. However, the manner in which these features are encoded in representations remains poorly understood. We present a systematic study of the linear geometry of contextualized word representations in ELMO and BERT. We show that a variety of linguistic features (including structured dependency relationships) are encoded in low-dimensional subspaces. We then refine this geometric picture, showing that there are hierarchical relations between the subspaces encoding general linguistic categories and more specific ones, and that low-dimensional feature encodings are distributed rather than aligned to individual neurons. Finally, we demonstrate that these linear subspaces are causally related to model behavior, and can be used to perform fine-grained manipulation of BERT’s output distribution.
Fight Detection from Still Images in the Wild.

Detecting fights from still images shared on social media is an important task required to limit the distribution of violent scenes in order to prevent their negative effects. For this reason, in this study, we address the problem of fight detection from still images collected from the web and social media. We explore how well one can detect fights from just a single still image. We also propose a new dataset, named Social Media Fight Images (SMFI), comprising real-world images of fight actions. Results of the extensive experiments on the proposed dataset show that fight actions can be recognized successfully from still images. That is, even without exploiting the temporal information, it is possible to detect fights with high accuracy by utilizing appearance only. We also perform cross-dataset experiments to evaluate the representation capacity of the collected dataset. These experiments indicate that, as in the other computer vision problems, there exists a dataset bias for the fight recognition problem. Although the methods achieve close to 100% accuracy when trained and tested on the same fight dataset, the cross-dataset accuracies are significantly lower, i.e., around 70% when more representative datasets are used for training. SMFI dataset is found to be one of the two most representative datasets among the utilized five fight datasets.
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote

We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev-Cantelli inequality (a.k.a. one-sided Chebyshev's), which is amenable to efficient minimization. The new form resolves the optimization challenge faced by prior oracle bounds based on the Chebyshev-Cantelli inequality, the C-bounds [Germain et al., 2015], and, at the same time, it improves on the oracle bound based on second order Markov's inequality introduced by Masegosa et al. [2020]. We also derive the PAC-Bayes-Bennett inequality, which we use for empirical estimation of the oracle bound. The PAC-Bayes-Bennett inequality improves on the PAC-Bayes-Bernstein inequality by Seldin et al. [2012]. We provide an empirical evaluation demonstrating that the new bounds can improve on the work by Masegosa et al. [2020]. Both the parametric form of the Chebyshev-Cantelli inequality and the PAC-Bayes-Bennett inequality may be of independent interest for the study of concentration of measure in other domains.
Students’ perception on one day one chapter one week one article program in fostering students’ literacy

Literacy is necessary for the upcoming years’ time, the 21st century. Life will be far easier for those who are literate in many aspects of life. To be technology literate, reading literacy is believed to be the very first literacy to be acquired. When one has reading literacy, other literacies might be easier to be achieved. As an effort to keep promoting the freshmen’s reading literacy, similar to Gerakan Literasi, English Education Department introduced a program namely “One Day One Article One Week One Chapter”. The principal reason of establishing this program is building the freshmen’s reading literacy and with awareness of supporting and continuing program Gerakan Literasi in which they used to read for around 15 minutes every morning before class when they were in Senior High School. research aims at investigating the students perception on this program. This research is qualitative by using a survey which was distributed to class A of the first semester students of English Education Department in academic year 2016/2017 to know students’ perception on the program. The finding reveals that the perception of the freshmen in the program is positive. In other words, this program is worthy to be sustained.