Reviewerly/exHarmony huggingface dataset:
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning

State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs. In this paper, we address this issue by showing that multi-hop and more complex logical reasoning can be accomplished separately without losing expressive power. Motivated by this insight, we propose an approach to multi-hop reasoning that scales linearly with the number of relation types in the graph, which is usually significantly smaller than the number of edges or nodes. This produces a set of candidate solutions that can be provably refined to recover the solution to the original problem. Our experiments on knowledge-based question answering show that our approach solves the multi-hop MetaQA dataset, achieves a new state-of-the-art on the more challenging WebQuestionsSP, is orders of magnitude more scalable than competitive approaches, and can achieve compositional generalization out of the training distribution.
Neural Bootstrapper

Bootstrapping has been a primary tool for uncertainty quantification, and their theoretical and computational properties have been investigated in the field of statistics and machine learning. However, due to its nature of repetitive computations, the computational burden required to implement bootstrap procedures for the neural network is painfully heavy, and this fact seriously hurdles the practical use of these procedures on the uncertainty estimation of modern deep learning. To overcome the inconvenience, we propose a procedure called \emph{Neural Bootstrapper} (NeuBoots). We reveal that the NeuBoots stably generate valid bootstrap samples that coincide with the desired target samples with minimal extra computational cost compared to traditional bootstrapping. Consequently, NeuBoots makes it feasible to construct bootstrap confidence intervals of outputs of neural networks and quantify their predictive uncertainty. We also suggest NeuBoots for deep convolutional neural networks to consider its utility in image classification tasks, including calibration, detection of out-of-distribution samples, and active learning. Empirical results demonstrate that NeuBoots is significantly beneficial for the above purposes.
Industrial Training Report: Universiti Sultan Zainal Abidin (UniSZA) Library, Gong Badak Campus / Che Wan Karmila Che Wan Ahmad

Library profession is specialized and technical in nature and so industrial training for the library science students must have both the elements of theory and practical skills incorporated. The industrial training is the student learns, practice and exposes the real working environment. Industrial training can be known as practical training. From practical training, student also learns about the ability to working under pressure, work ethics, and demands of the organization. Usually, practical training has supervision from the staff of the organization. In order to fulfil the requirements of IM 244 program, this training is requirement from the University Technology MARA (UiTM) for student of final year in Bachelor of lnformation Science (Hons.) Library Management. This paper will present about industrial training at Library of Universiti Sultan Zainal Abidin (UniSZA) Campus Gong Badak, Kuala Terengganu that have been approved by the Office of Registration of Universiti Sultan Zainal Abidin (UniSZA). Student must undergo and carried out this industrial training for five months, started from 03 February 2019 until 30 June 2019.
Designing Counterfactual Generators using Deep Model Inversion

Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models. Commonly referred to as counterfactuals, the synthesized explanations are required to contain discernible changes (for easy interpretability) while also being realistic (consistency to the data manifold). In this paper, we focus on the case where we have access only to the trained deep classifier and not the actual training data. While the problem of inverting deep models to synthesize images from the training distribution has been explored, our goal is to develop a deep inversion approach to generate counterfactual explanations for a given query image. Despite their effectiveness in conditional image synthesis, we show that existing deep inversion methods are insufficient for producing meaningful counterfactuals. We propose DISC (Deep Inversion for Synthesizing Counterfactuals) that improves upon deep inversion by utilizing (a) stronger image priors, (b) incorporating a novel manifold consistency objective and (c) adopting a progressive optimization strategy. We find that, in addition to producing visually meaningful explanations, the counterfactuals from DISC are effective at learning classifier decision boundaries and are robust to unknown test-time corruptions.
Contact Tracing During The COVID-19 Pandemic In District Health Directorate Example in Istanbul, Turkey

Abstract Background: After March 2020, with the first case of novel coronavirus, Turkish health care system switched to state of emergency to manage the COVID-19 pandemic. In order to control the pandemic, a scientific committee was established, guidelines were prepared, and filiation/contact tracing studies were started. Field investigation and isolation teams were established, and District Health Directorates (DHD) became frontline primary care organizations to manage contact tracing in Turkey. Case presentation: In this report, pandemic management and contact tracing were evaluated in example of Kartal DHD from Istanbul, Turkey. The report contains the details about the planning process, Kartal DHD Pandemic Management priorities, aim and planning of this policy, coordination and cooperation with other institutes to overcome this extraordinary state. Also, evaluation of the contact tracing and telemedicine usage in the pandemic management is examined as well with future implications. Conclusions: From the SWOT analysis to planning process, this report attempts to describe how this state of emergency was managed and how effective the contact tracing management process was. With its detailed key points of the pandemic management and future perspectives, it has been written in terms of preparation for the future pandemics.
A Study on Improvement of NCS based Hotelier Certification System: Focusing on the Utilization of AHLA Certification

The study aims to analyze the problems of the current NCS-based hotelier certification system in Korea and derive suggestions for improving it. Delphi and Focus Group Interview(FGI) were used as research methods, and the survey was conducted form 22 March to 17 April 2021.<BR> The research proposed the following measures to revitalize the current certification system: Firstly, provide appropriate compensation to the certified hotelier by adopting the certification acquirer’s mandatory employment. Secondly, introduce the private certificate and use it with the national accreditation certification schemes by departmental and positional classification. Fourthly, increase the proportion of foreign languages in the certification examination. Finally, the research suggests that utilizing the certification system implemented by the AHLA would be the most appropriate alternative to expanding the availability, activating its application, and segregating departmental and positional segregation appropriately of the current certification system in Korea.
DId COVID 19 help authors to publish in high impact journal

The progressing of countries is measured by scientific research, Iraq made a good progressing in recent three years, whereas the Ministry of Higher Education and Scientific Research announced that Iraq ranked fifty-fourth among the publishing countries in Scopus. The study aim is to look after if COVID 19 help Iraq to get this rank? The information was collected by questionnaires to general people and a phone interview with chief- editors of Iraqi journals had been made. The results showed that the COVID 19 play an important role in helping authors to collect any information about paper publishing in high impact journals and this is due to the ease of access to information that was previously restricted with a specific time and place and became available online because of the COVID 19 lockdown. Despite the seriousness of COVID 19 but as we know that always there is a positive aspect of everything happening around us, COVID 19 helped us to know an important advantage of e-learning that we do not know before.
Risk-Aware Transfer in Reinforcement Learning using Successor Features

Sample efficiency and risk-awareness are central to the development of practical reinforcement learning (RL) for complex decision-making. The former can be addressed by transfer learning and the latter by optimizing some utility function of the return. However, the problem of transferring skills in a risk-aware manner is not well-understood. In this paper, we address the problem of risk-aware policy transfer between tasks in a common domain that differ only in their reward functions, in which risk is measured by the variance of reward streams. Our approach begins by extending the idea of generalized policy improvement to maximize entropic utilities, thus extending policy improvement via dynamic programming to sets of policies and levels of risk-aversion. Next, we extend the idea of successor features (SF), a value function representation that decouples the environment dynamics from the rewards, to capture the variance of returns. Our resulting risk-aware successor features (RaSF) integrate seamlessly within the RL framework, inherit the superior task generalization ability of SFs, and incorporate risk-awareness into the decision-making. Experiments on a discrete navigation domain and control of a simulated robotic arm demonstrate the ability of RaSFs to outperform alternative methods including SFs, when taking the risk of the learned policies into account.
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization

Recently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zero-shot cross-domain setting. Despite achieving good performance on some public benchmarks, we observe that existing text-to-SQL models do not generalize when facing domain knowledge that does not frequently appear in the training data, which may render the worse prediction performance for unseen domains. In this work, we investigate the robustness of text-to-SQL models when the questions require rarely observed domain knowledge. In particular, we define five types of domain knowledge and introduce Spider-DK (DK is the abbreviation of domain knowledge), a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-DK are selected from Spider, and we modify some samples by adding domain knowledge that reflects real-world question paraphrases. We demonstrate that the prediction accuracy dramatically drops on samples that require such domain knowledge, even if the domain knowledge appears in the training set, and the model provides the correct predictions for related training samples.
MANAJEMEN PENGELOLAAN ARSIP DINAMIS KEPEGAWAIAN KANTOR KEMENTERIAN AGAMA KABUPATEN MALUKU TENGAH

This study aims to determine the management of dynamic records management, the obstacles that arise, as well as efforts to overcome the obstacles that occur in the management of dynamic archives at the Office of the Ministry of Religion of Central Maluku Regency. This research is descriptive using a qualitative approach so that to support this research activity, data collection from informants is carried out. Data analysis is preceded by collecting and collecting data or information in the field, after that it is classified according to the type and group, then it is analyzed or interpreted qualitatively. Based on the results of the analysis, it is known that the management of dynamic archives at the Office of the Ministry of Religion of Central Maluku Regency follows the Decree of the Minister of Religion No. 44 of 2010 concerning Archives Guidelines, that the management of dynamic records includes the creation of records, the use, maintenance and depreciation of records. Some of the obstacles faced in structuring dynamic archives are inadequate facilities used, lack of archiving employees, lack of ability and knowledge of employees who handle archives, archive room conditions that do not meet standards such as air temperature. Several efforts to overcome these obstacles in the management of dynamic archives are maximizing the use of facilities and infrastructure, optimizing employees who really deserve to occupy positions that handle the archives field, increasing the knowledge of employees who take care of archives by including archivist training, and procurement of air conditioning. especially in a room that lacks air circulation.
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization

There have been many recent advances in representation learning; however, unsupervised representation learning can still struggle with model identification issues. Variational Auto-Encoders (VAEs) and their extensions such as $\beta$-VAEs have been shown to locally align latent variables with PCA directions, which can help to improve model disentanglement under some conditions. Borrowing inspiration from Independent Component Analysis (ICA) and sparse coding, we propose applying an $L_1$ loss to the VAE's generative Jacobian during training to encourage local latent variable alignment with independent factors of variation in the data. We demonstrate our results on a variety of datasets, giving qualitative and quantitative results using information theoretic and modularity measures that show our added $L_1$ cost encourages local axis alignment of the latent representation with individual factors of variation.
Improved Cuckoo Search Algorithm for Future Selection

The classification of high-dimensional dataset is challenging as it contains large amount irrelevant and noisy features. Thus, feature selection is performed in the dataset to eliminate these redundant features. It reduces the dimensionality of the dataset and increases the classification accuracy. Hence, for selecting the relevant features in high dimensional data, an improved cuckoo search algorithm (ICSA) was proposed in this paper. After feature selection, the dataset undergo classification using KNN classifier and SVM classifier. The experimental process illustrates that the improved cuckoo search algorithm effectively increases the classification accuracy by reducing the number of features in the dataset. For analysing the proposed algorithm, seven UCI repository dataset have been utilised. Also, the ICS algorithm is compared with other existing algorithms for the given dataset. From the investigation process, it was concluded that the proposed algorithm selects lesser number of features and also enhances the classification accuracy than the other existing algorithms.
Relative Flatness and Generalization

Flatness of the loss curve is conjectured to be connected to the generalization ability of machine learning models, in particular neural networks. While it has been empirically observed that flatness measures consistently correlate strongly with generalization, it is still an open theoretical problem why and under which circumstances flatness is connected to generalization, in particular in light of reparameterizations that change certain flatness measures but leave generalization unchanged. We investigate the connection between flatness and generalization by relating it to the interpolation from representative data, deriving notions of representativeness, and feature robustness. The notions allow us to rigorously connect flatness and generalization and to identify conditions under which the connection holds. Moreover, they give rise to a novel, but natural relative flatness measure that correlates strongly with generalization, simplifies to ridge regression for ordinary least squares, and solves the reparameterization issue.
An Efficient and Fast Security Technique for Online Services and Applications

Nowadays, online based services and applications have exploded around the world due to the advancement of Internet technology so that any type of information can be transmitted and received very easily. These online basedinformation may be belongs to E-Commerce system, government oriented system or E-learning system. In this regard, secure and effective security requirements are required to prevent such applications from the malicious attacks. When a sender wants to send an electronic document to the receiver, an attacker can modify it and send the altered document to that receiver. Digital Signature is applied to avoid such situations and it provides not only the confidentiality and integrity of the document but also provides non-repudiation so that the signature can’t be denied by the signer. In this paper, we have discussed about the comparative study of Elliptic Curve Digital Signature algorithm (ECDSA) with Digital Signature algorithm (DSA) to achieve a better security mechanism for authenticity and integrity in such systems based on the performance of computation time and security strength. Thereby, our experimental comparison results show that the ECDSA is more suitable to ensure the confirming online services to the end users through insecure media.
Neural Network On Multilayer Perception And Its Self Efficacy On Employeee Engagement In Higher Educational Institutions

This study examines the Neural networks determine the complex relationship between the variables through the mathematical modelling. It performs a sensitive analysis and computes the importance of the predictors in percentage through “Independent variable importance analysis”. A Mathematical diagram will be pictorially represented where a complex relationship will be determined through the multi-layer perceptron. The normalized importance score predicts the importance of the variable.
СИСТЕМАТИЧЕСКИЙ ОБЗОР И АНАЛИЗ ОСОБЕННОСТЕЙ ИДЕНТИФИКАЦИИ ПО ГОЛОСУ

Voice identification is the process of identifying a speaker by a given utterance by comparing voice biometrics of a utterance with those utterance models that were saved in advance. Voice identification technologies have gained a new direction due to advances in artificial intelligence and are widely used in various fields. Character extraction is one of the most important aspects of voice identification, which significantly affects the identification process and performance. This systematic review is conducted to identify, compare and analyze various approaches, methods and algorithms for extracting features for voice identification, to provide background information on character retrieval approaches for voice identification applications and future research. The study examined models: based on patterns, based on vector quantization, dynamic time transfer, histogram model, stochastic models, Gaussian mixture models and hidden Markov model, based on Mel-frequency cepstral coefficients, generative or vector quantization, discriminatory models (usually using machine learning methods such as SVM and ANN).This study showed that the current trend of identification research is to develop a robust, universal voice identification structure for solving important voice identification problems, such as adaptability, complexity, multilingual recognition, and resistance to noise. The results presented in this study are based on past publications, quotations and the number of implementations, the quotes being the most relevant. This article also presents the general process of voice identification.
Building a cyber security model for small and medium sized enterprises

This paper presents a review of literary sources in the field of cyber security modeling and management. The main objective is to help SMEs improve their level of protection against cyber attacks. The author strives to build a cyber security model that can be easily deployed in small and medium businesses.
El papel de la mediación cultural en el aprendizaje

The study of mediation activity within participatory and cultural projects has become an essential topic in recent times, as it has been considered as an interactional activity leading to moments of meaning-building in a group. When these moments of exchange take place, the constitution of both material and symbolic productions is analyzed and highlighted. This article explains all this from a participatory project related to the label of European Capital of Culture, Mons 2015 and shows how learning is created within the the collective during the activity of mediation, and materially translated during the cultural event.
A Contrastive Learning Approach for Training Variational Autoencoder Priors

Variational autoencoders (VAEs) are one of the powerful likelihood-based generative models with applications in many domains. However, they struggle to generate high-quality images, especially when samples are obtained from the prior without any tempering. One explanation for VAEs' poor generative quality is the prior hole problem: the prior distribution fails to match the aggregate approximate posterior. Due to this mismatch, there exist areas in the latent space with high density under the prior that do not correspond to any encoded image. Samples from those areas are decoded to corrupted images. To tackle this issue, we propose an energy-based prior defined by the product of a base prior distribution and a reweighting factor, designed to bring the base closer to the aggregate posterior. We train the reweighting factor by noise contrastive estimation, and we generalize it to hierarchical VAEs with many latent variable groups. Our experiments confirm that the proposed noise contrastive priors improve the generative performance of state-of-the-art VAEs by a large margin on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ 256 datasets. Our method is simple and can be applied to a wide variety of VAEs to improve the expressivity of their prior distribution.
ANALISIS CELAH KEAMANAN APLIKASI WEB E-LEARNING UNIVERSITAS ABC DENGAN VULNERABILITY ASSESMENT

The development of information and communication technology today brings convenience to human life. One of the things that is growing quite rapidly is a web-based application. The proliferation of web-based applications is a challenge for web-based application developers in developing security aspects. Vulnerability Assessment of the E-Learning web application aims to detect vulnerabilities, describe vulnerabilities, assess vulnerabilities based on the Common Vulnerability Scoring System, and provide solutions. The research stages used were the Vulnerability Assessment and Penetration Testing Life Cycle. In looking for vulnerabilities in this study using the Home version of the Nessus Vulnerability Scanning. Based on the results of the vulnerability scanning, it was found low vulnerability, medium vulnerability, high vulnerability, and critical vulnerability. Each vulnerability certainly has a different impact on vulnerability, but on a critical vulnerability, namely the Elasticsearch Transport Protocol Unspecified Remote Code Execution has the most serious impact with a base score of 9.8, so the overall risk level on the Web E-Learning application is High. So it can be concluded that the E-Learning Web application at ABC University is said to be vulnerable, because it has a serious impact that affects Confidentiality, Integrity, and Availability of the E-Learning web application through its vulnerabilities. Therefore, ABC University must immediately make improvements and evaluations of the security of the E-Learning Web Application so that the risk of vulnerability in the E-Learning Web Application can be reduced.