Reviewerly/exHarmony huggingface dataset:
PERKEMBANGAN PENGELOLAAN ARSIP ELEKTRONIK DI INDONESIA: TINJAUAN PUSTAKA SISTEMATIS

Introduction. In Indonesia, technology has developed in various fields, this study explores the management of archives in Indonesian institutions still uses conventional archives or manages records electronically. The result of our findings also convey information on the development of electronic records management that has been carried out through a systematic literature review.Methods. A systematic literature review method was used for exploring electronic records management study. The use of this method is designed to search for, assess, and synthesize the best available evidence. The goal is to get informative, evidence-based answers related to the research questions.Analysis. The research will be analyzed based on the theory of Population, Intervention, Comparison, Outcomes and Context (PICOC). Results and Discussion. Carried out through a systematic literature review method, our results found 20 documents that had links with electronic archive management activities. The institution applies electronic archive management by using software technology such as computer-based applications, Android-based applications on mobile phones, and website-based network applications to various processes of archiving activities.Conclusion Our results suggest that the management of institutional records in Indonesia should follow the development by implementing electronic management.
Survey of Onion Routing Approaches: Advantages, Limitations and Future Scopes

The rapid advancement of technology has led to the evolution of the Internet, which brought changes in the lives of humans drastically. Since then slowly, all the devices started connecting via the internet user has the freedom and ability to store the information and extract it when needed. But this requires the implementation of active security measures to ensure that the data is confidential and secured. Onion routing was implemented to secure the data from eavesdropping and traffic analysis which guarantees the security of data with the help of private keys. This paper presents a detailed analysis of onion routing algorithms and its variants based on the implementation, features, security concerns and weakness.
A Study on education system centered virtuous action in Ancient China

This paper is to study the fact that the educational system of ancient China was composed of virtuous action, which can be used in the modern times where technology and information have developed. Therefore, it can be a study that reminds us that virtue is emphasized more in the age of the fourth industrial revolution. The purpose of education is to reveal what feudal morality demands from people, knowledge education is the basis of moral education and necessary to cultivate morals. The ideal human resource development has been based on the moral education and its basic and content, and the reason why moral education should be the center of the whole curriculum is because human beings are moral beings. But uniform moral education has been one of the causes of the development of natural science and technology in China for hundreds of years. This is a problem to be raised, and furthermore, it is time to study the utilization of moral education.
Gaussianization Flows.

Iterative Gaussianization is a fixed-point iteration procedure that can transform any continuous random vector into a Gaussian one. Based on iterative Gaussianization, we propose a new type of normalizing flow model that enables both efficient computation of likelihoods and efficient inversion for sample generation. We demonstrate that these models, named Gaussianization flows, are universal approximators for continuous probability distributions under some regularity conditions. Because of this guaranteed expressivity, they can capture multimodal target distributions without compromising the efficiency of sample generation. Experimentally, we show that Gaussianization flows achieve better or comparable performance on several tabular datasets compared to other efficiently invertible flow models such as Real NVP, Glow and FFJORD. In particular, Gaussianization flows are easier to initialize, demonstrate better robustness with respect to different transformations of the training data, and generalize better on small training sets.
A personalized search model using online social network data based on a holonic multiagent system

Personalized search utilizes user preferences to optimize search results, and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data. However, the behavioral data are noisy because users often clicked some irrelevant documents to find their required information, and the new user cold start issue represents a serious problem, greatly reducing the performance of personalized search. This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results, mine the knowledge of user interests, user influence and user relationships from online social networks, and use this knowledge to optimize the results returned by search engines. The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model. The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.
Deep Dive into Deep Neural Networks with Flows

Deep neural networks are becoming omnipresent in reason of their growing popularity in media and their daily use. However, their global complexity makes them hard to understand which emphasizes their black-box aspect and the lack of confidence given by their potential users. The use of tailored visual and interactive representations is one way to improve their explainability and trustworthy. Inspired by parallel coordinates and Sankey diagrams, this paper proposes a novel visual representation allowing tracing the progressive classification of a trained classification neural network by examining how each evaluation data is being processed by each network's layer. It is thus possible to observe which data classes are quickly recognized, unstable, or lately recognized. Such information provides insights to the user about the model architecture's pertinence and can guide on its improvement. The method has been validated on two classification neural networks inspired from the literature (LeNet5 and VGG16) using two public databases (MNIST and FashionMNIST).
Collaborative similarity metric learning for face recognition in the wild

Utilising different representations of face images is known to be helpful in face recognition. Inthis study,theauthorspropose two fusion techniques that make use of multiple face image features bycollaboratively training a similarity metric learner, based on Siamese neuralnetworks. This training procedure takes two (or possibly more) features of twoface images and outputs a similarity score that depicts whether the faces belongto the same person or not.Theauthorsinvestigate two approaches of collaborative similarity metric learning (CoSiM),both of which are based on training Siamese neural networks jointly, as a meansof early fusion. The experiments are employed on hand-crafted features such asscale-invariant feature transform (SIFT) and variants of the local binarypattern (LBP), on the YouTube Faces and the Labeled Faces in the Wild data sets.The authors provide theoretical and empirical comparisons of the proposed modelsagainst the related methods in the literature. It is shown that the proposedtechnique improves on the verification accuracy, compared to singlefeature-based baselines. By only utilising simple features like SIFT and LBP,the proposed techniques are shown to yield comparable results to the state ofthe art techniques, which depend on deep convolutional architectures or higherlevel features.
Interleaving Software Craftsmanship Practices in Medical Device Agile Development

Scaled Agile methodologies have revolutionized the way safety critical applications work. A majority of organizations have benefited from increased success rates in software development, improved quality and time to market while being compliant with regulations. However, one of the common pitfalls is a lack of focus on code quality in an urge to release software as quickly as possible. This paper highlights the transformation approach adopted by Philips HealthTech to improve code quality of its medical devices. It discusses how culmination of a series of actions related to Software Craftsmanship and Scaled Agile methodology helped in reducing release cycle time, increasing the number of features delivered and reducing field defects. Steps taken by a dedicated team, to enable businesses to focus on code quality hygiene and to assess the extent of improvements, are also discussed. Key take away from this paper is "Quality = Speed" i.e., driving agility and bringing in the needed cultural change in teams and organization.
Succinct Population Protocols for Presburger Arithmetic

In [Dana Angluin et al., 2006], Angluin et al. proved that population protocols compute exactly the predicates definable in Presburger arithmetic (PA), the first-order theory of addition. As part of this result, they presented a procedure that translates any formula φ of quantifier-free PA with remainder predicates (which has the same expressive power as full PA) into a population protocol with 2^𝒪(poly(|φ|)) states that computes φ. More precisely, the number of states of the protocol is exponential in both the bit length of the largest coefficient in the formula, and the number of nodes of its syntax tree.In this paper, we prove that every formula φ of quantifier-free PA with remainder predicates is computable by a leaderless population protocol with 𝒪(poly(|φ|)) states. Our proof is based on several new constructions, which may be of independent interest. Given a formula φ of quantifier-free PA with remainder predicates, a first construction produces a succinct protocol (with 𝒪(|φ|³) leaders) that computes φ; this completes the work initiated in [Michael Blondin et al., 2018], where we constructed such protocols for a fragment of PA. For large enough inputs, we can get rid of these leaders. If the input is not large enough, then it is small, and we design another construction producing a succinct protocol with one leader that computes φ. Our last construction gets rid of this leader for small inputs.
Can Twitter Help to Predict Outcome of 2019 Indian General Election: A Deep Learning Based Study

Traditionally, elections polls have been widely used to analyse trend and to predict likely election results. However, these methods are very expensive and labour intensive. With the widespread development of several social media platforms, a large amount of unstructured data become easily available, which in turn could be processed and analysed to extract meaningful information about several topics and events such as election, sports, natural hazards etc. Hence, in this study, we utilise twitter data to analyse the 2019 Indian general election and to predict possible outcomes. We have collected 41 million election related tweets during the months of April and May 2019. The proposed approach works by initially performing hashtags based tweets segregation to generate data for training the classification model. Subsequently, we augment different word embeddings with deep learning classification model to support improved classification accuracy. The model achieves 87.30% classification accuracy in amalgamation with fasttext word embedding. Finally, the opinion analysis is performed on the classified tweets to determine possible election outcomes. To evaluate our proposed model, the output results are validated with the available ground truth and have shown a close correlation to the actual results. Furthermore, we have also performed an in-depth analysis of the selected political parties tweets, to provide an insight into top trending hashtags and mentions used by people during election campaigns.
Optimization of Recurrent Neural Networks on Natural Language Processing

Recurrent neural networks (RNNs) are successful in various sequence modelling tasks, including natural language processing (NLP). However, parallelising the training process of RNNs proves to be difficult as computation at each timestep is dependent upon the output of the previous timestep. Many approaches have been taken to improve parallelisation of RNNs in recent years and achieved remarkable results. This paper investigate ways to further improve training speed and accuracy of RNNs in sentiment classification by combining methods of improving recurrent structure and recurrent units. Results show that not all combinations can result in an improvement, but rather significant improvements can be produced with the right arrangement of recurrent models.
Hybridization of population-based ant colony optimization via data mining

We propose a hybrid application of Population Based Ant Colony Optimization that uses a data mining procedure to wisely initialize the pheromone entries. Hybridization of metaheuristics with data mining techniques has been studied by several researchers in recent years. In this line of research, fr equent patterns in a number of initial high-quality solutions are extracted to guide the subsequent iterations of an algorithm, which results in an improvement in solution quality and computational time. Our proposal possesses certain differences from and contributions to existing literature. Instead of one single run that incorporates both the main metaheuristic and the data mining module inside, we propose to carry out independent runs and collect elite sets over these trials. Another contribution is the way we use the knowledge gained from the application of the data mining module. The extracted knowledge is used to initialize the memory model in the algorithm rather than to construct new initial solutions. One additional contribution is the use of a path mining algorithm (a specific sequence mining algorithm) rather than Apriori-like association mining algorithms. Computational experiments, conducted both on symmetric Travelling Salesman Problem and symmetric/asymmetric Quadratic Assignment Problem instances, showed that our proposal produces significantly better results, and is more robust than pure applications of population-based ant colony optimization.
“LET’S WRITE A CAPTION!”: UTILIZING INSTAGRAM TO ENHANCE ESP STUDENTS’ WRITING SKILLS

The integration of technology in language classroom is a prerequisite in the twenty-first century teaching. Social media as part of technology advancement has a great potential to improve students’ motivation and the efficacy of learning a language. Therefore, the present study sheds light on the effect of the use of Instagram on ESP students’ writing ability. It involved 36 Indonesian ESP students majoring in Sharia Banking of a university in Surakarta Indonesia. The pre-experimental research was conducted in eight meetings, including the writing pre-test and post-test. The students were assigned to write a descriptive essay by means of Instagram as the instructional medium. A questionnaire was also given to reveal their perceptions toward the use of Instagram to practice writing. The result of the data analysis showed that there was a significant difference on the students’ writing scores between the pre-test and the post test. The finding of this study is expected to provide significant contribution to the realm of English for specific purposes (ESP) teaching in Indonesia. Based on the findings, some insights are given for teachers and future researchers about the potential use of Instagram on the teaching of writing.
Comparative Evaluation of Speech Recognition Systems Based on Different Toolkits

Speech recognition is a method that allows machines to convert the incoming speech signals into text commands. This paper presents a brief survey on automatic speech recognition systems based on HTK, Julius, MATLAB, Sphinx and Kaldi. A description of the mentioned speech recognition systems is discussed, and the structure and performance of these different systems are presented.
Statistics for HCI: Making Sense of Quantitative Data

Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistical techniques including confidence inter
People detection and tracking methods for intelligent surveillance system

The ability to automatically detect and track people is a key component of intelligent surveillance system (ISS). ISS is able to automatically analyze surveillance camera data with limited human intervention. In this research, robust people detection and tracking methods are studied for public surveillance system. The proposed method composed of foreground-background segmentation and people detection using mixture of gaussians (MOG) and histogram of oriented gradients (HOG) and then followed by multi object tracking (MOT) method for people tracking. The proposed method is tested on a closed circuit television (CCTV) system at the engineering College of Sebelas Maret University (UNS) Surakarta. All of the people tracking methods has been tested, and the result is good enough with the success up to 100%.
Correlation properties of single photon binary waveforms used in quantum radar/lidar

Recently, much of the quantum radar/lidar research is focused on correlating single photon detection events with no delay line on the idler path. In other words, measuring the idler immediately, and correlating these events with later received photon events from the returning signal. This research approach has raised some questions due to the fact that all measurements done are classical, yet researchers are still observing sensor improvement in comparison to classical techniques. This therefore implies that the benefits from quantum radar/lidar using these techniques should be able to be explained entirely classically. This paper explores this concept by asserting that the correlation between the signals used in quantum remote sensing is largely due to the fact that the signal and idler photons are created simultaneously (which is only possible from an entangled source). We show, using very simple computer simulations, that having a single photon correlated (binary) waveform leads to correlation SNR advantages only in the low photon level regime, agreeing with previous literature.
A survey on domain adaptation theory

All famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the same distribution. When the distribution changes, most statistical models must be reconstructed from newly collected data, which for some applications can be costly or impossible to obtain. Therefore, it has become necessary to develop approaches that reduce the need and the effort to obtain new labeled samples by exploiting data that are available in related areas, and using these further across similar fields. This has given rise to a new machine learning framework known as transfer learning: a learning setting inspired by the capability of a human being to extrapolate knowledge across tasks to learn more efficiently. Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation. In this sub-field, the data distribution is assumed to change across the training and the test data, while the learning task remains the same. We provide a first up-to-date description of existing results related to domain adaptation problem that cover learning bounds based on different statistical learning frameworks.
Spatial Preserved Graph Convolution Networks for Person Re-identification

Person Re-identification is a very challenging task due to inter-class ambiguity caused by similar appearances, and large intra-class diversity caused by viewpoints, illuminations, and poses. To address these challenges, in this article, a graph convolution network based model for person re-identification is proposed to learn more discriminative feature embeddings, where a graph-structured relationship between person images and person parts are together integrated. Graph convolution networks extract common characteristics of the same person, while pyramid feature embedding exploits parts relations and learns stable representation with each person image. We achieve a very competitive performance respectively on three widely used datasets, indicating that the proposed approach significantly outperforms the baseline methods and achieves the state-of-the-art performance.
Research Issues on Generative Adversarial Networks and Applications

Generative Adversarial Networks introduces a challenging problem in the field of image generation and computer vision. Generating especially 3D face images is becoming very significant and difficult. This is openings in many interesting applications including Virtual Reality, Augmented Reality, Computer games, teleconferencing, Virtual try-on, Special effect, and so on. This paper contains three sections. The first section describes the Generative Adversarial Networks. The second section describes applications and methods and finally, the last section describes the future research directions of Generative Adversarial Networks.
Implementation of Storage in Virtual Private Cloud using Simple Storage Service on AWS

In this digital era, the emergence of many smart applications makes the human lives in a way more smart but at the same time it also increases the amount of data to an unprecedented rate. This makes the traditional data storage framework, accessibility and backup to remain more under risk. Recently, to overcome this an on-demand storage service will be provided by the cloud. Despite the hype, the cloud also comes up with the data security concerns. The primary objective of this research work is to develop a framework that permits confirmed clients to get access to the sensitive information within the organization. In contrast to the traditional system, the proposed system helps in data recovery if there arise any circumstances, the storage can be expanded by checking the accessibility of users logged into the storage system to access the data.
Evaluation of a Failure Prediction Model for Large Scale Cloud Applications

Modern cloud-based applications, including smart homes and cities require high levels of reliability and availability. All cloud services, including hardware and software experience failures because of their large scale and heterogeneity nature. In this paper, the main objective is to develop a failure prediction model that can early detect failed jobs. The advantage of the proposed model is to enhance resource utilization and to increase the efficiency of cloud applications. The proposed model is evaluated based on three public available traces, which are the Google cluster, Mustang, and Trinity. Moreover, four different machine learning algorithms have been applied to the traces in order to select the best accurate model. Furthermore, we have improved the prediction accuracy using different feature selection techniques. The evaluation results show that the proposed model has achieved a high rate of precision, recall, and f1-score.