Reviewerly/exHarmony huggingface dataset:
How to cheat the page limit

Abstract Every conference imposing a limit on the length of submissions must deal with the problem of page limit cheating: authors tweaking the parameters of the game such that they can squeeze more content into their paper. We claim that this problem is endemic, although we lack the data to formally prove this. Instead, this paper provides a far from exhaustive summary of ways to cheat the page limit, a case study involving the papers accepted for the Research and Applied Data Science tracks at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2019, and a discussion of ways for program chairs to tackle this problem. Of the 130 accepted papers in these two ECMLPKDD 2019 tracks, 68 satisfied the page limit; 62 (47.7%) turned out to spill over the page limit, by up to as much as 50%. To misappropriate a phrase from Darrell Huff's “How to Lie with Statistics,” we intend for this paper not to be a manual for swindlers; instead, nefarious paper authors already know these tricks, and honest program chairs must learn them in self‐defense. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Fairness in Data Mining
On-Device Information Extraction from SMS using Hybrid Hierarchical Classification

Cluttering of SMS inbox is one of the serious problems that users today face in the digital world where every online login, transaction, along with promotions generate multiple SMS. This problem not only prevents users from searching and navigating messages efficiently but often results in users missing out the relevant information associated with the corresponding SMS like offer codes, payment reminders etc. In this paper, we propose a unique architecture to organize and extract the appropriate information from SMS and further display it in an intuitive template. In the proposed architecture, we use a Hybrid Hierarchical Long Short Term Memory (LSTM)-Convolutional Neural Network (CNN) to categorize SMS into multiple classes followed by a set of entity parsers used to extract the relevant information from the classified message. The architecture using its preprocessing techniques not only takes into account the enormous variations observed in SMS data but also makes it efficient for its on-device (mobile phone) functionalities in terms of inference timing and size.
Robust Bayesian model selection for heavy-tailed linear regression using finite mixtures

In this paper, we present a novel methodology to perform Bayesian model selection in linear models with heavy-tailed distributions.We consider a finite mixture of distributions to model a latent variable where each component of the mixture corresponds to one possible model within the symmetrical class of normal independent distributions.Naturally, the Gaussian model is one of the possibilities.This allows for a simultaneous analysis based on the posterior probability of each model.Inference is performed via Markov chain Monte Carlo-a Gibbs sampler with Metropolis-Hastings steps for a class of parameters.Simulated examples highlight the advantages of this approach compared to a segregated analysis based on arbitrarily chosen model selection criteria.Examples with real data are presented and an extension to censored linear regression is introduced and discussed. IntroductionStatistical practitioners generally use model selection criteria in order to select a best model in different applications.However, model selection has been shown not to be an easy task and each criterion performs better under different situations.For more complex models, it is not clear which criterion is preferable (Carlin, 2006;Chen, 2006;Gelman, Hwang and Vehtari, 2014).Recently, Gelman, Hwang and Vehtari (2014) studied and compared different model criteria and concluded that "The current state of the art of measurement of predictive model fit remains unsatisfying".From their study, it is clear that different criteria-Akaike information criterion (AIC), Deviance information criterion (DIC), Widely Applicable Information Criterion (WAIC), fail in selecting the most adequate model under a variety of circumstances (see Appendix B for a description of some criteria).For example, settings with strong prior information or when the posterior distribution is not well summarized by its mean or in a spatial or network setup (for more details, see Gelman, Hwang and Vehtari, 2014).Other authors also comment about the model selection problem, e.g., "In summary, model choice is to Bayesians what multiple comparisons is to frequentists: a really hard problem for which there exist several potential solutions, but no consensus choice" (Carlin, 2006), "we saw that no single measure is dominant in all three cases.The L-measure performed better when the true model becomes more complex and BIC performed better when the true model is more parsimonious."(Chen, 2006).Under the Bayesian paradigm, a more robust and elegant solution is available, at least in theory, by considering one "full model" that embeds all the individual models of interest.More specifically, this means that a multinomial random variable (r.v.) with each category corresponding to one of the individual models is specified.This way, model selection may be performed based on the posterior distribution of this r.v., that is, the posterior probability of each model.Nevertheless, this approach may be challenging in some cases, specially
Database Generator to Support Product Derivation in SPL

Software product line (SPL) is a methodology to develop application variants with feature variability to accommodate user needs in a specific market. This methodology can be supported by the Abstract Behavioral Specification (ABS) modeling language. With the use of delta-oriented programming (DOP) in ABS, the development process to produce application variants in SPL can be automated. However, feature variability of an application variant could affect the design and implementation of the database schema of the application during product derivation phase in SPL, if the feature is related to the data storage of the application. This crucial thing is not handled by the management technique of variability in SPL which makes database schema might be inconsistent and incompatible with the application requirements. In this study, we successfully implement a tool to generate a compatible relational database schema for an application variant. This tool has also been adopted to support the automation process of our working product line for charity organizations.
The comparison study of kernel KC-means and support vector machines for classifying schizophrenia

Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.
Pemanfaatan Realtime Database di Platform Firebase Pada Aplikasi E-Tourism Kabupaten Nabire

Tourism objects in the district of Nabire relatively not developed much and has not been handled optimally due to lack of promotion. E-Tourism which is an electronic application for the tourism sector that can facilitate the user to visit a tourist site at a location. The currently available apps can not make real-time data changes regarding the position of the app's user coordinates to find out where to find the sights. This study aims to provide information to application users about the position of the location of the tour and the position of users who can change in real-time using firebase. This research uses experimental method, that is pinned firebase platform on E-Tourism so that application can utilize various real-time database feature in firebase. The system is tested by moving the user's position of the app to see changes in position data in real-time and will be shown on the application map. The next test by showing the location of tourism as well as the path traveled by the user to reach the sights. The results of this study show the position of the user that can change in real-time when the user to switch position without having to refresh the data manually (refresh). Applications can also display a list of tourist sites and the path taken to get to the tourist sites located in Nabire district.
A Fast Method for Image Matching and Registration Based on SIFT Algorithm and Image Pyramid

Abstract In this paper, a fast image registration algorithm based on SIFT is proposed, which is slow in extracting feature points from high-resolution images. In this paper, the original image is transformed into an image pyramid to obtain a sub-sampled image with a lower resolution. At the same time, the Laplace image pyramid is obtained. Feature points of the sub-sampled image are extracted through SIFT algorithm, and then they are fused with the original image to obtain feature points of the original image. Experimental results show that the algorithm can quickly find the feature points and obviously improve the computational efficiency.
A Feasibility Study on the Implementation of Visibility Algorithms for Fault Diagnosis in Aircraft Fuel Systems

This paper discusses the applicability of Visibility Algorithms to detect faults in condition monitoring applications. The general purpose of Visibility Algorithms is to transform time series into graphs and study them through the characterization of their associated network. Degradation of a component results in changes to the network. This technique has been applied using a test rig of an aircraft fuel system to show that there is a correlation between the values of key metrics of visibility graphs and the severity of four failure modes. We compare the results of using Horizontal Visibility algorithms against Natural Visibility algorithms. The results also show how the Kullback-Leibler divergence and statistical entropy can be used to produce condition indicators. Experimental results show that there is little dispersion in the values of condition indicators, leading to a low probability of false positives and false negatives.
Minat Membaca dan Implementasinya dalam Bimbingan dan Konseling

Reading interst is a strong desire that accompanied a sense of pleasure of enjoyment of reading with self­­-will. Reading is a demand for individuals because reading is an effective activity to gain insight and knowledge. Surah Al-Alaq which is the first letter to come down is a reading order. Furthermore, the mandate of regulations and legislation also invites reading culture and encourages people to enjoy reading. The problematics of some of the research results indicate that Indonesian interest in reading is still relatively low. Therefore how important are efforts to foster interest in reading. Improving interest in reading must be done through families, education units and the community. One of the factors influencing reading interest is the teacher. Counseling teacher/ Counselor is a teacher assigned to carry out guidance and counseling activities in schools responsible for providing various service activities in developing various student potentials and encouraging national education goals. Guidance counseling at schools is expected to be more intensive in order to improve students interest in reading through various types of counseling services. As for some services that can be applied in improving interest in reading such as orientation services, information, group guidance and individual counseling.
Intrinsic and Extrinsic Automatic Evaluation Strategies for Paraphrase Generation Systems

Paraphrase is an expression of a text with alternative words and orders to achieve a better clarity.Paraphrases have been found vital for augmenting training dataset, which aid to enhance performance of machine learning models that intended for various natural language processing (NLP) tasks.Thus, recently, automatic paraphrase generation has received increasing attention.However, evaluating quality of generated paraphrases is technically challenging.In the literature, the importance of generated paraphrases is tended to be determined by their impact on the performance of other NLP tasks.This kind of evaluation is referred as extrinsic evaluation, which requires high computational resources to train and test the models.So far, very little attention has been paid to the role of intrinsic evaluation in which quality of generated paraphrase is judged against predefined ground truth (reference paraphrases).In fact, it is also very challenging to find ideal and complete reference paraphrases.Therefore, in this study, we propose semantic or meaning oriented automatic evaluation metric that helps to evaluate quality of generated paraphrases against the original text, which is an intrinsic evaluation approach.Further, we evaluate quality of the paraphrases by assessing their impact on other NLP tasks, which is an extrinsic evaluation method.The goal is to explore the relationship between intrinsic and extrinsic evaluation methods.To ensure the effectiveness of proposed evaluation methods, extensive experiments are done on different publicly available datasets.The experimental results demonstrate that our proposed intrinsic and extrinsic evaluation strategies are promising.The results further reveal that there is a significant correlation between intrinsic and extrinsic evaluation approaches.
Survey on Software Tools that Implement Deep Learning Algorithms on Intel/x86 and IBM/Power8/Power9 Platforms

Neural networks are becoming more and more popular in scientific field and in the industry. It is mostly because new solutions using neural networks show state-of-the-art results in the domains previously occupied by traditional methods, eg. computer vision, speech recognition etc. But to get these results neural networks become progressively more complex, thus needing a lot more training. The training of neural networks today can take weeks. This problems can be solved by parallelization of the neural networks training and using modern clusters and supercomputers, which can significantly reduce the learning time. Today, a faster training for data scientist is essential, because it allows to get the results faster to make the next decision.In this paper we provide an overview of distributed learning provided by the popular modern deep learning frameworks, both in terms of provided functionality and performance. We consider multiple hardware choices: training on multiple GPUs and multiple computing nodes.
The Role of Paraphrasing in Writing Research Papers

Almost all people in this world have experienced in writing a research paper. Nevertheless, there are only some people who understand how to write appropriately. Plagiarism still becomes a big problem among the writers. This happens because they often get difficulty in integrating sources into the text. As the writers, they should know how to overcome that problem. The existence of paraphrasing can be a way to avoid the plagiarism. The writers can paraphrase a direct quote of someone using their own words. This can be a great way to increase the quality of research papers.
Investigate the Extrinsic and Intrinsic Motivational Factors of Knowledge Transfer in the Hospitals

The value of services in knowledge-intensive organizations like the hospitals is created by the tacit knowledge of the health staff.The weak knowledge transfer activities among the health staff effects on the performance level of the health care services.Hospitals are dependent on continuously learning from mistakes and to make improvements and then to transfer this newly acquired knowledge between units and workers in the organization.It is important to enhance the knowledge transfer behaviors of knowledge source and recipients in the hospitals.This study aims to investigate the extrinsic and intrinsic motivational factors that would be adopted to improve the knowledge transfer processes among the health staff.The data of this study were collected from 475 doctor and nurses that work in three Jordanian hospitals; Albashir Hospital, Jordan University Hospital and Al-Issra Hospital.The collected data were analyzed using many tests such as validity, reliability, demographic and descriptive analysis.The significant results show that there are many extrinsic motivational factors would be adopted to improve the knowledge transfer by the knowledge source and recipients such as reward, ideal salaries, promotions, satisfaction of work position, stability of labor and reputation feedback.On the other hand, there are many intrinsic motivational factors that would be applied to enhance the knowledge transfer such as arduous relationship, altruism, shared understanding, champions and enjoyment to help others.This study helps the hospitals to identify the motivational factors that would be adopted to improve the knowledge transfer processes in order to enhance the quality of health care services.
Association Rule Mining from large datasets of clinical invoices document

The concept of massive data generation nowadays affects several domains such as marketing including electronic invoices of large retailers, web access log files, healthcare, life sciences and so on. All these web activities introduced a new way to pay through the concept of electronic invoices (eInvoice), replacing the paper invoices. For these reasons, eInvoicing can be thought of as an innovative digital infrastructure for the issue, transmission, and storage of invoices. The availability of large volumes of eInvoices allows the discovery of new knowledge through data mining in these domains. Thus, users by using data mining can extract knowledge from large invoices documents. In this paper, we present a software tool for mining association rules from invoices produced in healthcare centers. In particular, the tool adopt a novel preprocessing methodology that provides merging, cleaning, formatting and summarization of eInvocies. The methodology can improve the quality of a huge amount of clinical invoices reducing the quantity of irrelevant data, making the remaining data suitable to mine information in form of association rules. The core of the tool allows to extract association rules from eInvoices; as a case study, we discuss the mined rules, highlighting the relationships among the purchased goods.
A Critical Analysis of Gold Prospecting Methods

Exploration and prospecting for mineral resources deposits are perceived to not be an easy process due to big deposits being discovered in early times.Numerous gold prospecting methods have been used in gold prospecting.The aim of this research is to critically analyze both traditional and modern gold prospecting methods that are being used in gold exploration.The research objective is to critically evaluate the numerous studies that focus on gold prospecting methods in an effort to provide an effective analysis of gold prospecting methods.In doing so, the study has used a qualitative method to review the existing literature.The study reviews academic journals and articles, books, and periodicals which were published between 2010 and 2019.This study has achieved to critically analyze both traditional and modern gold prospecting methods.The study finds out that the traditional gold prospecting methods include gold panning and dry washing.Additionally, the current gold prospecting methods include biological prospecting, geochemical prospecting and geophysical prospecting methods.
About STEM from the Perspective of STS

The movement of STEM has become the synonym for the renewal of science education. Nevertheless, the movement of STEM is not unambiguous. The article discusses two contradicting interpretations of STEM. The first one descends from the Weltanschauung of Modernity, the second one – from the critics of Modernity. According to the former, STEM education deals with the purification and integration of the disciplines of science education and answers the needs of the high-tech labour market. The latter one emphasizes the syncretic and anthropological character of the ecological mind and the changing patterns of consumption and production in favour of sustainable development. This version of STEM is closely related to the development of STS, which paves the way from purification and integration to the translation and human and non-human networking (ANT). The article searches for the answer to the question of how to deal with the confronting STEM versions in the process of the renewal of science education.
Learning End-to-End Lossy Image Compression: A Benchmark

Image compression is one of the most fundamental techniques and commonly used applications in the image and video processing field. Earlier methods built a well-designed pipeline, and efforts were made to improve all modules of the pipeline by handcrafted tuning. Later, tremendous contributions were made, especially when data-driven methods revitalized the domain with their excellent modeling capacities and flexibility in incorporating newly designed modules and constraints. Despite great progress, a systematic benchmark and comprehensive analysis of end-to-end learned image compression methods are lacking. In this paper, we first conduct a comprehensive literature survey of learned image compression methods. The literature is organized based on several aspects to jointly optimize the rate-distortion performance with a neural network, i.e., network architecture, entropy model and rate control. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. With this survey, the main challenges of image compression methods are revealed, along with opportunities to address the related issues with recent advanced learning methods. This analysis provides an opportunity to take a further step towards higher-efficiency image compression. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance, especially on high-resolution images. Extensive benchmark experiments demonstrate the superiority of our model in rate-distortion performance and time complexity on multi-core CPUs and GPUs. Our project website is available at https://huzi96.github.io/compression-bench.html.
A Fuzzy Consensus Clustering Based Undersampling Approach for Class Imbalanced Learning

The class imbalance problem is widely studied in the machine learning community, it is present in many real world applications such as spam filtering, anomaly detection and medical diagnosis. In this paper, we propose an adaptive fuzzy c-means based consensus clustering approach for class imbalanced learning, the number of base clusters are determined through a balancing optimization approach while the initial starting points for each base partition is determined in a sequential manner. The final partition is constructed via the co-association matrix. Finally, the center samples in the final cluster partition are selected to form the reduced data set with the minority class samples. In this way, the most representative majority class samples are chosen while the boundary samples are eliminated. The validity of the proposed method is tested with real world data sets which demonstrates superior performance compared to other clustering based re-sampling schemes. Thus, the fuzzy consensus clustering based under-sampling method can be used for real life imbalanced problems.