Reviewerly/exHarmony huggingface dataset:
Sistem Pendukung Keputusan untuk Menentukan Praja Terbaik Menggunakan Metode Simple Additive Weighting (Studi Kasus Institut Pemerintahan Dalam Negeri Kampus Sumatera Barat)

The Institute of Domestic Administration (IPDN) of the West Sumatra Campus is a tertiary institution attended by students from all over Indonesia. This activity is a mandatory activity for all cadets and must be followed every semester. Values ​​determine the graduation of a cadet in his education. For this reason, a good system is needed in determining the value. This study aims to find effective and appropriate values ​​in each service. The method used is Simple Additive Weighting for data that determine ranking. The results of this study have an accuracy of 85% of the ranking data manually to the test data. Thus, this system is more effective in the search process and ranking accuracy. Then this research is a system that can be used as a reference in ranking the best value of welfare.
OFR-Net: Optical Flow Refinement with a Pyramid Dense Residual Network

This paper proposes a new and effective convolutional neural network model termed OFR-Net for optical flow refinement. The OFR-Net exploits the spatial correlation between images and optical flow fields. It adopts a pyramidal codec structure with residual connections, dense connections and skip connections within and between the encoder and decoder, to comprehensively fuse features of different scales, locally and globally. We also introduce a warp loss to restrict large displacement refinement errors. A series of experiments on the FlyingChairs and MPI Sintel datasets show that the OFR-Net can effectively refine the optical flow predicted by various methods.
Peran Orang Tua dalam Pengenalan Budaya Literasi untuk Meningkatkan Kecerdasan Bahasa Pada Anak Usia Dini 5-6 Tahun

The role of parents in introducing literacy culture of early childhood can be seen from the efforts of parents at home. The family is the place where the character of the child is formed, introduce the child to read a book that suitable with his developmental age, invite the child to hear some stories and create conducive atmosphere for them. Literacy culture of early childhood is not only focus on reading and writing, but also involves communication process which are listening, children are able to think logically and critically, and the development of linguistic intelligence orally. Among the parents’ attempts to introduce literacy culture of early childhood are: (1) family library and playing; (2) library membership registering; (3) library tour; (4) story telling; (5) watching a foreign tv channel; and (6) field trip. stimulate the literacy culture of early childhood.
Transparency in the patent system – Artificial intelligence and the disclosure requirement

The patent system faces fundamental challenges by the recent breakthroughs in Artificial Intelligence (AI). While issues such as patentability and non-obviousness of AI-related inventions - covering both AI-generated output and AI methods as such - have already attracted attention, this chapter analyses whether patent law's disclosure requirement is (also) under attack by AI. According to the disclosure requirement, the inventor has a legal obligation to disclose his or her invention in order to acquire an exclusive right. As the functioning of the disclosure requirement is a common justification of the patent system, the issue at hand is highly relevant: If the disclosure requirement were indeed fundamentally challenged by AI, the patent system would run the risk of losing its legitimacy. This chapter also frames the research question through the lens of transparency. The fact that the disclosure requirement requires patentees to disclose their inventions is crucial for the quality of information in the patent register. Together with the fact that patent registers are open to the public, the disclosure requirement ultimately ensures a certain transparency regarding human inventiveness. With AI entering the picture - which is often scolded for being non-transparent - the question is whether and how this transparency is affected. We argue that the disclosure requirement indeed is challenged by AI, but that the challenges are less grievous than in other areas of patent law. To be more precise: the use of AI may lead to new disclosure deficits, and some AI-related patents may be considered insufficiently disclosed because they lack reproducibility. However, timely anticipation of these risks allows patent law to adapt, mainly because AI is not only a threat but could also remedy imbalances in the disclosure requirement: addressing these issues properly, AI may lead even to more instead of less transparency. The argument will unfold in four steps: We first examine the disclosure requirement in patent law (section 2) and the state of the art regarding AI (section 3). We will then combine these parts in order to address the looming challenges of the disclosure requirement confronted with AI (section 4) Lastly, we will also highlight new opportunities brought about by AI (section 5), before concluding (section 6).
Forensically Sound Piecewise Hashing: Integrity checks with DEIC

The integrity of a digital evidence is believed to be the paramount trait in the world of cyber forensics. Cyber- crime investigators face myriad challenges in the process similar to accommodating the call for bulk digital evidence. In due course extraction of useful information while maintaining the integrity and definitive protection against data degradation is mandatory. In this manuscript, we propose a novel approach by applying cryptographic hashing technique to only selected significant portions of the digital evidence, so even if the overall hash does not match, investigators could still verify the integrity of those critical section of the evidence. We put forward two notions in this manuscript, former is Heterogeneous piecewise hashing which is flexible version of piecewise hashing strategy and latter, novel evidence certification strategy which formalizes evidence provability process completely.
The design of Apache IoTDB distributed framework

Apache Internet of Things Database (IoTDB) is a new open-source timeseries database management system. A distributed data management system not only needs to solve the problem of metadata synchronization between nodes caused by data partition and multiple replicas, but also needs to support efficient query request processing. To solve the problem of metadata synchronization among nodes, we propose a dual-layer granularity metadata management strategy. Based on the consistency hash partitioning method and Raft protocol, we designed a distributed framework that supports both strong consistency query and eventual consistency query. Based on the single-machine version of Apache IoTDB, we carried out the system implementation and experimental test. Compared with the single-level granularity management strategy, the test results showed that the two-level granularity metadata management strategy takes less memory resources and improves the write performance by $5%\\sim10%$. Also, the results showed that the read and write performance of the distributed Apache IoTDB increases linearly with the extension of cluster size.
Few-Shot Learning for Abstractive Multi-Document Opinion Summarization

Opinion summarization is an automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product. The task is practically important and has attracted a lot of attention. However, due to a high cost of summary production, datasets large enough for training supervised models are lacking. Instead, the task has been traditionally approached with extractive methods that learn to select text fragments in an unsupervised or weakly-supervised way. Recently, it has been shown that abstractive summaries, potentially more fluent and better at reflecting conflicting information, can also be produced in an unsupervised fashion. However, these models, not being exposed to the actual summaries, fail to capture their essential properties. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation. We start by training a language model to generate a new product review given available reviews of the product. The model is aware of the properties: it proceeds with first generating property values and then producing a review conditioned on them. We do not use any summaries in this stage and the property values are derived from reviews with no manual effort. In the second stage, we fine-tune the module predicting the property values on a few available summaries. This lets us switch the generator to the summarization mode. Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
Mining Frequent Utility Sequential Patterns in Progressive Databases by U-Pisa

Sequential pattern mining is one of the most important aspects of data mining world and has a significant role in many applications like market analysis, biomedical analysis, weather forecasting etc. In the category of mining sequential patterns the usage of progressive database as an input database is relatively new and has a wide impact in decision-making system. In progressive sequential pattern mining, we discover the frequent sequences progressively with the help of period of Interest. As the traditional approaches of frequency based framework are not much more informative for decision making, in recent effort utility framework has been incorporated instead of frequency. This addressed many typical business concerns such as profit value associated with each pattern. In this paper, we applied the concept of frequent utility over the progressive database and discovered the sequential pattern efficiently. To do so we proposed an algorithm called U-Pisa which works progressively with the help of a quantitative progressive database. We conducted sub-stantial experiments on the proposed algorithm and proved that this process performs well.
Towards a Principled Evaluation of Likeability for Machine-Generated Art

We want to thank the artists who helped us with our research: Brooke Cheng, Dwayne Jones, Joseph Wilk, Luisa Fabrizi, Mark Hernandez, Tais Mauk, Michelle Cheung, Julia Peter, Francisco Rojo, Mathilde Mouw-Rao, Iain Nash, and Achim Koh.
Making chemistry FAIR

Compared with even 50 years ago, today’s chemistry lab is a very different place. More researchers are carrying out more experiments than ever before, using increasingly sophisticated and automated tools and generating a deluge of data. An analysis of the scientific literature suggests that research output (including articles, books, and data sets) is growing by 8–9% a year (J. Assoc. Inf. Sci. Technol. 2015, DOI: 10.1002/asi.23329). But the way data from experiments are shared and reused hasn’t kept pace, chemists say. Useful findings and raw data can languish in PhD theses stored in libraries or in PDFs on servers. Some researchers and policy makers would like to change that, pushing for the chemistry community to implement what are called the FAIR principles of data management. Those stakeholders’ efforts are being bolstered by funders like the European Research Council (ERC) and the US National Institutes of Health, which are mandating that
A Study on the Ecumenical Activities of Pastor Sun Ai Lee, an Asian Feminist

Sun-Ai Lee is well known in Korean ecumenical circles and among Asian women. She will be remembered as the pioneer of the Asian feminist Ecumenical Movement.She tried to help women in Asia to give voice to their social restrictions. She published the first Asian feminist theological magazine, In God’s Image, and worked as the Asian coordinator of EATWOT where she represented Asian women. Also, she established the Asia Women’s Resource Center (AWRC) to build the ground work for Asian feminist theology. Sun-Ai Lee’s theology is rooted in the context of oppression against women in Asian religious cultures. Her theological focus is based on equality and ecumenical partnership.<BR> The author of this essay argues that we can renew our faith and attitude by learning Asian feminist theology and studying the life and ecumenical feminist contributions of Sun-Ai Lee who lived a life of love towards Asian women.
ABC and indirect inference

Indirect inference (II) is a classical method for estimating the parameter of a complex model when the likelihood is unavailable or too expensive to evaluate. The idea was popularised several years prior to the main developments in ABC by Gourieroux et al. (1993); Smith (1993), where interest was in calibrating complex time series models used in financial applications. The II method became a very popular approach in the econometrics literature (e.g. Smith (1993); Monfardini (1998); Dridi et al. (2007)) in a similar way to the ubiquitous application of ABC to models in population genetics. However, the articles by Jiang and Turnbull (2004) and Heggland and Frigessi (2004) have allowed the II approach to be known and appreciated by the wider statistical community. In its full generality, the II approach can be viewed as a classical method to estimate the parameter of a statistical model on the basis of a so-called indirect or auxiliary summary of the observed data (Jiang and Turnbull, 2004). A special case of II is the simulated method of moments (McFadden, 1989), where the auxiliary statistic is a set of sample moments. In this spirit, the traditional ABC method may be viewed as a Bayesian version of II, where prior information about the parameter may be incorporated and updated using the information about the parameter contained in the summary statistic. However, much of the II literature has concentrated on developing the summary statistic from an alternative parametric auxiliary model that is analytically and/or computationally more tractable. The major focus of this book chapter is on approximate Bayesian methods that harness such an auxiliary model. These are referred to as parametric Bayesian indirect inference (pBII) methods by Drovandi et al. (2014a)...
Effect of crypto currency on the government backed currency

Abstract. As international financial systems are becoming political tools to manipulate the exchange of money for good, cryptocurrency, and its potential to avoid the international banking system becomes more popular. Thus, the question becomes what happens to the nation’s monitory system if cryptocurrency’s electronic payment transaction and system replace the current system?  Most nation’s central bank controls the financial systems, which affect institutions, rules, regulations, and laws of the land that affect this system. Robinson (2016) suggests money as a commodity in the national scope. International banks use BIC for transferring money with each other. Cryptocurrency, so far, does not need BIC to transfer money from one location to another.  Bitcoin, the system is a peer-to-peer internet currency allowing verified, and decentralized transfers of value between individuals and businesses. This article is a short introduction discussing the legacy money and cryptocurrency and the potential of cryptocurrency as a new currency and its possible implications. Keywords. Cryptocurrency, Blockchain, Currency, Money, Bitcoin, BIC, SWIFT. JEL. O30, O35, O36.
Interpretable Methods for Identifying Product Variants

For e-commerce companies with large product selections, the organization and grouping of products in meaningful ways is important for creating great customer shopping experiences and cultivating an authoritative brand image. One important way of grouping products is to identify a family of product variants, where the variants are mostly the same with slight and yet distinct differences (e.g. color or pack size). In this paper, we introduce a novel approach to identifying product variants. It combines both constrained clustering and tailored NLP techniques (e.g. extraction of product family name from unstructured product title and identification of products with similar model numbers) to achieve superior performance compared with an existing baseline using a vanilla classification approach. In addition, we design the algorithm to meet certain business criteria, including meeting high accuracy requirements on a wide range of categories (e.g. appliances, decor, tools, and building materials, etc.) as well as prioritizing the interpretability of the model to make it accessible and understandable to all business partners.
Comparison of Mamdani and Sugeno Inference Methods in Calorie Burn Calculation for Activity Using Treadmill

This research provides a comparison between the performance of Mamdani and Sugeno Fuzzy Inference System in Calorie Burn calculation for treadmill workout exercise. The results are compared against the calculation calorie burn value using traditional formula. The objective of this results to get a system which provides calorie burn results closer to the traditionally calculated value. The Mamdani and Sugeno systems are designed with two input membership functions namely Incline and Speed and one output membership function called Calorie Burn as used in the traditional calculation method. The fuzzy rule table is designed using 7 Membership function for Incline and speed inputs and 9 Membership function for calorie burn output. The Mamdani and Sugeno system are tested with the same fuzzy rules for better comparison. The results are tabulated with theoretical values compared with Mamdani and Sugeno system for 5 incline levels and 7 speed levels. It can be observed that the Sugeno system results are closer to the traditional calculation results. This is shows that the sugeno system works better than Mamdani system for linear system. Also it depicts the calculated values and hence it can be used as a replacement for tedious mathematical analysis.
Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

We focus on the problem of training convolutional neural networks on gigapixel histopathology images to predict image-level targets. For this purpose, we extend Neural Image Compression (NIC), an image compression framework that reduces the dimensionality of these images using an encoder network trained unsupervisedly. We propose to train this encoder using supervised multitask learning (MTL) instead. We applied the proposed MTL NIC to two histopathology datasets and three tasks. First, we obtained state-of-the-art results in the Tumor Proliferation Assessment Challenge of 2016 (TUPAC16). Second, we successfully classified histopathological growth patterns in images with colorectal liver metastasis (CLM). Third, we predicted patient risk of death by learning directly from overall survival in the same CLM data. Our experimental results suggest that the representations learned by the MTL objective are: (1) highly specific, due to the supervised training signal, and (2) transferable, since the same features perform well across different tasks. Additionally, we trained multiple encoders with different training objectives, e.g. unsupervised and variants of MTL, and observed a positive correlation between the number of tasks in MTL and the system performance on the TUPAC16 dataset.
Optical, mechanical, and photoelastic anisotropy of biaxially stretched polyethylene terephthalate films studied using transmission ellipsometer equipped with strain camera and stress gauge

The anisotropic properties of polyethylene terephthalate film resulting from its manufacturing process are quantitatively investigated in terms of its optical, mechanical, and photoelastic aspects. Transmission ellipsometers and a Jones‐matrix‐based analysis software together with a 4 × 4 Berreman‐matrix‐based analysis software are adopted to determine the wavelength‐dependent in‐plane birefringence, the principal refractive indices, and the orientation of the optical axis. Mechanical anisotropy is characterized in terms of the elastic compliance tensor components using the measured azimuthal angle‐dependent Young's modulus and Poisson's ratio. From the measured variation of the wavelength‐dependent in‐plane birefringence as a function of tensile stress, the dispersive photoelastic coefficients are obtained for a few sample azimuthal angles, and the components of the photoelastic tensor are determined. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 152–160
JIMS 8i – International Journal of Information, Communication and Computing Technology (IJICCT)

Jims8i – Infotech Journal known as IJICCT - International Journal of Information, Communication and Computing Technology is published since 2013. Two issues, January- June and July- December, are published every year. The goal of the journal is to provide a platform to the academicians, researchers, practitioners and thought-Gurus to share their knowledge and research in the field of computer science and its applications. The journal welcomes high quality original research papers, survey reports, as well as the discussion papers in the field of Information Technology. The editorial team tries to ensure that plagiarism is minimized by checking using latest software tools. However the final responsibility of the originality of the paper submitted lies with the Author(s). Technical Articles, Doctoral Abstracts and Book Reviews for the forthcoming issue are also solicited and we would appreciate if you could contribute the same. The papers/articles received for publication are sent for Double Blind Review process before they are accepted for publication.
DOES THE PSYCHO-PEDAGOGICAL MODULE TRAIN REAL SKILLS?

Thehereby paper aims to investigate the academic satisfaction degree ofthepsycho-pedagogical module graduates concerining the within formedcompetences and skills. One started from the assumption: the students whogo through the psycho-pedagogical module are pleased with this, becausethey are trained, in addition to the specific competences,also a series oftransversal competences, importantfor life. The research group comprised221 students (1st, 2nd, 3rd academic years- bachelor's degree), studentswho have completed or are about to complete the psycho-pedagogicalmodule. High level of statistical correlations were found: .904 **, .905 **,.906 ** (p- 0.01) between the degree of satisfaction and the competencesformed.
A STUDY ON TRAINING AND ITS EFFECTIVENESS IN MANUFACTURING INDUSTRY

One of the foremost important activities of HRM department is training. The technique of training has been a key factor and a serious to realize the objectives of human resources management. Training effectiveness &evaluation is a critical aspect of the development of knowledge of workforce. The objective of this study is to examine effectiveness of Training. To survive in the globalised era, organizations need to continuously develop the knowledge, skill and ability of their human resources throughout the year, the organization spend a huge amount of money for the purpose of training and development of the human resources. The main objective of the study is to understand the outcome of the training in real output scenario. The objective of the study was evaluated with both primary and secondary data. The data’s were collected from 200 respondents using questionnaire. The type of research design that has been used for the study is the descriptive research design. The descriptive research means the research which is done to know the current situation of the study. The data has been collected using structured questionnaire. The sampling design used in the study is stratified random sampling. The data has been analysed using percentage analysis method . From the analysis it is observed that the training helps to reduce the learning time for employees who is starting the new jobs, transfer or promotion and ensure that they become fully component as quickly and effectively as possible.