Reviewerly/exHarmony huggingface dataset:
A User Profile Based Medical Recommendation System

With the rapidly development of Internet, online medical platform has become an essential part of medicines trade. In order to help users quickly find satisfying products in a large number of commodities, the recommendation system has been proposed. The traditional recommendation algorithm usually only takes the user-item rating into consideration, which leads low accurate of prediction. In this paper, we propose a user profile based recommendation method, which uses deep learning to analyze user behavior and construct user multi-dimensional attribute features. user profile can be constructed by analyzing information of drugs. By analyzing the historical information of user’s action, including purchasing, browsing, and collecting, we can dynamically predict rating of user on drug by a trained neural network. The experimental verification on B2B medical platform shows that the accuracy of prediction is higher than other algorithms. The proposed system can not only improve user experience, but also increase the sales of the platform.
Navigation of Resources from Tangible Object Recognition to Improve Virtual Tours in Botanical Gardens

In order to create an immersive application that allows access to digital resources through the creation of a navigation structure based on recognition of tangible objects, a study is presented where a group of 12 people interact with the proposed model and evaluate the experience with the immersive model. The results showed a great acceptance in terms of ease of use and comfort surpassing the average usability scale system, the aspects to be taken into account has to do with the functionalities that are expected to implement according to the comments of the participants so it is concluded that the development of the application meets expectations. It is expected to add more features with the motivation of achieving a great reception by the community taking into account the future integrations and learning that makes part of the possibilities that this technology offers both visitors and researchers in the areas addressed by the implementation of this application.
Law and Order in Africa – Policing Social Networks

Liberalisation of markets and access to global technologies has helped drive the massive expansion of telecommunications, the Internet and social networks in Africa, creating new spaces to be policed. Enthusiasm for liberalisation is partly explained by the technology being bundled with a wide and increasingly sophisticated array of facilities for interception, wiretapping and shutdowns. This has allowed almost any level of surveillance from the minimal to the Orwellian. Consequently, governments have been able to extend their established practices of law and order into telecommunications, Internet access and social networks, for example, filtering keywords, blocking temporarily or permanently individual websites and services, and identifying individuals for questioning and enhanced interrogation. It is possible to monitor citizens at least as effectively as in the physical world and often less obtrusively. Much of the surveillance activity has been undertaken by intelligence services, which have access to the necessary budgets, and operate outside the, generally, limited checks and balances of constitutional courts and parliaments. In practice very few are required to observe human rights. There are almost no effective restraints on the adoption of sophisticated surveillance technologies, other than the limited availability of skills. Constitutional rights to privacy and dignity are systematically ignored, as are complaints about shutdowns.
Management bias as a cause of employee weak performance: Case Study at Jadara University

The study seeks to define the role of management bias on the weak performance of employees.The research sample comprised of 107 male and female employees.To achieve this research purpose, there were 79 questionnaires valid for statistical analysis, adopting the descriptive approach.Questionnaire was used to measure the effect of management bias on employees' weak performance.The study concluded that the university dependence on scientific foundations, qualifications, and abilities in selecting and recruiting employees was intermediate, which indicates that the university does not often follow the right regulations in selecting employees, indicating the bias existing in employees' selection.In light of the aforementioned results, the study recommended the management at Jadara University to establish an independent committee of professionals to recruit efficient and qualified employees.
Pengelolaan Arsip Elektronik di Unit Kearsipan PT Semen Padang

AbstractThe purpose of this paper is to describe how the stages of archival storage using the E-Archive application in the PT Semen Padang Archive Unit. describe how the stages of discovery of archives by using the E-Archive application in the PT Semen Padang Archive Unit. Based on the research that has been done, it can be concluded as follows. 1) Stages of archival storage using the E-Archive application at the Kerasipan Union PT Semen Padang, (a) preparing official letters or manuscripts that will be provided, (b) scanning scripts or letters, (c) creating folders on the computer as a repository for scanned letters or official scripts, (d) creating hyperlinks, namely linking the list of archives with the results of the scan, making complete administration of the media expert, consisting of, decree, minutes of approval of the transfer of media, minutes of legalization of transfer of media, list of archives suggested by media experts, and list of archives over media. 2) The stages of finding an archive using the E-Archive application in the PT Semen Padang Archive Unit are by (1) determining the types of letters needed, (2) searching for codes based on names or titles that have been indexed in the E-Archives application, after entering the title or code you want to find, the subject of the archive that you want to use appears.Keywords: E-Arhcive, storage.
Monitoring the Ratio Error of High-Voltage CVTs Based on Local Outlier Identification

The capacitor voltage transformer (CVT) is one of the most important measurement equipment in the high-voltage (HV) power system, its metering accuracy is a key factor to ensure the safety of the power system and the fairness of the electricity trade. The conventional off-line calibration method to obtain the ratio error of CVTs is time-consuming and needs power cut, so it is no longer a suitable approach in the HV power system. In this paper, an on-line ratio error monitoring method for CVTs has been proposed, this method is based on local outlier identification. Considering the voltage fluctuations in the power system, the output voltages of CVTs are used to construct recognition models firstly, in which other fluctuation components have been eliminated except the ratio error, then the abnormal model could be detected and blamed to the abnormal ratio error. The simulation on the field data illustrates that the proposed method could identify the abnormal ratio error of CVTs of 0.2 accuracy class, and this method is suitable for the actual conditions in power system.
Regularizing Reasons for Outfit Evaluation with Gradient Penalty

In this paper, we build an outfit evaluation system which provides feedbacks consisting of a judgment with a convincing explanation. The system is trained in a supervised manner which faithfully follows the domain knowledge in fashion. We create the EVALUATION3 dataset which is annotated with judgment, the decisive reason for the judgment, and all corresponding attributes (e.g. print, silhouette, and material \etc.). In the training process, features of all attributes in an outfit are first extracted and then concatenated as the input for the intra-factor compatibility net. Then, the inter-factor compatibility net is used to compute the loss for judgment. We penalize the gradient of judgment loss of so that our Grad-CAM-like reason is regularized to be consistent with the labeled reason. In inference, according to the obtained information of judgment, reason, and attributes, a user-friendly explanation sentence is generated by the pre-defined templates. The experimental results show that the obtained network combines the advantages of high precision and good interpretation.
The Library of the Headquarters of the Border Guard Special Corps. To the 125th Anniversary of the Corps Foundation

Due to the lack of studies on the history of librarianship in the Border Guard Special Corps (BGSC), this article considers for the first time the history of creation and development of the library at its headquarters. The purpose of the article is the reconstruction of the library stocks, first of all its quantitative indices and thematic content, basing on “The Library Catalogue of the Headquarters of the Border Guard Special Corps” of 1906. The author widely used the method of comparative analysis, which allowed to reveal the catalogue shortcomings, common to pre-revolutionary libraries, and specific features of catalogue compilation, to identify similarities and differences in the subject matter of the available publications and their distribution between Military Department divisions with similar book collections (using the example of “Systematic Catalogue of books of the Library of Headquarters of the Moscow Military District, published in the same 1906, and three additions to it). The core of the source base of the study were the library catalogues and orders of the chiefs of the Border Guard. This study allowed the author to establish the date of creation of the library, which should be considered January 30, 1895, as on that day the Corps Headquarters Order No. 12 recorded the first receipt of books and quantitative indices of the stock, which included 33 authors (names) in 110 volumes. According to the catalogue, by 1906 the library included 454 authors (names) of publications in 1396 volumes. The article revealed the existence of one more library at the headquarters of the BGSC, arranged and operated at Corps Museum. The results show that organising and development of libraries at the headquarters of BGSC and its structural units went in one course with the establishment and further existence of libraries of the Military Department. They had similar departments, same sources of accession, including from the General Staff, depended on the attitude of higher administration and the initiative of the officers themselves.
Non-finitely axiomatisable modal product logics with infinite canonical axiomatisations

Our concern is the axiomatisation problem for modal and algebraic logics that correspond to various fragments of two-variable first-order logic with counting quantifiers. In particular, we consider modal products with Diff, the propositional unimodal logic of the difference operator. We show that the two-dimensional product logic Diff×Diff is non-finitely axiomatisable, but can be axiomatised by infinitely many Sahlqvist axioms. We also show that its ‘square’ version (the modal counterpart of the substitution and equality free fragment of two-variable first-order logic with counting to two) is non-finitely axiomatisable over Diff×Diff, but can be axiomatised by adding infinitely many Sahlqvist axioms. These are the first examples of products of finitely axiomatisable modal logics that are not finitely axiomatisable, but axiomatisable by explicit infinite sets of canonical axioms.
A novel level set method for medical image segmentation

A new level set method is proposed to segment medical images by introducing intensity inhomogeneity and a double well potential function. First of all, an adaptive weighted median filter (AWMF) is used to define the region with respect to intensity inhomogeneity. Then, the analysis is performed by incorporating an energy function with intensity inhomogeneity-free images. Thereafter a new double well potential function is introduced that reduces the computational time. Finally, segmentation is performed on medical images by minimizing the overall energy function. The experimental results show the robustness of the proposed method in comparison to the existing methods in terms of computational time and Jaccard Index (JI).
Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.
Learn to Predict Sets Using Feed-Forward Neural Networks

This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tagging and object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality. Depending on the problem under consideration, we define different training models for set prediction using deep neural networks. We demonstrate the validity of our set formulations on relevant vision problems such as: 1)multi-label image classification where we achieve state-of-the-art performance on the PASCAL VOC and MS COCO datasets, 2) object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO v3, and 3) a complex CAPTCHA test, where we observe that, surprisingly, our set-based network acquired the ability of mimicking arithmetics without any rules being coded.
PENERAPAN SISTEM INFORMASI MANAJEMEN UNTUK MENJALIN KERJASAMA DENGAN WALI MURID

This research found out the implementation of management information systems in SMP N 1 Talamau. We used qualitative research methods. The result showed that the existing management information system meets standards, both in terms of students, curriculum, human resources, facilities and quality of users, who are able to manage the activities that exist in schools. Then, the role of parents is also important in implementing management information systems to improve the quality of education in SMP N 1 Talamau, implementing special strategies that utilize information technology-based management information systems, namely information and consultation rooms that function as a center for consultation between schools and parents in providing access to parents to see the development of students' academic values.
Dimensionality Reduction with Extreme Learning Machine Based on Manifold Preserving

As a nonlinear dimensionality reduction technique, manifold learning method is used to solve the computational complexity of high-dimensional data, which has been widely used in the visualization and feature extraction. However, most of existing methods are sensitive to noise and usually cannot get the accurate projection function, which limit its practical application. To overcome these problems, we propose a nonlinear dimensionality reduction method with Extreme Learning Machine based on Manifold Preserving called MP-ELM. MP-ELM takes both manifold structure and distance information into account, which can heighten dimensionality reduction effect and enhance noise immunity. The results of the visualization experiment on artificial data, clustering and face recognition experiment on several real-word database show that our method significantly outperforms the compared dimensionality reduction methods.
ANALISIS KEANDALAN DATA PENAKAR HUJAN OTOMATIS SEBAGAI ACUAN KOREKSI PERKIRAAN HUJAN RADAR

The reliability test of automatic rain gauge data in the Mt. Merapi region is highly needed considering that the data is used as a reference to correct the X-band MP radar rainfall estimates error. Rain gauge stations containing unreliable data need to be eliminated so that radar rainfall estimates can be adjusted based on reliable data only. This paper discusses the technique of examining the reliability of rain gauge data using a two-way analysis of variance. The method used in variance analyzing is the Scheffe test, where the difference in the mean value of the two rain gauge stations is compared with the critical value of the Scheffe test. The criteria appointed to declare 'reliable' is if the number of stations that have a significant difference with the reviewed station is less than two. A total of 5939 10-minute rainfall intensity data from 21 rain gauge stations in the Mt. Merapi region were used in the analysis. The results show that six of the 21 rain gauge stations contain unreliable data to use as a reference for correcting radar rainfall estimates. The Scheffe test is considered excellent and straightforward to apply to find out the variance of mean values between rain gauge stations.
widgyts: Custom Jupyter Widgets for Interactive Data Exploration with yt

widgyts is a custom Jupyter widget library to assist in interactive data visualization and exploration with yt.yt (Turk et al., 2011) is a python package designed to read, process, and visualize multidimensional scientific data.yt allows users to ingest and visualize data from a variety of scientific domains with a nearly identical set of commands.Often, these datasets are large, sparse, complex, and located remotely.Creating a publication-quality figure of an area of interest for this data may take numerous exploratory visualizations and subsequent parameter-tuning events.The widgyts package allows for interactive exploratory visualization with yt, enabling users to more readily determine which parameters and selections they need to best display their data.The widgyts package is built on the ipywidgets (Grout, Frederic, Corlay, & Ragan-Kelley, 2019) framework, which allows yt users to browse their data using a Jupyter notebook or a Jupyterlab instance.widgyts is developed on GitHub in the Data Exploration Lab organization.Issues, questions, new feature requests, and any other relevant discussion can be found at the source code repository (Munk & Turk, 2019).
Time-aware Large Kernel Convolutions

To date, most state-of-the-art sequence modeling architectures use attention to build generative models for language based tasks. Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$. Alternatively, they utilize depthwise convolutions with softmax normalized kernels of size $k$ acting as a limited-window self-attention, resulting in time complexity of $O(k{\cdot}n)$. In this paper, we introduce Time-aware Large Kernel (TaLK) Convolutions, a novel adaptive convolution operation that learns to predict the size of a summation kernel instead of using a fixed-sized kernel matrix. This method yields a time complexity of $O(n)$, effectively making the sequence encoding process linear to the number of tokens. We evaluate the proposed method on large-scale standard machine translation, abstractive summarization and language modeling datasets and show that TaLK Convolutions constitute an efficient improvement over other attention/convolution based approaches.
Sensor Selection via Maximizing Hybrid Bayesian Fisher Information and Mutual Information in Unreliable Sensor Networks

The sensor selection problem is addressed for unreliable sensor networks. The Bayesian Fisher information (BFI) matrix, mutual information (MI) and their relationship are investigated under Gaussian mixture noise conditions. To overcome the flaw that the sensor selection methods based on either BFI matrix or MI could not provide coincident results, the multiple objective optimal (MOP) -based sensor selection approach is developed via minimizing the number of selected sensors while maximizing corresponding BFI matrix and MI. The variable weight decision making (VWDM) and technique for order of preference by similarity to ideal solution (TOPSIS) approaches are then proposed to find the candidate that can better trade off the cost and two performance metrics. Comparison results demonstrated that the proposed method can find a more informative sensor group, and ultimately, its overall localization performance outperforms the sensor selection methods based on BFI or MI.
Crime Mapping Model based on Cloud and Spatial Data: A Case Study of Zambia Police Service

Crime mapping is a strategy used to detect and prevent crime in the police service.The technique involves the use of geographical maps to help crime analysts identify and profile crimes committed in different residential areas, as well as determining best methods of responding.The development of geographic information system (GIS) technologies and spatial analysis applications coupled with cloud computing have significantly improved the ability of crime analysts to perform this crime mapping function.The aim of this research is to automate the processes involved in crime mapping using spatial data.A baseline study was conducted to identify the challenges in the current crime mapping system used by the Zambia Police Service.The results show that 85.2% of the stations conduct crime mapping using physical geographical maps and pins placed on the map while 14.8% indicated that they don't use any form of crime mapping technique.In addition, the study revealed that all stations that participated in the study collect and process the crime reports and statistics manually and keep the results in books and papers.The results of the baseline study were used to develop the business processes and a crime mapping model, this was implemented successfully.The proposed model includes a spatial data visualization of crime data based on Google map.The proposed model is based on the Cloud Architecture, Android Mobile Application, Web Application, Google Map API and Java programming language.A prototype was successfully developed and the test results of the proposed system show improved data visualization and reporting of crime data with reduced dependency on manual transactions.It also proved to be more effective than the current system.