Reviewerly/exHarmony huggingface dataset:
An interpretable framework for investigating the neighborhood effect in POI recommendation

Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect , which captures a user’s POI visiting behavior based on the user’s preference not only to a POI, but also to the POI’s neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user’s POI visiting behavior. Second, we propose a deep learning–based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization–based POI recommendation techniques.
The Effectiveness of Online Learning in Learning Chinese Language

Mid-March 2020, Indonesia began to be hit by the COVID-19 pandemic, which forced the government to set up online learning. The purpose of this study was to find out how effectiveness online learning was applied by Mandarin language educators at the junior high school level at Methodist Charles Wesley. Analysis of the data used is data analysis Miles and Huberman. The results of this study are: (1)Educators and students have adequate facilities in online learning. (2)Online learning has flexibility in its implementation. However, online learning has its own challenges, namely: weak internet signal, high internet costs and lack of teacher supervision of students. Keywords: Online Learning, Effectiveness, COVID-19.
Model-Based Reinforcement Learning for Infinite-Horizon Discounted Constrained Markov Decision Processes

In many real-world reinforcement learning (RL) problems, in addition to maximizing the objective, the learning agent has to maintain some necessary safety constraints. We formulate the problem of learning a safe policy as an infinite-horizon discounted Constrained Markov Decision Process (CMDP) with an unknown transition probability matrix, where the safety requirements are modeled as constraints on expected cumulative costs. We propose two model-based constrained reinforcement learning (CRL) algorithms for learning a safe policy, namely, (i) GM-CRL algorithm, where the algorithm has access to a generative model, and (ii) UC-CRL algorithm, where the algorithm learns the model using an upper confidence style online exploration method. We characterize the sample complexity of these algorithms, i.e., the the number of samples needed to ensure a desired level of accuracy with high probability, both with respect to objective maximization and constraint satisfaction.
A Description Logic for Analogical Reasoning

Ontologies formalise how the concepts from a given domain are interrelated. Despite their clear potential as a backbone for explainable AI, existing ontologies tend to be highly incomplete, which acts as a significant barrier to their more widespread adoption. To mitigate this issue, we present a mechanism to infer plausible missing knowledge, which relies on reasoning by analogy. To the best of our knowledge, this is the first paper that studies analogical reasoning within the setting of description logic ontologies. After showing that the standard formalisation of analogical proportion has important limitations in this setting, we introduce an alternative semantics based on bijective mappings between sets of features. We then analyse the properties of analogies under the proposed semantics, and show among others how it enables two plausible inference patterns: rule translation and rule extrapolation.
SEGMENTASI CITRA DENGAN HISTOGRAM THRESHOLDING MENGGUNAKAN ANALISIS CLUSTER HIRARKIS

Object segmentation in the image aims to separate the object area from the background area. Before the segmentation process, the image undergoes some initial processing to obtain good object segmentation results. Pre-processing is an image processing operation to improve image quality. An image histogram is a graph depicting the quantitative distribution of the value of the degree of gray pixels in or a certain part of the image. From the table we can see the comparison of the values for thresholding of the 4 methods used in image testing image 1 gets the best results with a value of 59, image 2 with a value of 105, image 3 with a value of 161, image 4 with a value of 121 and image 5 with a value of 102 and from the table value it can be seen that the proposed method gives better ME values than the 3 methods another (otsu, KI, Kwon).
A Refined Phase Unwrapping Method for High Noisy Dense Fringe Interferogram Based on Adaptive Cubature Kalman Filter

Cubature Kalman filter phase unwrapping (CKFPU) is an effective algorithm in unwrapping the interferograms. The local phase slope estimation is a key factor that affects the unwrapped accuracy. However, the estimation accuracy of local phase slop is relatively low in high noisy and dense stripes areas, which usually leads to the unsatisfactory unwrapped results. In order to effectively solve this issue, the rewrapped map of the unwrapped phase (obtained by CKFPU algorithm), which is a filtered interferogram with clearer fringes and more detailed information, is proposed in this paper to improve the phase slope estimation. In order to solve the problem of imprecise error variance for the new phase slope estimation, an adaptive factor is introduced into the CKFPU algorithm to increase the stability and reliability of the phase unwrapping algorithm. The proposed method is compared with the standard CKFPU algorithm using both simulated and real data. The experimental results validate the feasibility and superiority of the proposed method for processing those high noise dense fringe interferograms.
Reclamation Tax Calculation at Revenue Service, Regional Financial and Asset Management (Dppkad) Pariaman City(Perhitungan Pajak Reklamepada Dinas Pendapatan, Pengelola Keuangan Dan Aset Daerah (Dppkad) Kota Pariaman)

Billboard tax calculations conducted by the Revenue, Financial and Asset Management Agency (DPPKA) Kab. Dharmasraya still uses a manual system so that performance is not optimal. The research conducted aims to optimize the calculation of advertisement tax and provide fast service time for ad users and taxpayers. With this research, it is expected to be able to assist the DPPKA in calculating the advertisement tax effectively and efficiently. The research methods used are field research, library research and laboratories. So that a new system analysis is produced by using tools such as Use Case Diagrams, Sequence Diagrams, Class Diagrams, Activity Diagrams and Deployment Diagrams.
Enhancement of Cone-Beam Computed Tomography Dental-Maxillofacial Images by Sampling Kantorovich Algorithm

In this paper, we establish a procedure for the enhancement of cone-beam computed tomography (CBCT) dental-maxillofacial images; this can be useful in order to face the problem of rapid prototyping, i.e., to generate a 3D printable file of a dental prosthesis. In the proposed procedure, a crucial role is played by the so-called sampling Kantorovich (SK) algorithm for the reconstruction and image noise reduction. For the latter algorithm, it has already been shown to be effective in the reconstruction and enhancement of real-world images affected by noise in connection to engineering and biomedical problems. The SK algorithm is given by an optimized implementation of the well-known sampling Kantorovich operators and their approximation properties. A comparison between CBTC images processed by the SK algorithm and other well-known methods of digital image processing known in the literature is also given. We finally remark that the above-treated topic has a strong multidisciplinary nature and involves concrete biomedical applications of mathematics. In this type of research, theoretical and experimental disciplines merge in order to find solutions to real-world problems.
Perancangan Sistem Informasi E-Learning pada SMAN ABC

The Covid-19 pandemic requires schools to divert their learning in their respective homes simultaneously with government regulations related to large-scale social access (PSBB) which aim to avoid crowds and add new clusters in the educational environment. This is a challenge for schools that do not yet have a bold learning platform and for teachers who still cannot use technology to provide their learning boldly. The web-based e-learning platform is expected to be able to assist teachers and students in carrying out learning without any technical guidance on its use. By using Rapid Application Development (RAD) in its design, it is hoped that it can produce an e-learning platform that is easy to use by teachers and students from SMAN ABC. This research is only up to the Design Workshop.
Internal control, reporting failures and lack of scientific methodology for reporting – Status of the Al- Bireh- Palestine municipality –: الرقابة الداخلية وإخفاقاتها في إعداد التقارير وغياب المنهجية العلمية لإعداد التقارير – حالة بلدية البيرة – فلسطين –

The question that the research seeks to answer is the relationship of good supervisory administrative reports to achieving the goals of the organization, and helping managers to show the spirit of work interest in making their administrative decisions and taking those decisions away from their personal aims and interests. The scientific and applied importance of research appears in an attempt to shed light on the importance of internal control and its correct scientific and technical reports in developing and maximizing the benefit of the organization. The researcher applies his hypotheses to the municipality of Al- Bireh- Palestine as an applied case and studies the monitoring reports from the beginning of 2013 until the beginning of 2020 and the violations that were shown or not shown by the supervisory reports in some of the decisions taken at the time and whether the weakness of these reports had a role in the growth and increase of violations and deviation from the regulations and the policies in place at the time or not. The research assumes in general that the more supervisory reports are prepared in a wrong way technically and administratively, with the knowledge or ignorance of the source of those reports, the more deviation the administration and its decisions are from the course, objectives, laws and policies set for this organization.
THE CONCEPT OF «DOCUMENT» IN HISTORICAL DOCUMENTARY STUDIES

This article discusses the concept of a document in historical document science. It is established that the development of society and the differentiation of social processes led to an increase in the array of documents, made adjustments to its standard and specific features and types of material carriers, etc. There was a need to organize the storage and organization of existing document files and develop effective mechanisms for processing and using documents. All these factors have led to scientific reflection and the emergence of new areas of document science and historical document science. It is confirmed that in different branches of the science of the document-information cycle, different definitions of the concept of a document are used today. The presence of its terminology, i.e., words or phrases that express characteristic concepts from a particular field of science, determines the specifics of a particular science. In almost every field of knowledge, there are one or more understanding versions according to the specifics of those objects to which the document's status is given. It is understood differently in computer science, library, bibliography, archives, museum studies. The concept of a document in particular fields of activity (library, museum, archival affairs, and bibliography) is interpreted separately. Hence its ambiguity complicates scientific communication and mutual understanding between specialists in the field of document and communication, but at the same time generates discussion and development of a new vision of the object-subject component. Considering the transition of information from document to source, it is necessary to use the concept of documentary sources of information as a set of documents of scientific, industrial, educational, reference and popular science, intended for general reading and subject-matter analysis to find the necessary data. The concept of "document" reflects the characteristics of real objects, serving as an object of practical activities for the creation, collection, analytical and synthetic processing, storage, retrieval, dissemination, and use of documentary information in society. Defining the concept of "document" is the main task of theoretical and historical document science. In the process of developing the meaning and content of the concept, the document has changed. The result is the presence of various definitions that expand the content of the term or focus on a particular type.
Recommendation with Diversity: Mass Diffusion Model Based on Trust Network and Object Reputation

The traditional mass diffusion recommendation algorithm only relies on the user’s object collection relationship, resulting in poor recommendation performance for users with small purchases (i.e., small-degree user), and it is difficult to balance the accuracy and diversity of the recommendation system. This paper introduces the trust relationship into the resource allocation process of the traditional mass diffusion algorithm and proposes the Dual Wing Mass Diffusion model (DWMD), which constructs a dual wing graph based on trust relationships and object collection relationships. Implicit trust is mined according to the network structure of the trust relationship and integrated into the resource allocation process, and then merging the positive effects of object reputation on a recommendation through tunable scaling parameters. The user controls the tunable scaling parameter to achieve the best recommendation performance. The experimental results show that the DWMD method significantly improves diversity and novelty while ensuring high accuracy and effectively improves the accuracy and diversity balance. The improved recommendation performance for small-degree users proves that the trust relationship can effectively alleviate the generalized cold start problem of the recommendation algorithm for users who collect a small number of objects.
A self adaptive new crossover operator to improve the efficiency of the genetic algorithm to find the shortest path

&lt;p&gt;Route planning is an important part of road network. To select an optimized route several factors such as flow of traffic, speed limits of road. are concerned. Total cost of such a network depends on the number of junctions between the source and the destination. Due to the growth of the nodes in the network it becomes a tough job to determine the exact path using deterministic algorithms so in such cases genetic algorithms (GA) plays a vital role to find the optimized route. Crossover is an important operator ingenetic algorithm. The efficiency of thegenetic algorithmis directlyinfluenced by the time of a crossover operation. In this paper a new crossoveroperator closest-node pairing crossover (CNPC) is recommended which is explicitly designed to improve the performance of the genetic algorithm compared to other well-known crossover operators such as point based crossover and order crossover. The distance aspect of the network problem has been exploited in this crossover operator. This proposed technique gives a better result compared to the other crossover operator with the fitness value of 0.0048. The CNPC operator gives better rate of convergence compared to the other crossover operators.&lt;/p&gt;
Translation Genius and Social Networks

The paper attempts to consider changes in the translator's activity caused by the turn towards digital transformation in the institutional sphere. The analysis is based on material from German social networks, their content being the subject matter for the modern translator who is a specialist in cross-cultural and cross-lingual communication. The study describes the algorithm for implementing social management at the macrostructural and compositional levels. The emphasis is also placed on the institutional spheres that are most susceptible to digital transformation, which include nutrition and healthy lifestyle, education and many others. Total digital transformation of these institutional spheres is primarily evidenced by an increase in neologisms recorded in electronic databases, and dictionaries with an indication of their contextual use and their further popularization, first of all, in mass media texts. The results of the analysis lead the authors to conclude that training a new generation of interpreters and translators should include mastering the skill of working with big data, different corpora of texts, neologisms marked by or containing the industry component, as well as the need to develop glossary-type dictionaries for branches of knowledge. Considering the controversial content of social networks, the authors of the paper come to the conclusion that it is necessary to develop the code of ethics for the social networks translator.
Learning 3-D Human Pose Estimation from Catadioptric Videos

3-D human pose estimation is a crucial step for understanding human actions. However, reliably capturing precise 3-D position of human joints is non-trivial and tedious. Current models often suffer from the scarcity of high-quality 3-D annotated training data. In this work, we explore a novel way of obtaining gigantic 3-D human pose data without manual annotations. In catedioptric videos (\emph{e.g.}, people dance before a mirror), the camera records both the original and mirrored human poses, which provides cues for estimating 3-D positions of human joints. Following this idea, we crawl a large-scale Dance-before-Mirror (DBM) video dataset, which is about 24 times larger than existing Human3.6M benchmark. Our technical insight is that, by jointly harnessing the epipolar geometry and human skeleton priors, 3-D joint estimation can boil down to an optimization problem over two sets of 2-D estimations. To our best knowledge, this represents the first work that collects high-quality 3-D human data via catadioptric systems. We have conducted comprehensive experiments on cross-scenario pose estimation and visualization analysis. The results strongly demonstrate the usefulness of our proposed DBM human poses.
ME-MCTS: Online Generalization by Combining Multiple Value Estimators

This paper addresses the challenge of online generalization in tree search. We propose Multiple Estimator Monte Carlo Tree Search (ME-MCTS), with a two-fold contribution: first, we introduce a formalization of online generalization that can represent existing techniques such as "history heuristics", "RAVE", or "OMA" -- contextual action value estimators or abstractors that generalize across specific contexts. Second, we incorporate recent advances in estimator averaging that enable guiding search by combining the online action value estimates of any number of such abstractors or similar types of action value estimators. Unlike previous work, which usually proposed a single abstractor for either the selection or the rollout phase of MCTS simulations, our approach focuses on the combination of multiple estimators and applies them to all move choices in MCTS simulations. As the MCTS tree itself is just another value estimator -- unbiased, but without abstraction -- this blurs the traditional distinction between action choices inside and outside of the MCTS tree. Experiments with three abstractors in four board games show significant improvements of ME-MCTS over MCTS using only a single abstractor, both for MCTS with random rollouts as well as for MCTS with static evaluation functions. While we used deterministic, fully observable games, ME-MCTS naturally extends to more challenging settings.
Exploration of Technology-aided Education: Virtual Reality Processing Plant for Chemical Engineering Process Design

This work-in-progress study will explore technology aided education in the form of a Virtual Reality (VR) application used to support learning outcomes in a chemical engineering capstone course.VR has the ability to immerse users in a simulated environment and provide them with experiential learning opportunities.Most undergraduate chemical engineering students are required to design a chemical plant for their capstone design project without ever having visited or interacted with a full-scale processing plant and could benefit from the immersive experience that the VR tool would offer.This study will be conducted over a two-year period from September 2019 to May 2021.During the first-year, surveys and design challenges will be conducted without the use of the VR chemical processing plant.The data from the first year will establish a baseline that evaluates how learning outcomes are being met by the course without the VR application.During the second year the surveys will be given again in conjunction with the VR educational tool.The tool will give students the ability to view and interact with the unit operations inside a chemical processing plant without special training, expensive protective equipment and security clearance.Students will complete a number of challenges in VR and will be evaluated on their comprehension and invited to provide feedback on the effectiveness of the VR tool.The effects of VR on student comprehension, retention, and chemical processing design competency will be evaluated based on the data collected.This paper will discuss the initial design of the VR chemical processing plant, data from the non-VR cohort and a description of the research methods to be used during the final portion of the research.
Sistem Informasi Tracer Study Menggunakan Metode FAST

The output of the success of the education process needs to be traced by tertiary institutions to find out how many graduates are relevant to the work with the field of knowledge they master is through tracer study. The implementation of the Tracer Study on the STMIK MIC Cikarang campus is still done manually, resulting in the campus difficulty reaching more alumni in filling out the questionnaire. Plus, the campus had to go the extra mile in recapitulating the results of the questionnaire. In this study, the FAST (Framework for Application System Technologies) method is used with the aim of designing a Tracer Study information system that can facilitate the management of alumni data and the results of the Tracer Study. By using this method, a system that can manage alumni data can be designed, and can present alumni data reports. And can provide email notifications given to alumni to update their personal data, so that data is up to date. This system can also recap the results of the tracer study questionnaire conducted by alumni, which can then present the report in graphical form