Reviewerly/exHarmony huggingface dataset:
EVALUASI APLIKASI PAJAK BERBASIS ANDROID DALAM PROSES PELAPORAN PAJAK

Paying activities such as income tax and several other taxes, is something that must be done by people who work and have income and this often becomes a scourge among the community. This is caused by many factors making it so difficult to do. Even if there is a tax consulting service, they will definitely think back to visit the tax consultant, how much the cost is, will they help the public make their tax reports, and various other questions about tax payment and reporting activities in the community. The general public especially taxpayers still find it difficult to get information and pay and take care of taxes that must be paid. This paper aims to evaluate how the Android application can help and make it easier for taxpayers to find out tax information. The research method used is the method of observation and interviews, while for the application development method using the MDLC (Mobile Development Life Cycle) method. The software used is Windows 10 Pro, Android Studio, Java development 7, Sublime Text 3. The results of the application built based on alpha testing are functionally issued results that are as expected and based on beta testing according to respondents, the application is as needed and runs well. From this paper, it is expected to provide a new discourse for taxpayers and can help the general public in carrying out their tax reporting activities and also make it easy for them to consult with tax advisors if they encounter obstacles in their tax reporting process. Â  Keywords: Android Application, Tax, Consultation
Reducing Data Volume in Instance Based Learning

During the training phase of the Nearest-Neighbor (NN) algorithm, considered the most popular Instance-Based Learning (IBL) algorithm, all training instances are stored as the description of the learned concept. IBL algorithms postpone the generalization process, which usually happens during the training phase, until the classification phase starts i.e., when an unclassified data instance needs to be classified. When training sets have a high volume of instances, to store them all becomes unfeasible mainly due to storage requirements. Several IBL algorithms overcome storage related problems by implementing data volume reduction i.e., by storing only a representative subset of the training set. The investigation described in this paper focuses on four IBL algorithms that implement data reduction, which have been empirically evaluated in data sets from the UCI Repository. Their performance, considering storage reduction and classification accuracy, are presented and discussed.
Research Guides: MSU Library Omeka Exhibitions: Omeka Proposal Form

Online exhibits showcase MSU's Special Collections in an appealing and organized fashion. MSU Library hosts Omeka classic for use by MSU students and faculty to create stand-alone online exhibits highlighting their research Proposal Form
Features and Methods for Automatic Posting Account Classification

Manual processes in accounting can introduce errors that affect business decisions. Automation (or at least partial automation of accounting processes) can help to minimise human errors. In this paper, we investigate methods for the automation of one of the processes involved in invoice posting – the assignment of account codes to posting entries – using various classification methods. We show that machine learning-based methods can reach a precision of up to 93% for debit account code classification and even up to 98% for credit account code classification.
Micro-blog Sentiment Analysis Based on Emoticon Preferences and Emotion Commonsense

Existing micro-blog sentiment analysis basically calculates sentiment values from explicit features, but the hidden sentiment in text often have an important influence on the judgment of sentiment preference, a new sentiment analysis method is proposed here. First, the dynamic micro-blog data are collected and pretreated by combining Weibo crawlers and Web API. According to the characteristics of micro-blog to construct an emoji dictionary. Then, the semantic similarity and tendentiousness are calculated based on the extraction and classification of sentiment words of ConceptNet. Finally, the emoticons and the weight of marked emotion commonsense are used to calculate the sentiment preference value of the whole micro-blog text, which makes the judgment of the sentiment polarity more accurate. Experimental results show the effectiveness of this method.
Bypassing Non-Outsourceable Proof-of-Work Schemes Using Collateralized Smart Contracts.

Centralized pools and renting of mining power are considered as sources of possible censorship threats and even 51% attacks for decentralized cryptocurrencies. Non-outsourceable Proof-of-Work (PoW) schemes have been proposed to tackle these issues. However, tenets in the folklore say that such schemes could potentially be bypassed by using escrow mechanisms. In this work, we propose a concrete example of such a mechanism which is using collateralized smart contracts. Our approach allows miners to bypass non-outsourceable PoW schemes if the underlying blockchain platform supports smart contracts in a sufficiently advanced language. In particular, the language should allow access to the PoW solution. At a high level, our approach requires the miner to lock some collateral covering the reward amount and protected by a smart contract acting as an escrow. The smart contract allows the pool to collect the collateral as soon as the miner collects any block rewards. We propose two variants of the approach depending on when the collateral is bound to the block solution. Using this, we show how to bypass previously proposed non-outsourceable Proof-of-Work schemes (with the notable exception for strong non-outsourceable schemes) and show how to build mining pools for such schemes.
APLIKASI SISTEM PAKAR UNTUK MENDIAGNOSIS PENYAKIT KUSTA DENGAN METODE FORWARD CHAINING

Leprosy, known as leprosy or hansens disease, is a disease that attacks the skin, the peripheral nervous system, the linning of the upper respiratory tract, and the eyes. Leprosy can cause skin injuries, nerve damage, muscle weakness, and numbness. Leprosy is caused by the bacterium Mycobacterium Leprae. This bacterium takes 6 months to 40 years to develop in the human body. Signs and symptoms of leprosy can 1 to 20 years after the bacteria infect the patients body. The goal achieved in this research is to make an application of an expert system that is useful to help obtain information and initial allegations in the diagnosis of leprosy. For this study using the forward chaining method. Forward chaining is a  search technique that starts from known fact and then matches the fact with the if part of the IF-THEN rules. The results of this study are that the public, especially patients with leprosy, can solve the problem of leprosy and can provide temporary solutions and alternative treatments for leprosy.
Generative forms of multimedia content. (Opening keynote talk)

Multimedia analysis has recently made spectacular improvements in both quality and in sophistication. Over the last half-decade we have seen extreme progress in tasks like image and video tagging, object detection and activity recognition, generating descriptive captions and more. Some of these have been deployed and are in widespread use in our smartphones and on our social media platforms. We have also seen recent research work, including our own, on computing more abstract features of multimedia, such as person-counting from CCTV, computing visual salience, estimating aesthetics of images and videos, and computing video memorability. The common methodology used across most of these applications is of course machine learning, in all its forms, from convolutional neural networks to simple regression and support vector machines. Much of the research in our field is about wrestling with machine learning to optimise its performance in multimedia analysis tasks and this recent run of extreme progress does not look like ending anytime soon, though it will reach its high water mark. When it does reach the point at which it cannot get any better, what then ? Generative machine learning (ML) is a recent form of media analysis which turns the conventional approach on its head and its methodology is to train a model and then generate new data. Example applications of generative ML deoldify which colourises black and white images and video clips, and Generative Adversarial Networks (GANs) which can generate DNA sequences, 3D models of replacement teeth, impressionist paintings, and of course video clips, some known as deepfakes. Putting aside the more nefarious applications of deepfakes, what is the potential for generative forms of multimedia ? In the short to medium term we can speculate that it would include things like movie augmentation but it how far can it go and could it replicate human creativity ? In this talk I will introduce some of the recent forms of generative multimedia and discuss how far I believe we could go with this exciting new technology.
Tailoring the Knowledge Data Discovery process to e-commerce reviews. = Tailoring the Knowledge Data Discovery process to e-commerce reviews.

This thesis mainly concerns data driven technologies and the related potentiality to bring out, from textual data, previously unknown knowledge. The goal, for the current work case, is to extract potentially useful information from Amazon products reviews. To this aim, Knowledge Data Discovery (KDD) process, applied on textual data collection, has been tailored to Amazon reviews dataset. First of all a broad description of text mining with its benefits and pitfalls has been introduced along with existing algorithm and methodologies; then, ESCAPE engine has been studied, tailored and proposed as a not-time consuming and low-computational cost solution. The proposed tool approaches and integrates all the building blocks of KDD processes such as data processing and characterization, self-Tuning exploratory data analytics, and knowledge validation and visualization. Two different approaches with self-tuning algorithms for the exploratory phase are also included. Before running the experiments, Amazon Web Service platform (AWS) case and the importance of data analysis for business choice have been discussed. A large number of experiments, applying the two approaches, have been performed on almost twenty products reviews dataset, some of them specifically built during the development of the thesis according to the work needs. Experimental results have been finally analysed, validated and visualized with several techniques in order to show, from a technical point of view, the performances of both the two ESCAPE approaches and the strategies used within them, and, from a business analysis point of view, interesting features among the different products categories comments.
PEMBELAJARAN MENYENANGKAN MODEL TEAM GAMES TOURNAMENT MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR PENDIDIKAN KEWARGANEGARAAN

One of the outcomes of Classroom Action Research conducted at Gunung Putri Bogor State Junior High School 3 is a research article published in a scientific journal. The purpose of the study is to encourage a significant increase in activity and learning outcomes, so as to create enjoyable learning. The action was taken by changing the conventional method to the cooperative method of the Team Games Tournament model for Citizenship Education learning, the subject matter, Youth Pledge in a Unity in Diversity Frame. The research subjects of class VIII even semester 2018 totaled 40 students. The results of the study, in the first cycle there have been improvements compared to pre-cycle. In the second cycle there was a significant increase in learning activities and outcomes. All learning outcomes have reached the Minimum Completion Criteria Value. Recommendations are expected that educators apply in educating as a variety of learning methods. Â  Keywords : learning, fun, playing ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Asma. (2011). Model Pembelajaran Kooperatif . Jakarta: Depdiknas Dirjen Dikti. Â  Cholisisn. (2010). Materi Ilmu Kewarganegaraan-Pendidikan Kewarganegaraan . Yogyakarta: Universitas Negeri Yogyakarta. Â  Robert, S. (2010). Cooperative Learning: Teori, Riset dan Praktik. (Terjemahan) . Jakarta: Nusa Media. Â  Shoimin, A. (2014). Model Pembelajaran Inovatif Dalam Kurikulum 2013 . Yogyakarta: AR-Ruz Media. Â  Suarjana. (2000). Model Pembelajaran Teams Games Tournament. Pendidikan Teknologi Informasi Dan Komunikasi (PTIK) . Universitas Sebelas Maret. Surakarta. , 13 No. 1 . Â  Suharsimi, A. S. dan S. (2015). Penelitian Tindakan Kelas (Edisi Revisi) . Jakarta: Bumi Aksara. Â  Syamsu, R. dan Herniwati. (2018). Survey Awal Pembelajaran Pendidikan Kewarganegaraan di Kelas VIII. Bogor: SMP Negeri 3 Gunung Putri-Bogor. Â  Â
Exploring the Machine Learning Algorithms and its Classification

Over previous years, Machine Learning (ML) has developed from endeavor of some computer enthusiasts developing the probability of computers learning, and a portion of Mathematics that seldom deliberated computational methods, to an independent survey discipline, which has not only offered the essential base for statistical-computational principles of learning processes. This manuscript concentrates on describing the perception and evolution of ML, few popular ML algorithms. ML methods might be applied on any broad area. ML will be procedure of self-learning from practices and performances without human interference. The study of ML methods will be displayed here. And provided the success companies see deriving value from massive number of accessible data, everyone wants in. However, whereas the thought of ML might seem overwhelming, and main thoughts are fairly easy. Now we have displayed an establishment for understanding the thoughts behind few popular ML methods and its classification.
An efficient revocable ID-Based key insulated signature scheme to achieve authentication of smart meter

In the smart grid, a large number of smart meters distributed at the edge of the network transmit electricity data to the control center through public network. Therefore, it is crucial to authenticate smart meter. Traditional authentication schemes that based on signature are usually rely on the assumption that the private key is absolutely secure, and private key exposure will endanger the security of the whole scheme. Further, most of these schemes are designed with bilinear pairings, which results in a high cost in computation and communication. So, an efficient revocable ID-based key insulated signature scheme is proposed in this paper. Computational analysis shows that our scheme has less cost than other schemes in computation and communication, which is suitable for smart meters with limited computational capability. Besides, our scheme can revoke the misbehaving or malicious smart meter conveniently and quickly.
Fuzzy Rule-Based Models

The chapter is focused on the fundamentals of fuzzy rule-based models, their key properties, design and development.
Formation of Cognitive Search Universal Educational Actions by Younger Schoolchildren

The article reveals methodological approaches to the formation of cognitive search universal educational actions by younger schoolchildren, which are actively used when working with information in the course of studying various subjects of primary school. Examples of assignments for fourth-grade students in mathematics are given, the purpose of which is to analyze the information presented in graphical form. The results of the activity of fourth-graders are characterized, the difficulties that arise in them in the process of completing tasks are assessed.
CREATIVE TECHNIQUES TO PROVIDE ESTATE LIQUIDITY

This chapter discusses strategies to help family businesses and their owners deal with liquidity problems. Although the federal estate tax imposed on the death of a majority family business owner was probably the most common cause of a liquidity crisis, the estate tax has never been the sole cause of a crisis, and is now a less common liquidity threat for smaller family businesses. Unexpected events like recessions, increased competition, technological changes, and even interest rate spikes can all trigger a cash flow crisis. Planned expansions and scheduled loan balloon payments can also create a cash flow crisis. The chapter discusses the place of insurance planning in a liquidity analysis. The tax code provides relief measures including extensions to pay estate tax, and estate plans can be designed to minimize liquidity problems. The chapter details these techniques which are typically unavailable without advance planning or a redeploying of business or family assets.
STUDY OF THE MINERALIZED ZONES OF THE BUKANTAU MOUNTAINS ON SPACE IMAGES IN THESHORT-WAVE INFRARED RANGE

Mineralized zones in the Bukantau Mountains were determined based on processing satellite images in the shortwaveinfrared range. Field certification work and testing (in ditches) were carried out in the areas of mineralizedzones. The results of laboratory analysis of samples showed a gold content of up to 3.0 g / t. The preliminaryresults of cosmogeological studies were obtained. It was the basis for identifying the prospective phase Fazogirprospective for gold mineralization.
Upper approximating probabilities of convergence in probabilistic coherence spaces

We develop a theory of probabilistic coherence spaces equipped with an additional extensional structure and apply it to approximating probability of convergence of ground type programs of probabilistic PCF whose free variables are of ground types. To this end we define an adapted version of Krivine Machine which computes polynomial approximations of the semantics of these programs in the model. These polynomials provide approximations from below and from above of probabilities of convergence; this is made possible by extending the language with an error symbol which is extensionally maximal in the model.
Association Rules Mining for STO Dataset of HSES Knowledge Portal System

HSES knowledge portal system is one of the web portals. In this portal, we collect information into student, teacher, and other datasets. Now we combine these datasets with common features with class descriptors. This is named as STO dataset which is handled using traditional tools and techniques. This large dataset is generally known as big data. Increasing the use of social media and new innovations generate a vast amount of data. The quantity of data makes HSES-KP very powerful and useful. This portal needs to manage and analyze big data for better association rules and outcomes. Association rule mining is the technique for finding frequent patterns, associations, correlations, or causal structures among sets of items in transaction databases. Lift, support, and confidence are user-defined measures of interestingness. In this paper, we review the background and the various techniques of association rules mining. This study grows up with a discussion of challenges and future directions.
LibGuides: Library Emergency Remote Support: Student Support Services

Library support and information for Fisher students studying remotely and faculty teaching remotely. Information about library support for students who are studying remotely
Música para dos cuartetos de saxofones, percusión y electrónica. Investigación entorno a los modos de sincronización y mutua afectación entre medios emisores de sonido como herramienta compositiva

Various compositional models that relate the acoustic medium to the electronic medium, delving into the nature of each of these mediums from its multiple dimensions and glimpsing them through irrestric and irreverent musical notation and conception mechanisms. Some of the elements that are explored throughout the investigative work are: New temperaments in proportional divisions other than twelve, their classification and application. The synchrony models between electronic and acoustic sound. Linguistic music: the perceptual migration of linguistic sound towards its creative implementation on the level of sound. The revaluation of the coincident sound: traditional harmony as a set of preconceived specific timbres. The notation and the psychological impact of the graph on the performer.The political compromise of composition and the ethological connotation of what it means to be a composer decanted towards the defense of heterogeneity and the rejection of extreme utilitarianism in the art of music.Double quartet of saxophones, percussion and electronics.
RANCANG BANGUN GAME EDUKASI PUZZLE DAN KUIS ARAB MELAYU

There is still a Lack of supporting media in learning Malay Arabic, elementary school students have difficulty recognizing hijaiyah letters. At present learning is still running using paper and textbooks so that the method of delivering limited material, also added background beliefs that have elementary school students are different, so the learning process becomes slow and the teacher has difficulty explaining the learning of Arabic Malay with material clear and precise. In the process of learning Malay Arabic, he must be able to master the types of Malay Arabic writing, the history of Malay Arabic, the symbolization of the Malay Arabic script, and the writing of the Malay Arabic script. This research process uses the Game Development Life Cycle (GDLC) method and uses the Unified Modeling Language (UML) tool. Also use construct 2 software in designing applications. Puzzle game mode will be directed to draw and place images that have been randomized by the system, in the quiz game users are directed to choose the available answers. The results of this study are puzzle educational game applications and Arabic quizzes.
Calculi for Classical Logic

The sequent calculus $${\textit{\textbf{G}}}_{1}$$ for the classical first-order logic in Sect. 1.2 is best seen as a refutation procedure. Inference rules are regarded as searching step of a counter-model of the endsequent when we turn inferences upside down. In Sect. 2.1 we follow the idea to prove the completeness of the cut-free fragment $${\textit{\textbf{G}}}_{1}$$ by a canonical proof search. This together with Soundness 1.1 yields the cut-elimination theorem (Hauptsatz, normal form theorem). Furthermore the same method is employed to applied calculi, predicative second order logic and the $$\omega $$ -logic.
Before the Breach: Five Tips for Upgrading Data Security

This article offers five tips to help prevent a data breach, and how to protect your data if you are affected.
Research Guides: Criminal Justice Basics: Contact Us

Your UC Libraries guide for general research assistance for Criminal Justice. Where to get contact information for Criminal Justice Librarians.
Time-aware parallel collaborative filtering movie recommendation based on Spark

Most of the traditional collaborative filtering (CF) recommendation models mainly consider the users' ratings on items, often ignore the time context information of users. However this information is non-trivial to improve the effectiveness of recommender system. A time-aware parallel CF movie recommendation based on Spark is proposed in this paper. The CF algorithm based on matrix factorisation can associate users' interests with items through implicit features and solve the sparse matrix problem. The time-aware CF algorithm considers the dynamic features associated with the items and users, which improves the recommendation accuracy by introducing discrete time parameter into the matrix factorisation model. To solve the problem of the slow processing speed of high volume data, distributed computing based on Spark is used to achieve the parallelisation of the algorithm. The experimental results on real dataset MovieLens show that the proposed method performs significantly better than traditional CF recommendation, which can alleviate the problem of data sparsity and significantly improve the processing speed and recommendation accuracy.