Reviewerly/exHarmony huggingface dataset:
Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle

This paper addresses the issue of how we can detect changes of changes, which we call metachanges, in data streams. A metachange refers to a change in patterns of when and how changes occur, referred to as “metachanges along time” and “metachanges along state”, respectively. Metachanges along time mean that the intervals between change points significantly vary, whereas metachanges along state mean that the magnitude of changes varies. It is practically important to detect metachanges because they may be early warning signals of important events. This paper introduces a novel notion of metachange statistics as a measure of the degree of a metachange. The key idea is to integrate metachanges along both time and state in terms of “code length” according to the minimum description length (MDL) principle. We develop an online metachange detection algorithm (MCD) based on the statistics to apply it to a data stream. With synthetic datasets, we demonstrated that MCD detects metachanges earlier and more accurately than existing methods. With real datasets, we demonstrated that MCD can lead to the discovery of important events that might be overlooked by conventional change detection methods.
Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.
Professional Vocation Development in the Future Border Guard Officers’ Activity

The issues of professional vocation of future border guard officers is vital and topical for their further operational and service activity.The purpose of this article is to determine and substantiate pedagogical conditions for development of professional vocation as the key to success in the activity of future border guard officers.To achieve the purpose and set tasks a triangulation of research methods was used: theoreticalanalysis, generalization, systematization in order to clarify the status of elaboration of the research problem in educational theory and practice, definition of the professional vocation characteristics of the future border guard officers; empiricalobservation, questionnaires, study of the positive experience of interdependence of professional vocation and success in operational and service activity of border guards, self-assessment -to diagnose motivation for learning and a level of development of cadets' professional vocation.Study involved 299 cadets, 89 persons of the managerial, scientific and pedagogical personnel.According to the results of the study and taking into account the specifics of the operational and service activity of the border guard officers, often involving cooperation with foreign counterparts, the pedagogical conditions of the professional vocation development have been determined and substantiated.
PENERAPAN DATA MINING DALAM MENGELOMPOKAN PRODUKSI JAGUNG MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS

Corn needs are currently experiencing a fairly rapid development can be seen in terms of the domestic market, here researchers want to increase the productivity and quality of corn production. The data that will be used is the data from the Central Statistics Agency. The method in this study is the K-means clustering algorithm and the application used is Rapidminer which will be grouped into 2 clustering, namely high and low. The results of this study are 2 high level cluster provinces, 32 low level cluster provincesKeywords: Corn, Data mining, K-means Clustering c
Listening Comprehension Proficiency Development of Information Technology Students in ESP classroom

University of Kyiv (Ukraine), and involved 221 undergraduate students of Computer Science and Cybernetics Faculty
Digital Forensics, A Need for Credentials and Standards

Despite the phenomenal growth in the digital world and crimes committed using digital techniques and tools, there are literally no foundational requirements to perform digital forensic investigations.While there are several private and mostly for-profit organizations that "sell" training and certifications regarding digital forensics credentials, at the federal and state level in the United States, there seem to be nothing of the kind.
Paradigma Dakwah Kultural: Dimensi Sufisme dalam Kontruksi Karakter Bima pada Pewayangan Jawa

&lt;p&gt;Wayang as a pride of Javanese culture is well known not only for artistic dimension, but also for a spiritual dimension. It can be seen from a compositions of wayang characters, namely Bima. This research is a qualitative research that focuses on library research about Mahabharata books, Javanese culture (wayang), Walisongo, and dakwah. This research is data analysis that uses descriptive analysis, as an effort to describe and analyze the siprirtiual dimensions are manifested in aspects of dakwah and sufism in the construction of the Bima character. The results of this study indicate that the character of Bima shows the construction for dimensions of dakwah and Sufism. The dimension of dakwah lies in the process of islamization of Hindu that contained in wayang that finally changes the character and storyline of the wayang itself. While the dimension of Sufism is in the process of conveying the values of Sufism, such as in the depiction of the soul (nafs) and manunggaling Kawula Gusti in the Dewaruci play.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Efficient Möbius Transformations and Their Applications to D-S Theory

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been done to reduce the complexity of computations used in information fusion with Dempster’s rule. The main approaches exploit either the structure of Boolean lattices or the information contained in belief sources. Each has its merits depending on the situation. In this paper, we propose sequences of graphs for the computation of the zeta and Möbius transformations that optimally exploit both the structure of distributive lattices and the information contained in belief sources. We call them the Efficient Möbius Transformations (EMT). We show that the complexity of the EMT is always inferior to the complexity of algorithms that consider the whole lattice, such as the Fast Möbius Transform (FMT) for all DST transformations. We then explain how to use them to fuse two belief sources. More generally, our EMTs apply to any function in any finite distributive lattice, focusing on a meet-closed or join-closed subset.
Accelerating Extreme Learning Machine on FPGA by Hardware Implementation of Given Rotation-QRD

Currently, Extreme Learning Machine (ELM) is one of the research trends in the machine learning field due to its remarkable performances in terms of complexity and computational speed.However, the big data era and the limitations of general-purpose processor cause the increasing of interest in hardware implementation of ELM in order to reduce the computational time.Hence, this work presents the hardware-software co-design of ELM to improve the overall performances.In the co-design paradigm, one of the important components of ELM, namely Given Rotation-QRD (GR-QRD) is developed as a hardware core.Field Programmable Gate Array (FPGA) is chosen as the platform for ELM implementation due to its reconfigurable capability and high parallelism.Moreover, the learning accuracy and computational time would be used to evaluate the performances of the proposed ELM design.Our experiment has shown that GR-QRD accelerator helps to reduce the computational time of ELM training by 41.75% while maintaining the same training accuracy in comparison to pure software of ELM.
A Novel P2P based System with Blockchain for Secured Voting Scheme

The revolution in the system of records has reached a great extent and paves a way for a powerful technology known as blockchain for storing data and performing transactional operations in a decentralized network. Blockchain was first developed to serve as a public transaction ledger for the cryptocurrency named “Bitcoin”. Later, One such application is the online balloting scheme. A ballot is a device that is used to cast the votes in an election. In ballot based voting, still there is no system to avoid proxy casting and recasting. We do not have any technology to view our casted votes. In the electronic voting system based on Blockchain, addresses some of the limitations in the existing systems and labels some of the issues of e-voting. The idea in blockchain enabled balloting scheme is to integrate Aadhaar card and Mobile number of the people using which the OTP is generated and then the voter is allowed to cast their vote. Ethereum is an open source blockchain based technology that uses both private blockchain and remix platform and it acts as a tool in storing the data. The user can cast their votes from anywhere (nearby booth) and the corresponding contestant are notified to the users based on their constituency and candidate promises. If NOTA votes crossed above 50%, then all the candidates are disqualified and are permitted to contest for the next election. If any of the citizen do not poll their vote, then a warning message will be sent to the respective person. They should provide a valid reason for not voting within a period of 6 months, if the reason is invalid then necessary action will be taken by the government. If a member of citizen wants to view his/her voting status then they can view it by entering their aadhar number and the block number that is sent to them through the mobile app. The implementation of this system addresses most of the issues faced in the balloting scheme and is used to avoid proxy casting and recasting and is also used to achieve above 95% of the vote.
Modelling Semantic Categories Using Conceptual Neighborhood

While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categories and individuals. Intuitively, if individuals are represented as vectors, we can think of categories as (soft) regions in the embedding space. Unfortunately, meaningful regions can be difficult to estimate, especially since we often have few examples of individuals that belong to a given category. To address this issue, we rely on the fact that different categories are often highly interdependent. In particular, categories often have conceptual neighbors, which are disjoint from but closely related to the given category (e.g.\ fruit and vegetable). Our hypothesis is that more accurate category representations can be learned by relying on the assumption that the regions representing such conceptual neighbors should be adjacent in the embedding space. We propose a simple method for identifying conceptual neighbors and then show that incorporating these conceptual neighbors indeed leads to more accurate region based representations.
Tech United Eindhoven @Home 2019 Champions Paper

This paper provides an overview of the main developments of the Tech United Eindhoven RoboCup @Home team. Tech United uses an advanced world modeling system called the Environment Descriptor. It allows straightforward implementation of localization, navigation, exploration, object detection & recognition, object manipulation and robot-robot cooperation skills based on the most recent state of the world. Other important features include object and people detection via deep learning methods, a GUI, speech recognition, natural language interpretation and a chat interface combined with a conversation engine. Recent developments that aided with obtaining the victory during RoboCup 2019 include pointing detection, usage of HSR’s display, a people detector and the addition of a custom keyboard in the chat interface.
Generalized Preference Learning for Trust Network Inference

Trust inference is essential in a plethora of data mining and machine learning applications. Unfortunately, conventional approaches to trust inference assume trust networks are available, while in practice they must be derived from social network features. This is however a difficult task which has to cope with challenges relating to scarcity, redundancy and noise in the available user interactions and other social network features. In this work, we introduce the new problem of Trust Network Inference (TNI), that is, inferring a trust network from a sequence of timestamped interaction networks. To solve the TNI problem, we propose a principled approach based on a preference learning paradigm, under a preference-based racing formulation. The proposed approach is suitable for addressing the above challenges, moreover it is versatile (i.e., independent from the social network platform) and flexible w.r.t. the use of topological and content-based information. Extensive experimental evaluation focusing on two distinct ground-truth scenarios, has provided evidence of the meaningfulness and uniqueness of our TNI approach, which can be regarded as key-enabling for any application that requires to handle a trust network associated with a social environment.
Calibration of a stereo microscope based on non-coplanar feature points with iteratively weighted radial alignment constraint

A commercial stereo light microscope, such as the OLYMPUS SZX7, has nonnegligible lens distortion for three-dimensional microscopic image correlation. To tackle the problem, an effective calibration method of using a stereo-vision system is proposed. It is implemented using non-coplanar feature points with weighted radial constraints, where the weights are incorporated into the objective function to estimate the internal and external parameters of the vision system iteratively. In the calculation of the distortion coefficients, a non-parametric distortion model expressed by a bicubic spline surface is used. The iterative convergence of the objective function and the influence of the attitude number of the calibration target on the calibration results are analysed, and verified with a digital stereo light microscope established with OLYMPUS SZX7. Both shape and displacement measurements have been performed to demonstrate the proposed method. The experimental results show that the proposed method can achieve higher accuracy compared with Zhang's classical method and the unweighted two-step method.
ANALISA TESTIMONIAL DENGAN MENGGUNAKAN ALGORITMA TEXT MINING DAN TERM FREQUENCY- INVERSE DOCUMENT FREQUENCE (TF-IDF) PADA TOKO ALLMEEART

E-commerce or often referred to as an online shop is the latest trend of the community in carrying out shopping activities, first before the rise of e-commerce companies like today the community to meet their needs still rely on distros around the customer lives, or to a shopping place but now it has switch to shoop online. The advantages offered by online shoop are the relatively low prices, no need to shop locations, and guarantee goods, it has an impact on retail shops that are increasingly lonely. Testimonials are one of the techniques carried out to convince customers to shop at e-commerce they have, testimonials are the responses of buyers for their experience of shopping in an e-commerce application starting from the payment process until the goods are received, the more positive experiences conveyed in the testimonials, the customer who have not shopped on an e-commerce application will be more convinced to shop. Testimonials on an e-commerce application are not always positive, there are times when testimonials are delivered by negative buyers. The customer's problem is the unavailability of percentages or information on the number of buyers with positive and negative shopping experiences because in general testimonials are only delivered in the form of a list.Keywords: Testimonial Analysis, Text Mining Algorithm, Term Frequency-Inverse Document Frequency (TF-IDF)
PENERAPAN KLASIFIKASI C4.5 DALAM MENINGKATKAN SISTEM PEMBELAJARAN MAHASISWA

The purpose of this research is to classify the improvement of student learning systems using the C4.5 datamining method. The source of the research data was obtained from the education section of STKOM Tunas Bangsa through interviews and questionnaires to semester 5 students (160 students) in the 2019-2020 academic year study program. The attributes used in the classification of student learning systems include: Teaching System (C1), Teaching Aids (C2), Environment (C3), Infrastructure Facilities (C4) and Assignment (C5). The calculation results mention the attribute Assignment (C5) is the attribute that most influences the improvement of student learning systems. The test was also carried out using the help of Rapidminer software and obtained an accuracy of 95%.Keywords: Datamining, Classification, C4.5, Student Learning, STIKOM Tunas Bangsa
Zernike like functions on spherical cap: principle and applications in optical surface fitting and graphics rendering

Zernike circular polynomials (ZCP) are widely used in optical testing, fabrication and adaptive optics. However, their ability to characterize information on spherical cap is often limited by aperture angle, with highly curved surface being particularly challenging. In this study, we propose a simple and systematic process to derive Zernike like functions that are applicable to all types of spherical cap. The analytical expressions of three function sets are calculated using Gram-Schmidt algorithm. They are hemispherical harmonics (HSH), Zernike spherical function (ZSF) and longitudinal spherical function (LSF). HSH satisfies Laplacian equation and composes a subset of spherical harmonics (SH). ZSF and LSF can be applied to arbitrary spherical cap and their orthogonality is invariant to aperture. The achieved functions, with their complete and orthogonal performance, can serve as essential tools for surface characterization required for a wide range of applications like large-angle lenses description in illumination design, aberration analysis in high aperture systems, human cornea measurement fitting, geomagnetic field modelling, etc. Moreover, they are important for graphics rendering in virtual reality and games by solving the reflectance equation efficiently.
Human Evaluation of Neural Machine Translation: The Case of Deep Learning

Recent advances in artificial neural networks now have a great impact on translation technology.A considerable achievement was reached in this field with the publication of L'Apprentissage Profond.This book, originally written in English (Deep Learning), was entirely machinetranslated into French and post-edited by several experts.In this context, it appears essential to have a clear vision of the performance of MT tools.Providing an evaluation of NMT is precisely the aim of the present research paper.To accomplish this objective, a framework for error categorisation was built and a comparative analysis of the raw translation output and the post-edited version was performed with the purpose of identifying recurring patterns of errors.The findings showed that even though some grammatical errors were spotted, the output was generally correct from a linguistic point of view.The most recurring errors are linked to the specialised terminology employed in this book.Further errors include parts of text that were not translated as well as edits based on stylistic preferences.The major part of the output was not acceptable as such and required several edits per segment, but some sentences were of publishable quality and were therefore left untouched in the final version.
Chemical fractionation of trace elements in mangrove sediments from the Songkhla Lake, Thailand using BCR technique

The aim of this work was to determine the concentration of trace elements (Zn, As, Cd and Pb) in mangrove sediments using the European community bureau of reference (BCR) sequential extraction method.The sampling was conducted in August 2016 at 8 stations of Songkhla Lake in southern Thailand.Trace element concentrations were determined using the inductively coupled plasma mass spectrophotometer (ICPMS).Zn (37.20-64.42%)and Cd (63.44-77.50%)were found dominant in the acid-soluble fraction (F1).Pb (62.01-89.48%)was detected dominantly in reducible fraction (F2).As (49.92-73.45%)was found dominant in F2 (at stations 2, 4, 6, and 8) and in oxidizable fraction (F3) with the concentrations of 48.71-68.02%(at stations 1, 3, 5, and 7).All trace elements (Zn, As, Cd, and Pb) were bound to the bioavailable fractions (first three fractions) and were low in residual fraction (F4).Calculation of ecological risk assessment of Cd revealed that the individual concentration factor (ICF) >6, the global concentration factor (GCF) >24 and the risk assessment code (RAC) >50% in all stations were classified as high concentration and very high risk for aquatic organisms.
Infrared Enhancement for Water Surface Imaging Based on Theory of Radiative Transfer and Edge Weight Analysis

There always exist contrast reduction and details degradation when the infrared imaging system works on water surface because of fog and water mist. An image enhancement method is proposed based on radiative transfer theory. Firstly, the infrared enhancement model is built according to radiative transfer theory, with which we construct an optimization frame. Secondly, edge matrix is designed for weight analysis, which is particularly important for next estimation of fine transmissivity and optical path radiation. Finally, enhancement is achieved with edge matrix. The main contribution and novelty includes the derivation of enhancement based on radiative transfer theory, design of edge matrix, and estimation of fine transmissivity and optical path radiation. The experimental comparisons show the proposed method can generate results with a good visual effect, which is proved by both subjective and objective assessment. The experiments also indicate that the proposed algorithm runs fast to apply in real system.
Indirect threats as an illegal speech act

This article demonstrates how speech act theory and specifi cally the notion of felicity conditions can help elucidate the threatening aspects of otherwise vague and unspecifi c messages.Based on a discussion of language crimes, illegal speech acts and the question of intent, I propose a list of felicity conditions for threats that account for their primary purpose as attempts to intimidate a victim.Examples for discussion are taken from a data set of indirect, written threats extracted from verdicts by Danish higher courts.Contrary to previous claims, it is shown that it is not only possible but linguistically quite straightforward to analyze even indirectly phrased messages as instances of threats.