Reviewerly/exHarmony huggingface dataset:
Learning GPS Point Representations to Detect Anomalous Bus Trajectories

Discovering anomalous bus trajectories can benefit transportation agencies to improve their services by helping them to deal with unexpected events such as detours or accidents. In this work, we propose a deep-learning strategy, which we name Spatial-Temporal Outlier Detector (STOD), that predicts the spatial/temporal anomaly degree of a bus trajectory by using learned representations of its GPS points. To calculate the score, STOD learns the regular behavior of bus trajectories by building a model that predicts the route id of buses. The degree of uncertainty on this prediction, measured by the entropy of the output class probability distribution, indicates the anomaly score of the trajectory. To perform the classification, STOD represents each point of a trajectory by the concatenation of two different representations. The first one (PAC embedding) is generated by the Point Activity Classifier (PAC) by leveraging temporal and spatial features on a stacked deep-learning model to predict the semantics of the point in terms of its bus activity (in route, bus stop, traffic signal, and other stops). The second representation (Geo embedding) captures the spatial relationship between a point and its geographical neighbors by applying a word embedding technique on the set of all trajectories. The experimental evaluation shows that our model is effective for filtering noisy trajectories since it outputs higher anomaly scores for both spatial and temporal anomalous trajectories than regular ones.
PEMBERI MAKAN OTOMATIS PADA KUCING MENGGUNAKAN RASPBERRY PI BERBASIS ANDROID

Keep a pet at home takes time and effort. For people who have very dense flurry of activity certainly keep a pet such as a cat would be very hard to do. A Raspberry Pi microcontroller is designed for the purpose of automatic feeding so it is easy to use. The workings of the tool are automatic scheduling using an Android-based smartphone so that the servo motor will open and close so that the cat food is taken out into the food container that has been provided. By using an Android-based smartphone, the feeding schedule can be set by the hour for each funnel. Equipped with a buzzer as a reminder of cat owners if the available food stock is low and must be immediately refilled. The programming language used is Python language. Based on testing and performance of "Automatic Cat Feeding Using Raspberry Pi Android Based" has shown results in accordance with the design that is able to open and close the funnel that fills the cat food container with a servo motor automatically by setting a predetermined time.
Adaptation and Effects of Cloud Computing on Small Businesses

One of the popular technologies in the industry today is cloud computing and the use of cloud computing in small businesses is now fashionable, it has a remarkable impact on them by bringing a new way of working, accessing their data and storing it. The aim of the study is to examine the impacts and effects of migration to cloud computing on small businesses. The methodology used for this study is based on a review of past studies, which explains in broad terms each research question of this study. The results of this study shows that there are several benefits that cloud computing has to offer small businesses if cloud computing is adopted and that includes flexibility, cost reduction and automatic software/hardware upgrades etc. Cloud computing has a large storage capacity that helps small businesses store and access large amounts of data quickly and easily, and there are a number of cloud computing data recovery techniques that help to recover lost or damaged data in the cloud. In the same way that security is a major concern for all technologies, it is also a concern for data stored in the cloud; security techniques such as encryption, cryptography are mostly used to ensure that key areas of data security (data confidentiality, data integrity and data availability) are protected. The results of this study helps small businesses to understand the impact of cloud computing on their businesses and the various security measures that need to be taken while adapting cloud computing technology.
Further constructions of bent functions and their duals

In 2012, Carlet et al. developed two secondary constructions of bent functions (Advances in Mathematics of Communications, 6: 305-314) and proposed some applications for their constructions. However, the duals of bent functions in their constructions were not presented. In order to find more general applications to these constructions and obtain new classes of bent functions, an open problem was proposed by Carlet in 2014. Hence, in this study, a class of PS vectorial bent functions for answering that open problem, which also addresses another open problem on vectorial bent functions proposed by Mesnager in 2014, is constructed. In addition, a new secondary construction of bent functions that generalises one of Carlet et al.'s constructions in 2012 is presented. Based on that, two new classes of bent functions were obtained and their duals were presented explicitly. In particular, some self-dual bent functions are constructed. Moreover, it can be proved that our bent functions can be EA-inequivalent to those constructed by Carlet et al. in 2012.
Does mobile money services adoption foster intra-African goods trade?

Using a propensity score matching methodology, we study the causal effect of mobile money services adoption on intra-African goods trade. We find that countries that adopted MM register a higher goods trade share in GDP of about 0.6 percent in comparison to non-adopters.
OpenTapioca: Lightweight Entity Linking for Wikidata.

We propose a simple Named Entity Linking system that can be trained from Wikidata only. This demonstrates the strengths and weaknesses of this data source for this task and provides an easily reproducible baseline to compare other systems against. Our model is lightweight to train, to run and to keep synchronous with Wikidata in real time.
Impact of Processing and Extraction on the Minor Components of Green Spanish-Style Gordal Table Olive Fat, as Assessed by Innovative Approaches

This work aims to study the effect of the green Spanish-style table olive processing and extraction method of fat on its minor components. For this purpose, it uses standard multivariate analysis (developed for Euclidean space), Compositional Data (CoDa) analysis (for data in the simplex) and Multiple Factor analysis (MFA). Overall, processing had a scarce effect on most of the minor components except ethyl and methyl esters and diacylglycerols, which markedly increased during fermentation; however, these compounds in table olive do not have the negative connotations that those in olive oil do since they are normal metabolites from the yeast microflora habitually present during the process. The work also showed that the quantification of minor components in table olive fat was an extraction-dependent method since Soxhlet increased the concentrations of fatty alcohols, triterpene dialcohols, sterols, waxes and polar compounds. Regarding statistical methods, CoDa analysis strategies were successfully applied to produce more appropriate clustering and Principal Component Analysis (PCA) segregation than standard tools. Moreover, MFA allowed for study of the components individually and by groups; the relationships among groups led to the most appropriate clustering and PCA segregation of samples and revealed the effect of the chemical groups’ evolution on the similarity/dissimilarity between samples. Therefore, MFA was the statistical analysis that led to the most information on the effect of processing and extraction methods. Its combination with appropriate CoDa logratios could be an exciting challenge.
A Thermal Infrared Image Enhancement Method Based on Histogram

To improve the enhancement of infrared thermal images, this paper proposes an algorithm based on an improved histogram clipping method.The algorithm determines the difference between the number of pixels in the histogram bins of the original thermal infrared image and the traditional histogram equalization image.Subsequently, based on a specific range criterion, the calculated difference of various histogram bins is divided into different blocks.Next, the histogram is redistributed, and a transform function is determined to obtain the enhanced thermal infrared image.The algorithm is based on an improved global histogram equalization method, which achieves a suitable balance between contrast enhancement, histogram shape, and detail information.The experimental results demonstrate that the average values of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean-square error (MSE)-27.5,0.923, and 590 respectively are better than those of other algorithms.Thus, this method effectively enhances the thermal infrared image.
GloVeInit at SemEval-2020 Task 1: Using GloVe Vector Initialization for Unsupervised Lexical Semantic Change Detection

This paper presents a vector initialization approach for the SemEval2020 Task 1: Unsupervised Lexical Semantic Change Detection.Given two corpora belonging to different time periods and a set of target words, this task requires us to classify whether a word gained or lost a sense over time (subtask 1) and to rank them on the basis of the changes in their word senses (subtask 2).The proposed approach is based on using Vector Initialization method to align GloVe embeddings.The idea is to consecutively train GloVe embeddings for both corpora, while using the first model to initialize the second one.This paper is based on the hypothesis that GloVe embeddings are more suited for the Vector Initialization method than SGNS embeddings.It presents an intuitive reasoning behind this hypothesis, and also talks about the impact of various factors and hyperparameters on the performance of the proposed approach.Our model ranks 12th and 10th among 33 teams in the two subtasks.The implementation has been shared publicly. 1
PSubCLUS: A Parallel Subspace Clustering Algorithm Based On Spark

Clustering is one of the most important unsupervised machine learning tasks.It is widely used to solve problems of intrusion detection, text analysis, image segmentation etc. Subspace clustering is the most important method for high-dimensional data clustering.In order to solve the problem of parallel subspace clustering for high-dimensional big data, this paper proposes a parallel subspace clustering algorithm based on spark named PSubCLUS which is inspired by SubCLU, a classical subspace clustering algorithm.While Spark is the most popular big data parallel processing platform currently, PSubCLUS uses the Resilient Distributed Datasets (RDD) provided by Spark to store data points in a distributed way.The two main performing stages of this algorithm, one-dimensional subspace clustering and iterative clustering, can be executed in parallel on each worker node of cluster.PSubCLUS also uses a repartition method based on the number of data points to achieve load balancing.Experimental results show that PSubCLUS has good parallel speedup and ideal load balancing effect, which is suitable for solving the parallel subspace clustering of high-dimensional big data.
Selection of handwritten signature dynamic indicators for user authentication

Selection and evaluation of handwritten signature indicators in user authentication systems are considered. An important and unresolved issue of information security is the effective identification of the user who accesses confidential information. Traditional password protection has a number of disadvantages. As an alternative to the password system or its addition, user identification by biometric characteristics is considered. An identifier that uses biometric characteristics is inextricably linked to the user and it is almost impossible to use it without authorization. The decision about the truth of the user of biometric authentication systems is made on the basis of comparison of his template with the data entered when trying to authenticate. The template is formed on the basis of studying the selected individual characteristics of the user. To do this, we use the biometric characteristics of users, which reflect the dynamic, behavioral characteristics of the person. It is proposed to use a handwritten signature as a biometric characteristic of the user. A handwritten signature is a socially and legally recognized biometric characteristic used for human authentication. It has a rather complex structure and high detail - all this makes solving the problem of user identification by mathematical methods quite complex and requires high computational costs. Another significant disadvantage is that handwritten authentication systems require the installation of additional specialized equipment, which makes the use of such systems as an ordinary means of authentication very expensive. To solve this problem, a scheme for implementing a computer data protection system against unauthorized access based on handwritten signatures using mobile devices based on the Android operating system as signature input devices is proposed. A method of biometric vector generation for use in dynamic biometric user authentication systems is proposed. The optimal characteristics are investigated and the efficiency of using the proposed form biometric characteristics vector is estimated. Certificates of copyrights registration for software applications developed during the work were received.
Exploiting Distributional Semantics Models for Natural Language Context-aware Justifications for Recommender Systems

In this paper1 we present a methodology to generate context-aware natural language justifications supporting the suggestions produced by a recommendation algorithm. Our approach relies on a natural language processing pipeline that exploits distributional semantics models to identify the most relevant aspects for each different context of consumption of the item. Next, these aspects are used to identify the most suitable pieces of information to be combined in a natural language justification. As information source, we used a corpus of reviews. Accordingly, our justifications are based on a combination of reviews’ excerpts that discuss the aspects that are particularly relevant for a certain context.In the experimental evaluation, we carried out a user study in the movies domain in order to investigate the validity of the idea of adapting the justifications to the different contexts of usage. As shown by the results, all these claims were supported by the data we collected.
Humans Meet Models on Object Naming: A New Dataset and Analysis

We release ManyNames v2 (MN v2), a verified version of an object naming dataset that contains dozens of valid names per object for 25K images.We analyze issues in the data collection method originally employed, standard in Language & Vision (L&V), and find that the main source of noise in the data comes from simulating a naming context solely from an image with a target object marked with a bounding box, which causes subjects to sometimes disagree regarding which object is the target.We also find that both the degree of this uncertainty in the original data and the amount of true naming variation in MN v2 differs substantially across object domains.We use MN v2 to analyze a popular L&V model and demonstrate its effectiveness on the task of object naming.However, our fine-grained analysis reveals that what appears to be human-like model behavior is not stable across domains, e.g., the model confuses people and clothing objects much more frequently than humans do.We also find that standard evaluations underestimate the actual effectiveness of the naming model: on the single-label names of the original dataset (Visual Genome), it obtains -27% accuracy points than on MN v2, that includes all valid object names.
Support Vector Machine VS Information Gain: Analisis Sentimen Cyberbullying di Twitter Indonesia

This study aims to clarify tweets on twitter using the Support Vector Machine and Information Gain methods. The clarification itself aims to find a hyperplane that separates the negative and positive classes. In the research stage, there is a system process, namely text mining, text processing which has stages of tokenizing, filtering, stemming, and term weighting. After that, a feature selection is made by information gain which calculates the entropy value of each word. After that, clarify based on the features that have been selected and the output is in the form of identifying whether the tweet is bully or not. The results of this study found that the Support Vector Machine and Information Gain methods have sufficiently maximum results.
(Stem and Word) Predictability in Italian verb paradigms: An Entropy-Based Study Exploiting the New Resource LeFFI

In this paper we present LeFFI, an inflected lexicon of Italian listing all the available wordforms of 2,053 verbs. We then use this resource to perform an entropy-based analysis of the mutual predictability of wordforms within Italian verb paradigms, and compare our findings to the ones of previous work on stem predictability in Italian verb inflection.
Short-Term Forecasting in Bitcoin Time Series Using LSTM and GRU RNNs

In recent years, bitcoin has become a very attractive investment in financial industry, which is not controlled by governments, but is based on trust between transfers under the technology of block chain. Hence, forecasting future bitcoin cryptocurrency values is a problem that has attracted the attention of many researchers in the field, while proving to be a very challenging problem. This work presents an experimental analysis using LSTM and GRUs for forecasting bitcoin values in a minute-granulated time for the entire next day. To this end we also present our methodology for conducting the experiments. The final goal is to create the core of a financial prediction tool around the RNNs. In our experiments, we achieved interesting results such as a SMAPE of 0.0002, a RMSE of US$ 3.844 and a rRMSE of 0.0028 in a day where bitcoin rates vary from US$ 13.2K and US$ 14.6K, surpassing the results of SMAPE found in the literature and proposed limit of SMAPE smaller than 0.007 for forecasts.
Cryptographic Algorithm Using Newton-Raphson Method and General Bischi-Naimzadah Duopoly System

Image encryption is an excellent method for the protection of image content. Most authors used the permutation-substitution model to encrypt/decrypt the image. Chaos-based image encryption methods are used in this model to shuffle the rows/columns and change the pixel values. In parallel, authors proposed permutation using non-chaotic methods and have displayed good results in comparison to chaos-based methods. In the current article, a new image encryption algorithm is designed using combination of Newton-Raphson’s method (non-chaotic) and general Bischi-Naimzadah duopoly system as a hyperchaotic two-dimensional map. The plain image is first shuffled by using Newton-Raphson’s method. Next, a secret matrix with the same size of the plain image is created using general Bischi-Naimzadah duopoly system. Finally, the XOR between the secret matrix and the shuffled image is calculated and then the cipher image is obtained. Several security experiments are executed to measure the efficiency of the proposed algorithm, such as key space analysis, correlation coefficients analysis, histogram analysis, entropy analysis, differential attacks analysis, key sensitivity analysis, robustness analysis, chosen plaintext attack analysis, computational analysis, and NIST statistical Tests. Compared to many recent algorithms, the proposed algorithm has good security efficiency.
Global Horizontal Irradiance Modeling for All Sky Conditions Using an Image-Pixel Approach

Ground images with a sky camera have become common to evaluate cloud coverage, aerosols, and energy collection. In parallel, the growth of solar energy has led to an impulse to evaluate and forecast the solar potential in a site before investments, which has increased the importance of solar power measurements. Facing that scenario, this work presents a novel sky camera model that allows to measure the global horizontal irradiance (GHI). Initially, images from a fisheye camera were stored and a pixel-based approach model was created for cloud segmentation. A total of 813 k vectors of features were used as input to the support vector machine for classification (SVC), which yielded a success rate of about 98.6% in accuracy. The Sun’s position was also segmented and an artificial neural network (ANN) regression model for GHI with 17 input features was created based on segmentation of the Sun, clouds, and sky. The training/validation stage of the ANN used 89,964 samples and the test stage reached about 97.4% in Pearson’s correlation. The RMSE was 72.3 W/m2 for GHI and the normalized RMSE, nRMSE, revealed 12.9% for GHI. That nRMSE value was comparable to or lower than other studies, despite the high fluctuations in the observed GHI.
Visual-Textual Alignment for Graph Inference in Visual Dialog

As a conversational intelligence task, visual dialog entails answering a series of questions grounded in an image, using the dialog history as context.To generate correct answers, the comprehension of the semantic dependencies among implicit visual and textual contents is critical.Prior works usually ignored the underlying relation and failed to infer it reasonably.In this paper, we propose a Visual-Textual Alignment for Graph Inference (VTAGI) network.Compared with other approaches, it makes up the lack of structural inference in visual dialog.The whole system consists of two modules, Visual and Textual Alignment (VTA) and Visual Graph Attended by Text (VGAT).Specially, the VTA module aims at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics.The VGAT module views the visual features with semantic information as observed nodes and each node learns the relationship with others in visual graph.We also qualitatively and quantitatively evaluate the model on VisDial v1.0 dataset, showing our VTAGI outperforms previous state-of-the-art models.
KORELASI KETERAMPILAN MEMBACA PEMAHAMAN TEKS BERITA DENGAN KETERAMPILAN MENULIS TEKS BERITA SISWA KELAS VIII SMP NEGERI 17 PADANG

There are three objectives in this study as follows. First, this study describes the reading comprehension skills of the eighth grade students of SMP Negeri 17 Padang. Second, this study describes the writing skills of the eighth grade students of SMP Negeri 17 Padang. Third, this study was to analyze the correlation between reading comprehension skills of the news text and writing skills of the eighth grade students of SMP Negeri 17 Padang. This type of research is descriptive quantitative research. The research design applied in this study is a correlational relationship of two variables. The population in this study were students of class VIII SMP Negeri 17 Padang in the academic year 2019/2020. The samples in this study were 32 students. Samples were taken using simple random sampling or simple random sampling. The research instruments are objective tests and performance tests. Based on data analysis, three results were obtained from this study. First, reading comprehension skills of class VIII students of SMP Negeri 17 Padang with good qualifications with an average score of 81.80. Second, the skills of writing news texts for eighth grade students of SMP Negeri 17 Padang with good qualifications with an average score of 72.30. Third, there is a significant relationship between the relationship between reading comprehension skills and news text writing skills of eighth grade students of SMP Negeri 17 Padang which has a significant level of 95%. Based on the hypothesis test, it is accepted that the skills in writing news texts are also in sufficient qualifications (76-85%). Based on the results of the study, H₀ is rejected and H₁ is accepted because the test results prove that t count is greater than t table, namely 8.01&gt; 1.63.