Reviewerly/exHarmony huggingface dataset:
Convergence of Blockchain and IoT: An Edge Over Technologies

The term Internet of Things (IoT) defines the continuous growth of the always-online, data-collecting devices. Blockchain is a shared ledger or a database, distributed across an open or private processing system that expedites the procedure of recording business and data management in a business network. It empowers the design of decentralized transactions, smart contracts, and intelligent assets that can be managed over internet. It formulates the revolutionary decision-making governance systems with more egalitarian users and autonomous organizations that can control over Internet without any third-party involved. Various researchers have proposed the merging of the two technologies to ensure a secure and permanent solution to record data which is also processed by “smart” machines in the IoT. The IoT applications are also distributed in nature and the blockchain technology could be an imminent factor in deciding how the IoT connected devices communicate with each other. With the aid of blockchain, the security of IoT devices can be decentralized to a great extent thereby, eliminating the possibility of threats that hover around it at present. This chapter outlines an introduction to the blockchain technologies and its decentralized architecture, especially from the perspective of challenges and limitations. The main objective is to explore the trending research topics, benefits and drawbacks of blockchain towards making in a smart environment. The study explores its potential applications for business and future directions that are all set to transfigure the smart world.
INTESTINE DISEASE DIAGNOSIS SYSTEM USING CERTAINTY FACTOR METHOD

Inside the human body there are many important organs, one of which is the intestine. Intestinal disease / digestive disease is a disease that most often attacks the digestive tract in humans. There are several intestinal diseases that are dangerous and there are also harmless intestinal diseases. In this research, researchers created an android-based expert system application that can provide information to the users about diseases that are being suffered through the symptoms experienced by the user. The process of making expert system applications using the certainty factor algorithm. The certainty factor algorithm is used to accommodate the uncertainty of an expert's. The mechanism that be used in the certainty factor algorithm on each symptom uses a measure of increased belief (MB) and measure of increased disbelief (MD). Expert system applications that have been built to detect intestinal diseases based on Android have been successfully implemented with a presentation of accuracy of 99.7265625%. by that percentage, it show us that the diagnosis of symptoms of the selected disease is in suitable by the experienced of user, and has the accuracy determined by the system
Aplicação de Números Aleatórios Artificiais e Método Monte Carlo na Análise de Confiabilidade de Redes Geodésicas

A Geodetic Network is a network of point interconnected by direction and/or distance measurements or by using Global Navigation Satellite System receivers. Such networks are essential for the most geodetic engineering projects, such as monitoring the position and deformation of man-made structures (bridges, dams, power plants, tunnels, ports, etc.), to monitor the crustal deformation of the Earth, to implement an urban and rural cadastre, and others. One of the most important criteria that a geodetic network must meet is reliability. In this context, the reliability concerns the network's ability to detect and identify outliers. Here, we apply the Monte Carlo Method (MMC) to investigate the reliability of a geodetic network. The key of the MMC is the random number generator. Results for simulated closed levelling network reveal that identifying an outlier is more difficult than detecting it. In general, considering the simulated network, the relationship between the outlier detection and identification depends on the level of significance of the outlier statistical test.
Natural Language Semantics With Pictures: Some Language &amp; Vision Datasets and Potential Uses for Computational Semantics

Propelling, and propelled by, the "deep learning revolution", recent years have seen the introduction of ever larger corpora of images annotated with natural language expressions.We survey some of these corpora, taking a perspective that reverses the usual directionality, as it were, by viewing the images as semantic annotation of the natural language expressions.We discuss datasets that can be derived from the corpora, and tasks of potential interest for computational semanticists that can be defined on those.In this, we make use of relations provided by the corpora (namely, the link between expression and image, and that between two expressions linked to the same image) and relations that we can add (similarity relations between expressions, or between images).Specifically, we show that in this way we can create data that can be used to learn and evaluate lexical and compositional grounded semantics, and we show that the "linked to same image" relation tracks a semantic implicature relation that is recognisable to annotators even in the absence of the linking image as evidence.Finally, as an example of possible benefits of this approach, we show that an exemplar-model-based approach to implicature beats a (simple) distributional space-based one on some derived datasets, while lending itself to explainability.* Work done while author was at Bielefeld University. 1 There are interesting subtleties here.In our everyday language, we are quite good at ignoring the image layer, and say things like "the woman is using a computer", instead of "the image shows a woman using a computer", or "this is a computer", instead of "this is an image of a computer".This also seems to carry over to tense, where we can say "is using", instead of "was using at the time when the picture was taken".There are however contexts in which talk about the image as image is relevant, and this can happen in large corpora such as discussed here.So this is something to keep in mind.
Pengembangan Media Story Telling Berbasis Montase Sederhana Sebagai Suplemen Bahan Ajar Tema Peduli Terhadap Makhluk Hidup Kelas IV Sekolah Dasar

The background that encourage of this study is the lack of thematic learning media involving student creativity and supporting school literacy activities as a source of student learning materials. The purpose of the study to determine the feasibility of a simple story-based media story telling as a supplement of teaching materials theme of Care Against 4th grade elementary school. The type of research used is Research and Development (RnD) with Borg and Gall thinking model. Data analysis techniques used descriptive qualitative and quantitative descriptive. The feasibility result based on data analysis by experts obtained the average percentage of 92.46% for media feasibility and 91.31% for the feasibility of learning materials. The acquisition of teacher response to media acceptance obtained percentage in SD Negeri Mranggen 01 equal to 95,58%, SD Negeri Mranggen 02 equal to 88,25% and SD Negeri Mranggen 04 got percentage equal to 91,17% Result questionnaire student response learn 90,64% Elementary School of Mranggen 01, 93.21% in SD Negeri Mranggen 02 and 85.36% in SD Negeri Mranggen 04. Media story telling simple montage based as a supplement teaching materials subject to the theme of living beings have met the criteria very well and deserve to be used in learning process
Learning Task-Specific Representation for Novel Words in Sequence Labeling

Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance. This is commonly referred to as the out-of-vocabulary (OOV) problem. In this work, we address the OOV problem in sequence labeling using only training data of the task. To this end, we propose a novel method to predict representations for OOV words from their surface-forms (e.g., character sequence) and contexts. The method is specifically designed to avoid the error propagation problem suffered by existing approaches in the same paradigm. To evaluate its effectiveness, we performed extensive empirical studies on four part-of-speech tagging (POS) tasks and four named entity recognition (NER) tasks. Experimental results show that the proposed method can achieve better or competitive performance on the OOV problem compared with existing state-of-the-art methods.
The Expected-Length Model of Options

Effective options can make reinforcement learning easier by enhancing an agent's ability to both explore in a targeted manner and plan further into the future. However, learning an appropriate model of an option's dynamics in hard, requiring estimating a highly parameterized probability distribution. This paper introduces and motivates the Expected-Length Model (ELM) for options, an alternate model for transition dynamics. We prove ELM is a (biased) estimator of the traditional Multi-Time Model (MTM), but provide a non-vacuous bound on their deviation. We further prove that, in stochastic shortest path problems, ELM induces a value function that is sufficiently similar to the one induced by MTM, and is thus capable of supporting near-optimal behavior. We explore the practical utility of this option model experimentally, finding consistent support for the thesis that ELM is a suitable replacement for MTM. In some cases, we find ELM leads to more sample efficient learning, especially when options are arranged in a hierarchy.
Search-Based Reversible Logic Synthesis Using Mixed-Polarity Gates

Synthesis methods of irreversible circuits cannot be used for reversible circuits because of their logical differences. An algorithm for the synthesis of reversible circuits using its Positive-Polarity Reed–Muller (PPRM) expansions is presented in this chapter. The proposed algorithm used Hamming Distance (HD) approach to select the transformation path. A variety of reversible gates are selected through finding the possible matching reversible gate for path selection. It has the capability to allow the algorithm to synthesize reversible function in terms of quantum cost, which is a challenging task for existing synthesis algorithms. The proposed algorithm has been applied to synthesize all 3-variable reversible functions and has shown to obtain a good result. From the experimental results, it has been shown that with the m-NCT gate library added, the results are improved significantly.
Digital Literacy Enhancement Status in Kenya’s Competency-Based Curriculum

Digital literacy (DL) is a one of the competency recommended for educators to be embedded in the Kenyan Competency Based Curriculum (CBC) in preparation for learners for the 21st century skills. The Ministry of Education deployed devices to lower primary at a ratio of 1 to 1. The aim of the research was to determine the level and frequency of embedding the digital literacy abilities after the teachers and school leaders in a 3-day preparation and 8 weeks of implementation with virtual support through Communities of learning. The methodology was a self-administered survey which evaluated the seven abilities based on the level and frequency of implementation. The dot product of level and frequency were determined as a percentage. The analysis explored the average percentages and the relationship between level and frequency of implementation. The findings revealed that there was a correlation between the level and the frequency of implementation. All abilities where at developing stage on average. The mode and mean on levels and frequency were on average similar apart from few cases where it was above. All the abilities had a bell curve on implementation with access, integrate and evaluate skewed to the right. It can be concluded that despite the deployment of devices there is still very low implementation of DL and there is a significant relationship between the level and frequency of implementation. There is need to interrogate possible factors to low implementation beyond devices and educator preparation with virtual support.
Efficient Similarity Search for Sets over Graphs

Measuring similarities among different nodes is important in graph analysis tasks, such as link prediction, and recommendation. Among different similarity measures, SimRank is one of the most popular and promising ones, and has received a lot of research attention. While most current studies focus on single-pair, single-source/top-k, and all-pairs SimRank computation, few of them have studied finding similar pairs given a set of node pairs, which has attractive applications in personalized search and recommendation tasks. In this paper, we present Carmo, an efficient algorithm for retrieving the top-k similarities from an arbitrary set of pairs. In addition, we introduce two types of indexes to boost the efficiency of Carmo: one is hub-based, the other is tree-based. We show the effectiveness and efficiency of our proposed methods by extensive experiments.
SISTEM INFORMASI PENGELOLAAN DATA LAUNDRY PADA RUMAH LAUNDRY BEKASI

Laundry house is a business engaged in services in washing and drying, especially clothing. To serve the public in the field of laundry services, the laundry house in managing the data is still done manually and has not been computerized so there are still errors in the transaction process. Problems that occur in a laundry house such as incorrectly recording the type of package, the calculation is still using a calculator tool, in making the report must see the notes that have been collected previously, must see previous records recorded in the ledger, and be vulnerable to losing previous note notes so that it takes a long time and the results obtained are less accurate. The purpose of this study is to build a laundry data management program using the waterfall method which consists of 5 stages, namely, software requirements analysis, design, programming code, testing and support or maintenance. While the data collection is through observation, interviews and searching for literature related to this research so that it can solve problems related to laundry data management activities, especially in the process of receiving laundry services transactions to be more effective and efficient and can improve service to customers. The results of the study show that this laundry data management program can help and simplify the process of receiving laundry services at the laundry house.
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness

Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and L2 feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extract similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.
Competency Mastery of Information Literacy and Collaboration on Prospective Teacher Students

This study aims to describe and explore the achievement of competencies in information literacy and collaboration skills as part of 21st century competency in accounting education students at the Muhammadiyah University of Surakarta (UMS). The respondents of this study were 207 students with a mixed method approach through convergent parallel design. The results of the study showed that most students had information literacy skills but were still relatively low. This can be seen from students who had mostly used the internet, but did not use trusted sources. This condition is exacerbated by students not filtering and validating the information so that the information submitted tends to be biased, only a few used credible journals as a reference while the rest used sources whose information credibility was still doubtful. In collaboration skills, the achievement of this competency varies between students who are active in campus organizations, non-campus organizations, and do not follow any organization. Those who were active in the organization seem to be better at mastering this competency. This can be seen in the aspect of contributions in groups and accepting responsibility. Courage to accept new responsibilities was not fully owned, some were still hesitant to accept the responsibility given to him for various reasons, especially those classified as inactive organizations. In this aspect, it appears that students who were active in non-campus organizations considered a mandatory task to be disliked. Whereas in the aspect of collective responsibility, students both active and non-active in organizations had the same competencies where they wanted and were able to work together in teams.
Camera External Self-Calibration for Intelligent Vehicles

An algorithm for the camera external self-calibration is presented in this paper which is quite convenient and practicable as no calibration board is required. The camera is fixed rigidly on a vehicle with a planar motion constraint. Based on that, the rotation parameters are decomposed into two parts and the vehicle is manipulated to take two certain motions to solve those two parts in turn. Furthermore, given that the velocity is available, the translation can be solved by introducing a virtual aligned camera, which can improve the accuracy and reduce the computational time. Both simulations and experiments are carried out where several relevant algorithms are adopted as comparisons. Corresponding results validate the performance and handiness of the proposed method.
Assessing completeness of a WEB site from quality perspective

Assessing quality of a web site is most important especially because people world over are dependent on the content hosted on the WEB site for various purposes. May factors are to be considered for assessing the quality. Quality of every factor must be considered individual and also the total quality of the WEB site must be computed. This paper is primarily focused on the factor called “Completeness” which is one of the factors that can be considered for computing the quality of the WEB sites. There can be much of the disconnected in the content hosted on the web site such as missing href, columns in the tables and forms. The more of the disconnectedness in the content that is hosted on the WEB site, the less the quality, as the information hosted on the WEB site is incomplete and less readable
Belief Revision Operators with Varying Attitudes Towards Initial Beliefs

Classical axiomatizations of belief revision include a postulate stating that if new information is consistent with initial beliefs, then revision amounts to simply adding the new information to the original knowledge base. This postulate assumes a conservative attitude towards initial beliefs, in the sense that an agent faced with the need to revise them will seek to preserve initial beliefs as much as possible. In this work we look at operators that can assume different attitudes towards original beliefs. We provide axiomatizations of these operators by varying the aforementioned postulate and obtain representation results that characterize the new types of operators using preorders on possible worlds. We also present concrete examples for each new type of operator, adapting notions from decision theory.
Reading Comprehension Problems on English Texts Faced By High School Students in Medan

In the English teaching and learning process, there are four skills that are identified as&#x0D; very important; listening, speaking, reading and writing. The Indonesian government&#x0D; places English as a compulsory subject for junior and senior high school. But in practice,&#x0D; teaching English is not too maximal. Reading skills in this case reading English texts is&#x0D; very important because by reading it is open the opportunity to absorb other existing&#x0D; knowledge. Reading comprehension is an advanced level of reading skills. Senior high&#x0D; school students are expected to have mastered the comprehension skills of reading&#x0D; English texts at an advanced level. This is because most likely at the level of lectures&#x0D; these students are required to read English reference books. Therefore, research&#x0D; that reveals the problems that exist in the teaching and learning process of Reading&#x0D; Comprehension English texts needs to be done. Data sources were the articles about&#x0D; teaching English, focusing in the problem of reading comprehension. Data were words&#x0D; and sentences from the articles. The analysis technique used was thematic analysis.&#x0D; Based on the research, problem face by student in reading comprehension of English&#x0D; text was majoring in three theme; 1. Problems from the student self, 2. Problems from&#x0D; the teacher, and 3.External problems. Problems from the student self consist of three&#x0D; part; student basic English ability, less attraction on the subject and not motivated.&#x0D; Problems from the teacher were not using any method while teaching and used&#x0D; inappropriate method. External problems were material and environment.
Unsupervised Learning of Depth and Ego-Motion with Spatial-Temporal Geometric Constraints

In this paper, we propose an unsupervised joint deep learning pipeline for depth and ego-motion estimation that explicitly incorporated with traditional spatial-temporal geometric constraints. The stereo reconstruction error provides the spatial geometric constraint to estimate the absolute scale depth. Meanwhile, the depth map with absolute scale and a pre-trained pose network serve as a good starting point for direct visual odometry (DVO), resulting in a fine-grained ego-motion estimation with the additional back-propagation signals provided to the depth estimation network. The proposed joint training pipeline enables an iterative coupling optimization process for accurate depth and precise ego-motion estimation. The experimental results show the state-of-the-art performance for monocular depth and ego-motion estimation on the KITTI dataset and a great generalization ability of the proposed approach.
Sequence to Sequence Learning for Query Expansion

As fas as we are aware, using Sequence to Sequence algorithms for query expansion has not been explored yet in Information Retrieval literature. We tried to fill this gap in the literature with a custom Query Expansion system trained and tested on open datasets. One specificity of our engine compared to classic ones is that it does not need the documents to expand the introduced query. We test our expansions on two different tasks : Information Retrieval and Answer preselection. Our method yielded a slight improvement in performance in both two tasks . Our main contributions are :• Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction.• We assess the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings’ space.We afterwards started a quantitative and qualitative analysis of the weights learned by our network. In the second part, I will discuss what is learned by a Recurrent Neural Network compared to what we know about human language learning.