Reviewerly/exHarmony huggingface dataset:
Immediate Detection of Data Corruption by Integrating Blockchain in Cloud Computing

Data has become an invaluable asset in recent times. Although, so many technologies for storing and processing these data, are in existence, cloud computing is known as the best in most of the parameters. Cloud computing allows its users to store and process a huge amount of data that are in the remote servers, thus reducing the burden on user side. Since, the users have to hand over the control over their data to an unknown authority, this method gives birth to a new challenge of data protection in terms of confidentiality, integrity and availability. There have been numerous researches on data integrity protection, so far, using cryptographic tools and data replication strategies. Yet, there is always a need to trust on a third party auditor for performing data integrity verifications. This has led to the threat of being cheated on, if the cloud authority colludes with the third party verifier. Also, the verification strategies that are already proposed and executed failed to identify the data corruption at the time of occurrence itself. To overcome this problem, we propose a data integrity verification scheme that uses blockchain technology for storing the data in the cloud. The blockchain properties like immutability and decentralization are integrated to identify the tampering of data immediately. Hence, the third party auditor is removed by enabling the minors to take control over the blocks in the blockchain. This paper shows the design and working model of the integration of blockchain in cloud storage.
A Comparative Analysis of Unsupervised Language Adaptation Methods

To overcome the lack of annotated resources in less-resourced languages, recent approaches have been proposed to perform unsupervised language adaptation.In this paper, we explore three recent proposals: Adversarial Training, Sentence Encoder Alignment and Shared-Private Architecture.We highlight the differences of these approaches in terms of unlabeled data requirements and capability to overcome additional domain shift in the data.A comparative analysis in two different tasks is conducted, namely on Sentiment Classification and Natural Language Inference.We show that adversarial training methods are more suitable when the source and target language datasets contain other variations in content besides the language shift.Otherwise, sentence encoder alignment methods are very effective and can yield scores on the target language that are close to the source language scores.
Identifying PIDs playing FAIR

This is an extended, revised version of Philipson (2017). Findability and interoperability of some PIDs, Persistent Identifers, and their compliance with the FAIR data principles are explored, wher ...
EFEKTIVITAS MODEL PEMBELAJARAN LITERASI KRITIS BERBASIS PENDEKATAN KONSEP UNTUK MENINGKATKAN KETERAMPILAN MENULIS KREATIF SISWA SEKOLAH DASAR

The purpose of this study is to analyze whether conference writing-based literacy learning is able to improve creative writing skills in elementary school students and to analyze whether there are differences in creative writing skills between students who are given literacy learning based on conference writing methods and those not given literacy based learning conference writing method. The research method used is a quantitative research model with an experimental method. The research design used in this study was Quasi Experimental Design with the form of Nonequivalent Control Group Design. In the design of this study researchers used one experimental group with a control group that began with the pretest in each group. The experimental group was given treatment while the control group was not given treatment. At the end of the study posttest will be given to the two groups. In the normality test it is stated that the data is not normal so it is tested with the Wilcoxon formula. Based on the hypothesis analysis test it can be concluded that there are differences in creative writing skills in students before and after using the critical literacy learning model based on the concept approach.
Training of future specialists in economics with the help of online service LearningApps

Increasing the motivation of students, involving them in interaction within the educational process allows educators to improve the quality of learning, to form the necessary competences. It is proved that interactive learning technologies have a great potential of learning and development, provide maximum activity of participants of the educational process, optimal learning time and its effectiveness. The article introduces the use of interactive exercises developed through the LearningApps online service. LearningApps is a Web 2.0 application for supporting learning and teaching processes with small interactive modules. Those modules can be used directly in learning materials, but also for self-studying. LearningApps is a constructor for creating interactive tasks using a variety of templates that can be used in student learning. The LearningApps interactive environment has the ability to receive and embed an HTML code to place interactive tasks on website pages, teachers'and students' blogs. Using LearningApps allows a teacher to prepare a variety of didactic materials on the Internet that can be used on smartphones, multimedia boards, computers and other devices. The article presents interactive exercises in economic disciplines that encourage students to study, gain knowledge, skills and experience actively. A brief description of the use of LearningApps online tasks such as Find Words, First Million, Find Pairs, Puzzles, Fill in the Gaps, Crossword, and Race for Future Economics Professionals is given. The main benefits of using interactive exercises developed with LearningApps are outlined.
Question Answering Using Hierarchical Attention on Top of BERT Features

Machine Comprehension (MC) tests the ability of the machine to answer a question about a given passage.It requires modeling complex interactions between the passage and the question.Recently, attention mechanisms have been successfully extended to machine comprehension.In this work, the question and passage are encoded using BERT language embeddings to better capture the respective representations at a semantic level.Then, attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph.Our experiments were performed on the datasets provided in MRQA shared task 2019 1
Research and Development of Models and Program for Optimal Product Line Control

The article is devoted to the development of mathematical models and program for methods of optimal control of product lines. The classes of the lines are analyzed, the mathematical model of the line as a control object for the manufacturer, retailer and customer are proposed. The analysis of previous developments was carried out: market models with asymmetric information structure, models of manufacturers of the production segment and alternative simulation models of the product line. The study of the dynamics and steady state of the product line was carried out. To study the dynamics of the product line, a simulation model of «producers, product lines, consumers» was used, in which the choice of consumers is simulated in the samples. The problem of optimal aggregation of a multidimensional nonlinear, stochastic and non-stationary object «product line» has been set and solved. Optimal control program has been developed on the basis of optimal aggregation. Examples of modeling are given.
Vision-Based Multirotor Following Using Synthetic Learning Techniques

Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights).
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis

Insightful findings in political science often require researchers to analyze documents of a certain subject or type, yet these documents are usually contained in large corpora that do not distinguish between pertinent and nonpertinent documents.In contrast, we can find corpora that label relevant documents but have limitations (e.g., from a single source or era), preventing their use for political science research.To bridge this gap, we present adaptive ensembling, an unsupervised domain adaptation framework, equipped with a novel text classification model and time-aware training to ensure our methods work well with diachronic corpora.Experiments on an expert-annotated dataset show that our framework outperforms strong benchmarks.Further analysis indicates that our methods are more stable, learn better representations, and extract cleaner corpora for fine-grained analysis.
NavigaTorch: Projection-based Robot Control Interface using High-speed Handheld Projector

We propose "NavigaTorch", a projection-based robot control interface that enables the user to operate a robot quickly and intuitively. The user can control and navigate a robot from the third-person viewpoint using the projected video as visual feedback. We achieved flickerless image feedback and a quick response in robot operation by developing a handheld pixel-level visible light communication (PVLC) projector. Our contribution is the development of a robot control interface based on high-speed projection technology and the exploration of the design methodology of projection-based robot control using a handheld projector.
Amazon at MRP 2019: Parsing Meaning Representations with Lexical and Phrasal Anchoring

This paper describes the system submission of our team Amazon to the shared task on Cross Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).Via extensive analysis of implicit alignments in AMR, we recategorize five meaning representations (MRs) into two classes: Lexical-Anchoring and Phrasal-Anchoring.Then we propose a unified graph-based parsing framework for the lexical-anchoring MRs, and a phrase-structure parsing for one of the phrasalanchoring MRs, UCCA.Our system submission ranked 1st in the AMR subtask, and later improvements shows promising results on other frameworks as well.
Effective-Practical and Creative (Heuristic) Pedagogical Technologies of Students' Self-Development

Creative self-development through educational technology is relevant in modern education.The article shows the basic components of educational technology that can be applied in teaching.The usage of pedagogical technologies by teachers allows them to engage students and create a favorable climate during study.At the same time, students are developing their creative thinking, personal, intellectual and creative abilities.
Asymptotically Optimal Sampling-Based Planners

An asymptotically optimal sampling-based planner employs sampling to solve robot motion planning problems and returns paths with a cost that converges to the optimal solution cost, as the number of samples approaches infinity. This comprehensive article covers the theoretical characteristics of asymptotic optimality of motion planning algorithms, and traces its origins, analysis models, practical performance, extensions, and applications.
Effective Fusion of Multi-Modal Data with Group Convolutions for Semantic Segmentation of Aerial Imagery

In this paper, we achieve a semantic segmentation of aerial imagery based on the fusion of multi-modal data in an effective way. The multi-modal data contains a true orthophoto and the corresponding normalized Digital Surface Model (nDSM), which are stacked together before they are fed into a Convolutional Neural Network (CNN). Though the two modalities are fused at the early stage, their features are learned independently with group convolutions firstly and then the learned features of different modalities are fused at multiple scales with standard convolutions. Therefore, the multi-scale fusion of multi-modal features is completed in a single-branch convolutional network. In this way, the computational cost is reduced while the experimental results reveal that we can still get promising results.
Edge-assisted collaborative image recognition for augmented reality

Mobile Augmented Reality (AR), which overlays digital information with real-world scenes surrounding a user, provides an enhanced mode of interaction with the ambient world. Contextual AR applications rely on image recognition to identify objects in the view of the mobile device. In practice, due to image distortions and device resource constraints, achieving high performance image recognition for AR is challenging. Recent advances in edge computing offer opportunities for designing collaborative image recognition frameworks for AR. In this demonstration, we present CollabAR, an edge-assisted collaborative image recognition framework. CollabAR allows AR devices that are facing the same scene to collaborate on the recognition task. Demo participants develop an intuition for different image distortions and their impact on image recognition accuracy. We showcase how heterogeneous images taken by different users can be aggregated to improve recognition accuracy and provide a better user experience in AR.
Exploring Graph-Algebraic CCG Combinators for Syntactic-Semantic AMR Parsing

We describe a new approach to semantic parsing based on Combinatory Categorial Grammar (CCG).The grammar's semantic construction operators are defined in terms of a graph algebra, which allows our system to induce a compact CCG lexicon.We introduce an expectation maximisation algorithm which we use to filter our lexicon down to 2500 lexical templates.Our system achieves a semantic triple (Smatch) precision that is competitive with other CCG-based AMR parsing approaches.
Social Media Makes Things Possible For Librarians: A Critical Note

The rapid progress and development of social media and its associated tools have made things easy for the library staff and library professionals. Social media is the fastest tool of communication in today’s world, where everyone can reach the other within a click. At the same time, the library professionals are getting familiar with social media and its related tools to apply in their respective libraries.
Sparse Coding in Authorship Attribution for Polish Tweets

The study explores application of a simple Convolutional Neural Network for the problem of authorship attribution of tweets written in Polish.In our solution we use two-step compression of tweets using Byte Pair Encoding algorithm and vectorisation as an input to the distributional model generated for the large corpus of Polish tweets by word2vec algorithm.Our method achieves results comparable to the state-of-the-art approaches for the similar task on English tweets and expresses a very good performance in the classification of Polish tweets.We tested the proposed method in relation to the number of authors and tweets per author.We also juxtaposed results for authors with different topic backgrounds against each other.
Patient-Specific Explanations for Predictions of Clinical Outcomes

Machine learning models that are used for predicting clinical outcomes can be made more useful by augmenting predictions with simple and reliable patient-specific explanations for each prediction.This article evaluates the quality of explanations of predictions using physician reviewers. The predictions are obtained from a machine learning model that is developed to predict dire outcomes (severe complications including death) in patients with community acquired pneumonia (CAP).Using a dataset of patients diagnosed with CAP, we developed a predictive model to predict dire outcomes. On a set of 40 patients, who were predicted to be either at very high risk or at very low risk of developing a dire outcome, we applied an explanation method to generate patient-specific explanations. Three physician reviewers independently evaluated each explanatory feature in the context of the patient's data and were instructed to disagree with a feature if they did not agree with the magnitude of support, the direction of support (supportive versus contradictory), or both.The model used for generating predictions achieved a F1 score of 0.43 and area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval [CI]: 0.81-0.87). Interreviewer agreement between two reviewers was strong (Cohen's kappa coefficient = 0.87) and fair to moderate between the third reviewer and others (Cohen's kappa coefficient = 0.49 and 0.33). Agreement rates between reviewers and generated explanations-defined as the proportion of explanatory features with which majority of reviewers agreed-were 0.78 for actual explanations and 0.52 for fabricated explanations, and the difference between the two agreement rates was statistically significant (Chi-square = 19.76, p-value < 0.01).There was good agreement among physician reviewers on patient-specific explanations that were generated to augment predictions of clinical outcomes. Such explanations can be useful in interpreting predictions of clinical outcomes.
A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

Nowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, there is a semantic gap between human concept and features extracted from the images and it has become an important problem which decreases retrieval precision. In this paper, a convolutional neural network (CNN) is used to extract deep and high-level features from the images. Next, an optimization problem is defined in order to model the retrieval system. Heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have shown an effective role in solving the complex problems. A recent introduced heuristic algorithm is Grasshopper Optimization Algorithm (GOA) which has been proved to be able to solve difficult optimization problems. So, a new search method, modified grasshopper optimization algorithm (MGOA) is proposed to solve modeled problem and to retrieve similar images efficiently, despite of total search in database. Experimental results showed that the proposed system named CNN-MGOA achieves superior accuracy compared to traditional methods.
Neural network for univariate and multivariate nonlinearity tests

Abstract This paper aims to introduce a multiple output artificial neural network as a tool for testing nonlinearity in multivariate time series. Unlike previous studies, we use weights from trained networks to determine the support space for defining random weights of nonlinear regressors and obtain greater power. Moreover, this paper uses two hidden layer neural networks for univariate and multivariate nonlinearity tests. Simulation results show that the proposed method is more powerful than the Terasvirta, Lin, and Granger test in most functional forms and more powerful than the Tsay test in some cases. Taking into account univariate and multivariate time series, the neural network approach is much more powerful than both these tests.
Perfect Concurrent Fault Detection in CMOS Logic Circuits Using Parity Preservative Reversible Gates

Reversible logic has 100% fault observability meaning that a fault in any circuit node propagates to the output stage.In other words, reversible circuits are latent-fault-free.Our motivation is to incorporate this unique feature of reversible logic to design CMOS circuits having perfect or 100% Concurrent Error Detection (CED) capability.For this purpose, we propose a new fault preservative reversible gate library called Even Target -Mixed Polarity Multiple Control Toffoli (ET-MPMCT).By using ET-MPMCT, we ensure that the evenness/oddness of applied 1's at input, is preserved at all levels of a circuit including output level unless there is a faulty node.A single fault always destroys the parity of input at the output.Our design strategy has two steps for a given function: 1) implement the function with our proposed reversible ET-MPMCT gate library; and 2) apply reversible-to-CMOS gate conversion.For the first step, we propose two approaches.For our first approach in step 1, we first need to have a reversible form of the given function if it is irreversible.Then, synthesize the reversible function using Mixed Polarity Multiple Control Toffoli (MPMCT) gate library by conventional reversible logic synthesis techniques.Finally, the synthesized circuit is converted to ET-MPMCT constructed circuit which is fault preservative.Our second approach, is an ESOP -Exclusive Sum of Products -based synthesis approach modified for our proposed fault preservative ET-MPMCT gate library.It does work with both irreversible and reversible functions.We synthesize our circuits with both approaches in step 1 and choose the circuit with lower number of reversible gates to be fed to step 2 of our design strategy.In second step of our design strategy, we convert our fault preservative reversible circuits into their CMOS counterparts.The performance of our designs is compared with other CED schemes in the literature in terms of area, power consumption, delay and detection rate.Simulations are done with Cadence Genus tool using TSMC 40nm technology.Clearly, results are in favor of our proposed techniques.