Reviewerly/exHarmony huggingface dataset:
CAPTION: Correction by Analyses, POS-Tagging and Interpretation of Objects using only Nouns

Recently, Deep Learning (DL) methods have shown an excellent performance in image captioning and visual question answering. However, despite their performance, DL methods do not learn the semantics of the words that are being used to describe a scene, making it difficult to spot incorrect words used in captions or to interchange words that have similar meanings. This work proposes a combination of DL methods for object detection and natural language processing to validate image's captions. We test our method in the FOIL-COCO data set, since it provides correct and incorrect captions for various images using only objects represented in the MS-COCO image data set. Results show that our method has a good overall performance, in some cases similar to the human performance.
An improved localization method for lesion area in gynecological ultrasound image

Abstract The false positive and false negative rates of current image localization methods in gynecological lesion area are high because the effectiveness is affected by random noise. Therefore, by using Bhattacharyya coefficient-based scale-invariant feature transform (B-SIFT), a novel localization method of lesion area in gynecological ultrasound image is proposed in this paper. Firstly, Rayleigh mean filtering is used to suppress the noise in the ultrasound image based on Rayleigh distribution characteristics of the noise. Then, the segmentation method of the lesion region is designed by using the scale-invariant feature transform (SIFT). Furthermore, the feature extraction function B-SIFT is proposed to locate the lesion region based on the Bhattacharyya coefficient. Finally, two lesion characteristics of Bhattacharyya coefficients are defined, and the B-SIFT-based feature region descriptors are obtained by constructing an eigenvector normalized based on the Bhattacharyya coefficients. Experimental results show that the proposed method has a high positioning accuracy, strong recall ratio, low energy consumption, and low time consumption, which is more effective and feasible than the traditional method for localization of lesions.
Modal Logic of Hearsay between Beliefs with Inconsistent Individual Ideas

In analysing logic of hearsay, the inconsistency of individual notions makes difficulty. Modal logic is suitable for analysing logic of hearsay. The most previous semantics of modal logic such as using possible worlds makes the formula x = y ⊃ □x = y valid. However, in logic of hearsay, it can happen that the quoting person recognises x = y although the quoted person recognises x ≠ y. As such, the formula x = y∧¬□x = y is satisfiable in logic of hearsay. In order to deal with this, this study propose a new semantics with belief structure.
Efficient Protocols for Oblivious Linear Function Evaluation from Ring-LWE.

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function \(f(x) = ax + b\), where a sender inputs the field elements a, b, and a receiver inputs x and learns f(x). OLE can be used to build secret-shared multiplication, and is an essential component of many secure computation applications including general-purpose multi-party computation, private set intersection and more.
Semantics of a Relational λ-Calculus (Extended Version)

We extend the λ-calculus with constructs suitable for relational and functional-logic programming: non-deterministic choice, fresh variable introduction, and unification of expressions. In order to be able to unify λ-expressions and still obtain a confluent theory, we depart from related approaches, such as λProlog, in that we do not attempt to solve higher-order unification. Instead, abstractions are decorated with a location, which intuitively may be understood as its memory address, and we impose a simple coherence invariant: abstractions in the same location must be equal. This allows us to formulate a confluent small-step operational semantics which only performs first-order unification and does not require strong evaluation (below lambdas). We study a simply typed version of the system. Moreover, a denotational semantics for the calculus is proposed and reduction is shown to be sound with respect to the denotational semantics.
Teachers’ Techniques in Teaching Vocabulary at the First Year Students of MTs. Asholihiyah Lopan Ketak East Lombok

This research deals with teachers’ techniques in teaching English vocabulary at the first year students of MTs Assholihiyah Lopan Ketak East Lombok. It is expected to serve as an attempt to contribute to more teachers’ techniques in teaching vocabulary at junior high school in especially in Assholihiyah Lopan Ketas East Lombok as well as the reference to the teachers who teach English vocabulary. It applied a qualitative approach. The subject of the research was one of English teacher. The data was collected through questionnaire and interview. The data was analyzed through data reduction, display, and conclusion. The result was the teacher of class 1 (1 and 2) used the identifying picture and categorizing object technique. And the teacher in class 1 (3) used categorizing objects technique to teach English vocabulary. Those techniques found from the textbook, so the teacher develops this technique by herself. In presenting English especially vocabulary, the teacher should be creative in choosing the material and able to stimulate the students interest.
Variation of Indonesian Language Teaching Techniques in Islamic Elementary School

This research is aimed to identify some techniques used to teach The Indonesian language for elementary school. This qualitative research is conducted in Miftahul Ulum elementary school which is located in Kepohbaru Bojonegoro. The data collection techniques consist of interview, questionnaire, and documentation. The result shows that there are 14 techniques used in the lower level and 13 techniques in the upper level. From the varied techniques applied, there are three techniques which support listening skill development, six techniques support speaking skill development, four techniques support reading skill development, and four techniques support writing skill development. The variations of using language teaching technique have implication in mapping methods and techniques before learning is implemented. The mapping should appropriate for the students’ development phase.
SMC2, Sequential Inference in State-Space Models

SummaryIn Chap. 16, we discussed PMCMC algorithms, i.e., MCMC samplers that (a) rely on particle filters to approximate the intractable likelihood; yet (b) leave invariant the exact posterior distribution of the considered (state-space) model. Potentially, PMCMC algorithms suffer from the same limitations as all MCMC samplers: they do not offer an easy way to estimate marginal likelihoods; they are too expensive for sequential scenarios; and calibrating their tuning parameters may be cumbersome (recall the numerical experiments of Sect. 16.5.2).In Chap. 17, we discussed SMC samplers, which, among other benefits, address these shortcomings in situations where the likelihood may be computed exactly. The next logical step is to develop SMC samplers for models with intractable likelihoods.Such SMC samplers are often called SMC2, and are the subject of this chapter. The first part presents a basic, generic version of SMC2 algorithms. The second part develops a more elaborate variant, which makes it possible to perform simultaneously and sequentially parameter inference, model choice, and state prediction for state-space models.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text

The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.
Enhanced Image Restoration Via Supervised Target Feature Transfer

Deep learning has obtained remarkable success for image restoration. However, most existing deep image restoration models are trained by minimizing the pixel-level reconstruction error between restored images and target images (ground truth), while neglecting the rich information from the intermediate feature layers, thus hindering the representational power of networks. To address this problem, we propose a Supervised Target Feature Transfer (STFT) framework to enhance the power of feature expression of the deep image restoration models. Specifically, we introduce a self-supervised antoencoder-based target feature extractor to extract compact feature representation of target images, which serves as supervision signals to train the deep backbone models at the same time. With such feature-level supervised information, deep backbone model can be enhanced by transfer learning of such target features. Moreover, we theoretically analyze our STFT training strategies and demonstrate that it imposes learnable prior information on the backbone restoration model. Extensive experiments demonstrate the effectiveness of our proposed framework compared with the state-of-the-art image restoration models.
Transfer Learning by Mapping and Revising Boosted Relational Dependency Networks

Statistical machine learning algorithms usually assume that there is considerably-size data to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data by relying on a model learned in a source domain where data is easy to obtain to be a starting point for the target domain. On the other hand, real-world data contains objects and their relations, usually gathered from noisy environment. Finding patterns through such uncertain relational data has been the focus of the Statistical Relational Learning (SRL) area. Thus, to address domains with scarce, relational, and uncertain data, in this paper, we propose TreeBoostler, an algorithm that transfers the SRL state-of-the-art Boosted Relational Dependency Networks learned in a source domain to the target domain. TreeBoostler first finds a mapping between pairs of predicates to accommodate the additive trees into the target vocabulary. After, it employs two theory revision operators devised to handle incorrect relational regression trees aiming at improving the performance of the mapped trees. In the experiments presented in this paper, TreeBoostler has successfully transferred knowledge among several distinct domains. Moreover, it performs comparably or better than learning from scratch methods in terms of accuracy and outperforms a transfer learning approach in terms of accuracy and runtime.
IMPLEMENTATION OF AGILE METHODOLOGY FOR MULTI OWNER DATA SHARING USING CLOUD COMPUTING SERVICES

Cloud services are popular because they can reduce costs and computer and network complexity.Cloud services are popular. Since the cloud customers are not expected to spend in IT services, buy equipment,or obtain permits, the advantages include low operating costs, fast returns on investment, easy deployment,flexibility, unlimited usage, and application of emerging creative technologies. Cloud companies specializing inother fields (e.g. e-mails) may often offer innovative tools that cannot be managed or established by a singleorganization. Scalability, durability and performance are several key advantages for consumers. In this study weimplemented a methodology of agile approach for multi-owner data sharing using cloud services.
STRATA: Building Robustness with a Simple Method for Generating Black-box Adversarial Attacks for Models of Code.

Adversarial examples are imperceptible perturbations in the input to a neural model that result in misclassification. Generating adversarial examples for source code poses an additional challenge compared to the domains of images and natural language, because source code perturbations must adhere to strict semantic guidelines so the resulting programs retain the functional meaning of the code. We propose a simple and efficient black-box method for generating state-of-the-art adversarial examples on models of code. Our method generates untargeted and targeted attacks, and empirically outperforms competing gradient-based methods with less information and less computational effort. We also use adversarial training to construct a model robust to these attacks; our attack reduces the F1 score of code2seq by 42%. Adversarial training brings the F1 score on adversarial examples up to 99% of baseline.
Blender as a tool for generating synthetic data

Acquiring data for neural network training is an expensive and labour-intensive task, especially when such data isdifficult to access. This article proposes the use of 3D Blender graphics software as a tool to automatically generatesynthetic image data on the example of price labels. Using the fastai library, price label classifiers were trained ona set of synthetic data, which were compared with classifiers trained on a real data set. The comparison of the resultsshowed that it is possible to use Blender to generate synthetic data. This allows for a significant acceleration of thedata acquisition process and consequently, the learning process of neural networks.
Interactive Re-Fitting as a Technique for Improving Word Embeddings

Word embeddings are a fixed, distributional representation of the context of words in a corpus learned from word co-occurrences. While word embeddings have proven to have many practical uses in natural language processing tasks, they reflect the attributes of the corpus upon which they are trained. Recent work has demonstrated that post-processing of word embeddings to apply information found in lexical dictionaries can improve their quality. We build on this post-processing technique by making it interactive. Our approach makes it possible for humans to adjust portions of a word embedding space by moving sets of words closer to one another. One motivating use case for this capability is to enable users to identify and reduce the presence of bias in word embeddings. Our approach allows users to trigger selective post-processing as they interact with and assess potential bias in word embeddings.
Aspect-Based Sentiment Analysis in Education Domain

Analysis of a large amount of data has always brought value to institutions and organizations. Lately, people's opinions expressed through text have become a very important aspect of this analysis. In response to this challenge, a natural language processing technique known as Aspect-Based Sentiment Analysis (ABSA) has emerged. Having the ability to extract the polarity for each aspect of opinions separately, ABSA has found itself useful in a wide range of domains. Education is one of the domains in which ABSA can be successfully utilized. Being able to understand and find out what students like and don't like most about a course, professor, or teaching methodology can be of great importance for the respective institutions. While this task represents a unique NLP challenge, many studies have proposed different approaches to tackle the problem. In this work, we present a comprehensive review of the existing work in ABSA with a focus in the education domain. A wide range of methodologies are discussed and conclusions are drawn.
A Readability Level of Reading Materials for Vocational High School Students in Lombok, NTB

This study aims to find out a readability level of reading materials for the students in one of the Vocational High Schools in Lombok, NTB using Cloze procedure, Flesh formula, and Fry Graph formula for the 12th grade. This study is a descriptive quantitative research. The sample of the study were 32 students using English textbooks presented for 12th graders in one of Vocational High Schools in Lombok, NTB. There were three genres performed; descriptive, recount, and narrative text. For the Flesh formula, and Fry Graph, the researcher’s observations were as the instrument of the study, meanwhile, for Cloze procedure, a test was employed to collect the data needed. The data was analyzed quantitatively described in the form of numerical description and percentage. The result revealed that analyzing the readability level through Cloze procedure was 42%, designated that the reading materials were instructional/assisted to read, and Flesh formula was 82.7%, indicated that the reading materials were easily understandable by the students, and Fry Graph formula showed that the reading materials were readable for the students. Thus, reading materials for students in one of the Vocational High Schools in Lombok, NTB was readable for 12th graders to read.
A Robust Approach with Text Analytics for Bengali Digit Recognition Using Machine Learning

Vernacular digit or text recognition has always been a challenge courtesy of the undecipherable images and uniqueness in every human being’s handwriting. In this paper, we have aimed to classify the Bengali numerals obtained from the NumtaDB dataset with the implementation of a convolutional neural network (CNN). Moreover, we have also conducted a survey based on Bengali numerals and performed a sentiment analysis on the recorded responses of our subjects using the same classification technique and thereafter have touched upon an area of application of vernacular digit recognition which has seldom gone unnoticed. We have also endeavoured to provide an in-depth literature survey of the works that have been done in this area previously. Our classification model gives us a whopping accuracy of 98.40%.
Conversational Recommender System by Bayesian Methods

We present a Bayesian approach to conversational recommender systems. After any interaction with the user, a probability mass function over the items is updated by the system. The conversational feature corresponds to a sequential discovery of the user preferences based on questions. Information-theoretic criteria are used to optimally shape the interactions and decide when the conversation ends. Most probable items are consequently recommended. Dedicated elicitation techniques for the prior probabilities of the parameters modelling the interactions are derived from basic structural judgements based on logical compatibility and symmetry assumptions. Such prior knowledge is combined with data for better item discrimination. Our Bayesian approach is validated against matrix factorization techniques for cold-start recommendations based on metadata using the popular benchmark data set MovieLens. Results show that the proposed approach allows to considerably reduce the number of interactions while maintaining good ranking performance.
A STUDY ABOUT STABILITY OF TWO AND THREE SPECIES POPULATION MODELS

In this article, we have discussed the stability of second order linear and non-linear systems by characteristic roots. In the case of non-linear system, we linearize the nonlinear system under certain specified conditions and study the stability of critical points of the linearized systems. Necessary theories have been presented, applied, and illustrated with examples. A self-contained theory for a homogeneous linear system of third order is built by using the basic concept of the differential equation.
MULTIMEDIA INTEGRATION IN ENGLISH CLASS

This study aimed to determine the extent to which multimedia technology can be applied in the process of teaching and learning English and affect student performance. Data obtained from classroom observations and interviews. The analysis shows how the implementation of multimedia or educational applications provides an improvement in the teaching and learning process. The implementation of multimedia particularly educational applications in computer gives students the opportunity to practice using English, as well as increasing students' interest in participating in the classroom activities. In the process of integrating multimedia technologies, the development of the students' skills can be seen. In short, this study shows that the implementation of technology raises a positive impact on students in the learning process in the classroom.
Cryptographic Divergences: New Techniques and New Applications.

In the recent years, some security proofs in cryptography have known significant improvements by replacing the statistical distance with alternative divergences. We continue this line of research, both at a theoretical and practical level. On the theory side, we propose a new cryptographic divergence with quirky properties. On the practical side, we propose new applications of alternative divergences: circuit-private FHE and prime number generators. More precisely, we provide the first formal security proof of the prime number generator PRIMEINC [8], and improve by an order of magnitude the efficiency of a prime number generator by Fouque and Tibouchi [16, 17] and the washing machine technique by Ducas and Stehle [15] for circuit-private FHE.
Word Embedding and Feature Reduction for Sentiment Analysis Using GA

Sentiment analysis (SA), also called as opinion mining is the technique for the removal of opinions of a specific entity or feature from reviews dataset. The opinions of other users help in decision making process of people. This paper studies different methods that are aimed at SA. These approaches vary from semantic based methods, machine learning, neural networks, syntactical methods with each having its own strength. Although hybrid approach also exists where the idea is to combine strengths of two or more methods to increase the accuracy. A framework in which sentiment analysis is done by using word embedding and feature reduction techniques is also proposed. Word embedding is a technique in which low-dimensional vector representation of words is provided. Feature reduction method is used with Support Vector Machine (SVM) classifier. The framework will perform sentiment analysis of user opinions by using a machine learning approach and provides a recommendation system for the ease of decision making for users. The proposed system in this paper has solved the scalability problem and improved the accuracy.
An Efficient Agreement Mechanism in CapsNets By Pairwise Product

Capsule networks (CapsNets) are capable of modeling visual hierarchical relationships, which is achieved by the "routing-by-agreement" mechanism. This paper proposes a pairwise agreement mechanism to build capsules, inspired by the feature interactions of factorization machines (FMs). The proposed method has a much lower computation complexity. We further proposed a new CapsNet architecture that combines the strengths of residual networks in representing low-level visual features and CapsNets in modeling the relationships of parts to wholes. We conduct comprehensive experiments to compare the routing algorithms, including dynamic routing, EM routing, and our proposed FM agreement, based on both architectures of original CapsNet and our proposed one, and the results show that our method achieves both excellent performance and efficiency under a variety of situations.