Reviewerly/exHarmony huggingface dataset:
Commonsense Knowledge in Word Associations and ConceptNet

Humans use countless basic, shared facts about the world to efficiently navigate in their environment.This commonsense knowledge is rarely communicated explicitly, however, understanding how commonsense knowledge is represented in different paradigms is important for both deeper understanding of human cognition and for augmenting automatic reasoning systems.This paper presents an in-depth comparison of two large-scale resources of general knowledge: ConceptNet, an engineered relational database, and SWOW a knowledge graph derived from crowd-sourced word associations.We examine the structure, overlap and differences between the two graphs, as well as the extent to which they encode situational commonsense knowledge.We finally show empirically that both resources improve downstream task performance on commonsense reasoning benchmarks over text-only baselines, suggesting that large-scale word association data, which have been obtained for several languages through crowd-sourcing, can be a valuable complement to curated knowledge graphs. 1
Implementasi Program Literasi dalam Meningkatkan Pendidikan Karakter Siswa

This research is motivated by the fact that the development of the era in the school environment, it is found that many students whose interest in reading and writing has begun to decrease, one of which is due to the lack of getting used to reading, so that it has an impact on the character of students in the school. Several school principals made breakthroughs by implementing literacy activities to restore students' interest in reading and writing to their respective schools. Regarding educational institutions as educational units at various levels, starting from elementary, middle, and upper levels, both government and private, nowadays it is increasingly necessary to own and manage school libraries. This study aims (1) to determine the implementation of literacy programs in improving character education of students at SMP Negeri 1 Lumbung. (2) To determine the results of the literacy program in improving character education for students at SMP Negeri 1 Lumbung. (3) To determine the obstacles and solutions of literacy programs in improving character education for students at SMP Negeri 1 Lumbung. This research is a qualitative field research with descriptive methods. This is because this study aims to reveal data in the field, namely by describing and interpreting something objectively about something that happened at the time the research was carried out, with the aim of obtaining an overview of data about the actual social reality. The results of the research that the authors conducted lead to conclusions, namely: 1) The implementation of the literacy program at SMP Negeri 1 Lumbung has reached the learning stage. In the development stage, participants are encouraged to show their involvement in their thoughts and emotions with the reading process through productive activities orally and in writing. 2) The result of the implementation of the literacy program in improving the character education of students at SMP Negeri 1 Lumbung is that most of the education process depends on literacy abilities and awareness. The literacy culture that is embedded in students affects the level of success both in school and in social life. Strengthening moral education or character education in the present is very necessary to overcome the moral crisis that is currently hitting this nation. 3) The most prominent obstacle in implementing literacy programs is the lack of student motivation and the solution to these obstacles is to increase student development and provide motivation to students to actively read.
Security Analysis of Capsule Network Inference using Horizontal Collaboration.

The traditional convolution neural networks (CNN) have several drawbacks like the Picasso effect and the loss of information by the pooling layer. The Capsule network (CapsNet) was proposed to address these challenges because its architecture can encode and preserve the spatial orientation of input images. Similar to traditional CNNs, CapsNet is also vulnerable to several malicious attacks, as studied by several researchers in the literature. However, most of these studies focus on single-device-based inference, but horizontally collaborative inference in state-of-the-art systems, like intelligent edge services in self-driving cars, voice controllable systems, and drones, nullify most of these analyses. Horizontal collaboration implies partitioning the trained CNN models or CNN tasks to multiple end devices or edge nodes. Therefore, it is imperative to examine the robustness of the CapsNet against malicious attacks when deployed in horizontally collaborative environments. Towards this, we examine the robustness of the CapsNet when subjected to noise-based inference attacks in a horizontal collaborative environment. In this analysis, we perturbed the feature maps of the different layers of four DNN models, i.e., CapsNet, Mini-VGG, LeNet, and an in-house designed CNN (ConvNet) with the same number of parameters as CapsNet, using two types of noised-based attacks, i.e., Gaussian Noise Attack and FGSM noise attack. The experimental results show that similar to the traditional CNNs, depending upon the access of the attacker to the DNN layer, the classification accuracy of the CapsNet drops significantly. For example, when Gaussian Noise Attack classification is performed at the DigitCap layer of the CapsNet, the maximum classification accuracy drop is approximately 97%.
Analysing the Behaviour of Distributed Files in Hadoop for NoSQL Database against DML Queries

Big data mean for the massive quantity of heterogeneous data. Now a day the big data management becomes a challenging task for data analysts. There are several components are available in Hadoop for bigdata management and evaluating performance. Hence there are some issues while selecting the components and tools for big data management. Some kind of issues leads to data mismatch and using unnecessary file systems. Another issue while dealing with large database of various users which includes database users, database administrators and users who extracts information in sequence from data warehouses. So this paper is going to provide some NoSQL database and HBase Performance to test a single node. Here the performance of query become slowly because of single node cluster setup and to perform and setup had need of multi node clusters to improve query performance. Compare to existing SQL database management the proposed Hadoop file system with NoSQL will give good performance and reduce time.
2D AND 3D PLOTTER USING ARDUINO NANO

This paper aims to develop and implement a low cost and high accuracy mini-CNC machine for 2D and 3D plotting using Arduino Nano. This system can be widely used in small-scale engraving or Image plotting applications where the accuracy and speed are the main performance parameters.
Self-supervised Reinforcement Learning with Independently Controllable Subgoals

To successfully tackle challenging manipulation tasks, autonomous agents must learn a diverse set of skills and how to combine them. Recently, self-supervised agents that set their own abstract goals by exploiting the discovered structure in the environment were shown to perform well on many different tasks. In particular, some of them were applied to learn basic manipulation skills in compositional multi-object environments. However, these methods learn skills without taking the dependencies between objects into account. Thus, the learned skills are difficult to combine in realistic environments. We propose a novel self-supervised agent that estimates relations between environment components and uses them to independently control different parts of the environment state. In addition, the estimated relations between objects can be used to decompose a complex goal into a compatible sequence of subgoals. We show that, by using this framework, an agent can efficiently and automatically learn manipulation tasks in multi-object environments with different relations between objects.
The Students’ Ability In Writing Skill: Using Personal Pronoun In Narrative text

This study aims to determine the students’ ability of second grade SMP Talitakum to use personal pronouns in narrative text. The subject of this study are students of second grade and the object of this study was the students' ability to use pronouns in narrative text. The sample of this study are 16 students. Data collection techniques in this study use test and interviews. Based on the data analyzed, the researchers concluded that the students' ability to use pronouns in the narrative text was 68.43% and the students' ability to use personal pronouns in the narrative text is 62.93%. The study concludes that the difficulty affecting students in using pronouns was a lack of understanding of pronouns and narrative text even though they had been taught by the teacher and the lack of grammar books.
Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy

The goal of question answering (QA) is to answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, model accuracy analysis reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic). But QA's lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.
A Study on the Measurement of the Exposure Time using the Slow Motion Recording of a Smartphone

Poster: ECR 2019 / C-3240 / A Study on the Measurement of the Exposure Time using the Slow Motion Recording of a Smartphone  by:  M. Yamauchi 1, N. Shimizu1, C. Yamauchi-Kawaura2, S. Wakumura1, R. Sano1; 1Nagakute, Aichi/JP, 2Nagoya/JP
Impact of Service Quality and Customer Satisfaction: Evidence from Banking Industry

This study aims to investigate impact of service quality on customer satisfaction. Information data were collected by questionnaires from two hundred customers of four private sector banks located in Peshawar district using convenience sampling technique. Results indicate that customer service quality has a positive influence on customer satisfaction.
ALGORITHM FOR USING FUZZY LOGIC IN MANAGEMENT AND DECISION-MAKING MODELS

In real conditions, when the task of formally describing the control process of a rather complex process arises, it is necessary to take into account several external factors (parameters) and their values, which potentially tend to Infinity. At the same time, the system's response is not limited to just one control action. To automate the process of composing all possible combinations of linguistic descriptions of variables at the stage of fuzzy conditional statements and the decision-making mechanism on the use of control actions in the development of control and decision-making models, it is proposed to use fuzzy logical models. Ways to construct algorithms for converting input perturbations of complex systems into conceptual relations for automating the control process and supporting decision-making are considered. The fuzzy logic apparatus relation is used to formalize, process, and make decisions about the use of system control signals in response to external disturbances. Fuzzy control systems combine information from human experts (natural language) with measurements and mathematical models. Fuzzy Systems will turn the knowledge base into a mathematical formulation that has proven very effective in many applications. When designing a fuzzy system, many questions need to be answered, in particular in creating linguistic models to describe the functioning of complex systems, in particular radar mapping systems with recognition of objects on the ground and making decisions for controlling unmanned systems. Thus, at the stage of composing a set of fuzzy instructions (statements), it is of interest to formalize the following processes, such as determining all possible combinations of terms of linguistic variables and making a decision on the application of control actions, depending on external factors. In the process of formalizing the process of determining all possible combinations and terms of linguistic variables, it is necessary to create fuzzy instructions (rules) for managing a system or object for fuzzy-logical control models and decision-making in the process of developing models for the functioning of complex systems.
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing

Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language. To better understand and leverage what PLMs have learned, several techniques have emerged to explore syntactic structures entailed by PLMs. However, few efforts have been made to explore grounding capabilities of PLMs, which are also essential. In this paper, we highlight the ability of PLMs to discover which token should be grounded to which concept, if combined with our proposed erasing-then-awakening approach. Empirical studies on four datasets demonstrate that our approach can awaken latent grounding which is understandable to human experts, even if it is not exposed to such labels during training. More importantly, our approach shows great potential to benefit downstream semantic parsing models. Taking text-to-SQL as a case study, we successfully couple our approach with two off-the-shelf parsers, obtaining an absolute improvement of up to 9.8%.
Impact of sample size on the stability of risk scores from clinical prediction models: a case study in cardiovascular disease

Abstract Background: Stability of risk estimates from prediction models may be highly dependent on the sample size of the dataset available for model derivation. In this paper, we evaluate the stability of cardiovascular disease risk scores for individual patients when using different sample sizes for model derivation; such sample sizes include those similar to models recommended in national guidelines, and those based on recently published sample size formula for prediction models. Methods: We mimicked the process of sampling N patients from a population to develop a risk prediction model by sampling patients from the Clinical Practice Research Datalink. A cardiovascular disease risk prediction model was developed on this sample and used to generate risk scores for an independent cohort of patients. This process was repeated 1000 times, giving a distribution of risks for each patient. N = 100 000, 50 000, 10 000, N min (derived from sample size formula) and N epv10 (meets 10 events per predictor rule) were considered. The 5 – 95 percentile range of risks across these models was used to evaluate instability. Patients were grouped by a risk derived from a model developed on the entire population (population derived risk) to summarise results. Results: For a sample size of 100 000, the median 5 – 95 percentile range of risks for patients across the 1000 models was 0.77%, 1.60%, 2.42% and 3.22% for patients with population derived risks of 4-5%, 9-10%, 14-15% and 19-20% respectively, for N = 10 000 it was 2.49%, 5.23%, 7.92% and 10.59%, and for N using the formula derived sample size, it was 6.79%, 14.41%, 21.89% and 29.21%. Restricting this analysis to models with high discrimination, good calibration or small mean absolute prediction error reduced the percentile range, but high levels of instability remained. Conclusions: Widely used cardiovascular disease risk prediction models suffer from high levels of instability induced by sampling variation. Many models will also suffer from overfitting (a closely linked concept), but at acceptable levels of overfitting there may still be high levels of instability in individual risk. Stability of risk estimates should be a criterion when determining the minimum sample size to develop models.
DEVELOPING “BEBERAN” GAME BOARD AS AN INNOVATIVE MEDIA TO IMPROVE STUDENTS’ SPEAKING SKILLS OF PRE-SERVICE TEACHERS OF ISLAMIC ELEMENTARY EDUCATION

This study aimed to develop a “Beberan” game board as a practical, efficient, and innovative media to improve students’ speaking skills. It was carried out using the research and development method by applying Borg, Gall, and Sugiyono theories. Furthermore, it involved seven steps: analysis, designing product, preliminary test, experts’ judgments, product revisions, field test, and final product of media. A total of 30 students of the pre-service teacher study program of the Islamic elementary education, Al-Anwar Islamic College, were the subjects. The instruments used were observation, interview, and test. Based on the data obtained, the results showed that developing the “Beberan” game board as an innovative media improved students’ speaking skills at the beginner level. This improvement was observed in the result obtained from the pre-test and post-test scores, which increased from 67.867 to 72.133.
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning

While the last few decades have witnessed a huge body of work devoted toinference and learning in distributed and decentralized setups, much of thiswork assumes a non-adversarial setting in which individual nodes---apart fromoccasional statistical failures---operate as intended within the algorithmicframework. In recent years, however, cybersecurity threats from maliciousnon-state actors and rogue entities have forced practitioners and researchersto rethink the robustness of distributed and decentralized algorithms againstadversarial attacks. As a result, we now have a plethora of algorithmicapproaches that guarantee robustness of distributed and/or decentralizedinference and learning under different adversarial threat models. Driven inpart by the world's growing appetite for data-driven decision making, however,securing of distributed/decentralized frameworks for inference and learningagainst adversarial threats remains a rapidly evolving research area. In thisarticle, we provide an overview of some of the most recent developments in thisarea under the threat model of Byzantine attacks.
Assisted Learning for Organizations with Limited Data

In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance. The organizations have sufficient computation resources but are subject to stringent data-sharing and collaboration policies. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In assisted learning, an organizational learner purchases assistance service from an external service provider and aims to enhance its model performance within only a few assistance rounds. We develop effective stochastic training algorithms for both assisted deep learning and assisted reinforcement learning. Different from existing distributed algorithms that need to frequently transmit gradients or models, our framework allows the learner to only occasionally share information with the service provider, but still obtain a model that achieves near-oracle performance as if all the data were centralized.
Does Vision-and-Language Pretraining Improve Lexical Grounding?

Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world. Vision-and-Language (VL) models, trained jointly on text and image or video data, have been offered as a response to such criticisms. However, while VL pretraining has shown success on multimodal tasks such as visual question answering, it is not yet known how the internal linguistic representations themselves compare to their text-only counterparts. This paper compares the semantic representations learned via VL vs. text-only pretraining for two recent VL models using a suite of analyses (clustering, probing, and performance on a commonsense question answering task) in a language-only setting. We find that the multimodal models fail to significantly outperform the text-only variants, suggesting that future work is required if multimodal pretraining is to be pursued as a means of improving NLP in general.
Design Design of Information System Monitoring Complaints Targeting COVID-19 Vaccination for the General Public Based on the Web (Case Study of Bandung Health Office)

Bandung Health Office as a Health Agency that always provides quality services is currently focused on handling pandemics, especially in the implementation of vaccination programs as a strategy to combat the COVID-19 outbreak. Therefore, monitoring of complaints experienced by the community after vaccination is required. The problem experienced today is the unavailability of containers to accommodate and manage data on COVID-19 vaccination complaints using the information system. The purpose of this research is to design a website-based information system that supports the process of monitoring and handling COVID-19 vaccination complaints in the Bandung Health Office by presenting informative and transparent data so as to facilitate the public in reporting and obtaining complaint information easily. The system development method uses waterfall model, while the system functional design method uses object-oriented modeling approach with Unified Modelling Language (UML). Black box test results obtained a 100 percentage of system functionality success. Evaluation test results with System Usability Scale (SUS) questionnaire obtained the final test score of 77.6 with Acceptable category and B Grade Scale. So based on the test results, the system can run easily according to the needs of the user. This research resulted in a web-based information system that makes it easier for health workers to efficiently manage COVID-19 vaccination complaint reporting data.
ENVIRONMENTAL ISSUES OF SURFICIAL URANIUM DEPOSITS: OUM DHEROUA CASE STUDY (ISLAMIC REPUBLIC MAURITANIA)

Uranium deposits and resources are considered as an important raw material base for the implementation of scenarios for the green and clean energy transition. Traditionally discussed risks of potential environmental impacts of Uranium projects development could be subdivided by deposit type. Surficial type mineralization connected to the calcretes in shallow paleovalleys or playas has many specific features which might be analysed separately. Case study of Oum Dheroua Uranium project in the Islamic Republic of Mauritania shows an unexpected lower estimation of environmental risks comparatively to conventional Uranium projects despite to open-pit mining technology. The reasons for such estimation, connected to geographic location, the inclusion of Uranium minerals in natural ecosystems and low scale of deposits (both in grade and size sense). Potential by-products (Vanadium and Strontium) are not part of environmental factors assessment.
AN ADVANCED APPROACH TO RECOGNIZE HUMAN ACTIVITIES VIA DEEP LEARNING

The study of wearable and handheld sensors recognizing human activity improved our understanding of human behaviours and human objectives. Many academics seek to identify the activities of a user from raw data using the fewest necessary resources. In this article, we propose a network of profound beliefs, a full-service architecture for the recognition of activities (DBN-LSTM). This DBN-LSTM method improves the human predictability of raw data and reduces the complexity of the model as well as the requirement for comprehensive workmanship. A geographically and temporally rich network is CNN-LSTM. Our proposed model for the UCI HAR Public Data Set can achieve 99% accuracy and 92% precision.