Reviewerly/exHarmony huggingface dataset:
In search of Indonesian-based digital literacy curriculum through TULAR NALAR

Indonesia is one of the largest social media users in the world. In its January 2020 report, We Are Social places Indonesia in the third position with the addition of internet users (17% with more than 25 million users). However, a big problem arises because of the low level of digital literacy among its citizens. One of the efforts to prevent the spread of hoaxes is digital literacy and building critical thinking through the curriculum. The digital literacy curriculum has been developed by various organizations such as UNESCO, UNICEF, Newseum, and the Ministry of Education and Culture of Indonesia (Information and Communication Technology Curriculum). This study adapts and elaborates the curriculum into a TULAR-NALAR curriculum product. The research methodology used is Design-Based Research (DBR) with curriculum development referring to ADDIE (Analyze, Design, Develop, Implement, and Evaluate) compiled by Robert Maribe Branch.
Research on Knowledge Extraction Based on Semantic Similarity

To solve the thorny problem of &#x2018;information explosion&#x2019; in tunnel construction design, the knowledge extraction model based on semantic similarity (KEMSS) is proposed in this paper. In this model, the key words of designer&#x0027;s demand and the tunnel geological exploration and bidding texts are preprocessed; secondly, the Bert pretraining model is used to represent the two as semantic vectors containing context information; then the two feature vectors interact in the interaction layer to extract the matching features between texts; finally, semantic similarity is calculated by matching layer to extract the target text of the tunnel geological exploration and bidding texts. KEMSS is proposed for the semantic matching between short text and long text from a new perspective in this paper, which only needs a small part of the corpus in the field of tunnel construction to fine-tune based on knowledge transfer. The comparative experiments show that the accuracy and range of knowledge extraction of KEMSS are better than those of the comparative baseline models, which verifies the feasibility and effectiveness of the proposed model.
The construction of the eLTER Pan-European research infrastructure to support multidisciplinary environmental data integration and analysis

&amp;lt;p&amp;gt;One of the major goals of the upcoming European integrated Long-Term Ecosystem &amp;amp;#160;critical zone and socio-ecological Research Infrastructure (eLTER RI) is to provide reliable and quality-controlled long-term environmental data from various disciplines for scientific analysis as well as the assessment of environmental policy impacts. For this purpose, eLTER has been designing and piloting a federated data infrastructure for integration and dissemination of a broad range of &amp;lt;em&amp;gt;in situ&amp;lt;/em&amp;gt; observations and related data.&amp;lt;br&amp;gt;Implementing such a pan-European environmental data infrastructure is a lengthy and complex process driven by user needs, shareholder requirements and general service and technology best practises. The European LTER community has laid the foundations of this eLTER Information System. For further improvements, user needs have recently been collected by (a) targeted interviews with selected stakeholders to identify requirements, (b) workshops mapping requirements to potential RI services, and (c) analysis work for designing the RI service portfolio for. The requirements collections are used to derive functional (i.e. the behaviour of essential features of the system) and non-functional (i.e. the general characteristics of the system) requirements for the IT infrastructure and services. These collected requirements revolve around the development of workflows for the ingestion, curation and publication of data objects including the creation, harvesting, discovery and visualisation of metadata as well as providing means to support the analysis of these datasets and communicating study results.&amp;lt;br&amp;gt;Considering that downstream analyses of data from both eLTER and other RIs are a key part of the RI&amp;amp;#180;s scope the design includes virtual collaborative environments where different data and analyses can be brought together and results shared with FAIR principles as the default for research practice. The eLTER RI will take advantage of data stored in existing partner data systems, harmonised by a central discovery portal and federated data access components providing a common information management infrastructure for bridging across environmental RIs.&amp;lt;br&amp;gt;This presentation will provide an overview of the current stage of the eLTER RI developments as well as its major components, provide an outlook for future developments and discuss the technical and scientific challenges of building the eLTER RI for interdisciplinary data sharing.&amp;lt;/p&amp;gt;
Implementation of a 3-wheeled Wall Following Robot Navigation System using Coppelia

This article will design a controller for a three-wheeled wall-following robot based on Copalia software. The problem with the wall-following robot is how to control a follower robot to move constantly along the wall in the intended direction. The robot controller uses Sugeno fuzzy logic as a rule base for stationary and moving states. This controller is created through distance and orientation navigation. Both are estimated by the robot model and corrected by the sensor measurement results. In cases where the wall is not available, for example, an open door, the robot will stop then there will be feedback to take the next step. The designed controller has been verified experimentally, where the results show an error rate of five millimeters from the actual distance
CASTA: Clinical Assessment System for Tuberculosis Analysis

Mantaux test is used for analyzing tuberculosis. Various factors like drug history, immunological status, age, coexisting illness, administration flaws influence its outcome, so also its interpretation. There is a lot of inconsistency involved in the existing approaches in measuring the wheal size in skin tests. This work presents a computer aided clinical assessment system that analyzes the outcomes of the mantaux test. The assessment system uses medical photography to acquire forearm images from patients and image processing techniques to obtain geometric features from the image. These features are used to quantify the size of the wheals of the skin test. The size of the wheal is quantified using area, perimeter, length and breadth of the bounding box. The system is integrated with a smartphone application built using React Native which is connected to the backend servers which help in running the image processing algorithms and delivering the results. The NodeJS server and the Flask server connected to AWS S3 Bucket store all the images. The servers are deployed on an Oracle Cloud instance which ensures all-time online availability. The application and the computing approaches presented in this work minimize the errors and inconsistencies involved in the reading and interpretation of skin test outcomes; moreover, using computer aided diagnosis would enhance automation of healthcare informatics at clinics and hospitals.
Application of a dense network cross-correlation event detector to seismicity induced by hydraulic stimulations in Espoo/Helsinki, Finland

&amp;lt;p&amp;gt;We have developed a cross-correlation based event detector with the primary application focus on dense, surface-installed temporary seismic networks monitoring the seismicity from a set target area. The event detector is capable of handling a terabytes-sized, noisy multi-channel dataset with hundreds of stations on a regular desktop PC. The detector works in 4 steps: 1. Generate templates from known events in a database. 2. Run cross-correlation for templates on continuous data. 3. Filter initial detections based on the surface network geometry. 4. Relocate the events in the filtered dataset applying correlation time delays and amplitude based magnitude corrections. The detector is accompanied with an event viewer designed to rapidly browse the resulting catalogue. We apply the detector on the induced seismicity of a planned Enhanced Geothermal System (EGS) doublet in Espoo, Finland. The company St1 Oy performed two hydraulic stimulations at around 6 kilometer depth beneath the Aalto University campus in Otaniemi, Espoo, Finland, in June-July 2018 and in May 2020. For both stimulations, the Institute of Seismology, University of Helsinki, installed a temporary ~100 geophone network to monitor the stimulation and post-stimulation stages. The network consisted of three-component 4.5 Hz geophones, mostly DATA-CUBE3s recording at 400 Hz. The geophone stations were organized into large arrays consisting of up to 25 stations, smaller 3-4 station arrays, and additional single stations for better azimuthal coverage. We present here the results from applying the detector on the datasets collected from the Otaniemi-EGS. The anatomy, such as magnitude of completeness, of the resulting event catalogue consisting of thousands of induced events will be discussed with the goal of publishing the finalized catalogue in near future. We also briefly discuss the impact of adding sub-surface borehole stations, and applying the detector to a sparser surface station network to broaden the scope of usefulness.&amp;amp;#160;&amp;lt;/p&amp;gt;
Combatting the financing of hate groups

Purpose This study aims to make the reader aware of recent changes in the white supremacist movement and how such changes have altered the ways in which the movement can be combatted. Design/methodology/approach The authors study the movement in two periods: from 1970 to 2005 and from 2006 onward. The authors contrast the two periods and discuss the legal and financial issues within each. Findings The authors find that while legal concepts such as vicarious liability and respondeat superior apply today just as they did before, new tools are needed to fight the new means of financing the movement. Research limitations/implications The main limitation of this study is the lack of quantitative data. Because the “alt-right” became popular around 2015, there has not been enough time for the construction of detailed data sets. Practical implications While many law papers have explored the white supremacist movement, the financing side has gone under-analyzed in scholarly research. This is important in light of the rise of the internet, online payment processors, cryptocurrencies and remote organizing and fundraising. Social implications The 2017 Charlottesville rally was organized and financed via podcasts, online forums, encrypted chats and anonymous payments. Since then, the movement has mostly gone underground and has become more violent and radical as many members have come to believe that marches and politics do not help them. Originality/value To the best of the authors’ knowledge, there are no papers in finance that deal extensively with this topic. The authors believe that the severity of the issue and the importance of its funding make this study a valuable source of information. The recent changes occurring within the movement are likely to become even more critical to its success or failure in the future.
A Machine Translation Approach from Japanese to Tamil

This paper presents a rule-based approach to Japanese-Tamil Machine Translation(MT) system. Our approach proves the possibility of the MT between Japanese and Tamil, and we give special attention and describe the Tamil synthesis in this paper.
Enhancing planetary imagery with the holistic attention network algorithm

&amp;lt;p&amp;gt;The recent developments in computer vision research in the field of Single Image Super Resolution (SISR)&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;can help improve the satellite imagery data quality and, thus, find application in planetary exploration.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The aim of this study is to enhance planetary surface imagery, in planetary bodies that there are&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;available data but in a low resolution. Here, we have applied the holistic attention network (HAN)&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;algorithm to a set of images of Saturn&amp;amp;#8217;s moon Titan from the Titan Radar Mapper instrument in its&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Synthetic Aperture Radar (SAR) mode, which was on board the Cassini spacecraft. HAN can find&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;correlations among hierarchical layers, channels of each layer, and all positions of each channel, which&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;can be interpreted as an application and intersection of previously known models. The algorithm used&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;in our case-study was trained on 5000 grayscale images from HydroSHED Earth surface imagery dataset&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;resampled over different resolutions. Our experimental setup was to generate High Resolution (HR)&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;imagery from eight times lower resolution (x8 scale). We followed the standard workflow for this&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;purpose, which is to first train the network enhancing x2 scale to HR, then x4 scale to x2 scale, and&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;finally x8 scale to x4 scale, using subsequently the results of the previous training. The promising results&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;open a path for further applications of the trained model to improve the imagery data quality, and aid&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;in the detection and analysis of planetary surface features.&amp;lt;/p&amp;gt;
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requiring complex interactions, existing algorithms can suffer from an inability to accurately anticipate the influence of self-actions on other agents. Incorporating an ability to reason about other agents' potential responses can allow an agent to formulate more effective strategies. This paper adopts a recursive reasoning model in a centralized-training-decentralized-execution framework to help learning agents better cooperate with or compete against others. The proposed algorithm, referred to as the Recursive Reasoning Graph (R2G), shows state-of-the-art performance on multiple multi-agent particle and robotics games.
Strategy Synthesis for Zero-sum Neuro-symbolic Concurrent Stochastic  Games

Neuro-symbolic approaches to artificial intelligence, which combine neural networks with classical symbolic techniques, are growing in prominence, necessitating formal approaches to reason about their correctness. We propose a novel modelling formalism called neuro-symbolic concurrent stochastic games (NS-CSGs), which comprise probabilistic finite-state agents interacting in a shared continuous-state environment, observed through perception mechanisms implemented as neural networks. Since the environment state space is continuous, we focus on the class of NS-CSGs with Borel state spaces. We consider the problem of zero-sum discounted cumulative rewards and prove the existence of the value of NS-CSGs under Borel measurability and piecewise-constant restrictions on the components of the model. From an algorithmic perspective, existing methods to compute values and optimal strategies for CSGs focus on finite state spaces. We present, for the first time, implementable value iteration and policy iteration algorithms to solve a class of uncountable state space CSGs, namely NS-CSGs, and prove their convergence. Our approach works by exploiting the underlying game structures and then formulating piecewise linear or constant representations of the value functions and strategies of NS-CSGs. We illustrate our approach by applying a prototype implementation of value iteration to a dynamic vehicle parking case study.
Effects of Clusters and Business Environment Context on Knowledge Absorption Capacity and Measurement with A Panel Analysis

The theoretical hypotheses of absorptive capacity (AC) are relatively simple; validation and measurement are more complicated. There are still discussions about AC's best proxy or measurement because archival or survey methods may not provide dependable results. Additionally, absorption theories have not researched the effectiveness of a spatial or geographically relevant bounding as a cluster. Exterior factors define absorptive capacities, but their contingent effects on variables and forms are unclear. Limited studies on the external networks and business environment and change in theory versus execution remain essential research subjects. This research aims to explore the effects of clusters and the business environment on absorptive capacity from the perspective of contingency theory. Based on a sample of 38 nations, World Bank, and Global Innovation Index within the 2013-2018 period data, panel analysis shows that cluster inclusion and business environment relative to the nation can strengthen the absorptive capacity. The results also show that the proxy variable of absorptive capacity is significantly related to results.
RASA Chatbot Using AI

The growth of technologies like Artificial Intelligence (AI), Big Data &amp; Internet of Things (IoT), etc. has marked many advancements in the technological world since the last decade. These technologies have a wide range of applications. One such application is “Chatterbot or “Chatbot”. Chatbots are conversational AIs, which mimics the human while conversing &amp; eliminates the need of human by automating mundane tasks. In the study undertaken, we have created a chatbot in education domain &amp; it is named as “College Enquiry Chatbot”, This chatbot is a web-based application that analyses and understands user's queries and provides an instant and accurate response. Rasa technology is used to construct this chatbot. It's an open-source technology, which uses its two main packages i.e., Rasa Core &amp; Rasa Natural Language Understanding (NLU) in order to build a Contextual AI Chatbot. NLU is used to infer the intent and to extract the necessary entities from user input &amp; the Rasa Core provides the output by building a probabilistic model with the help of Recurrent Neural Network (RNN). Evaluation of the model is done by getting a confusion matrix and performance measures like Precision, Accuracy &amp; F1 Score which come out to be 0.628, 0.725 and 0.669 respectively on average basis. This chatbot's accuracy, lack of dependability on human resources, 24 x 7 accessibility and low maintenance creates various opportunities for its implementation. This conversational agent can not only be used in educational institutions but also in places where enquiry becomes a tedious task.
Indonesian Teachers’ Perceptions of Students’ Problems and The Possible Causes when Speaking English in The EFL Classroom

English Speaking skill plays an important role in communication for EFL learners, including those in Indonesia. However, the problem of speaking English is a frequently encountered topic of discussion in the body of literature and in educational contexts. There are various problems that are listed as the possible causes of language learners’ incompetence in speaking English from teachers’ perceptions which have to be investigated. In order to examine the teachers’ perceptions of students’ problems and possible causes when speaking English in the EFL classrooms, I interviewed three English teachers in a junior high school. Semi structured-interview was used to generate data, which were then analysed by using the Interactive Model of data analysis. The results indicated that lack of vocabulary, lack of confidence, fear of making mistakes and language anxiety as students’ problems in speaking. In addition, the teachers perceived the possible causes derived from both intrinsic and extrinsic factors such as students’ motivation or willingness to practice speaking English and the environmental factors like family support which hindered the students from delivering their best speaking performance.
A Study on Window-based Path Driving through Identifying the Target Walking in an Ultrasonic Sensor-based Follow-up Collaborative Robot

This study proposes a method for window-based path driving through identifying the user's walking and works in an ultrasonic sensor-based follow-up collaborative robot. Target location estimation methods of an ultrasonic sensor-based follow-up collaborative robot are divided into distance-based and coordinate-based following methods. It is difficult to smoothly use the distance-based following method in an unspecified indoor space due to the time delay in its following process since it follows a target with a safe distance. A preliminary study that converts the distance into location information in the target following process in order to compensate for it causes fails in the following due to frequent path changes when errors occur in reference distance values because of irregularities such as specular reflection in an ultrasonic sensor. Therefore, this study proposes a window-based following method according to the identification of user's walking and works in a robot through converting the current location of the robot into a calibrated coordinate system. The driving of the proposed system is based on encoders attached to the robot and the cumulative error is minimized by initializing the coordinate in the user's identification of works to prevent errors caused by slip due to long-term operation.
Speckle Interference and Displacement

This section discusses speckle interference and displacement, including rigid body movement, intensity correlation for open or closed fringes, carrier fringe patterns in speckle interferometry for open-fringe generation, and practical removal of environmental instabilities.
Lightweight spatial pyramid pooling network for real-time semantic segmentation

Abstract In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed.
Phase Detection

Phase can be detected instantaneously using a single speckle pattern or using the time to gather a series of speckle patterns. The instantaneous approach, also called single-shot phase detection, gives a single speckle pattern in which a spatial carrier is embedded, but phase extraction from this kind of pattern is limited in bandwidth. More than one speckle pattern is needed to process the whole bandwidth and to extract phase values using phase-shifting procedures. The acquisition of more than one speckle pattern requires time, and time is related to the properties of the object under analysis, such as deformation or translation, and ambient thermal drifts. In theory, the object’s properties must remain unchanged for each measurement, but in practice, they keep evolving for each measurement. Due to thermal and other noise effects in the acquisition process, the phase values are usually noisy and distorted, and smoothing procedures must be introduced in the numerator and denominator of the arctangent function that is always used in the final steps of phase extraction.