Reviewerly/exHarmony huggingface dataset:
Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method

Advances in developing dialogue systems regarding speech recognition, language understanding, and speech synthesis. Dialogue systems to support human interaction with a robot efficiently by using spoken language. Facilities that provide convenience in carrying out daily activities for someone, such as older people, are necessary. The existence of Human-Robot Interaction (HRI), so that this interaction can give orders to the robot to do work that cannot be done by humans. This study presents a dialogue management system for HRI with a comparison sentence similarity method between TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity Algorithm and Jaccard Coefficient and using Finite State Machine (FSM). Dialogue Management is a way to find the response of the answer. When the user says something or in other words, is responsible for managing the flow of the conversation to command the robot. TF-IDF is used to give the weight of the term relationship and comparison between Cosine Similarity and Jaccard Coefficient for comparison method to determine the classification of similarity sentences from the dialogue manager to improve the intent of the dialogue, for the FSM method to set the sequence flow dialogue. We use Google Cloud Speech API as an engine for speech to text using Kinect V2 as an audio sensor. There are eight scenarios created in this system. The speech recognition process using Google Speech for an average of 2.62 seconds shows a reasonably fast response. TF-IDF Cosine Similarity method can produce enough accuracy of 97.43%, and Jaccard Coefficient indicates an accuracy level of 91.57%. The state of the FSM method can be considered as an efficient structure for building dialogue management.
Vostok: 3D scanner simulation for industrial robot environments

Computer vision will drive the next wave of robot applications. Latest three-dimensional scanners provide increasingly realistic object reconstructions. We introduce an innovative simulator that allows interacting with those scanners within the operating environment, thus creating a powerful tool for developers, researchers and students. In particular, we present a novel approach for simulating structured-light and timeof-flight sensors. Qualitative results demonstrate the efficiency and reliability in industrial environments. By using the programmability of modern GPUs, it is now possible to make greater use of parallelized simulative approaches. Apart from the easy modification of sensor parameters, the main advantage in simulation is the opportunity of carrying out experiments under reproducible conditions, especially for dynamic scene setups. Moreover, thanks to a great computational power, it is possible to generate huge amounts of synthetic data which can be used as test datasets for training machine learning models.
Bitcoin Covenants Unchained

Covenants are linguistic primitives that extend the Bitcoin script language, allowing transactions to constrain the scripts of the redeeming ones. Advocated as a way of improving the expressiveness of Bitcoin contracts while preserving the simplicity of the UTXO design, various forms of covenants have been proposed over the years. A common drawback of the existing descriptions is the lack of formalization, making it difficult to reason about properties and supported use cases. In this paper we propose a formal model of covenants, which can be implemented with minor modifications to Bitcoin. We use our model to specify some complex Bitcoin contracts, and we discuss how to exploit covenants to design high-level language primitives for Bitcoin contracts.
Digital literacy skills of math students through e-learning in COVID-19 era: a case study in Universitas Riau

Abstract The phenomenon of using social media as online media is getting more massive this decade. Young people as a millennial generation are the biggest users in the use of social media today. Research on digital literacy is still rare, especially in Indonesia. Based on this phenomenon, this study aims to map students’ digital mathematical literacy through e-learning in the Covid-19 era. This research uses a quantitative approach with a case study method. Informants were used as research samples of 8 postgraduate students at Riau University. Student digital literacy ability data was collected through online questionnaires using Google Form, then analyzed descriptively using SPSS Program version 23.00 for Windows. The findings obtained in this study indicate the importance of digital literacy capabilities that have a positive impact on knowledge, understanding and skills in using media, especially social media, which is now often used as a source of information by the public, especially by students. Seeing the results of the study, researchers recommended the need for universities or leaders so that students can improve their digital literacy abilities.
Policy Transfer via Kinematic Domain Randomization and Adaptation

Transferring reinforcement learning policies trained in physics simulation to the real hardware remains a challenge, known as the sim-to-real gap. Domain randomization is a simple yet effective technique to address dynamics discrepancies across source and target domains, but its success generally depends on heuristics and trial-and-error. In this work we investigate the impact of randomized parameter selection on policy transferability across different types of domain discrepancies. Contrary to common practice in which kinematic parameters are carefully measured while dynamic parameters are randomized, we found that virtually randomizing kinematic parameters (e.g., link lengths) during training in simulation generally outperforms dynamic randomization. Based on this finding, we introduce a new domain adaptation algorithm that utilizes simulated kinematic parameters variation. Our algorithm, Multi-Policy Bayesian Optimization, trains an ensemble of universal policies conditioned on virtual kinematic parameters and efficiently adapts to the target environment using a limited number of target domain rollouts. We showcase our findings on a simulated quadruped robot in five different target environments covering different aspects of domain discrepancies.
“All of the Rules of Jazz”

Though improvising computer systems are hardly new, jazz has recently become the focus of a number of novel computer music projects aimed at convincingly improvising alongside humans, with a particular focus on the use of machine learning to imitate human styles. The attempt to implement a sort of Turing test for jazz, and interest from organizations like DARPA in the results, raises important questions about the nature of improvisation and musical style, but also about the ways jazz comes popularly to stand for such broad concepts as “conversation” or “democracy.” This essay explores these questions by considering robots that play straight-ahead neoclassical jazz alongside George Lewis’s free-improvising Voyager system, reading the technical details of such projects in terms of the ways they theorize the recognition and production of style, but also in terms of the political implications of human-computer musicking in an age of algorithmic surveillance and big data.
Survivability and Vulnerability Analysis of Cloud RAID Systems under Disk Faults and Attacks

In this paper we model and analyze survivability and vulnerability of a cloud RAID (Redundant Array of Independent Disks) storage system subject to disk faults and cyber-attacks. The cloud RAID survivability is concerned with the system’s ability to function correctly even under the circumstance of hazardous behaviors including disk failures and malicious attacks. The cloud RAID invulnerability is concerned with the system’s ability to function correctly while occupying some state immune to malicious attacks. A continuous-time Markov chains-based method is suggested to perform the disk level survivability and invulnerability analysis. Combinatorial methods are then presented for the cloud RAID system level analysis, which can accommodate both homogeneous (based on binomial coefficients) and heterogeneous (based on multi-valued decision diagrams) disks. A detailed case study on a cloud RAID 5 system is conducted to illustrate the application of the proposed methods. Impacts of different parameters on the disk and system survivability and invulnerability are also investigated through numerical analysis.
A Bayesian Perspective on Training Speed and Model Selection

We take a Bayesian perspective to illustrate a connection between training speed and the marginal likelihood in linear models. This provides two major insights: first, that a measure of a model's training speed can be used to estimate its marginal likelihood. Second, that this measure, under certain conditions, predicts the relative weighting of models in linear model combinations trained to minimize a regression loss. We verify our results in model selection tasks for linear models and for the infinite-width limit of deep neural networks. We further provide encouraging empirical evidence that the intuition developed in these settings also holds for deep neural networks trained with stochastic gradient descent. Our results suggest a promising new direction towards explaining why neural networks trained with stochastic gradient descent are biased towards functions that generalize well.
Platforms for Insurance 4.0

Insurance 4.0 platforms are strategic components to support organizational efficiency, effectiveness, profitability, and to create new business models, products, and services. The use of innovative digital platforms in insurance 4.0 can yield several benefits. They support the daily business and administrative tasks, aid to take complex decision-making, and help in managing processes. The real objective of the platforms is to free insurance organizations from operational tasks and support their focus on strategic decisions and activities. The leading platforms at the basis of insurance 4.0 are the internet of things, with black boxes and similar, the cognitive insurance, with its essential components of big data analytics, artificial intelligence, and RPA—robotic process automation. Blockchain, smart contracts, and mobile can play essential roles in the insurance 4.0 transformation. This chapter confirms that to implement insurance 4.0, it is necessary to digitize insurance processes heavily. There might be challenges in this respect in current cultures, procedures, processes, capacities, and capabilities.
The development of constructions from the right edge: a multinomial regression analysis of clitic left and right dislocation in child French

The aim of the present research is to investigate the development of left and right dislocation in child French through a corpus study of three children until age 2;7 from the corpus of Lyon (Demuth & Tremblay, 2008). We extracted a total of 704 dislocations and analysed their syntactic properties. We show that (i) right dislocations are more frequent than left dislocations and (ii) left dislocations are significantly more complete than right dislocations (fewer omissions of verbs or pronouns). We compare these results to the hypothesis of Freudenthal, Pine, Jones & Gobet (2015, 2016) according to which some properties of child language can be explained by a learning mechanism from the right edge of the sentences from the input. We will show that this hypothesis can explain the general trend found in our data, but it is not sufficient to account for the entire development of dislocation in French.
Tokocabai Marketplace Application based on Web Using Extreme Programming Method

Chili is one of the horticultural commodities that is the biggest contributor to inflation for the Indonesian economy. This is caused by the high level of consumption of chili by the public, while the price of chili tends to fluctuate throughout the year. This can come from a variety of factors, one of which is the long chili product distribution chain. With the help of information technology that has developed rapidly, the problem of price fluctuations can be reduced by creating a digital market for buying and selling chili. The digital market can accelerate the process of chili transactions throughout Indonesia so that it can narrow the distribution chain. The application to be developed is a web-based marketplace called Tokocabai. Back-end development will use gRPC and GraphQL as an API service. Front-end development will use the Vue Js framework. Tokocabai application development uses extreme programming methods. The stages of extreme programming methods are planning, design, coding, testing, and release. The iteration that has been successfully carried out is twice with the number of user stories as many as 19. The number of user stories in iteration one is eight and the second iteration is 11. The feature categories that have been successfully implemented are auth, product catalog, profile, transaction, and product review.
Formal Verification of Ethereum Smart Contracts Using Isabelle/HOL

The concept of blockchain was developed with the purpose of decentralizing the trade of assets, suppressing the need for intermediaries during this process, as well as achieving a digital trust between parties. A blockchain consists in a public immutable ledger, constituted by chronologically ordered blocks such that each block contains records of a finite number of transactions. The Ethereum platform, that this paper builds upon, is implemented using a blockchain architecture and introduces the possibility of storing Turing complete programs. These programs, also known as smart contracts, can then be executed using the Ethereum Virtual Machine. Despite its core language being the EVM bytecode, they can also be implemented using a higher-level language that is later compiled to EVM, being Solidity the most used. Among its applications stand out decentralized information storage, tokenization of assets, and digital identity verification. In this paper we propose a method for formal verification of Solidity smart contracts in Isabelle/HOL. We start from the imperative language and big-step semantics proposed by Schirmer [23], and adapt it to describe a rich subset of Solidity, implementing it using the Isabelle/HOL proof assistant. Then, we describe the properties about programs using Hoare logic, and present a proof system for the language, for which results on soundness and (relative) completeness are obtained. Finally, we describe the verification of an electronic voting smart contract, which illustrates the degree of proof complexity that can be achieved using this method. Examples of smart contracts containing overflow and reentrancy vulnerabilities are also presented.
FOREIGN EXPERIENCE OF IMPLEMENTATION OF 3D MODELING AND RAPID PROTOTYPING FOR FORMING DIGITAL COMPETENCIES

The article is devoted to the use of 3D modeling and rapid prototyping in the process of teaching schoolchildren abroad. The relevance of this approach is shown on the example of the UK, Singapore, Japan and the USA, where the implementation of additive technologies is becoming one of the priority areas of education development. Various formats of using 3D modeling in the educational process are considered. The potential of 3D modeling and prototyping as an object of study and a teaching tool, as a tool to increase the involvement of schoolchildren and their motivation is analyzed. The methodological techniques used in foreign pedagogical practice of using 3D technologies are considered. Difficulties arising in the implementation of 3D modeling and prototyping in the educational process are indicated. The prospects for further research in this direction are shown. They are associated with the need for a more detailed study, understanding and systematization of foreign experience in order to further adapt to the conditions of the Russian school and develop, on its basis, original author's methods that are most effective for the formation of digital competencies of students.
Implementasi Metode Ekstraksi Fitur Gabor Filter dan Probablity Neural Network (PNN) untuk Identifikasi Kain Tapis Lampung

Tapis Fabric is a traditional clothing of the people of Lampung in the form of a shawl cloth or a sarong made of woven cotton thread with various motifs and ornaments, silver thread or gold thread by embroidered or punched. The pattern of filter cloth is quite complex, unlike the pattern of fabric in general, with its own uniqueness that has become the culture of Lampung society until now. This filter cloth will be investigated by identifying the three types of filter cloth, namely Sasab, Bintang Perak and Gunung Beradu and see the results of its identification. The method used to identify is by combining the Gabor Filter feature extraction method which has frequency and orientation parameters and Probability Neural Network classification methods. Previously, the combination of these two methods was used to identify objects with simple patterns. The results are quite good, such as detecting faces, leaf patterns, and other simple patterns. This research is expected to get maximum identification results on the filter cloth even though it has a pattern that is not simple and will be used as a research report to determine the suitability of the method used for the filter object.
Identifying Bots in Social Networks Using the Example of LiveJournal

Social networks have firmly entered the lives of billions of global Internet users worldwide. They communicate in social networks, play online games, make purchases, organise online events — exchange content from all walks of life [1, 2]. The most popular and well-known services in Russia are Vkontakte (vk.com), Youtube.com, Facebook.com, Odnoklassniki (Ok.ru), etc. The interfaces of such platforms allo — fake accounts. In this paper, we propose an approach to detect bots using the LiveJournal social network as an example. For this, we investigated the characteristics of the user’s egograph and performed a comparative analysis of the results of the classification algorithms.
Optimal Design of Fuzzy Logic Systems Through a Chicken Search Optimization Algorithm Applied to a Benchmark Problem

In this paper an implementation of the CSO (Chicken Search Optimization) algorithm in benchmark problems is presented. CSO algorithm is used on solving the problem to find the optimal distribution on the Membership Functions (MFs) in the Type-1 Fuzzy Logic System (T1FLS) applied to fuzzy controller specifically for the water tank problem. Optimization in the structure and parameters for designing for a fuzzy tracking benchmark controller is presented. An efficiently CSO algorithm for the optimization in Fuzzy Logic Controllers (FLC) is presented. When level of noise is added in the model the CSO shows an excellent development. CSO algorithm shows better results when is compared with others metaheuristic in the simulation results for this benchmark control problem.
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization

Brittle optimization has been observed to adversely impact model likelihoods for regression and VAEs when simultaneously fitting neural network mappings from a (random) variable onto the mean and variance of a dependent Gaussian variable. Previous works have bolstered optimization and improved likelihoods, but fail other basic posterior predictive checks (PPCs). Under the PPC framework, we propose critiques to test predictive mean and variance calibration and the predictive distribution's ability to generate sensible data. We find that our attractively simple solution, to treat heteroscedastic variance variationally, sufficiently regularizes variance to pass these PPCs. We consider a diverse gamut of existing and novel priors and find our methods preserve or outperform existing model likelihoods while significantly improving parameter calibration and sample quality for regression and VAEs.
Modeling Interactive Smart Spaces

The Internet of Things (IoT) enables the creation of Interactive Smart Spaces (ISSs) where different types of digital devices are integrated in the ambient or embedded in physical objects, and can sense human actions to control equipment, modify environmental parameters, or create multi-sensory effects. These IoT-enhanced interactive systems can support human activities in different contexts, e.g., education, entertainment, home assistance, rehabilitation, to name a few. This paper explores a human-centered perspective in the design of ISSs, which takes into account some salient characteristics of these systems and introduces new conceptual modeling issues going beyond representing hardware, software, and connectivity features of IoT devices. Interaction in particular emerges as a crucial modeling dimension, needed to capture the interplay occurring not only among multiple connected IoT devices but also among the users and the materials or the spaces embedding such devices. The paper presents a novel conceptual model for Interactive Smart Spaces and exemplifies the identified abstractions through a case study, modelling a complex ISS for children’ play and learning that has been installed at two local schools and two therapeutic centers in our country.
Automatic speech recognition in computer-assisted language learning for individual learning in speaking

This study is intended to help English as a Foreign Language (EFL) learners in Indonesia to reduce their anxiety level while speaking in front of other people. This study helps to develop an atmosphere that encourages students to practice speaking independently. The interesting atmosphere can be obtained by using Automatic Speech Recognition (ASR) where every student can practice speaking individually without feeling anxious or pressurized, because he/she can practice independently in front of a computer or a gadget. This study used research and development design as it tried to develop a product which can create an atmosphere that encourages students to practice their speaking. The instrument used is a questionnaire which is used to analyze the students’ need of learning English. This study developed a product which utilized ASR technology using C# programming language. This study revealed that the product developed using ASR can make students practice speaking individually without feeling anxious and pressurized.
Pull out all the stops: Textual analysis via punctuation sequences

Whether enjoying the lucid prose of a favorite author or slogging through some other writer's cumbersome, heavy-set prattle (full of parentheses, em dashes, compound adjectives, and Oxford commas), readers will notice stylistic signatures not only in word choice and grammar, but also in punctuation itself. Indeed, visual sequences of punctuation from different authors produce marvelously different (and visually striking) sequences. Punctuation is a largely overlooked stylistic feature in "stylometry", the quantitative analysis of written text. In this paper, we examine punctuation sequences in a corpus of literary documents and ask the following questions: Are the properties of such sequences a distinctive feature of different authors? Is it possible to distinguish literary genres based on their punctuation sequences? Do the punctuation styles of authors evolve over time? Are we on to something interesting in trying to do stylometry without words, or are we full of sound and fury (signifying nothing)?