Reviewerly/exHarmony huggingface dataset:
Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation

In this paper, a robust central difference Kalman filter is proposed to address the process uncertainty and non-Gaussian measurement noise induced by the vehicle's severe maneuver and abnormal measurements in MEMS-SINS/GNSS integrated navigation system.Compared with the state-of-the-art noise distribution based robust filter, in the proposed filter, the process uncertainty and measurement uncertainty are simultaneously suppressed based on a new constructed cost function, which is independent of noise distribution and more insensitive to the non-Gaussian noise.To be specific, the statistical linearization approach is first presented to derive a linear-like regression model.Then, by resorting to the innovation orthogonal theory and Cholesky triangular decomposition, the fading factor of cost function is adaptively and robustly determined in the process of iteration, where the filtering performance and the stability of the algorithm under the condition of process uncertainty are extremely enhanced.Furthermore, the correntropy using the mixture of two Gaussian functions as the kernel function is incorporated into the cost function to prevent the non-Gaussian measurement noise.Our extensive simulation and car-mounted experiment demonstrate that the proposed filter can achieve higher estimation accuracy and better robustness as compared with the related state-of-the-art methods.
Identity-Based Partially Blind Signature Scheme: Cryptanalysis and Construction

Blind signature is a special type of digital signature, the signer cannot see the specific content signed.However, blindness may cause users to abuse their rights.Partial blind signature allows the signer to embed pre-negotiated public information in the blind signature without losing blindness, which can prevent users from abusing their rights.Islam et al. presented an identity-based partial blind signature scheme and claimed that it is provable secure.However, in this paper we proved that the scheme is vulnerable against the tampering attacks with public information.We then proposed a new identity-based partial blind signature scheme, showed the security proofs under the assumption that the elliptic curve discrete logarithm problem (ECDLP) is difficult.The new scheme does not use pairing operations and enjoys less computation cost.
Demonstration Guided Actor-Critic Deep Reinforcement Learning for Fast Teaching of Robots in Dynamic Environments

Using direct reinforcement learning (RL) to accomplish a task can be very inefficient, especially in robotic configurations where interactions with the environment are lengthy and costly. Instead, learning from expert demonstration (LfD) is an alternative approach to gain better performance in an RL setting, which also greatly improves sample efficiency. We propose a novel demonstration learning framework for actor-critic based algorithms. Firstly, we put forward an environment pre-training paradigm to initialize the model parameters without interacting with the target environment, which effectively avoids the cold start problem in deep RL scenarios. Secondly, we design a general-purpose LfD framework for most of the mainstream actor-critic RL algorithms that include a policy network and a value function like PPO, SAC, TRPO, A3C. Thirdly, we build a dedicated model training platform to perform the human-robot interaction and numerical experimentation. We evaluate the method in six Mujoco simulated locomotion environments and our robot control simulation platform. Results show that several epochs of pre-training can improve the agent’s performance over the early stage of training. Also, the final converged performance of the RL algorithm is also boosted by external demonstration. In general the sample efficiency is improved by 30% with the proposed method. Our demonstration pipeline makes full use of the exploration property of the RL algorithm, which is feasible for fast teaching robots in dynamic environments.
Automatic Document Sketching: Generating Drafts from Analogous Texts

The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document.However, the high branching factor inherent to text generation impedes the ability of even the strongest language models to offer useful editing suggestions at a more global or document level.We introduce a new task, DOCUMENT SKETCHING, which involves generating entire draft documents for the writer to review and revise.These drafts are built from sets of documents that overlap in form -sharing large segments of potentially reusable text -while diverging in content.To support this task, we introduce a Wikipediabased dataset of analogous documents and investigate the application of weakly supervised methods, including use of a transformer-based mixture of experts, together with reinforcement learning.We report experiments using automated and human evaluation methods and discuss relative merits of these models.
A Multilabel Approach to Portuguese Clinical Named Entity Recognition

Objectives: Clinical Named Entity Recognition is a critical Natural Language Processing task, as it could support biomedical research and healthcare systems. While most extracted clinical entities are based on single-label concepts, it is very common in the clinical domain entities with more than one semantic category simultaneously. This work proposes BERT-based models to support multilabel clinical named entity recognition in the Portuguese language. Methods: For the experiment, we used the Label Powerset method applied to the multilabel corpus SemClinBr. Results: We compare our results with a Conditional Random Fields baseline, reaching +2.1 in precision, +11.2 in recall, and +7.4 in F1 with a clinical-biomedical BERT model (BioBERTpt). Conclusion: We achieved higher results for both exact and partial metrics, contributing to the multilabel semantic processing of clinical narratives in Portuguese.
¡Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline

We construct the first ever multimodal sarcasm dataset for Spanish.The audiovisual dataset consists of sarcasm annotated text that is aligned with video and audio.The dataset represents two varieties of Spanish, a Latin American variety and a Peninsular Spanish variety, which ensures a wider dialectal coverage for this global language.We present several models for sarcasm detection that will serve as baselines in the future research.Our results show that results with text only (89%) are worse than when combining text with audio (91.9%).Finally, the best results are obtained when combining all the modalities: text, audio and video (93.1%).
Perspective of blockchain technology application in corporate governance: Recent trands

The subject of the present paper is the perspective of blockchain technology application based on the experience of the Russian and foreign companies, ﬁnancial institutions, and public authorities. The purpose of the article is to study trends, identify areas of application, and analyze the risks and beneﬁts of blockchain technology application in corporate governance. The author used the methods of generalization, synthesis, comparative analysis of the approaches applied to determine the role played by blockchain in corporate governance, studied appropriate recent scientiﬁc publications, and also conducted the comparative analysis of the corporate governance goals and key characteristics of the above technology. The author has exhaustively analyzed the prospects of blockchain technology implementation in corporate governance taking into account the spread of the COVID-19 pandemic, which forms the novelty of the present paper. The author proposes to follow the below recommendations pertaining to certain issues of implementing blockchain technology into entity’s operations: to include the issue of suitability of blockchain technology integration into the entity’s operations in the meeting of the Board of Directors’ agenda, develop and approve at the Board level in-house documents to regulate the technology application within the entity as well as the strategy of its using to be followed by its integration into the entity’s general business strategy, approve the entity’s risk-appetite to use the technology within the frameworks of the overall entity’s risk management strategy, analyze the impact it may cause on the entity’s activities at the Board level and also ensure improving the employees’ training and competencies with respect to using blockchain technology. The author concludes that nowadays blockchain technology will be most intensively used in areas where it is a more effective alternative to existing systems in their current state. The conclusions and results obtained may be used in the course of developing Russian corporate governance practice as well as in the analytical and practical work performed by the public authorities and the business community.
Perbedaan Prestasi Belajar Mahasiswa Ditinjau dari Jalur Seleksi Masuk Perguruan Tinggi

The problem brought in this study is how the difference in student achievement is reviewed from the college entrance pathways. Based on that problem, this study aimed at determining whether or not there are differences in student achievement based on college entrance pathways. This study used a quantitative research approach, which included several hypothesis tests such as normality test, homogeneity test, anova test, and kruskall test. This research was conducted in 2020 with a sample of 1,215 students of IAIN Jember. The results showed that the Grade Point Average (GPA) of UMPTKIN students was significantly different, which was greater than the GPA of SPAN students and the GPA of Mandiri students class 2016-2017. For students of class 2017-2018, the GPA of UMPTKIN students was greater than the GPA of Mandiri students. For 2018-2019 students, the GPA of SPAN students was greater than the GPA of UMPTKIN students, and the GPA of UMPTKIN students was greater than that of Mandiri students. Whereas for 2019-2020 students, there was no significant difference between the 3 groups of students. From the data of 4 batches of students, it can be seen that students who entered through the Mandiri pathway have low achievement compared to the others. Meanwhile, students who entered through UMPTKIN and SPAN always compete to achieve their achievements.
Evaluating Medical Lexical Simplification: Rule-Based vs. BERT

Lexical simplification (LS) can decrease the communication gap between medical experts and laypeople by replacing medical terms with layperson counterparts. In this paper, we present: 1) a rule-based approach to LS using a consumer health vocabulary, and 2) an unsupervised approach using BERT to generate word candidates. Human evaluation shows that the unsupervised model performed better for simplicity and grammaticality, while the rule-based method was better at meaning preservation.
Usability Study of Enhanced Salat Learning Approach using Motion Recognition System

Salat learning is one of the most important processes for every Muslim. The current learning approach requires the teacher or expert to be present in the learning session, which is time and resource-consuming. Previous researchers use both wearable and non-wearable sensors to recognize salat movement. They focus on specific salat movement rather than complete salat movement cycle. This paper present a motion recognition system to enhance salat learning experience. The system helps users recognize the complete salat movements cycle based on salat law by using a multisensor setup for better tracking capability. Three evaluations are conducted to validate the system's performance and its contribution. The first evaluation is to measure success score in recognition accuracy and identify user error. The second evaluation is conducted to compare the proposed system with the traditional-based methodology, and the last evaluation focuses on the user experience and acceptance of the proposed system. The result from performance evaluation shows the system has high accuracy in recognizing salat movement. There is a significant difference in the error rate and success score when comparing the learning methodology. However, users provide positive feedback based on the survey conducted after using the proposed system.
Continuous kannada speech segmentation and speech recognition based on threshold using MFCC And VQ

&lt;p&gt;&lt;span&gt;Continuous speech segmentation and its recognition is playing important role in natural language processing. Continuous context based Kannada speech segmentation depends on context, grammer and semantics rules present in the kannada language. The significant feature extraction of kannada speech signal for recognition system is quite exciting for researchers. In this paper proposed method is divided into two parts. First part of the method is continuous kannada speech signal segmentation with respect to the context based is carried out by computing average short term energy and its spectral centroid coefficients of the speech signal present in the specified window. The segmented outputs are completely meaningful segmentation for different scenarios with less segmentation error. The second part of the method is speech recognition by extracting less number Mel frequency cepstral coefficients with less number of codebooks using vector quantization .In this recognition is completely based on threshold value.This threshold setting is a challenging task however the simple method is used to achieve better recognition rate.The experimental results shows more efficient and effective segmentation with high recognition rate for any continuous context based kannada speech signal with different accents for male and female than the existing methods and also used minimal feature dimensions for training data.&lt;/span&gt;&lt;/p&gt;
A Comprehensive Survey On Cloud Computing Simulators

Cloud Computing is one of the upcoming technologies which has gotten the attention of many researchers and investor. But cloud computing still faces challenges because it is not economical and impractical for research institutions and industries to set up a physical cloud for research and experiments on it (cloud computing). Due to this, the researchers have chosen to test their contributions with simulators. Therefore, the purpose of this study is to perform a survey on existing cloud simulators. These cloud simulators aid in modeling cloud application through the creation of virtual machine, data Centre, and other thing which can be easily added and configured to it in order to provide stress free analysis. Till this present time, many cloud simulators with various features have been proposed and available for use. In this paper a comprehensive study has been performed on major cloud simulators by highlighting their features, strength and weakness through analysis. After which comparative analysis was done on the simulation, from the study, none of the simulators have the feature to simulate mobile cloud computing issues. This study has not been published anywhere else.
Problems of Human Secure Interaction with the Internet Space

The paper addresses the problem of ensuring secure human interaction with the Internet space by cognitive assistants.Psychological prerequisites to cognitive assistant creation are given consideration to, and the requirements the assistants should meet are formulated and substantiated.Feasibility studies on technological aspects of cognitive assistant creation are carried out.The methodology is proposed for the development of cognitive assistants with associative thinking.It involves the development of conceptual models of the human-Internet interaction process based on stream recurrent neural networks with controlled elements taking into account the psychological, physiological, and information aspects of the interaction.A series of experiments with recurrent neural networks was conducted.
A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support

Using machine learning predictive models for clinical decision support has great potential in improving patient outcomes and reducing health care costs. However, most machine learning models are black boxes that do not explain their predictions, thereby forming a barrier to clinical adoption. To overcome this barrier, an automated method was recently developed to provide rule-style explanations of any machine learning model’s predictions on tabular data and to suggest customized interventions. Each explanation delineates the association between a feature value pattern and an outcome value. Although the association and intervention information is useful, the user of the automated explaining function often requires more detailed information to better understand the patient’s situation and to aid in decision making. More specifically, consider a feature value in the explanation that is computed by an aggregation function on the raw data, such as the number of emergency department visits related to asthma that the patient had in the prior 12 months. The user often wants to rapidly drill through to see certain parts of the related raw data that produce the feature value. This task is frequently difficult and time-consuming because the few pieces of related raw data are submerged by many pieces of raw data of the patient that are unrelated to the feature value. To address this issue, this paper outlines an automated lineage tracing approach, which adds automated drill-through capability to the automated explaining function, and provides a roadmap for future research.
A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks

In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets.While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions.We address this gap by (i) collecting a multilingual social media corpus containing customer service conversations (865k tweets), (ii) comparing various pipelines of pretraining and finetuning approaches, (iii) applying them on 5 different end tasks.We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings. 1
Pulse deinterleaving based on fusing PDWs and PRI extraction process for radar-assisted edge devices considering computational costs

Abstract Deinterleaving or radar pulse separation is a very important goal in terms of radar sources for identifying and implementing electronic warfare systems. In order to separate radar pulses, parameters measured by electronic warfare receivers such as electronic warfare support measures (ESM) and electronic signals intelligence (ELINT) are used in pulse separation. This paper presents a multi-parameter improved method for separating the pulse sequence of radar signals based on time of arrival (TOA) processing with sorting the other pulse descriptor words (PDW) parameters. In the proposed method, after extracting all the pulse repetition intervals (PRIs) based on TOA, the parameters of the angle of arrival, pulse width and carrier frequency (RF) are being used in pulse sorting to separate the received interleaved pulse sequences. The sequential difference histogram (SDIF) algorithm or cumulative difference histogram (CDIF) algorithm is used to extract all pulse repetition intervals. Also, in order to separate the sequence of the received pulses from all surroundings emitters, in addition to matching the potential PRI among the TOAs of the pulses and the similarity measurement in the other parameters of the pulse sequence (pulse sorting) have been used. This proposed algorithm is implemented in the integrated and complete design for deinterleaving of the radar pulses. The proposed method by considering low-cost computing sources considers a fast and low-complexity solution that can be used for edge-enabled distributed processors in aerial radar platforms as edge devices for military/combat unmanned aerial vehicles or networked missiles. The simulation results show that our method is completely effective.
Variational Quantum Chemistry Programs in JaqalPaq

We present example quantum chemistry programs written with JaqalPaq, a python meta-programming language used to code in Jaqal (Just Another Quantum Assembly Language). These JaqalPaq algorithms are intended to be run on the Quantum Scientific Computing Open User Testbed (QSCOUT) platform at Sandia National Laboratories. Our exemplars use the variational quantum eigensolver (VQE) quantum algorithm to compute the ground state energies of the H2, HeH+, and LiH molecules. Since the exemplars focus on how to program in JaqalPaq, the calculations of the second-quantized Hamiltonians are performed with the PySCF python package, and the mappings of the fermions to qubits are obtained from the OpenFermion python package. Using the emulator functionality of JaqalPaq, we emulate how these exemplars would be executed on an error-free QSCOUT platform and compare the emulated computation of the bond-dissociation curves for these molecules with their exact forms within the relevant basis.
Using Waterfall Method to Design Information System of SPMI STIMIK Sepuluh Nopember Jayapura

Abstract - Conventional filing of SPMI documents is a problem experienced by STIMIK Sepuluh Nopember Jayapura, archiving in this way can result in documents being lost or scattered, in addition to causing difficulties in finding documents during internal and external audits. This study aims to assist institutions in improving their filing systems, so that SPMI data or documents can be stored properly and easily obtained when needed. In designing the information system SPMI STIMIK Sepuluh Nopember Jayapura, using the waterfall method and data modeling using data flow diagrams, while in system testing using the blackbox method and user acceptance test. This research produces an SPMI information system that can be used to archive all types of documents owned by the internal quality assurance agency Based on table 2, as many as 86% of the answers of respondents who answered were very correct (SB), as many as 12% of respondents who answered correctly (B) and 2% answered doubtful or neutral.