Reviewerly/exHarmony huggingface dataset:
Simulation Platform for Autonomous Aerial Manipulation in Dynamic Environments

The aerial manipulator (AM) is a systematic operational robotic platform in high standard on algorithm robustness. Directly deploying the algorithms to the practical system will take numerous trial and error costs and even cause destructive results. In this paper, a new modular simulation platform is designed to evaluate aerial manipulation related algorithms before deploying. In addition, to realize a fully autonomous aerial grasping, a series of algorithm modules consisting a complete workflow are designed and integrated in the simulation platform, including perception, planning and control modules. This framework empowers the AM to autonomously grasp remote targets without colliding with surrounding obstacles relying only on on-board sensors. Benefiting from its modular design, this software architecture can be easily extended with additional algorithms. Finally, several simulations are performed to verify the effectiveness of the proposed system.
RESPONSIBILITY OF BUSINESS ACTIVITIES IN ELECTRONIC TRANSACTIONS THROUGH THE INTERNET SITE VIEWING FROM THE LEGAL PERSPECTIVE OF CONSUMER PROTECTION

At first the internet network could only be used by the educational environment (colleges) and research institutions. Then in 1995, the new internet can be used for the public. With the application can make it easier for people to access information on the internet. After the opening of the internet for the public interest, more and more business applications have sprung up on the internet. Users of electronic systems, also known as consumers, can quickly find out about information developments in various parts of the world. By relying solely on search engines such as Google and Yahoo, electronic system users around the world have easy internet access to various kinds of information. Compared to books and libraries, the internet symbolizes the spread (decentralization), knowledge (knowledge) of information and data to the extreme. The internet can also be used by the government in providing public services. The internet is the easiest means to meet the needs of finding information that consumers want.
Characteristics of Chinese Translation of Emily Dickinson's Poetry: A Big-Data Analysis Based on Nine Translations

Using a program written in python language to conduct a big-data statistical analysis of poetic form and vocabulary use of nine Chinese translations of Emily Dickinson's poetry, it is found that the current Chinese translations deviate significantly from the original in terms of poetic form in which deviation of punctuation is greater than that of stanzas and lines, and no translation completely retains formal characteristics of the original. Different characteristics in the use of vocabulary can be found in different translations. Some translations have too many additions and deletions, which makes language content deviate from the original too much. In translating Dickinson's poetry, the form and content of the original text should be faithfully translated. In particular, dashes of the original text should be kept completely, reduce the manifestation of the translator in the translation and avoid addition and deletion in translating.
Remittances and Output Volatility: The Role of Financial Development

This paper examines the impact of remittances on output volatility through the channel of financial development using data for 158 countries from 1971 to 2017. We estimate the role of financial development by looking at multiple features of financial institutions, such as depth, access and efficiency. We used multiple indicators as a proxy of financial development in the remittance-output volatility nexus and employed System Generalized Method of Moments (GMM) and Fixed Effects Instrumental Variable (FE- IV) models. Our findings are robust across specifications. We find a significant positive impact of all indicators of financial development on the remittance-output volatility relationship. The findings suggest that multifaceted financial development is needed for the effective management of output volatility through remittance inflows.
Clustering structure for species sampling sequences with general base measure

We investigate the clustering structure of species sampling sequences $(ξ_n)_n$, with general base measure. Such sequences are exchangeable with a species sampling random probability as directing measure. The clustering properties of these sequences are interesting for Bayesian nonparametrics applications, where mixed base measures are used, for example, to accommodate sharp hypotheses in regression problems and provide sparsity. In this paper, we prove a stochastic representation for $(ξ_n)_n$ in terms of a latent exchangeable random partition. We provide explicit expression of the EPPF of the partition generated by $(ξ_n)_n$ in terms of the EPPF of the latent partition. We investigate the asymptotic behaviour of the total number of blocks and of the number of blocks with fixed cardinality in the partition generated by $(ξ_n)_n$.
Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking

Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in isolation, we propose the LSTM-Interleaved Transformer which incorporates cross-document interactions for improved multi-hop ranking. The LIT architecture can leverage prior ranking positions in the re-ranking setting. Our model is competitive on the current leaderboard for the TextGraphs 2020 shared task, achieving a test-set MAP of 0.5607, and would have gained third place had we submitted before the competition deadline. Our code implementation is made available at https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020
Slow-Growing Trees

Random Forest's performance can be matched by a single slow-growing tree (SGT), which uses a learning rate to tame CART's greedy algorithm. SGT exploits the view that CART is an extreme case of an iterative weighted least square procedure. Moreover, a unifying view of Boosted Trees (BT) and Random Forests (RF) is presented. Greedy ML algorithms' outcomes can be improved using either "slow learning" or diversification. SGT applies the former to estimate a single deep tree, and Booging (bagging stochastic BT with a high learning rate) uses the latter with additive shallow trees. The performance of this tree ensemble quaternity (Booging, BT, SGT, RF) is assessed on simulated and real regression tasks.
Parallel sequence tagging for concept recognition

Background: Named Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts. In a traditional concept-recognition pipeline, these tasks are combined in a serial way, which is inherently prone to error propagation from NER to NEN. We propose a parallel architecture, where both NER and NEN are modeled as a sequence-labeling task, operating directly on the source text. We examine different harmonisation strategies for merging the predictions of the two classifiers into a single output sequence. Results: We test our approach on the recent Version 4 of the CRAFT corpus. In all 20 annotation sets of the concept-annotation task, our system outperforms the pipeline system reported as a baseline in the CRAFT shared task 2019. Conclusions: Our analysis shows that the strengths of the two classifiers can be combined in a fruitful way. However, prediction harmonisation requires individual calibration on a development set for each annotation set. This allows achieving a good trade-off between established knowledge (training set) and novel information (unseen concepts). Availability and Implementation: Source code freely available for download at https://github.com/OntoGene/craft-st. Supplementary data are available at arXiv online.
IMPLEMENTASI KEBIJAKAN INTERDEPENSI FAKTA-NILAI DALAM PROSES PENGINPUTAN DATA POKOK PENDIDIKAN (DAPODIK) DI SEKOLAH SATUAN PENDIDIKAN KERJASAMA GREEN SCHOOL BALI

Indonesia's national education planning program includes an integrated data collection called DAPODIK, or Basic Education Data, which serves as the main data source for the country's education system. Educational planning will fail if it is not carried out by professionals. In this dapodik system, all data required for the application is complete, original (legal), responsible, and up-to-date. improve education in Indonesia and save unnecessary expenses so that these savings can be used to support education programs that are more in line with the needs of the Indonesian people, using data from this dapodik application, the central government will be able to make decisions about staff ratios in education, professional allowances educators, and school operational costs.
Appointment of comprehensive forensic examinations in case of the study of digital traces in the investigation of economic crimes

Due to the ongoing digitalization of the economy, the study of digital traces in the framework of forensic examinations in criminal cases in the field of economic activity is one of the most relevant topics in forensic expert science. The purpose of this study is to identify and analyze the features of the production of complex forensic economic and computer–technical expertise in the study of digital traces, to determine the limits of competence of an expert economist in the expert study of digital traces. Within the framework of the study, the author set the following tasks: to analyze the types of crimes in the field of economics and the ways of their commission for the possibility of the formation of digital traces during the implementation of these offenses, to determine the types of digital traces characteristic of crimes in the field of economic activity, to determine the appropriateness of the appointment of comprehensive examinations, to analyze an example of expert practice for the use of special economic knowledge and special knowledge in the field of forensic computer-technical expertise.The author came to the conclusion that the appointment of a comprehensive forensic computer-technical and economic expertise is unnecessary in most cases, since the simultaneous joint participation of an expert economist and an expert in the field of IT technologies in writing an opinion and formulating a general conclusion is not necessary in most cases when investigating crimes in the field of economic activity. As a rule, the participation of a specialist in the field of IT technologies is sufficient to withdraw computer information from the carrier and then provide this information to an expert economist for research. At the same time, an expert economist is able to work independently with software products and computer files, for the study of which there is enough well-known knowledge.
Ethics without ideology or a new theory of synderesis

The article analyzes the problem of the correlation of morality and ideology. The author believes that the distinction between morality and ideology can be made only at the level of their genealogy: if ideology is a socio-cultural product, then morality is rooted in human nature, in "moral intuitions", which medieval scholastic philosophy called synderesis. In modern ethics, synderesis can be identified with the neurophysiological prerequisites of morality. Opponents of this approach argue that, firstly, it excludes free will, and secondly, it does not explain the content of moral values and norms in any way. The author of the article criticizes these statements and offers his original theory of synderesis. The article draws an analogy between ethics and intuitionistic mathematics, which considers mathematical objects as the results of intellectual construction based on initial intuitions. Ethical objects should also be considered as constructions based on innate intuitions. Moral intuitions do not exclude free will, since they rely on rationality inherent in our nature, the main property of which is the ability to make decisions regardless of external stimuli and internal states. In addition, these intuitions influence the content of our norms, as they are the "operators" of their construction and are included in the "final product".
RISK OF INJURY COMPARISON BETWEEN REGULAR AND KNUCKLE PUSH-UP BASED ON KINEMATIC PARAMETER ANALYSIS

Nowadays, it is difficult to access physical exercise facilities because people spent most of their time at work. Therefore, physical exercises that could be done with less equipment and space are more likely needed, and a push-up is one of them. There are several kinds of push-up which two are regular push-up and knuckle push-up. Both of them differ in hand posture, which carries a different risk of injury. In this research, kinematic parameters of joints on both of the push-up method that was acquired from optical motion capture using 120 fps GoPro camera and six markers were compared to identify which method would lead to a higher risk of injury. From analyzing the elbow joints acceleration, it was observed that the maximum value of both the linear and angular acceleration of knuckle push-up was larger. This means that the elbow joints suffered more load during knuckle push-up, leading to a higher risk of injury.
Sample selection for Face Identification System

The face identification problem is the task of identifying incoming users from their face images.The applications of this task include automating user identification at the building entrance.Consequently, many proposed algorithms are proposed for the given task ranging from handcraft computational models to deep learning models.This paper utilizes the existing effective algorithms and proposes the template selection strategy to enhance its recognition performance when the enrollment of multiple samples for each user is proposed.Besides, the paper investigates the effect of image quality on the recognition performance of the system and the efficacy of the proposed template selection strategy when applied in such a situation.Experiments are performed on the in-house dataset collected from the Thai population --to evaluate the empirical system performance when the system is deployed in Thailand to automatically identify the user at the building gate --as well as on the LFW public dataset consisting of 13,000 face images of 5,749 individuals with multiple ethnicities.The results show that, with the proposed sample selection, the identification error on the in-house dataset for the close-set identification decreased from 3.11 to 2.46% at 0.11% and 0.06% FMR.For the open-set identification, the FNMR decreased from 9.36% to 5.40% at 6.20% and 3.23% FAR, respectively.In addition, the experiments on the LFW dataset have also demonstrated the efficacy of the proposed system consisting of the proposed sample selection method and the selection of face recognition modules.That is, the performance improvement is noticeable as compared to the baseline where the sample selection method is not deployed and to the previous work when multiple samples are used for enrollment.
Stakeholder involvement in the curricula modernisation through a virtual foresight laboratory

Introduction . Involvement of stakeholders into curricula modernisation in modern educational institutions has always been significant due to the existing misbalance between the social requirements and outdated educational materials. Aim . The aim of the research is to analyse the three dimensions of stakeholder involvement into curricula modernisation in education on the basis of Kostanay regional university named after A.Baitursynov (Kostanay, Kazakhstan): maximal involvement, partial involvement, and formal participation. Methodology and research methods . The research is based on the 10 sessions 1.5 h each organised on the platform of the virtual foresight laboratory (VFL) specially designed for the realisation of maximal involvement of the participants. Two in-depth face-to-face interview sessions with the focus groups (FG) participants (1 entry interview and 1 post-sessional interview) were organised and processed. Results . The results of the sessions with 6 stakeholders represented focus group 1 that demonstrated the new maps of the future curriculum and modernised the text of the existing educational curriculum in terms of the goals, content and competencies requirements. The highlights include the following results of the study: the FG1 participants indicated their synergy in foreseeing the future through mapping, trends design and discussions in VFL; there was the increase of the learning gain among FG1 stakeholders in terms of the structure of the curriculum, educational programme, types of competencies, foresight research; the personal growth of stakeholder involvement into curricula modernisation was directly observed, as their gained experience was reflected in further modernisation of educational programmes of other majors. Scientific novelty . The research presents the key theoretical role of virtual foresight laboratory in curricula modernisation, basic theoretical issues on design of the future maps and foresight planning. Practical significance . Practical outcomes in curricula modernisation can be adapted to any educational institutions aimed at perspective planning and foresight applications.
Guessing about Guessing: Practical Strategies for Card Guessing with Feedback

In simple card games, cards are dealt one at a time and the player guesses each card sequentially. We study problems where feedback (e.g. correct/incorrect) is given after each guess. For decks with repeated values (as in blackjack where suits do not matter) the optimal strategy differs from the "greedy strategy" (of guessing a most likely card each round). Further, both optimal and greedy strategies are far too complicated for real time use by human players. Our main results show that simple heuristics perform close to optimal.
A comprehensive study on three pillars of cryptocurrency and blockchain technology

Bitcoin and crypto currencies are only two of the latest platitudes in financial markets.Cryptocurrencies have recently received a lot of media attention, which is one of the reasons for their present popularity.This study conceptually discusses the three pillars, cryptography, peer to peer network and proof of work, important to cryptocurrencies, due to their ever-increasing popularity as new financial instruments.The study conceptually discusses the chronology of money till cryptocurrency and their middle events.It further discusses the three necessary phenomena of cryptocurrency trading and the reason of its development as most trusted mode of payment among young population.
Bumpy Appearance Editing of Object Surfaces in Digital Images

Objects in nature have diverse appearances, and appearance is one of the elements constituting the unique visual aspect of an object.However, previous studies have shown that when an object is represented as a digital image, its appearance can change compared to the real object depending on the material.In this study, the focus was on the "bumpiness" of the object surface, whereby a method was proposed to edit the bumpiness of the object in the image.First, the statistics obtained from images were analyzed in relation to the sensibility value of the bumpiness perceived by humans.Since the statistics on the original image could not fully explain the perception of bumpiness, analyses were conducted on multiscale images.The results suggested that the mean, standard deviation, and top, defined as the average of the luminance values within the top 10 [percentile], were highly influential as cues for bumpiness perception.The results also indicated that the components in the range of 5-65 [cycles per image-width] were highly influential.Based on these analysis results, a method is proposed to edit bumpiness perception by modulating components in the low and medium frequency bands.The effectiveness of the proposed method is demonstrated by image modulation experiments on objects of various materials.
Economics and Environment Assessment for Design and Implementation of An Automatic Solar Tracking System

Our climate system is undergoing severe changes because of a global economy based on fossil fuels and rising greenhouse gas emissions.Furthermore, the use of non-renewable energy will be restricted in the near future.As a result, the use of renewable energy has begun to assist our environment with clean energy.Solar energy is frequently employed since it is a renewable and cost-effective source of power.The objective of this project is to design and implement an automatic solar tracking system by using MATLAB/Simulink and Proteus software to get the maximized power generated from the PV system.In this project, the inventive of an automated solar tracker benefitting from light dependent resistors added with ultrasonic anemometer as a wind sensor and a rotary position sensor to achieve a fully functioning an automatic solar tracker.The values of irradiance and power generated will be discussed on this paper based on the implementation of the Matlab and Proteus simulation.
Adversarial Grammatical Error Generation: Application to Persian Language

Grammatical error correction (GEC) greatly benefits from large quantities of high-quality training data. However, the preparation of a large amount of labelled training data is time-consuming and prone to human errors. These issues have become major obstacles in training GEC systems. Recently, the performance of English GEC systems has drastically been enhanced by the application of deep neural networks that generate a large amount of synthetic data from limited samples. While GEC has extensively been studied in languages such as English and Chinese, no attempts have been made to generate synthetic data for improving Persian GEC systems. Given the substantial grammatical and semantic differences of the Persian language, in this paper, we propose a new deep learning framework to create large enough synthetic sentences that are grammatically incorrect for training Persian GEC systems. A modified version of sequence generative adversarial net with policy gradient is developed, in which the size of the model is scaled down and the hyperparameters are tuned. The generator is trained in an adversarial framework on a limited dataset of 8000 samples. Our proposed adversarial framework achieved bilingual evaluation understudy (BLEU) scores of 64.5% on BLEU-2, 44.2% on BLEU-3, and 21.4% on BLEU-4, and outperformed the conventional supervised-trained long short-term memory using maximum likelihood estimation and recently proposed sequence labeler using neural machine translation augmentation. This shows promise toward improving the performance of GEC systems by generating a large amount of training data.