Reviewerly/exHarmony huggingface dataset:
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration

Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks. Despite recent progress, efficient exploration in meta-RL remains a key challenge in sparse-reward tasks, as it requires quickly finding informative task-relevant experiences in both meta-training and adaptation. To address this challenge, we explicitly model an exploration policy learning problem for meta-RL, which is separated from exploitation policy learning, and introduce a novel empowerment-driven exploration objective, which aims to maximize information gain for task identification. We derive a corresponding intrinsic reward and develop a new off-policy meta-RL framework, which efficiently learns separate context-aware exploration and exploitation policies by sharing the knowledge of task inference. Experimental evaluation shows that our meta-RL method significantly outperforms state-of-the-art baselines on various sparse-reward MuJoCo locomotion tasks and more complex sparse-reward Meta-World tasks.
Gender Peculiarities Of Emoticons Usage In The Russian And German Internet Discourses

The article analyzes characteristic features of Russian and German Internet discourse with emoticons in Internet users' messages.The authors investigate the characteristic features of the usage of emoticons taking into account the gender of their senders and receivers belonging to two linguistic cultures.The relevance of studying the gender characteristics of Internet users' communicative behavior based on cross-cultural analysis is stipulated by the scientific community's growing interest in anthropological studies, which focus on the communication of a gender linguistic personality in close connection with the structure of the mentality.The studied material allows arguing that despite emoticons being able to independently convey emotions, they are often combined with a verbal statement in Russian and German Internet messages.Considering emoticons as the main means of expressing emotions in the Internet discourse, the authors concluded that emoticons can rightly be considered as innovative means of graphical presentation of ideas, expanding the possibilities of writing, compensating for its lacking means of expression compared with oral speech.This study also revealed some gender preferences of emoticons usage, e.g. the choice of different kinds of emoticons.The research results lead to the conclusion that female and male characteristics of Russian and German Internet users' types of discourse have more similarities than differences.The degree of similarity in the usage of emoticons is stipulated by many of sociocultural factors (senders' and receivers' statuses, including closeness to another person), and the revealed minor differences can be associated with the manifestation of the temperamental characteristics of these nations.
Reflections of Developments in Information Technologies to Internal Audit: Blockchain Technology and Continuous Auditing

In recent years, several revolutionary developments related to information and communication technologies have been carried out. Important advances regarding the rapid creation of digital infrastructure in every field have made the Internet an indispensable part of people’s lives. These developments have not only changed the life of humanity, but also the ways in which business and other processes are executed. Nowadays, these types of activities can easily be performed in the online environment. The advent of computers and the Internet has significantly changed the way things are done in businesses and how financial information is transmitted. The number of businesses that use the Internet to perform their activities and publish their financial reports is growing rapidly in real time. In this real-time reporting, continuous auditing has become a necessity in order to ensure the accuracy and reliability of the information provided. The transformation provided by information technologies provides transparency and ease of access to information. For this reason, “Continuous Auditing” gains importance against the risks and dangers that may arise in the operation of such systems. In addition to the positive aspects of the Internet, it has also caused risks such as fraud, security and data theft to arise. The increase in such risks and the rapid increase in the rate of realization has caused people to focus on solutions to these problems. For this purpose, blockchain technology which offers a high level of confidence has been developed. The purpose of this study is to explain the concepts of continuous auditing and blockchain technology, to indicate the importance and superiority of these concepts, and to explain their connections with internal audit and among themselves. Additional aims of this study include a correct introduction to the concepts of blockchain technology and continuous auditing, elimination of the deficiency in national literature related to these concepts, and a presentation of a wider perspective.
An Application of Fuzzy-VIKOR Method in Environmental Impact Assessment of the Boog Mine Southeast of Iran

Mining activities are one of the essential environmental challenges. Rating the environmental components (ECs) that affect by mining activities is a strategic guide for Environmental-Impact-Assessment (EIA). VlseKriterijumska- Optimizacija- I- Kompromisno- Resenje (VIKOR) method is developed as an efficient decision-making method to assess the impacts of the granite quarry Boog (in Southeast of Iran) on the environment. VIKOR method focuses on quantifying the effect of each impacting factor (IF) on each designed EC. This paper represents an evaluation method relying on fuzzy numbers in decision methods to carry out the lack of certainty and ambiguity from experts’ subjective knowledge and experience. Shannon entropy theory is used to adjust subjective weights defined by decision makers to objective weights. The results catched through ranking the R, S and Q indices. In this case, the Air quality (R= 0.05, S= 0.16, Q= -0.05) is available as the more important EC that affected by the mining activities contaminations. Compareing the results with standard matrix method confirm that the Air quality has been effected more than the other parameters with 33.63%. Fuzzy-VIKOR is a systematic approach, which can easily extend to deal with quantitative environmental analysis and other mining engineering selection problems.
Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks

This is the system description of the CA-SIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021.To address two shared challenges among those tasks, the colloquial text and the imbalance annotation, we apply customized pre-trained language models and propose various training strategies.Experimental results show the effectiveness of our system.Moreover, we got an F1-score of 0.87 in task 8, which is the highest among all participates.
Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering

Although Question-Answering has long been of research interest, its accessibility to users through a speech interface and its support to multiple languages have not been addressed in prior studies.Towards these ends, we present a new task and a synthetically-generated dataset to do Fact-based Visual Spoken-Question Answering (FVSQA).FVSQA is based on the FVQA dataset, which requires a system to retrieve an entity from Knowledge Graphs (KGs) to answer a question about an image.In FVSQA, the question is spoken rather than typed.Three sub-tasks are proposed: (1) speech-to-text based, (2) end-to-end, without speech-to-text as an intermediate component, and (3) cross-lingual, in which the question is spoken in a language different from that in which the KG is recorded.The end-to-end and cross-lingual tasks are the first to require world knowledge from a multi-relational KG as a differentiable layer in an end-to-end spoken language understanding task, hence the proposed reference implementation is called Worldly-Wise (WoW).WoW is shown to perform endto-end cross-lingual FVSQA at same levels of accuracy across 3 languages -English, Hindi, and Turkish.
Magnitude Estimation and Onsite Earthquake Early Warning using Cumulative Absolute Velocity in Taiwan

&amp;lt;p&amp;gt;Real-time magnitude determination is one of the critical issues for earthquake early warning (EEW). Magnitude determination may have saturation situation using initial seismic signals after an earthquake occurrence. Previous studies utilized eventual cumulative absolute velocity (eCAV) to determine magnitude up to 9.0 without any saturation. However, to determine eCAV will be too late for EEW application. In order to shorten time to obtain eCAV, 4,754 strong motion records from 64 events with M&amp;lt;sub&amp;gt;L &amp;lt;/sub&amp;gt;large than 5.5 in Taiwan are used to establish the relationship between eCAV and initial shaking parameters (initial CAV, initial cumulative absolute displacement, initial cumulative absolute integral displacement,&amp;amp;#160; P&amp;lt;sub&amp;gt;d&amp;lt;/sub&amp;gt; and&amp;amp;#160; &amp;amp;#964;&amp;lt;sub&amp;gt;c&amp;lt;/sub&amp;gt;)&amp;amp;#160;from 1 s to 20 s after P arrival. Our preliminary results show that eCAV can be estimated using initial shaking parameters. Logarithm linear correlation coefficients vary from 0.78 to 0.97 with standard deviations from 0.27 to 0.10 for time windows from 1 s to 20 s after P arrival. Eventually, we can timely estimate eCAV for magnitude determination as well as or on-site EEW purpose.&amp;lt;/p&amp;gt;
Towards Robustness of Text-to-SQL Models against Synonym Substitution

Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks. Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective.
Analysis of the Results of Using the Means of Organisation of Distance Learning when Training the Future Primary School Teachers under the Conditions of Quarantine

The study analyses the results of using software and hardware during distance learning. The basis for the study was the Khmelnytskyi Humanitarian-Pedagogical Academy and the Municipal Institution of Higher Education “Mykhailo Hrushevskyi Barskyi HumanitarianPedagogical College”. A survey of students who studied computer science in the 1st and 2nd year of the specialty “Primary Education” was conducted. This allowed studying the attitude of the future primary school teachers towards different means of distance learning, determining the conditions for organising distance learning for students of both institutions, comparing the results of teaching computer science for the future primary school teachers by distance and conventional forms. It was found that most students are positive about the use of the platforms offered by the institution and consider their opportunities sufficient for their learning. Adaptation to the new platforms and tools took several weeks. The most popular among the tools were Google Classroom, blog, cloud documents, video tutorials on YouTube, online tests, and more. Quite a large number of students underestimate the effectiveness of video conferencing and prefer other types of electronic materials. However, when explaining new material, sounding presentations increases the level of knowledge acquisition. The main problems that students face during distance learning are the lack of access to computer devices and the Internet, as well as a considerable number of tasks in various disciplines, which causes overload and deterioration of both students’ well-being and the quality of their education. The study of students’ grades in the discipline “Computer Science” in 2019 and 2020 made it possible to compare the results at the beginning of quarantine and at the end of the semester. This approach allows determining the effectiveness of the use of distance learning. The results indicate that during the quarantine there was no considerable deterioration in student grades. Statistical analysis confirmed the identity of the results of semester control in groups that studied in the conventional and distance form
Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning

Classification is a common task in machine learning. Random features (RFs) stand as a central technique for scalable learning algorithms based on kernel methods, and more recently proposed optimized random features, sampled depending on the model and the data distribution, can significantly reduce and provably minimize the required number of features. However, existing research on classification using optimized RFs has suffered from computational hardness in sampling each optimized RF; moreover, it has failed to achieve the exponentially fast error-convergence speed that other state-of-the-art kernel methods can achieve under a low-noise condition. To overcome these slowdowns, we here construct a classification algorithm with optimized RFs accelerated by means of quantum machine learning (QML) and study its runtime to clarify overall advantage. We prove that our algorithm can achieve the exponential error convergence under the low-noise condition even with optimized RFs; at the same time, our algorithm can exploit the advantage of the significant reduction of the number of features without the computational hardness owing to QML. These results discover a promising application of QML to acceleration of the leading kernel-based classification algorithm without ruining its wide applicability and the exponential error-convergence speed.
An Agent-based Architecture for AI-Enhanced Automated Testing for XR Systems, a Short Paper

This short paper presents an architectural overview of an agent-based framework called iv4XR for automated testing that is currently under development by an H2020 project with the same name. The framework's intended main use case of is testing the family of Extended Reality (XR) based systems (e.g. 3D games, VR sytems, AR systems), though the approach can indeed be adapted to target other types of interactive systems. The framework is unique in that it is an agent-based system. Agents are inherently reactive, and therefore are arguably a natural match to deal with interactive systems. Moreover, it is also a natural vessel for mounting and combining different AI capabilities, e.g. reasoning, navigation, and learning.
Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction

Fuzzy logic is an extension of traditional reasoning, where x is a member of set A or not, or an x can be a member of set A with a certain degree of membership . The ability of fuzzy models to map fuzzy values is the reason for using fuzzy inference models in various cases that use fuzzy values to produce a clear or definite output. In this research, an analysis of the level of accuracy generated by the Sugeno and Mamdani inference model will be carried out in predicting rainfall at Polonia Station, Medan, North Sumatra. Prediction results will be analyzed for accuracy by comparing the results obtained by Sugeno fuzzy inference models and Mamdani using Mean Absolute Percent Error (MAPE). When compared to the results of the Mean Absolute Percent Error (MAPE) Sugeno fuzzy inference model of 1.33% and mamdani fuzzy inference model of 1.45%, then the accuracy rate for the Sugeno inference model is 100%-1.33% = 98.67% while the Mamdani fuzzy inference model is 100%-1.45 = 98.55%. The end result is that the membership function model used in the Sugeno fuzzy model is more accurate than the Mamdani fuzzy inference model in the test case of rainfall prediction at Polonia station in Medan. North Sumatra. The results of the analysis carried out for the Sugeno and Mamdani fuzzy models are influenced by the accuracy of the input values. Rainfall prediction is an important thing to study, weather conditions in certain areas can be predicted so that it can help people's daily activities, can determine a series of community social activities. For example, information on rainfall and its classification is widely used as a guide for agriculture, tourism and transportation, for example: Cropping Patterns, Harvest Predictions, Shipping and flight schedules
Strategies for Enhancing Programming in Local Juvenile Courts: Logic Models and Quality Assurance Procedures

The authors provide a case-study related to a recent project using program logic models as a primary component in the implementation of a formal quality assurance process in a local juvenile court. Program logic models illustrate the evolution of court personnel’s thoughts about how best to conceptualize programming. Juvenile court officials are developing and implementing formal “quality assurance” procedures to allow for ongoing planning and program development. The authors argue that quality assurance procedures hold great promise for assuring that juvenile court programming is efficient and effective and serves the needs of local communities.
Limits of quantum speed-ups for computational geometry and other problems: Fine-grained complexity via quantum walks

Many computational problems are subject to a quantum speed-up: one might find that a problem having an O(n^3)-time or O(n^2)-time classic algorithm can be solved by a known O(n^1.5)-time or O(n)-time quantum algorithm. The question naturally arises: how much quantum speed-up is possible? The area of fine-grained complexity allows us to prove optimal lower-bounds on the complexity of various computational problems, based on the conjectured hardness of certain natural, well-studied problems. This theory has recently been extended to the quantum setting, in two independent papers by Buhrman, Patro, and Speelman (arXiv:1911.05686), and by Aaronson, Chia, Lin, Wang, and Zhang (arXiv:1911.01973). In this paper, we further extend the theory of fine-grained complexity to the quantum setting. A fundamental conjecture in the classical setting states that the 3SUM problem cannot be solved by (classical) algorithms in time O(n^{2-a}), for any a>0. We formulate an analogous conjecture, the Quantum-3SUM-Conjecture, which states that there exist no sublinear O(n^{1-b})-time quantum algorithms for the 3SUM problem. Based on the Quantum-3SUM-Conjecture, we show new lower-bounds on the time complexity of quantum algorithms for several computational problems. Most of our lower-bounds are optimal, in that they match known upper-bounds, and hence they imply tight limits on the quantum speedup that is possible for these problems.
Look Ma, Backtracking without Recursion

I show how backtracking can be discovered naturally without using a recursive function (nor using a loop with an explicit stack). Rather, my approach involves a form of self application that can be elegantly expressed in an object-oriented program, and that is reminiscent of how recursion is done in lambda calculus. It also illustrates why reasoning about object-oriented programs can be hard.
Text Generation from Discourse Representation Structures

We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs).DRSs are document-level representations which encode rich semantic detail pertaining to rhetorical relations, presupposition, and co-reference within and across sentences.We formalize the task of neural DRS-to-text generation and provide modeling solutions for the problems of condition ordering and variable naming which render generation from DRSs non-trivial.Our generator relies on a novel sibling treeLSTM model which is able to accurately represent DRS structures and is more generally suited to trees with wide branches.We achieve competitive performance (59.48 BLEU) on the GMB benchmark against several strong baselines.(a)
A Survey of Race, Racism, and Anti-Racism in NLP

Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.
One-Shot Face Recognition Based on Multiple Classifiers Training

One-shot face recognition is a challenging problem which requires recognizing novel identities from only one seen face image. One-shot classes are simply neglected because of the lack of training samples. Therefore, these classes contribute less to the improvement of face recognition performance. The main goal of one-shot face recognition task is to use the novel face samples to enhance the ability of network not only in close-set classify, but also in open-set face verification. In this paper, Base data and Novel data is trained separately with two classifiers to reduce the impact of data imbalance. We propose Confidence Constrain Loss to train classifiers in parallel and get better classifiers fusion in the test phase. Besides, we use data augmentation with 3D face reconstruction to obtain a variety of oneshot set's training samples. Thus, our method can effectively increase the recognition accuracy in the novel set without reducing recognition accuracy in base set. Experiments on MS-celeb-1M low-shot dataset demonstrate that our method achieve state-of-the-art which has 98.90% coverage at precision=99% without using external data.