Reviewerly/exHarmony huggingface dataset:
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents

Text-based games simulate worlds and interact with players using natural language.Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds.However, it remains unclear to what extent artificial agents utilize semantic understanding of the text.To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent.Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts.To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including ZORK I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.
Decisions on the Orderliness and Collaborative Operation Mechanism of Each Subsystem under the Organizational Model of the Internet of Things

This paper analyzes the overall organic network system integrated by subnetworks, analyzes the network evolution process of the overall system of the Internet of Things (IoT) organization mode network, and analyzes the network through the network scale measurement, the node growth measurement, the node distribution measurement, and the node association measurement. We analyze the coordination mode of hardware resources, information resources, financial resources, human resources, organizations, and customer resources from the perspective of resource elements. And we analyze the vertical and horizontal work collaboration mode from the perspective of work activities. From the perspective of equity, two types of equity-based synergy and four types of contract-based synergy are proposed. It is proposed that the network stage of the IoT organization model is different from the previous stage of the important collaboration model, the platform-based collaborative model, which is analyzed from the coordination manager software platform and the IoT organization model infrastructure hardware platform. We use relevant data to construct a SIM model to measure the synergy of the development of the IoT industry system to quantify the overall collaborative development status of the IoT industry system. At the same time, the gray correlation analysis model GM (1, N) is used to correlate the complex system subsystems. The research results show that the development of the IoT industry has gone through a U-shaped development process from the uncoordinated stage to the coordinated development of the industrial system. Each subsystem has self-development capabilities and exhibits different interaction relationships with each other.
Innovations in the educational process and pedagogical technologies under the influence of crisis phenomena and global digitalization

Innovations in the educational process are necessary because the entire country's future well-being depends on the level of education of young people. Pedagogical technologies are being improved to provide high-quality modern education. In their study, the authors studied the features of innovations in the educational process and pedagogical technologies, the impact of the crisis and global digitalization on it, as well as global trends. The authors determined that the dominant processes and functions for acquiring new knowledge and transferring it to the economy's production sectors are increasingly organized according to the principle of networks and proposed the features and algorithm for the formation of a pedagogical (educational) cluster. To calculate the effectiveness of such a cluster, the authors have developed a complex system of indicators. The recommendations proposed in the study will help universities understand the clustering process, realize its importance, outline development paths and their place in the clustering of the educational process.
Analysis and Experimental Study of HDFS Performance

In the age of big data, the amount of data that people generate and use on a daily basis has far exceeded the storage and processing capabilities of a single computer system. That motivates the use of distributed big data storage and processing system such as Hadoop. It provides a reliable, horizontallyscalable, fault-tolerant and efficient service, based on the Hadoop Distributed File System (HDFS) and MapReduce. The purpose of this research is to experimentally determine whether (and to what extent) the network communication speed, the file replication factor, the files’ sizes and their number, and the location of the HDFS client influence the performance of the HDFS read/write operations.
Computer Based Test Using the Fisher-Yates Shuffle and Smith Waterman Algorithm

Tests are used to determine a person’s level of understanding of a subject. The inhibiting factors in tests are less varied questions, questions with insufficient difficulty, subjective assessments, and the length of time in their correction. This research aimed to develop a Computer Based Test (CBT) application. The type of questions in this CBT are multiple choice and essays. This CBT employs categorization of questions, randomization of the questions, and automatic assessment. Questions were categorized manually based on Bloom’s Taxonomy of a lecture. Then the randomization process was carried out using the Fisher-Yates Shuffle algorithm for each question category. The Smith Waterman algorithm was used to automatically assess the essay-type questions. The steps of the Smith Waterman algorithm were preprocessing, data comparison using Smith Waterman, and percentage similarities conversion to test scores. The results of the study showed that the CBT application was able to randomize questions using the Fisher-Yates Shuffle algorithm and automatically assess answers using the Smith Waterman algorithm. RMSE was used to measure of the accuracy of the Smith Waterman algorithm: a value of 1.86 was obtained.&#x0D; Keywords: Computer based test, assessment, Fisher-Yates Shuffle, Smith Waterman
Model Pembelajaran Inkuiri Untuk PAUD

One of the supports of successful learning is understanding and using the right model and in accordance with the theme of learning. In this study, further study and understanding of one of the learning models is the inquiry learning model. Many parents who do not understand how this learning model is not only applied in school, but also applied at home, because this learning is not always about education in school alone. This research is a literature study that uses documentation as a data collection method. The results show that: 1) this learning model could not only be applied in schools, but it was very important to be implemented at home, with direct intervention from parents, 2) by looking at the activities and learning steps offered by this model, it could improve learning outcomes and make learning more active and enjoyable, 3) each learning model will not only have advantages, but certainly has disadvantages. In this research some weaknesses and strengths were found in inquiry learning.
Building Low-Resource NER Models Using Non-Speaker Annotations

In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.Cross-lingual methods have had notable success in addressing these concerns, but in certain common circumstances, such as insufficient pretraining corpora or languages far from the source language, their performance suffers.In this work we propose a complementary approach to building low-resource Named Entity Recognition (NER) models using "non-speaker" (NS) annotations, provided by annotators with no prior experience in the target language.We recruit 30 participants in a carefully controlled annotation experiment with Indonesian, Russian, and Hindi.We show that use of NS annotators produces results that are consistently on par or better than cross-lingual methods built on modern contextual representations, and have the potential to outperform with additional effort.We conclude with observations of common annotation patterns and recommended implementation practices, and motivate how NS annotations can be used in addition to prior methods for improved performance. 1
Multi-server blind quantum computation protocol with limited classical communication among servers

A user who does not have a quantum computer but wants to perform quantum computations may delegate his/her computation to a quantum cloud server. For users securely using the service, it must be assured that no malicious server can access any vital information about the computation. The blind protocol was proposed as a mechanism for users to secure their information from unauthorized actions of the server. Among the blind protocols proposed thus far, a protocol with two servers sharing entanglement does not require any quantum resource from the user but does not allow the servers to interact even after the computation. We propose a protocol in this paper that extends this two-server protocol to multiple servers and is secure even if some servers communicate with each other after the computation. Dummy gates and a circuit modeled after brickwork states play a crucial role in the new protocol.
Challenging distributional models with a conceptual network of philosophical terms

Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading.Research on methods for small data is still in early stages and it is not clear which methods achieve the best results.We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case.We provide a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup.We compare three methods for creating specialized models from small datasets.Though the models do not perform well enough to directly support philosophers yet, we find that models designed for small data yield promising directions for future work.
Data Filtering using Cross-Lingual Word Embeddings

Data filtering for machine translation (MT) describes the task of selecting a subset of a given, possibly noisy corpus with the aim to maximize the performance of an MT system trained on this selected data.Over the years, many different filtering approaches have been proposed.However, varying task definitions and data conditions make it difficult to draw a meaningful comparison.In the present work, we aim for a more systematic approach to the task at hand.First, we analyze the performance of language identification, a tool commonly used for data filtering in the MT community and identify specific weaknesses.Based on our findings, we then propose several novel methods for data filtering, based on cross-lingual word embeddings.We compare our approaches to one of the winning methods from the WMT 2018 shared task on parallel corpus filtering on three real-life, high resource MT tasks.We find that said method, which was performing very strong in the WMT shared task, does not perform well within our more realistic task conditions.While we find that our approaches come out at the top on all three tasks, different variants perform best on different tasks.Further experiments on the WMT 2020 shared task for parallel corpus filtering show that our methods achieve comparable results to the strongest submissions of this campaign.
Philosophy of Music in the Image of the World: From Antiquity to the Modern Time

The article presents philosophical views on music in the context of the transformations of the worldview from Antiquity to the Modern Time. In this research author also mentions the contemporary issues, and uses her own philosophical concept of the music, which can be described as following: the value of music as a substance and the way of the valuable interaction of a person with the world affirm the essence of musical being, in which the invariable principle of Harmony, the principle of Chaos-Form movement, is preserved (see "The Value of the Music: Philosophical Aspect"). Music expresses the fluid essence of the world and changes of being in space and time. Philosophy of music as a field of philosophical knowledge considers music from ontological-epistemological and phenomenological-axiological prospectives, as something more than just a form of art. It explores the deep, ultimate foundations of the existence of music as such and the philosophical and aesthetic foundations of musical art. Since ancient times music has been a representation of the world in the human conscience and served as the harmonic equivalent of cosmological philosophy, mathematics, astronomy, and astrophysics (Pythagoras, Plato, Aristotle, Aristoxenus, Porphyry et als.). The scientific view on music was enriched in the Modern Time by the expanded view on the cosmo-sound space reflected in musical art, which at the same time transforms the mathematical ideas of geometricity, squareness etc. The tendency to create integral world music in the musical practice of the XX-XXI centuries explains the attempt of mankind to present music by modern methods of composing musical art as an expression of fear towards the secret Harmony of universal existence, and, on the other hand - as a form of search for salvation and mental balance, intuitively reflecting what is happening in natural science, which more and more points to the abyss of unknowable universe, and the unstable place of man in the world.
Facemask detection in real time based single shot based algorithm

Machine learning, the future technology is destined to provide ease of living with its applications spread in all aspects of our day-to-day life. A part of machine learning is Image Processing, another growing field in which images and videos are used to provide information. Use of Image Processing is widespread and one of its applications is for security purposes. Image Processing not only reduces human interference in video surveillance but also provides a more accurate system.This paper is prepared to present the implementation of SSD Mobilenets algorithms and models used in it for carrying out the task of detection and tracking of people who are wearing masks or not in a public area.
Semi-Supervised Joint Estimation of Word and Document Readability

Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for the other.Motivated by our analysis showing that there is a recursive relationship between word and document difficulty, we propose to jointly estimate word and document difficulty through a graph convolutional network (GCN) in a semi-supervised fashion.Our experimental results reveal that the GCN-based method can achieve higher accuracy than strong baselines, and stays robust even with a smaller amount of labeled data. 1
Stock Theft: Rural Livestock Farmers' Entrepreneurial Perspectives

The collation between stock theft and rural livestock farmers’ entrepreneurial perspectives receive limited attention in the field of Criminology and Criminal Justice, as well as ‘Business Administration’ nor entrepreneurial dominance industry. Conventionally, stock theft is nothing new in South Africa, traditional interpretations and recorded cases can be reportedly traced back to 1806. This study reflects the duality of stock theft, offering criminological [Theoretical] explanations and glimpses of attractive business enterprises by adopting the three elements of ‘Timmons Model of Entrepreneurial Process’ through the use of non-empirical research design: Systematic review, while featuring the most affected areas by stock theft in the Eastern Cape (EC), KwaZulu-Natal (KZN) and Mpumalanga (MP) Provinces respectively, while focusing on 2014-2020 financial years. This study establishes provides that this crime, instead of making more business avenues for rural livestock farmers, will continue to decrease the number of job opportunities. It is recommended that the South African government (I.e. national, provincial and local levels), agricultural industries, and the local South African Police Service Stock Theft Units (SAPS STUs) as a collective and commodity organisations will have to gravely consider offering adequate services required to bring workable solutions for initiation of entrepreneurial activities in the selected provinces.
APPLICATION OF LEAN TECHNOLOGIES OF EDUCATIONAL PROCESS CONTROL UNDER THE CONDITIONS OF EPIDEMIOLOGICAL UNCERTAINTY

The article discusses the relevance of the implementation of lean production principles in higher education institutions and their impact on the educational process in pandemic conditions. The SQDCM format is considered as an effective model providing management by objectives, connection with safety, quality, costs, and corporate culture. Value is seen as the most important category of lean management, which serves as a basis for self -organization of faculty and staff, and students in higher education institutions. The authors pay attention to the process of lean principles’ implementation in educational process, with Udmurt State University setting an example. Since having formed the values over the recent years, Udmurt State University has been able to effectively change educational processes in the settings of epidemiological uncertainty that has arisen in pandemic in Russia and around the world. The authors conclude, that lean approaches to building a university management system allow to maintain student involvement in educational activities, to see new opportunities for interaction in organizing the educational process and to master new valuable competencies, thus, to maintain a high level of education quality.
IITP-MT at CALCS2021: English to Hinglish Neural Machine Translation using Unsupervised Synthetic Code-Mixed Parallel Corpus

This paper describes the system submitted by IITP-MT team to Computational Approaches to Linguistic Code-Switching (CALCS 2021) shared task on MT for English → Hinglish.We submit a neural machine translation (NMT) system which is trained on the synthetic code-mixed (cm) English-Hinglish parallel corpus.We propose an approach to create code-mixed parallel corpus from a clean parallel corpus in an unsupervised manner.It is an alignment based approach and we do not use any linguistic resources for explicitly marking any token for code-switching.We also train NMT model on the gold corpus provided by the workshop organizers augmented with the generated synthetic code-mixed parallel corpus.The model trained over the generated synthetic cm data achieves 10.09 BLEU points over the given test set.
Emergent Social Learning via Multi-agent Reinforcement Learning

Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can learn to use social learning to improve their performance. We find that in most circumstances, vanilla model-free RL agents do not use social learning. We analyze the reasons for this deficiency, and show that by imposing constraints on the training environment and introducing a model-based auxiliary loss we are able to obtain generalized social learning policies which enable agents to: i) discover complex skills that are not learned from single-agent training, and ii) adapt online to novel environments by taking cues from experts present in the new environment. In contrast, agents trained with model-free RL or imitation learning generalize poorly and do not succeed in the transfer tasks. By mixing multi-agent and solo training, we can obtain agents that use social learning to gain skills that they can deploy when alone, even out-performing agents trained alone from the start.
Study on the Intentional Choice Mechanism of Course Selection Based on Swarm Intelligence Algorithm

With the passage of time and recent advances in science and information technology, the development in the area of course selection based on some defined criteria has made the choice of the mechanism easy and effective. In this paper, the approach is based on an innovative perspective, and swarm intelligence was introduced for the intentional choice mechanism of course selection. The study has considered the course selection in English as an example. Swarm intelligence algorithm and integrative course selection were combined with the recommendation algorithm and the intent of course selection in English course to discuss the relevant decision mechanism. Firstly, the comprehensive selection intentional recommendation algorithm and PSO algorithm were introduced, and the algorithm was initialized. Secondly, the operation process was described in detail, and the application process was analyzed. Then, it was introduced into the English course elective process. Finally, the experimental results of the study came with the conclusion through the test that the PSO algorithm has a higher degree of accuracy and can better judge individual behaviors, which contributes to the establishment of choice-choice mechanism. The effectiveness of the study was demonstrated through experiments.
Switching and Swapping of Quantum Information: Entropy and Entanglement Level

Information switching and swapping seem to be fundamental elements of quantum communication protocols. Another crucial issue is the presence of entanglement and its level in inspected quantum systems. In this article, a formal definition of the operation of the swapping local quantum information and its existence proof, together with some elementary properties analysed through the prism of the concept of the entropy, are presented. As an example of the local information swapping usage, we demonstrate a certain realisation of the quantum switch. Entanglement levels, during the work of the switch, are calculated with the Negativity measure and a separability criterion based on the von Neumann entropy, spectral decomposition and Schmidt decomposition. Results of numerical experiments, during which the entanglement levels are estimated for systems under consideration with and without distortions, are presented. The noise is generated by the Dzyaloshinskii-Moriya interaction and the intrinsic decoherence is modelled by the Milburn equation. This work contains a switch realisation in a circuit form—built out of elementary quantum gates, and a scheme of the circuit which estimates levels of entanglement during the switch’s operating.