Reviewerly/exHarmony huggingface dataset:
Hydroponics Fodder Grow Chamber

India is a developing country, but rural areas do not seem to be developing much. Basically most of the public is farmers, most of the farmers have limited agricultural land and also lack of water resources. in many parts of India rain and is not enough for traditional way farming. To avoid these problems our proposed system is structured a helpful touch of fresh and raw feed for cattle food within affordable natural conditions. This hydroponic system does not require any soil to grow fodders and will absorb 80% less water as compared to the traditional method of farming. Greater topic for work to upgrade the Hydroponic Fodder Grow room for proper management of cattle fodder in any period during the year. This paper suggests a clever plan no human power or less human power is required for It performance. this is usually a completely automated system. In seven to for eight days the room provides fodder as a ready-to-feed product any cattle or grass-eating animals. This process is aided by a Moisture sensor to produce a certain amount of water. Forage seeds use H2O, or solutions that enrich the nutrients of the drug food within the absence of soil. The amount of water is additionally calculated by microcontroller atmega328p. Also, we visit the average temperature and humidity of our room. This heat and humidity are the same and is controlled by a cool cooler and UV/LED light inside the room. Adequate water management and nutrients within the hydroponic system, moisture, humidity pH, water level and temperature should be measurement Using a microcontroller all these functions do it automatically and display at the top of the guided screen. These hydroponics require less space, and this is true usually inside a room of aluminum or fiber. that the environment of the room is completely controlled. That's the fodder prepared for use within 8 days up to 1fit This healthy cattle fodder. Mainly more production we use maize as fodder. Performing remote monitoring of fodder extension procedure, prohibited by employees, thereby reducing the manual process.
Financial institutions should decline hackers’ requests for voluntary compensation

Cybersecurity and data privacy have been prioritised by financial institutions due to legislative requirements, an increase in security breaches, and in fulfilment of their obligations to their customers. Despite this, financial institutions are still considered worthy targets by hackers. This article argues that financial institutions should decline requests for voluntary compensation from grey hat hackers who breach security controls and report discovered vulnerabilities. Following an overview of the motives of different groups of hackers, this article relies on the moral duty of respect for persons, mainly following Kantian ethics, to support the argument against voluntary compensation. The article concludes with an overview of the reasons that compensation is not an appropriate response.
Atomic Execution of Optimization Transactions on Permissioned Blockchains

A transaction is executed asynchronously by multiple nodes in a Blockchain and it is imperative that all executions result in the same output for the transaction to be valid. Transactions involving optimization problems can provide differing solutions on different executions. Common supply chain applications with planning, scheduling, resource allocation, and routing optimization are mixed integer linear programs with the above characteristic of generating different solutions. This inherent lack of determinism during the execution of optimization transactions impedes the adoption of Blockchain for the above supply chain applications. In this paper, we address the problem of non-determinism in optimization transactions for permissioned Blockchains. Our proposed scheme enables atomic execution of optimization transactions and we outline its implementation for Hyperledger Fabric.
A Commentary on Covid-19 Contact-Tracing Apps and Broader Societal Implications of Technosolutionism

This paper is a commentary on COVID-19 technological solutions, such as contact tracing apps, bringing into question the effectiveness of technosolutionism, and the sociotechnical implications resulting from such technologies. This paper argues that the abyss created from the lack of governance-led leadership in COVID-19 responses has exacerbated power asymmetries reliant upon technology sector led solutions that reflect current political dynamics and governance structures. In relation to these power asymmetries favoring technology companies, we are potentially creating opportunities for technocratic future visions to be further embedded into concepts of smart cities under the guise of public health. These visions and circumstances are dubbed as `imaginaries of health' in the analyses. The analysis is primarily focusing on COVID-19 contact tracing apps and US context, but also references other countries' findings, such as Australia and Singapore, as examples of contact tracing app's technological limitations. The conclusion of the paper incorporates an examination of sociotechnical effects, calls for the incorporation of alternative imaginaries, and includes suggestions for where these technologies may be better served with creative implementations, along with considerations for the development of further apps. This commentary serves as an examination of sociotechnical implications of technosolutionism under COVID-19 based technical solutions, such as contact tracing apps, with a predominant US-focus.
La grammaticalizzazione dell’it. fa

Abstract It. fa ‘ago’ represents a rare case of grammaticalization of a finite verbal form and a typical example of what we call ‘preadaptive change’. Its present categorical status, however, is an object of dispute between the different approaches of historical and theoretical linguistics: on the one hand fa is considered an adverbial, on the other hand a postposition, becoming a sort of test of the explanatory power of both the approaches. After a reconsideration of the data throughout the history of Italian and a scrutiny of the literature, we come to the conclusion that in the case of a grammaticalization process both the structural features and the inherited properties are necessary to assign the categorial status and in the case in question converge towards the adverbial function.
An Alternative Approach to Compress Text Files Using Fibonacci Sequence and Lucas Series

Data compression uses variable-length codes in which symbols that appear frequently are assigned with a specific code pattern (i.e. integer/alphanumeric). The ultimate goal behind assigning the characters with these code patterns is to facilitate easier compression and decompression. After concluding lots of research on different lossless text data compression techniques (i.e. Huffman coding, LZW coding, Shannon–Fano coding) along with the study of various concept of sequence and series (i.e. Fibonacci sequence (Bhattacharyya in complexity analysis of a lossless data compression algorithm using Fibonacci sequence. University of Calcutta, Kolkata, West Bengal (Bhattacharyya in Complexity analysis of a lossless data compression algorithm using Fibonacci sequence. Research Associate Department of Computer Science. University of Calcutta, Kolkata, West Bengal [3]) and Lucas Series), we have given an alternative approach through this research paper to improve the data compression ratio for the text file.
Research on Situational Awareness Security Defense of Intrusion Link Based on Data Element Characteristic Network Transmission Signal

When defending the intruding links of optical fiber network, the intruding signals are not classified, which leads to the low accuracy of feature extraction and the poor effect of intruding defense. For this reason, the inter-correlation characteristic is used to process the fiber signal. The time-domain eigenvectors, conjugate signals and feature sets of the intrusion signals in the optical fiber network are extracted from three aspects: wavelet domain, frequency domain and time domain by using the two-threshold method and the short-time energy. The classification model is designed by the neural network algorithm, the feature vectors of the intrusion signal are input into the classification model, and the error function is determined by the negative logarithmic likelihood function. The network transmission signal vulnerability is obtained by comparing the data metadata characteristics in the process of network data transmission. Big data is adopted to quantitatively analyze the data interception and monitoring risks in the transmission process, establishing the network transmission security mechanism, completing the quantitative assessment of data transmission risks, and realizing the security defense of network intrusion link situational awareness. Experimental results show that the proposed method has high accuracy in feature extraction and intrusion detection.
Academic - Industry Partnership: Development IT Sector in Kazakhstan

This paper provides grounds for serious concern with regard to the need for a new interaction model between academic and industry based on statistical data. Collecting analytical data on the current situation in research and academic potential enabled the formation of our view as to the need for changing the model of interaction between Kazakhstani universities and industry. This work reveals weak points and allows for focusing attention on developing communication between key actors, suggesting a new model, which would meet modern requirements. The goal of this article can be attained by a comprehensive analysis of existing data, synthesis of adequate knowledge on research interaction between universities and industrial sector; analysis of the need for collaboration, key barriers and interaction frameworks with the aim to provide suggestions which would facilitate cooperation and identify the most effective patterns of partnership. Further, the study also seeks to become aware of reasons so one can make certain sustainable collaborations between both aspects. Implications of the findings will be used to purpose at facilitating more university and industry partnerships in the future. Overall, this paper identifies a new model of an Academic-Industry interaction aimed at fostering industrial science and technology in the sphere of Kazakhstani IT sector.
Reward Redistribution for Reinforcement Learning of Dynamic Nonprehensile Manipulation

Recent reinforcement learning (RL) systems can solve a wide variety of manipulation tasks even in real-world robotic implementations. However, in some nonprehensile manipulation tasks (e.g. poking, throwing), the classical reward system fails as the robot has to manipulate objects whose motion trajectory is partly uncontrollable. Such tasks require a specific type of reward that would reflect this temporal misalignment. We propose a novel method, based on a delayed reward redistribution, that allows a robot to fulfill goals in an only partially controllable environment. The reward system in our architecture combines information from other sensors together with inputs from an unsupervised vision module based on a variational autoencoder (VAE). This delayed reward system then controls the training of the motor module based on a Soft Actor-Critic (SAC) neural network. We compare results for a delayed and nondelayed version of our system in a simulated environment and show that the delayed reward greatly outperforms the nondelayed version.
Calculate the Score of a Social Media Profile and Posts on Twitter and Other Social Media Sites on the Internet

In the age of social networking, finding the worth of one’s social presence and online interactions can have a significant impact on tackling the problem of fake news. We are proposing a scoring mechanism which can also be used to calculate the worth of one’s online presence, and also to suggests numerical score to a particular thought posted on the internet.
Variational Quantum Eigensolver and Its Applications

The Variational Quantum Eigensolver (VQE) algorithm is attracting much attention to utilize current limited quantum devices. The VQE algorithm requires a quantum circuit with parameters, called a parameterized quantum circuit (PQC), to prepare a quantum state, and the quantum state is used to calculate the expectation value of a given Hamiltonian. Creating sophisticated PQCs is important from the perspective of the convergence speed. Thus, we propose problem-specific PQCs of the VQE algorithm for optimization problems. Our idea is to dynamically create a PQC that reflects the constraints of an optimization problem. With a problem-specific PQC, it is possible to reduce a search space by restricting unitary transformations in favor of the VQE algorithm. As a result, we can speed up the convergence of the VQE algorithm. Experimental results show that the convergence speed of the proposed PQCs is significantly faster than that of the state-of-the-art PQC.
Training Mode of Applied Talents in Tourism Management Specialty Under Artificial Intelligence

In recent years, with the rapid development of AI, deep learning, mobile Internet and other technologies, products related to AI have been applied to many places, such as our work, life, study and so on, so that our work, study, life is more convenient and convenient. Whether colleges and universities can catch the fast train of AI era, improve their talent training mode, improve the teaching quality, enhance the overall competitiveness of the school, and transport more outstanding talents for the society is an important problem. Therefore, through practical experiments on two professional classes in a university in our city, we can know that AI technology can help students to improve their academic performance to a certain extent.
Algorithm for Constructing Logical Operations to Identify Patterns in Data

Neural networks have proven themselves in solving problems when the input and output data are known, but the cause and effect relationship between them is not obvious. A well-trained neural network will find the right answer to a given request, but will not give any idea about the rules that form this data. The paper proposes an algorithm for constructing logical operations, in terms of multi-valued logic, to identify hidden patterns in poorly formalized areas of knowledge. As the basic elements are considered many functions of the multi-valued logic of generalized addition and multiplication. The combination of these functions makes it possible to detect relationships in the data under study, as well as the ability to correct the results of neural networks. The proposed approach was considered for classification problems, in the case of multidimensional discrete features, where each feature can take k-different values and is equivalent in importance to class identification.
Affix-based Distractor Generation for Tamil Multiple Choice Questions using Neural Word Embedding

Assessment plays an important role in learning and Multiple Choice Questions (MCQs) are quite popular in large-scale evaluations. Technology enabled learning necessitates a smart assessment. Therefore, automatic MCQ generation became increasingly popular in the last two decades. Despite a large amount of research effort, system generated MCQs are not useful in real educational applications. This is because of the inability to produce the diverse and human alike distractors. Distractors are the wrong choices given along with the correct answer (key) to confuse the examinee. Especially, in educational domain (grammar learning) the MCQs deal with affix-based or morphologically transformed distractors. In this paper, we present a method for automatic generation of affix-based distractors for fill-in-the-blanks for learning Tamil Vocabulary. Affix-based distractor generation relies on certain regularities manifest in high dimensional spaces. We investigate the quality of distractors generated by a number of criteria, including Part-Of-Speech, difficulty level, spelling, word co-occurrence, semantic similarity and affixation. We evaluated our proposed method in grammar based Multiple Choice Questions (MCQs) dataset. The result shows that affix-based distractors, yield significantly more plausible outcomes in certain grammar based questions.
“Digital generation” and knowledge in human sciences: teaching communication experience

The article analyzes the current situation of digitalization in education, considers the characteristics and features of the transition to digital culture, presents the main levels of transformation of the concept of “digital culture” in the aspect of humanitarian knowledge, and identifies the main vectors of the formation of the studied type of culture. In this article, the authors aim to highlight some issues of pedagogical communication in the context of digitalization of the educational environment and the place and role of humanitarian knowledge in it. The digital space provides the teacher with ample opportunities for dialogue with the student. The task of the teacher in this case is to meet the cultural needs of students, to have the skills to work with information resources. The task of humanitarian education now is to fill the niche of dubious educational resources with interesting and professionally correct content, to form an objective attitude to the humanitarian component of education in society.
Nonparametric Bayesian Deep Visualization

Visualization methods such as t-SNE [1] have helped in knowledge discovery from high-dimensional data; however, their performance may degrade when the intrinsic structure of observations is in low-dimensional space, and they cannot estimate clusters that are often useful to understand the internal structure of a dataset. A solution is to visualize the latent coordinates and clusters estimated using a neural clustering model. However, they require a long computational time since they have numerous weights to train and must tune the layer width, the number of latent dimensions and clusters to appropriately model the latent space. Additionally, the estimated coordinates may not be suitable for visualization since such a model and visualization method are applied independently. We utilize neural network Gaussian processes (NNGP) [2] equivalent to a neural network whose weights are marginalized to eliminate the necessity to optimize weights and layer widths. Additionally, to determine latent dimensions and the number of clusters without tuning, we propose a latent variable model that combines NNGP with automatic relevance determination [3] to extract necessary dimensions of latent space and infinite Gaussian mixture model [4] to infer the number of clusters. We integrate this model and visualization method into nonparametric Bayesian deep visualization (NPDV) that learns latent and visual coordinates jointly to render latent coordinates optimal for visualization. Experimental results on images and document datasets show that NPDV shows superior accuracy to existing methods, and it requires less training time than the neural clustering model because of its lower tuning cost. Furthermore, NPDV can reveal plausible latent clusters without labels.
Mapping Between “Classifications for Mineral Resources and Mineral Reserves (GB/T 17766-2020)” and “United Nations Framework Classification for Resources (UNFC-2019)”

The aim of this paper is to provide the basis for development of the Bridging Document between China National Standard of “Classifications for mineral resources and mineral reserves (GB/T 17766-2020)” and “United Nations Framework Classification for Resources (UNFC-2019)”. This paper analyses GB/T 17766-2020 and UNFC-2019 updated recently. Based on the existing bridging document, this paper sets up correspondences between categories of the two systems from the view of definitions. The correspondences are tested by a case study. The mapping study shows that the mineral resources and mineral reserves in GB/T 17766-2020 can correspond respectively to the Viable Projects and Potentially Viable Projects classes. As the application scope of UNFC-2019 is different from that of GB/T 17766-2020, it will be key point to mapping in depth the categorization of G axes between the two classification systems. This paper provides correspondences on categories between GB/T 17766-2020 and UNFC-2019, and laid a good foundation to develop the bringing document between the two classification systems.
OWLStats: Distributed Computation of OWL Dataset Statistics

Nowadays, ontologies are used in various application areas, involving Artificial Intelligence, Natural Language Processing, Data Integration, and Knowledge Management. It is essential to know the internal structure, distribution, and coherence of the published datasets to make it easier to reuse, interlink, integrate, infer, or query. Therefore, there is a pressing need to obtain a clear view of OWL datasets became more prevalent. In this paper, we present OWLStats, a software component for computing statistical information about large scale OWL datasets in a distributed manner. We present the primary distributed in-memory approach for computing 32 different statistical criteria for OWL datasets utilizing Apache Spark, which can scale horizontally to a cluster of machines. OWLStats has been integrated into the SANSA framework. The preliminary results prove that OWLStats is linearly scalable in terms of data scalability.
Acquiring the proceedings of scholarly events to the collections of RAS Library for Natural Sciences

The research libraries thoroughly analyze new information resources emerging on the information market as well as the dynamics and vectors of information and communication processes in science. The author discusses the findings of the study of acquiring the proceedings of scholarly events to the collections of the Library for Natural Sciences of the Russian Academy of Sciences. This type of publications comprises information on the most recent developments, vectors in fundamental and applied research accomplished by national and foreign research organizations. With this information resource, researchers are able to find like-minded colleagues and to assess their own possibilities and prospects for their own studies. The reference materials, conference proceedings and other scholarly publications are of undoubtful interest to the scholarly community; however, the circulations are small, and, as a rule, the book-selling organizations do not distribute these publications. The circulation depends on the number of participants attending the scholarly event. Therefore the conference, symposium, seminar proceedings can be categorized as rare publications. The accumulated experience of collection development with these publications enables to analyze other types of small-circulation scientific publications acquired to the collections of the library.
Understanding How Users Experience the Physiological Expression of Non-humanoid Voice-based Conversational Agent in Healthcare Services

Interactions with voice-based conversational agents (VCAs) in non-humanoid forms are becoming increasingly pervasive, and researches on non-humanoid VCAs engaging diverse human traits have been conducted. However, there have never been studies employing living body's physiological states to be expressed solely through the voice of such VCAs yet. As physiological expressions of such VCAs can have potential for manifesting health-related issues in a human-like way, we selected healthcare scenarios as a case for exploring novel user experiences that they can induce. We conducted design workshops for identifying design considerations and design opportunities for the physiologically expressible VCAs in the healthcare service domain. Following these findings, we designed the new concept of physiologically expressible healthcare VCAs and conducted a Wizard-of-Oz user study. Finally, we summarize the unique user experiences on physiologically expressible VCA's healthcare services and user perceptions of its physiological expressions, and discuss design implications for physiologically expressible VCAs.