Reviewerly/exHarmony huggingface dataset:
Digitization of agricultural extension system for effective management of emergency in Nigeria

&#x0D; &#x0D; &#x0D; This paper presents challenges facing agricultural extension system in Nigeria most especially, during crises and emergencies, which necessitate the adoption of digitizing extension systems as a basis for improving farmers’ access to extension services during emergencies. The emergence of ICTs has given rise to digitization, which is the delivery of agricultural advice via audiovisual messages (video), interactive voice response (IVR) and short message services (SMS) among others. Efforts should be made by the stakeholders in agricultural extension to digitize the country's extension system by capitalizing on the existing enthusiasm among extension practitioners, and farmers using lessons of best practices from elsewhere.&#x0D; &#x0D; &#x0D;
Methodological justification of psychological variables of empirical research efficiency of modular-developmental interaction

The article is devoted to the problem of psychological filling with content the quality of higher education and the effectiveness of developmental interaction of its participants in the conditions of modern information society. Innovative modular-developmental interaction is considered as a complex subject of socio-psychological and educational-pedagogical research, which requires a separate scientific study and is presented simultaneously in several perspectives of psychological cognition: a) philosophical-analytical (from a concept to the category); b) methodological (from explanatory approaches to the author’s model); c) theoretical-psychological (from the designed model to the holistic knowledge system); d) instrumental-psychological (from educational scenarios and modular educational-book complexes to psychological methods and techniques of its implementation). For an empirical study of the effectiveness of modular-developmental interaction we have substantiated a number of psychological variables (innovations) on a well-established and systematically reflected basis, one of the differentiation dimensions of which were the types of developmental exchange (information-knowledge exchange, normative-business exchange, value-semantic self-exchange, spiritual-semantic superexchange), and the other one – innovation-psychological conditions of fundamental experimentation, in particular, groups of conditions for creating an innovation-psychological climate at HEI ((higher education institution) organizational-psychological, developmental-psychological, program-methodical (including psycho-artistic) and expert-psychological). Based on the methodological definition of these two criterion-based and logically differentiated vectors, a table-matrix is created, the internal content of which is a system of interrelated 16 psychological variables of a complexly structured psychological-pedagogical experiment. Each of these variables is characterized by empirical specificity of psychological content, and together they form a holistic functional field of experimental activity concerning practical verification of the effectiveness of initiated changes and the system of proposed innovations.
FAIR Interfaces for Geospatial Scientific Data Searches

Several factors must be considered in designing a highly accurate, reliable, scalable, and user-friendly geospatial data search interfaces. This paper examines four critical questions that ought to be considered during design phase: (1) Is the search interface or API that provides the search capability useable by both humans and machines? (2) Are the results consistent and reliable? (3) Is the output response format free to use, community-defined, and non-propriety? (4) Does the API clearly state the usage clauses? This paper discusses how certain data repositories at the US Department of Energy's Oak Ridge National Laboratory apply FAIR data principles to enable geospatial searches and address the above-mentioned questions.
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that all data are available to the recommendation platform. However, in practice, user-item interaction data (e.g.,rating) and user-user social data are usually generated by different platforms, and both of which contain sensitive information. Therefore, "How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature" remains an important challenge. In this work, we bring secure computation techniques into social recommendation, and propose S3Rec, a sparsity-aware secure cross-platform social recommendation framework. As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms. Moreover, to further improve model training efficiency, we propose two secure sparse matrix multiplication protocols based on homomorphic encryption and private information retrieval. Our experiments on two benchmark datasets demonstrate the effectiveness of S3Rec.
Performance Analysis of Machine Learning-Based Breast Cancer Detection Algorithms

Breast Cancer has now been a threat to the lives of countless women. This growth of breast tissue is metastatic, and therefore grows rapidly, infecting other body parts too. The probability of survival is high only if the tumor is detected in an early stage, the higher the stage, lower are the chances of survival of the patient. The presence of a minor tumor could be missed by the human eye, but the machine learning algorithms scan mammograms deeply and are able to detect even the smallest tumor. This work is a performance analysis of three Supervised Machine Learning Algorithms, namely, Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM), on two distinct datasets, i.e., Breast Cancer Wisconsin (Diagnostic) dataset and Breast Histopathology Images dataset. Univariate feature selection methods have been applied to select ten features in Breast Cancer Wisconsin (Diagnostic) dataset, and Wrapper Feature Selection methods have been applied to select three instances containing ten features in the Breast Histopathology Images dataset. The results exhibit that RF is the best suited algorithm for the Breast cancer Wisconsin (Diagnostic) dataset with an accuracy of 98.91%, while CNN is suitable for Breast Histopathology Image Dataset with an accuracy of 92.4%. Further, the effectiveness of this machine learning model is tested using the k-fold cross-validation technique.
Education as a factor of life and career success of the Russians

The problem of studying education, values of education, and educational potential of the Russians is relevant in the current context, when professional, continuing education, and self-education become the intrinsic elements of modern life. Education is the factor of achieving life and career success, a crucial competitive advantage on the job market, opens career growth opportunities, such as prestigious and top-paying job. It plays one of the key roles in social development and ensuring economic upturn of the country. The subject of this research is education within the structure of factors of life and career success. The goal lies in the analysis of education as the factor of success of the Russians. As a result of theoretical analysis, the author offers the original structural model of life and career success, new empirical data on education within the structure of values of the youth, as well as materials of qualitative research on life and career success among youth. The conducted analysis demonstrates that education, including higher education, does not hold the leading position within the structure of values of Russian youth, yielding to family, health and material well-being. It is neither among the top factors of life success. At the same time, Russian youth agree to the fact that education contributes to career growth. Young people rate the knowledge high and are willing to give their best for decent education. Self-education, lifelong learning expands the career-building opportunities, allows getting a prestigious and top-paying job. At the same time, professional success requires personal efforts of the individual and fulfillment of their potential. Certain role in this process is played by the system of useful social contacts. The current socioeconomic situation in the country also affects education and educational plans of youth.
An empirical study on temporal modeling for online action detection

Abstract Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. A typical OAD system mainly consists of three modules: a frame-level feature extractor which is usually based on pre-trained deep Convolutional Neural Networks (CNNs), a temporal modeling module, and an action classifier. Among them, the temporal modeling module is crucial which aggregates discriminative information from historical and current features. Though many temporal modeling methods have been developed for OAD and other topics, their effects are lack of investigation on OAD fairly. This paper aims to provide an empirical study on temporal modeling for OAD including four meta types of temporal modeling methods, i . e . temporal pooling, temporal convolution, recurrent neural networks, and temporal attention, and uncover some good practices to produce a state-of-the-art OAD system. Many of them are explored in OAD for the first time, and extensively evaluated with various hyper parameters. Furthermore, based on our empirical study, we present several hybrid temporal modeling methods. Our best networks, i . e . , the hybridization of DCC, LSTM and M-NL, and the hybridization of DCC and M-NL, which outperform previously published results with sizable margins on THUMOS-14 dataset (48.6% vs. 47.2%) and TVSeries dataset (84.3% vs. 83.7%).
Teachers and Students in the Digital Age: Attitude to Online Learning, Analysis of Aspects of Communication and Meaning Transfer

This research examines the problem of the attitude of students and teachers to the online learning format, to which many universities in the world were forced to switch due to the COVID-19 pandemic occurred in 2020. This research aims to study how teachers and students relate to the online learning format, as well as to analyze aspects of communication and meaning transfer in online and classroom lessons. The object of the study was 104 students of higher educational institutions, as well as 38 teachers of higher educational institutions. Students answered questions from February 14 to March 3, 2021, and teachers from February 16 to March 12, 2021. The responses were collected using Google Forms. To study different aspects of the perception of online learning and aspects of transferring understanding of the content of academic disciplines among students of higher educational institutions, 2 original questionnaires were developed: a questionnaire for students, which included 17 questions, and a questionnaire for teachers, which included 21 questions. The questions concerned the attitude toward the online learning format, aspects of communication, and meaning transfer during class, as well as aspects of transferring the understanding of academic subjects. The results obtained indicate that teachers and students perceive the online learning format in different ways. Teachers have some difficulties in working in an online format, while students do not see significant difficulties in this format in terms of communication and understanding of academic subjects.
HPKS: High Performance Kubernetes Scheduling for Dynamic Blockchain Workloads in Cloud Computing

Emerging blockchain technologies have been increasingly popular and reforming our daily lives. Fusing blockchain technology with existing cloud systems has a great benefits in both improving the functionality/performance and guaranteeing the security/privacy. However, most existing commercial systems fail to address the characteristics of PoS blockchain applications in the cloud. In real-world scenarios, jobs/pods may arrive and leave due to the workload changes. Traditionally, the selection process is based on the state of the workers, e.g., resource availability and specifications of pods. In this paper, we not only provide an optimal solution for offline workloads management which minimizes the number of used workers to reduce the total computational resource demand, but also propose a high performance Kubernetes scheduling scheme HPKS, which maximizes the utilization of workers. Specifically, extensive experiments based on real PoS blockchain applications shows that HPKS reduces the average worker nodes usage by 13.0%. Additionally, the overall increase of Makespan using HPKS is less than 3% when compared to the default scheduler available in Kubernetes.
Scaffolded input promotes atomic organization in the recurrent neural network language model

The recurrent neural network (RNN) language model is a powerful tool for learning arbitrary sequential dependencies in language data.Despite its enormous success in representing lexical sequences, little is known about the quality of the lexical representations that it acquires.In this work, we conjecture that it is straightforward to extract lexical representations (i.e.static word embeddings) from an RNN, but that the amount of semantic information that is encoded is limited when lexical items in the training data provide redundant semantic information.We conceptualize this limitation of the RNN as a failure to learn atomic internal states -states which capture information relevant to single word types without being influenced by redundant information provided by words with which they co-occur.Using a corpus of artificial language, we verify that redundancy in the training data yields non-atomic internal states, and propose a novel method for inducing atomic internal states.We show that 1) our method successfully induces atomic internal organization in controlled experiments, and 2) under more realistic conditions in which the training consists of childdirected language, application of our method improves the performance of lexical representations on a downstream semantic categorization task.
What's in Your Head? Emergent Behaviour in Multi-Task Transformer Models.

The primary paradigm for multi-task training in natural language processing is to represent the input with a shared pre-trained language model, and add a small, thin network (head) per task. Given an input, a target head is the head that is selected for outputting the final prediction. In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for. We find that non-target heads exhibit emergent behaviour, which may either explain the target task, or generalize beyond their original task. For example, in a numerical reasoning task, a span extraction head extracts from the input the arguments to a computation that results in a number generated by a target generative head. In addition, a summarization head that is trained with a target question answering head, outputs query-based summaries when given a question and a context from which the answer is to be extracted. This emergent behaviour suggests that multi-task training leads to non-trivial extrapolation of skills, which can be harnessed for interpretability and generalization.
Graph Algorithms for Multiparallel Word Alignment

With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently.Alignments are useful for typological research, transferring formatting like markup to translated texts and can be used in the decoding of machine translation systems.At the same time, massively multilingual processing is becoming an important NLP scenario and pretrained language and machine translation models that are truly multilingual are proposed.However, most alignment algorithms rely on bitexts only and do not leverage the fact that many parallel corpora are multiparallel.In this work, we exploit multiparallelity of corpora by representing an initial set of bilingual alignments as a graph and then predicting additional edges in the graph.We present two graph algorithms for edge prediction: one inspired by recommender systems and one based on network link prediction.Our experimental results show absolute improvements of F 1 of up to 28% over the baseline bilingual word aligner in different datasets.
DIRECT: Direct and Indirect Responses in Conversational Text Corpus

We create a large-scale dialogue corpus that provides pragmatic paraphrases to advance technology for understanding the underlying intentions of users. While neural conversation models acquire the ability to generate fluent responses through training on a dialogue corpus, previous corpora have mainly focused on the literal meanings of utterances. However, in reality, people do not always present their intentions directly. For example, if a person said to the operator of a reservation service “I don’t have enough budget.”, they, in fact, mean “please find a cheaper option for me.” Our corpus provides a total of 71,498 indirect–direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset. In addition, we propose three tasks to benchmark the ability of models to recognize and generate indirect and direct utterances. We also investigated the performance of state-of-the-art pre-trained models as baselines.
Composability of global phase invariant distance and its application to approximation error management

Abstract Many quantum algorithms can be written as a composition of unitaries, some of which can be exactly synthesized by a universal fault-tolerant gate set like Clifford+T, while others can be approximately synthesized. One task of a quantum compiler is to synthesize each approximately synthesizable unitary up to some approximation error, such that the error of the overall unitary remains bounded by a certain amount. In this paper we consider the case when the errors are measured in the global phase invariant distance. Apart from deriving a relation between this distance and the Frobenius norm, we show that this distance composes. If a unitary is written as a composition (product and tensor product) of other unitaries, we derive bounds on the error of the overall unitary as a function of the errors of the composed unitaries. Our bound is better than the sum-of-error bound, derived by Bernstein- Vazirani(1997), for the operator norm. This builds the intuition that working with the global phase invariant distance might give us a lower resource count while synthesizing quantum circuits. Next we consider the following problem. Suppose we are given a decomposition of a unitary, that is, the unitary is expressed as a composition of other unitaries. We want to distribute the errors in each component such that the resource-count (specifically T-count) is optimized. We consider the specific case when the unitary can be decomposed such that the R z ( θ ) gates are the only approximately synthesizable component. We prove analytically that for both the operator norm and global phase invariant distance, the error should be distributed equally among these components (given some approximations). The optimal number of T-gates obtained by using the global phase invariant distance is less than what is obtained using the operator norm. Furthermore, we show that in case of approximate Quantum Fourier Transform, the error obtained by pruning rotation gates is less when measured in this distance, rather than the operator norm.
A hybrid sentiment analysis approach using black widow optimization based feature selection

With the massive development of Internet technologies and e-commerce technology, people rely on the product reviews provided by users through web. Sentiment analysis of online reviews has become a mainstream way for businesses on e-commerce platforms to satisfy the customers. This paper proposes a novel hybrid framework with Black Widow Optimization (BWO) based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analyzed using performance metrics such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.
Applicability of On-Site P-Wave Earthquake Early Warning to Seismic Data Observed During the 2011 Off the Pacific Coast of Tohoku Earthquake, Japan

In this study, the on-site P-wave earthquake early warning (EEW) based on the site-specific spectral ratio of S-wave to P-wave to efficiently incorporate the site characteristics, which can potentially issue the earthquake warning by the time of T s-p , was developed. The spectral ratio of S-wave to P-wave that are related to the source effects, the path effects, and the site effects are significantly affected by the site effects contrast to the source effects and the path effects in practical. At first, the on-site P-wave EEW method which multiplies a site-specific spectral ratio of S-wave to P-wave prepared in advance by P-wave observed in the real-time at seismic stations is applied to seismic data for moderate-sized earthquakes with a magnitude ( Mj ) of 5.0–6.0, occurred in the eastern Japan, observed at both the sedimentary basin site and the rock site. As a result, this method predicted well the observed S-wave in the single indicator of SI within the logarithmic standard deviation of 0.25 as well as in the frequency of more than 0.5 Hz. It is, also, confirmed that the site-specific spectral ratio of S-wave to P-wave at a seismic station was stably retrieved from 20 data samples at least. To investigate the applicability of this method to earthquake ground motions induced by a large-scaled earthquake, finally, this method is applied to seismic data during the 2011 off the Pacific coast of Tohoku earthquake, Japan ( Mw 9.0). The prediction of S-wave using a time-window of 10 s after P-wave arrived, could not reproduce the observation with the underestimation; however, the prediction of S-wave using a time-window of more than 20 s containing P-wave propagated from an area generating strong motions in the fault, could reproduce the observation. Even in the case of the large-scaled earthquake, the on-site P-wave EEW method based on the site-specific spectral ratio of S-wave to P-wave at a seismic station availably works by using the gradually increasing time-windows after P-wave arrived in the single indicator of SI as well as in the frequency content, avoiding the mixture of S-wave into a part of P-wave.