Reviewerly/exHarmony huggingface dataset:
YABANCI DİL YARDIMI OLARAK MOBİL DESTEKLİ DİL ÖĞRENMESİ: BİR LİTERATÜR İNCELEMESİ

In recent years, mobile language learning, which can also be defined as mobile-assisted language learning (MALL), is commonly used in a wide range all over the world. MALL is utilized in language learning to foster new possibilities in providing more useful and attractive learning activities. Thus, the current study aimed to identify the effectiveness of MALL as a tool for language learning by examining 10 Ph.D. dissertations (5 from Turkey, 5 from several counties) and 10 journal articles published in different Social Science Citation Index (SSCI) journals between 2013 and 2019. The review was conducted to identify the research domains such as scope, research questions, methodologies, common emergent features and overall implications of those studies. The results showed that while Ph.D. dissertations from Turkey mostly focused on vocabulary learning through several MALL applications, Ph.D. dissertations from other countries had diverse research domains. Further, the article studies vary in terms of scopes, methodologies and contexts, providing insights for context-dependent practices such as restricted settings. Those studies also revealed the pros and cons and suggested employing MALL applications appropriately in facilitating language learning.
Automatic Differentiation Between Legitimate and Fake News Using Named Entity Recognition

Today, the increasing ease of publishing information online combined with a gradual shift of paradigm from consuming news via conventional media to non-conventional media calls for a computational and automatic approach to the identification of an article's legitimacy. In this study, we propose an approach for cross-domain fake news detection focusing on the identification of legitimate content from a pool of articles that are of varying degrees of legitimacy. We present a model as a proof of concept as well as data gathered from evaluating the model on Fake-News AMT, a dataset released for cross-domain fake news detection. The results of our model are then compared against a baseline model which has served as the benchmark for the dataset. We find all results in support of our hypothesis. Our proof-of-concept model has also outperformed the benchmark in the domains Technology and Entertainment as well as when it was run on the whole dataset at once.
A semi-supervised learning approach for CBIR systems with relevance feedback

Semi-supervised learning techniques are gaining importance in the scenario of constantly growing data collections. CBIR systems must be able to autonomously analyze the patterns available, to fully exploit unlabeled data with the final objective of identifying an optimal representation space where data belonging to the same semantic class are close to each other. In this work we propose to adopt relevance feedback as a mean of collecting information about the semantic classes perceived by the user and to exploit this information for a long-term learning process where a more effective feature space can be obtained by a proper metric learning technique and class labels can be automatically assigned to unlabeled patterns. The process can iterate as new data become available thus providing a tool for successfully managing new incoming data. The experimental results will confirm the advantages of the proposed learning approach.
Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara.

Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires significant amounts of pre-processing. More than the linguistic situation of Bambara itself, the socio-cultural context within which Bambara speakers live poses challenges for automated processing of this language. In this paper, we present the first parallel data set for machine translation of Bambara into and from English and French and the first benchmark results on machine translation to and from Bambara. We discuss challenges in working with low-resource languages and propose strategies to cope with data scarcity in low-resource machine translation (MT).
Blockchain 3.0

The rapid growth of R&D in the blockchain world can be explained by the industry’s gaining a deeper understanding of where the value is created and captured for a traditional internet company and a blockchain company. Joel Monegro and Naval Ravikant talk about the idea of fat protocols, where most of the innovation in the blockchain space will happen at the core technology level. Then a token layer can monetize the use of the underlying architecture and provide access to the application layer. To that end, we have seen three iterations of blockchains in development: the first version was the original protocol by Satoshi that provides a functional blockchain for transactions. The second version was protocols like Ethereum that provide smart contracts and on-chain operations. The next generation of blockchain protocols allow on-chain execution and the integration of services such as machine learning onto a blockchain for advanced tasks.
Multi-Year and Typical Year Data for Solar Energy System in South Korea

Typical meteorological year (TMY) data has significant importance for solar resource assessment, as well as for building performance analysis.The necessity of high-accuracy TMY data has been well known for many years for the financial viability of solar long-term planing project as it represents long-term weather features.However, the TMY does not mean the use of real-time data; it is therefore important to determine the accuracy of the TMY dataset.In this study, the solar energy performance of TMY was evaluated quantitatively by comparing it to multi-year average weather data (2008-2017) based on the statistical analysis method.The PV power output were produced by using 100kW photovoltaic system based on PVsyst software at six sites.The results showed that the difference between the TMY-PV power output and long-term PV power output (measured with percentage error) are smaller than 9%, which means there is close-fit agreement between TMY and long-term averages PV power output.These findings suggests that TMY can provide reliable estimation of PV power in the feasibility study of PV project.
Adaptive spatial scale person reidentification

Person reidentification (ReID) requires the discriminative features of an entire pedestrian image to handle the problems of inaccurate person bounding box detection, background confusion, and occlusion. Many recent person ReID methods have attempted to learn the feature information of an entire pedestrian image through parts feature representations, but it is often too time consuming. Person ReID relies on discriminative pedestrian features, and different spatial scales can distinguish features by differing degrees. We propose an innovative and effective adaptive spatial scale person ReID network model based on the residual neural network (ResNet) of an instance batch normalization. Through experimental visualizations, pedestrian features extracted by ResNet from four layers are analyzed, and two layers with discriminative features are selected. Using an adaptive dimension adjustment module, different spatial scale features are aggregated and merged by the aggregation layer. To effectively learn spatial channel correlations and prevent overfitting, a multilayer distribution normalization processing module is designed to implement end-to-end training and evaluate the person ReID networks. Compared with other methods, this network model showed excellent performance on four public person ReID datasets and is superior to most current methods.
Zero-Watermarking Algorithm in Transform Domain Based on RGB Channel and Voting Strategy

A zero-watermarking algorithm in transform domain based on RGB channel and voting strategy is proposed. The registration and identification of ownership have achieved copyright protection for color images. In the ownership registration, discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) are used comprehensively because they have the characteristics of multi-resolution, energy concentration and stability, which is conducive to improving the robustness of the proposed algorithm. In order to take full advantage of the characteristics of the image, we use three channels of R, G, and B of a color image to construct three master shares, instead of using data from only one channel. Then, in order to improve security, the master share is superimposed with the copyright watermark encrypted by the owner’s key to generate an ownership share. When the ownership is authenticated, copyright watermarks are extracted from the three channels of the disputed image. Then using voting decisions, the final copyright information is determined by comparing the extracted three watermarks bit by bit. Experimental results show that the proposed zero watermarking scheme is robust to conventional attacks such as JPEG compression, noise addition, filtering and tampering, and has higher stability in various common color images.
CogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation Identification

We describe our submission to the CogALex-VI shared task on the identification of multilingual paradigmatic relations building on XLM-RoBERTa (XLM-R), a robustly optimized and multilingual BERT model. In spite of several experiments with data augmentation, data addition and ensemble methods with a Siamese Triple Net, Translrelation, the XLM-R model with a linear classifier adapted to this specific task, performed best in testing and achieved the best results in the final evaluation of the shared task, even for a previously unseen language.
RETRACTED: Construction of Chinese synonymous nouns discrimination and query system based on the semantic relation of embedded system and LSTM

Distributed Semantic Model (DSM) establishes their standards for expressing the meaning of words and sentences. DSM provides a quantitative measure of the two language representation is how closely related; it is not possible to automatically classify the different semantic relationships. Chinese semantic analysis methods and stacked two-way long in Word2Vec model, Long Short-Term Memory (stack LSTM) model. The Word2Vec model to capture the word's semantic features was transferred as a high-dimensional word vector and the first words and evaluated the performance of two typical Word2Vec model: Skip grams and Continuous Bag-Of-Words (CBOW). After that, it will use the LSTM models are stacked for feature extraction of continuous word vector. Therefore, the concept of similarity of meaning is not yet in the DSM. An effort to solve the problem of underspecification will introduce the evolution embedded system. Also, are a different kind of test of the career of automatic learning the words of these semantic relationships, will evaluate them both that there is no teacher in the teaching environment, the distribution model is, in many cases, in general, to find that it is possible to specify a high similarity score for its synonym, deep learning classifier is the best in recognition of semantic relationships.
Organization of project activities in the system of advanced training of teachers

Project activity occupies an important place in the system of professional development. Focused on a rapidly changing environment, it provides the widest opportunities for innovative development of the field of additional education. The implementation of project activities allows you to form important for students of courses, leading decisions in the context of global educational competition and high uncertainty of the educational environment. The purpose of the article is to study the experience of organizing project activities in the system of professional development of teachers. Project activity is a joint educational creative activity of course participants that has a common goal and agreed methods to achieve it. The article reveals the experience of project activities of students in the framework of the course "Management in education". In the process of implementing project activities, the project competence of students is formed. Project activity occupies an important place in the system of professional development. Its implementation allows you to radically change the entire learning process, make it active, creative and independent.
Dynamic Complexity Tuning for Hardware-Aware Probabilistic Circuits

Probabilistic inference is a well suited approach to address the challenges of resource constrained embedded application scenarios. In particular, probabilistic models learned generatively are robust to missing data and are capable of encoding domain knowledge seamlessly. These traits have been leveraged to propose hardware-aware probabilistic learning and inference strategies that induce Pareto optimal accuracy versus resource consumption trade-offs. This paper proposes a model-complexity tuning strategy that relies on ensembles of probabilistic classifiers to identify the difficulty of the classification task on a given instance. It then dynamically switches to a higher or lower complexity setting accordingly. The strategy is evaluated on an embedded human activity recognition scenario and demonstrates a superior performance when compared to the Pareto-optimal trade-off obtained when the ensembles are deployed statically, especially in low cost regions of the trade-off space. This makes the strategy amenable to embedded computing scenarios, where one of the main constraints towards always-on functionality are the device’s strict resource constraints.
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature

The coronavirus disease (COVID-19) has claimed the lives of over one million people and infected more than thirty-five million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations inherent to scientific literature. Moreover, because these engines mainly rely upon semantic information, their ability to capture complex global relationships across documents is limited, which reduces the quality of similarity-based article recommendations for users. In this work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles. The CKG combines semantic information with document topological information for the application of similar document retrieval. The CKG is constructed using the latent schema of the data, and then enriched with biomedical entity information extracted from the unstructured text of articles using scalable AWS technologies to form relations in the graph. Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval. Analysis demonstrates the quality of relationships in the CKG and shows that it can be used to uncover meaningful information in COVID-19 scientific articles. The CKG helps power www.cord19.aws and is publicly available.
3D Curve Planning Algorithm of Aircraft Under Multiple Constraints

The trajectory planning of the aircraft is generally based on different mission requirements, under certain constraints to find an available optimal mission route. The traditional 3D trajectory planning algorithm is easy to fall into the local optimum. The search speed is full when the algorithm is searched. Some algorithms can only perform polyline search and fail to fully consider the physical reality of the aircraft. Aiming at the above problems, this paper proposes a path planning algorithm that combines $$ {\text{A}}^{*} $$ algorithm and Dubins curve comprehensive optimization. The algorithm in this paper adopts heuristic search algorithm, performs two pruning by setting parameters, and through reasonable parameter settings, in a short time, the UAV’s three-dimensional curve trajectory planning is quickly performed, which is greatly improved compared with other current algorithms.
Adversarial Training with Orthogonal Regularization

Deep neural networks have been successful in various domains, such as computer vision and natural language processing. On the other hand, researchers have discovered a vulnerability of convolutional neural networks to the samples with imperceptible perturbations, also known as, adversarial perturbations. It has been observed that adversarial perturbations can alter the predictions of a deep model. One of the most common approaches to increase the robustness of deep models is adversarial training. However, adversarial training often suffers from a degradation in generalization performance. In this study, orthogonal regularization is used along with adversarial training to facilitate both generalizability and adversarial robustness in deep models. According to the experiments on MNIST and CIFAR10 datasets, imposing orthogonality in weights improves both the generalization performance and adversarial robustness.
Timetracker capabilities in student project activities

The purpose of the work is to consider the possibilities of time trackers in the design activities of students. The article presents an analysis of modern time trackers that contribute to the self-organization of students to achieve educational goals. In the context of an increase in the share of independent training in universities and the expansion of distance learning, there is a need for students to master time management, which allows them to conduct activities most productively.
Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at #SMM4H 2020

The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications (#SMM4H) 2020 shared tasks – on detecting mentions of adverse effects (Task 2), extracting and normalizing them (Task 3), and detecting mentions of medication abuse (Task 4). Our approaches relied on a combination of traditional machine learning and deep learning models. On Tasks 2 and 4, our submitted runs performed at or above the task average.
A Pyranometer for Solar Radiation Measurement-Review

There is a problem of availability of sufficient and functional instruments for measuring solar radiation in Nigeria due to higher importation costs and maintenance. So, it is preferred to construct devices from locally available equipment. This paper reviews the pyranometer to measure solar radiation, to assess the availability of solar energy arriving on Earth, it is important to measure solar radiation at some locations. From the reviewed existing research works, Silicon Photodiode-based pyranometers are preferred to thermopile-based pyranometers as the former has lower cost, more portable and requires little maintenance.
Disparity-augmented trajectories for human activity recognition

Numerous methods for human activity recognition (HAR) have been proposed in the past two decades. Many of these methods are based on sparse representations, which describe the whole video content by a set of local features. Trajectories, as mid-level sparse features, are capable of describing the movements of interest points in two-dimensional (2D) space. However, 2D trajectories might be affected by viewpoint changes, potentially decreasing their accuracies. In this paper, we first propose and compare different 2D trajectory-based algorithms for human activity recognition. Then, we propose a new way of augmenting 2D trajectories with disparity information, without the calculation of the 3D reconstruction. Our obtained HAR results have shown a 2.76% improvement when using disparity-augmented trajectories, compared to using classical 2D trajectory information only. Furthermore, we have also tested our method on the challenging Hollywood 3D dataset, on which we have obtained competitive results, at a much faster speed.
Social origins of risk semiotics

The article examines the structure of the inter-relationship between society and its inherent risks, the main components of which are society’s accumulated experience in predicting and mitigating risks, the continuous complication of modern society and the new social risks it generates. The reasons for the formation of these components, the positive and negative results of their use by society are analyzed. The reactions of managers and scientists to existing social risks are described. The main difference between these reactions is the diametrically opposite attitude of managers and scientists to the phenomenon of reductionism in solving complex social problems. The article defines the role of interdisciplinary research areas (synergetics, systemology, the combined social analysis, science of science, etc.) in solving problems related to social risks. The proposed by A. G. Teslinov’s classification of existing worlds (the material world, the world of ideas, the social world and the world of signs) correlates with traditional disciplinary classifications. The place of a new scientific direction (risk semiotics) in the system of existing risk sciences, as well as among other artificial semiotics is established. The conclusion about the need for interrelated development of social semiotics and risk semiotics is substantiated.
Formation of bachelors’ competence readiness to work in a team

The purpose of the article is to analyze the experience of forming the competence of bachelors' readiness to work in a team. This competence is characterized by the ability of the student to realize their role in the team and to carry out social interaction, to focus on cooperation with team members. The requirements for training modern bachelors who are capable of continuous personal and professional growth through effective collective work are reflected in regulatory documents. In the structure of the competence of readiness to work in a team, we distinguish motivational, cognitive and functional components. Their formation is carried out in the course of studying the module "Man, society, culture", the disciplines "Fundamentals of management culture", "General psychology" and others. Where a significant role is played by the use of innovative educational technologies that contribute to building effective interaction between students. Special attention is paid to the development of such qualities of the student as sociability, openness, a sense of mutual assistance, positive emotional mood. Modern educational technologies of interaction have an active impact on the process of forming the bachelor's competencies of readiness to work in a team.