Reviewerly/exHarmony huggingface dataset:
The Role of Trademarks on Online Retail Platforms: An EU Trademark Law Perspective

Abstract This article analyses the possible implications of the application of AI on online retail platforms for the role of EU trademarks and in particular their functions in this digital environment. It comes to the conclusion that the influence of AI on the importance of functions of origin and quality may differ from that on the accessory functions. Also, to a certain extent, this influence may vary depending on the parameters under which AI is set to function. Nevertheless, regardless of the parameters, it seems likely that the importance of the origin and quality functions may be diminished. In contrast, the significance of the accessory trademark functions may depend on the parameters under which AI is set to operate. If the goal of an AI implemented by a platform is to satisfy the needs of a consumer whose main preference is to acquire products with a specific trademark then ideally such an AI should seek to achieve this goal and, as a result, that mark may become a criterion for offering a product or service or acquiring it on behalf of the user. In this case, accessory functions may gain importance on online retail platforms, in turn encouraging trademark owners to invest more in the goodwill and attractiveness of their marks. However, if the parameters of the AI are such that it offers or makes purchases based on criteria that only serve the commercial interests of online retail platforms or their clients, and the trademarks preferred by consumers are not among such criteria, the importance of accessory functions may be weakened.
The Wavelet-Based Denoising Of Images in Fiji, With Example Applications in Structured Illumination Microscopy

Filtration of super-resolved microscopic images brings often troubles with removing undesired image parts like, e.g., noise, inhomogenous background and reconstruction artifacts. Standard filtration techniques, e.g., convolution- or Fourier transform-based methods are not always appropriate, since they may lower image resolution that was acquired by hi-tech and expensive microscopy systems. Thus, in this article it is proposed to filter such images using discrete wavelet transform (DWT). Newly developed Wavelet_Denoise plugin for free available Fiji software package demonstrates important possibilities of applying DWT to images: Decomposition of a filtered picture using various wavelet filters and levels of details with showing decomposed images and visualization of effects of back transformation of the picture with chosen level of suppression or denoising of wavelet coefficients. The Fiji framework allows, for example, using a plethora of various microscopic image formats for data opening, users can easily install the plugin through a menu command and the plugin supports processing 3D images in Z-stacks. The application of the plugin for removal of reconstruction artifacts and undesirable background in images acquired by super-resolved structured illumination microscopy is demonstrated as well.
RBF Neural Network-Based Frequency Band Prediction for Future Frequency Hopping Communications

On the basis of the chaotic features of the frequency hopping signal, frequency band prediction for frequency hopping signal can enhance the interference effect of the signal greatly. However, poor prediction accuracy often limits its development in the military field. Therefore, for the sake of enhancing the frequency band prediction accuracy of frequency hopping signal, this paper studies the radial basis function (RBF) neural network frequency hopping signal frequency band prediction model based on the gradient descent method and improved the particle swarm optimization algorithm, respectively. The former uses a step-by-step algorithm to optimize the center value and weight so that the network can find the most suitable initial state. Then, the clustering selection optimization algorithm is employed to optimize the central value. In addition, it optimizes the weight by using a gradient descent method of the optimal learning rate. The latter optimizes the structure of the RBF neural network through the combination of the subtractive clustering algorithm and improved the particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the gradient RBF algorithm model performs better in terms of accuracy, but time efficiency is lower, while the PSO-RBF algorithm has better time efficiency.
Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT.

This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic tweet in the form of modern standard Arabic or dialect comes from. We solve the task in two parts. The first part involves pre-processing the provided dataset by cleaning, adding and segmenting various parts of the text. This is followed by carrying out experiments with different versions of two Transformer based models, AraBERT and AraELECTRA. Our final approach achieved macro F1-scores of 0.216, 0.235, 0.054, and 0.043 in the four subtasks, and we were ranked second in MSA identification subtasks and fourth in DA identification subtasks.
Smart Earthquake Disaster Prevention System

Strong earthquakes can cause buildings to collapse, fires, and jammed doors and elevators.In this study we used the Internet of Things to develop a smart earthquake disaster prevention system.The system continuously monitors the real-time earthquake warning data of the Central Weather Bureau (CWB).When real-time seismic data are updated, the system immediately obtains the intensity and location of the earthquake and calculates the intensity and time of arrival at different locations.When the earthquake is judged to be dangerous, the system immediately shuts off the gas supply.When residents are determined to be at home by a human body detector, a sound and light earthquake alarm is immediately activated, and real-time earthquake data are displayed on LINE instant messaging platform, short message service (SMS), and electronic displays.Doors are also automatically opened to prevent them from being stuck.Earthquake information is immediately sent to regional elevator management centers to instruct elevators to stop at the next floor, open the doors, and broadcast information on the upcoming strong earthquake.By developing this system, the latest earthquake warning information from the Central Meteorological Administration can be effectively captured, and necessary evacuation and disaster mitigation measures can minimize deaths and property losses from earthquakes.
A Mixed Transmission Estimation Iterative Method for Single Image Dehazing

Considering the frequent occurrence of hazy weather, single image dehazing has become an important research task. Physical model-based and deep learning-based methods are two competitive methods in single image dehazing, but achieving fidelity and effectively removing haze at the same time in real hazy scenes is still a challenging problem. In this work, we propose a mixed iterative model to restore high-quality clear images by integrating a physical model-based approach and a learning-based approach, which can combine the advantages of the physical model-based method in maintaining natural attributes and the advantages of the learning-based method in completely removing haze. For a hazy image, first, according to the haze density, it is divided into separate regions to calculate the local atmospheric light. Then, we utilize the dark channel prior and DehazeNet to jointly estimate the transmission, which can estimate an accurate recovered image that is more in line with the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. The experiments on both synthetic and natural hazy image datasets indicate that the proposed method can restore natural and clean images and has superior performance than other state-of-the-art algorithms in peak signal-to-noise ratio, structural similarity, information entropy, color natural index, and color histogram. And in the four indicators of the quantitative results of the synthetic hazy test set, we achieve the highest score in three of them and the second highest result in another. In the comparison of the two quantitative indicators of natural hazy images, our average objective results are both the highest.
Social Media Variety Geolocation with geoBERT.

This paper describes the Helsinki–Ljubljana contribution to the VarDial 2021 shared task on social media variety geolocation. Following our successful participation at VarDial 2020, we again propose constrained and unconstrained systems based on the BERT architecture. In this paper, we report experiments with different tokenization settings and different pre-trained models, and we contrast our parameter-free regression approach with various classification schemes proposed by other participants at VarDial 2020. Both the code and the best-performing pre-trained models are made freely available.
Changing the Mind of Transformers for Topically-Controllable Language Generation

Large Transformer-based language models can aid human authors by suggesting plausible continuations of text written so far.However, current interactive writing assistants do not allow authors to guide text generation in desired topical directions.To address this limitation, we design a framework that displays multiple candidate upcoming topics, of which a user can select a subset to guide the generation.Our framework consists of two components: (1) a method that produces a set of candidate topics by predicting the centers of word clusters in the possible continuations, and ( 2) a text generation model whose output adheres to the chosen topics.The training of both components is self-supervised, using only unlabeled text.Our experiments demonstrate that our topic options are better than those of standard clustering approaches, and our framework often generates fluent sentences related to the chosen topics, as judged by automated metrics and crowdsourced workers.
Shaping Social Literacy at Distance Lessons by Using Multimedia Technologies

The article studies innovation methods of distance interaction in education. The necessity to pass over to distance learning was caused by global pandemic. On March 11, 2020 the WHO called the outbreak of COVID-19 a pandemic, which drastically changed traditional mode of life, business and education all over the world. The demand for learning became more acute. For millions of people distance communications were the only link with the outer world. The need and necessity in distance access to school, secondary and higher education affected the development of new trends in the field of education. The change-over to distance learning in schools and universities requires application of different methods of involving schoolchildren and students in the process of learning and improving skills of independent thinking. The authors provided a number of recommendations dealing with multimedia technology application in distance learning, which aimed at the development of cognitive, communicative skills and skills of logical, creative and critical thinking and social literacy.
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning

Likelihood training and maximization-based decoding result in dull and repetitive generated texts even when using powerful language models (Holtzman et al., 2019).Adding a loss function for regularization was shown to improve text generation output by helping avoid unwanted properties, such as contradiction or repetition (Li et al., 2020).In this work, we propose fine-tuning a language model by using policy gradient reinforcement learning, directly optimizing for better generation.We apply this approach to minimizing repetition in generated text, and show that, when combined with unlikelihood training (Welleck et al., 2020), our method further reduces repetition without impacting the language model quality.We also evaluate other methods for improving generation at training and decoding time, and compare them using various metrics aimed at control for better text generation output.
L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset

Sentiment analysis is one of the most fundamental tasks in Natural Language Processing. Popular languages like English, Arabic, Russian, Mandarin, and also Indian languages such as Hindi, Bengali, Tamil have seen a significant amount of work in this area. However, the Marathi language which is the third most popular language in India still lags behind due to the absence of proper datasets. In this paper, we present the first major publicly available Marathi Sentiment Analysis Dataset - L3CubeMahaSent. It is curated using tweets extracted from various Maharashtrian personalities’ Twitter accounts. Our dataset consists of ~16,000 distinct tweets classified in three broad classes viz. positive, negative, and neutral. We also present the guidelines using which we annotated the tweets. Finally, we present the statistics of our dataset and baseline classification results using CNN, LSTM, ULMFiT, and BERT based models.
Ask2Transformers: Zero-Shot Domain labelling with Pretrained Language Models

In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels. We exploit the knowledge encoded within different off-the-shelf pre-trained Language Models and task formulations to infer the domain label of a particular WordNet definition. The proposed zero-shot system achieves a new state-of-the-art on the English dataset used in the evaluation.
Project Scheduling Management in the Software Industry

Project scheduling is the next step once project planning is completed. This requires knowledge and the depth of the activities, the dependencies between activities and, duration of the activities. Creating and designing a realistic project schedule is the biggest challenges a project manager faces. There are some common problems that can be impact towards project delay or performance: Assigning Resource or Resource Constraint, Managing the Unknown or Managing Change and Estimating Task Duration. These 3 factors and causes are contributing to project scheduling problem. The impact of resource constraint causes towards be impact towards project delay or performance. To achieve this goal, the survey has been conducted. Finally, the results of the surveys are presented in order to see which commons problems are the most happening and really effected project scheduling.
Confidence-Aware Embedding for Knowledge Graph Entity Typing

Knowledge graphs (KGs) entity typing aims to predict the potential types to an entity, that is, (entity, entity type = ?). Recently, several embedding models are proposed for KG entity types prediction according to the existing typing information of the (entity, entity type) tuples in KGs. However, most of them unreasonably assume that all existing entity typing instances in KGs are completely correct, which ignore the nonnegligible entity type noises and may lead to potential errors for the downstream tasks. To address this problem, we propose ConfE, a novel confidence-aware embedding approach for modeling the (entity, entity type) tuples, which takes tuple confidence into consideration for learning better embeddings. Specifically, we learn the embeddings of entities and entity types in separate entity space and entity type space since they are different objects in KGs. We utilize an asymmetric matrix to specify the interaction of their embeddings and incorporate the tuple confidence as well. To make the tuple confidence more universal, we consider only the internal structural information in existing KGs. We evaluate our model on two tasks, including entity type noise detection and entity type prediction. The extensive experimental results in two public benchmark datasets (i.e., FB15kET and YAGO43kET) demonstrate that our proposed model outperforms all baselines on all tasks, which verify the effectiveness of ConfE in learning better embeddings on noisy KGs. The source code and data of this work can be obtained from https://github.com/swufenlp/ConfE.
Toward the creation of WordNets for ancient Indo-European languages.

This paper presents the work in progress toward the creation of a family of WordNets for Sanskrit, Ancient Greek, and Latin. Building on previous attempts in the field, we elaborate these efforts bridging together WordNet relational semantics with theories of meaning from Cognitive Linguistics. We discuss some of the innovations we have introduced to the WordNet architecture, to better capture the polysemy of words, as well as Indo-European language family-specific features. We conclude the paper framing our work within the larger picture of resources available for ancient languages and showing that WordNet-backed search tools have the potential to re-define the kinds of questions that can be asked of ancient language corpora.
Correlation or regression, that’s the question

There are different ways to quantify the relation between two or more continuous variables. Some researchers use correlation coefficients; others will apply regression methods such as linear regression. In this paper, we show that the choice between correlation and regression is not purely a statistical one but largely depends on the research aims. Importantly, one should always inspect the data before using either of the two methods.
The results of department educational processes reengineering in dual and digital education concepts

The subject matter of the article is the results of department educational processes reengineering, which was conducted at the Department of Land Administration and Geographic Information Systems of O. M. Beketov National University of Urban Economy. The current article introduces the scientific community with the results of reengineering at the department, which made it possible to implement certain conceptual and fundamental provisions of the dual, digital, and cluster approaches in the development of education. The problem, which resolves the contradiction between the standardization of learning processes by traditional methods and innovative learning processes based on the use of intelligent information technologies, is devised. Based on the analysis of the tendencies in the development of pedagogy methodology, it was concluded that today the methodological paradigms in the science of teaching and the formation of a new methodological paradigm of educology were changed. Cybernetic pedagogy, which combines methods of traditional pedagogy and cybernetics, has had a significant impact on the formation of educalogy. This combination of methods created a support system for the educational processes of the department. The basic theoretical decisions of the department reengineering and results of experimental research conducted based on a support system of educational processes of the department are shown in the article. The results of two full-scale experiments are presented. The purpose of the first experiment was to evaluate the possibility of independent study of science-intensive educational material in the discipline "Mathematical processing of geodetic measurements," and for the second – to evaluate the possibility of creating an intellectual property exchange of scientific-pedagogical and pedagogical workers. Besides, the results of the experimental studies allowed to creation of innovative authoring training technology "Partnership," which provides for participating students in the formation of educational material (tutorials). From the above, it is possible to conclude about the effectiveness of reengineering procedures in the concept of dual and digital education.
An Optimized DiscretizationApproach using k-Means Bat Algorithm

This study has proposed arelatively new discretization approach using k-means and Bat algorithm in preparation phase of classification problem. In essence, bat algorithm is applied to find the best search space solution. Eventually, the best search space solution is utilized to produce cluster centroid. The cluster centroid is very useful to determine appropriate breakpoint for discretization. The proposed discretization approach is applied in the experiments with continuous datasets. Decision Tree, k-Nearest Neighbours and Naïve Bayes classifiers are used in the experiments. The proposed discretization approach is evaluated against other existing approaches: K-Means algorithm, hybrid K-Means with Particle Swarm Optimization (PSO) and hybrid K-Means with Whale Optimization Algorithm (WOA).The classification performance is evaluated in terms of accuracy, recall, f-measure and receiver operating characteristic curve (ROC). To test the performance of the proposed algorithm, nine benchmark continuous datasets are used. The proposed algorithm show the better results compare to other approaches. The proposed algorithm performs better in discretization to solve classification problems.
Mining Experts from Source Code Analysis: An Empirical Evaluation

Modern software development increasingly depends on third-­party libraries to boost productivity and quality. This development is complex and requires specialists with knowledge in several technologies, such as the nowadays libraries. Such complexity turns it extremely challenging to deliver quality software, given the pressure. For this purpose, it is necessary to identify and hire qualified developers, to obtain a good team, both in open source and proprietary systems. For these reasons, enterprise and open source projects try to build teams composed of highly skilled developers in specific libraries. However, their identification may not be trivial. Despite this fact, we still lack procedures to assess developers skills in widely popular libraries. In this paper, we first argue that source code activities can identify software developers’ hard skills, such as library expertise. We then evaluate a mining­-based strategy to reduce the search space to identify library experts. To achieve our goal, we selected the 9 most popular Java libraries and 6 libraries for microservices (i.e., 15 libraries in total). We assessed the skills of more than 1.5 million developers in these libraries by analyzing their commits in more than 17 K Java projects on GitHub. We evaluated the results by applying two surveys with 158 developers. First, with 137 library expert candidates, they observed 63% precision for popular Java libraries’ used strategy. Second, we observe a precision of at least 71% for 21 library experts in microservices. These low precision values suggest space for further improvements in the evaluated strategy.
Exploring Implicit Sentiment Evoked by Fine-grained News Events

We investigate the feasibility of defining sentiment evoked by fine-grained news events. Our research question is based on the premise that methods for detecting implicit sentiment in news can be a key driver of content diversity, which is one way to mitigate the detrimental effects of filter bubbles that recommenders based on collaborative filtering may produce. Our experiments are based on 1,735 news articles from major Flemish newspapers that were manually annotated, with high agreement, for implicit sentiment. While lexical resources prove insufficient for sentiment analysis in this data genre, our results demonstrate that machine learning models based on SVM and BERT are able to automatically infer the implicit sentiment evoked by news events.