Reviewerly/exHarmony huggingface dataset:
Aspects of organizational and technical solutions of the transport communications department

Nowadays, under my leadership, work is underway in the following main areas: 1. Scientific and methodological foundations for improving the quality of road construction products (Development of a set of models and methods for improving the quality of individual elements of the "road" system. Development of efficiency (quality) criteria for the production of building materials); 2. Modeling transport systems by economic and mathematical methods (Systematization of organizational and technological solutions for managing the processes of building transport systems by economic and mathematical methods); 3. Economic and mathematical models of the integrated use of various types of transport (Development of criteria for assessing the economic efficiency of using information modeling technology in the organization of construction production of transport systems in the interests of ensuring safety).
Enhanced Symbol Recognition based on Advanced Data Augmentation for Engineering Diagrams

Symbol recognition has generated research interest for image analytics of engineering diagrams.Techniques including structural, syntactic, statistical, Convolution Neural Network (CNN) were studied to identify gaps of research.Despite popularity, CNN requires huge learning dataset, which often involves costly procurement.To address this, combination between CycleGAN and CNN is proposed.CycleGAN generates more learning dataset synthetically, thus yielding opportunity to improve accuracy of symbol recognition.In the domain of for engineering symbols, standard CNN model is developed and used in experimental testing.Different ratios of training dataset were tested in multiple experiments using Piping and Instrument Diagram (P&IDs) drawings.Result of highest accuracy for symbol recognition is up to 92.85% against baseline and other method.The results determined that gradual reduction of training samples, the effectiveness of recognition accuracy performance after using proposed method was remained substantially stable.
Predicting scalar diversity with context-driven uncertainty over alternatives

Scalar implicature (SI) arises when a speaker uses an expression (e.g., some) that is semantically compatible with a logically stronger alternative on the same scale (e.g., all), leading the listener to infer that they did not intend to convey the stronger meaning.Prior work has demonstrated that SI rates are highly variable across scales, raising the question of what factors determine the SI strength for a particular scale.Here, we test the hypothesis that SI rates depend on the listener's confidence in the underlying scale, which we operationalize as uncertainty over the distribution of possible alternatives conditioned on the context.We use a T5 model fine-tuned on a text infilling task to estimate this distribution.We find that scale uncertainty predicts human SI rates, measured as entropy over the sampled alternatives and over latent classes among alternatives in sentence embedding space.Furthermore, we do not find a significant effect of the surprisal of the strong scalemate.Our results suggest that pragmatic inferences depend on listeners' context-driven uncertainty over alternatives.
5g Mobile Edge Assisted Metaverse Light Field Video System: Prototype Design and Empirical Evaluation

Metaverse is recently envisioned as the main driver for immersive multimedia in future networks. Light field video (LFV), regarded as the intermediate transition scheme towards metaverse, is conducive to sensing and reconstructing a realistic scene. However, the research on the LFV system still lacks investigation, hindering the application of metaverse in practice. This paper addresses major technical challenges in LFV delivery through mobile networks with the edge and cloud infrastructure. A novel 5G Mobile Edge Assisted Metaverse Light-field-video System (MEAMLS) is proposed, integrating LFV collection, metaverse content construction, and an extended reality viewing scheme. A custom-made LFV capture array is designed to capture real-world scenes, and a learning method-integrated edge server is applied to assist adaptive wide-baseline LFV coding. Aiming at reducing the latency of computation, a fast sparse reconstruction algorithm for metaverse content construction is established with edge-cloud collaboration. Intelligent service for users is deployed on edge to achieve viewport-driven extended reality viewing. By providing the same visual experience as reality, the proposed MEAMLS bridges the physical and virtual worlds. The system is prototyped on a realistic 5G testbed to validate the performance under indoor and outdoor circumstances empirically. Experimental results provide fundamental insights for the design of the mobile edge networks assisted LFV system from the perspective of transmission resource, adaptability, and deployment, respectively.
Science and art. Personalization

The crisis of the scientific format of thinking is considered and one of its manifestations is personalization, which takes place today in all basic forms of human activity. On the basis of structural dynamics and an appeal to ancient ideas about time, it is shown that Kuznetsov’s writing – a vivid example of modern visual media, is a new form of personalized art and an adequate response to the crisis of the scientific format of thinking.
University Students’ Perspective Towards Indonesian Language Compulsory Courses at Muhammadiyah and ‘Aisyiyah Universities

This study aims to describe: (1) the implementation suitability of the Indonesian Language Compulsory Course based on the Decision Director-General of the university no 84/E/KPT/2020, (2) the progress forms that students get after taking the Indonesian Language Compulsory Course, (3) the implementation of Indonesian Language Compulsory Course, and (4) the difficulty causes in implementing Indonesian Language Compulsory Course.This study uses qualitative to explore the information behind the data.The subject of the study is students of Muhammadiyah and 'Aisyiyah universities who had taken Indonesian language compulsory course.The object is students' perspectives on implementing the Indonesian language compulsory course.Data collection is a questionnaire that 123 respondents fill out from 16 Muhammadiyah and 'Aisyiyah Universities throughout Indonesia.The result shows that implementing Indonesian language compulsory course at Muhammadiyah and 'Aisyiyah universities have been carried out well.
Textual Content Moderation in C2C Marketplace

Automatic monitoring systems for inappropriate user-generated messages have been found to be effective in reducing human operation costs in Consumer to Consumer (C2C) marketplace services, in which customers send messages directly to other customers.We propose a lightweight neural network that takes a conversation as input, which we deployed to a production service.Our results show that the system reduced the human operation costs to less than one-sixth compared to the conventional rule-based monitoring at Mercari.
Analysis of hyperparameters in Sentiment Analysis of Movie Reviews using Bi-LSTM

Movie reviews are an important factor in determining a film’s success because instead of depending solely on the number of views as a parameter for the success of the movie, movie reviews are used to acquire additional insights into the movie. Existing systems use LSTM for sentiment analysis but there is no study available how various hyperparameters affect the performance of the model. Bi-LSTM along with dropout layers provide good accuracy in sentiment analysis. The suggested method outperforms CNN and Natural Language Toolkit in terms of accuracy.The proposed model is tested using different hyper parameters including dropout rate,number of Bi-LSTM layers and Bi-LSTM nodes. 64 LSTM nodes, 2 Bi-directional Layers, and a 0.2 Dropout rate should be used for optimal accuracy. Effect of different text vectorization algorithms and activation functions was also studied. The combination of Tf-idf text vectorization and the ReLU activation function yields the best results.
Using Graph Mining Method in Analyzing Turkish Loanwords Derived from Arabic Language

Loanwords are the words transferred from one language to another, which become essential part of the borrowing language. The loanwords have come from the source language to the recipient language because of many reasons. Detecting these loanwords is complicated task due to that there are no standard specifications for transferring words between languages and hence low accuracy. This work tries to enhance this accuracy of detecting loanwords between Turkish and Arabic language as a case study. In this paper, the proposed system contributes to find all possible loanwords using any set of characters either alphabetically or randomly arranged. Then, it processes the distortion in the pronunciation, and solves the problem of the missing letters in Turkish language relative to Arabic language. A graph mining technique was introduced, for identifying the Turkish loanwords from Arabic language, which is used for the first time for this purpose. Also, the problem of letters differences, in the two languages, is solved by using a reference language (English) to unify the style of writing. The proposed system was tested using 1256 words that manually annotated. The obtained results showed that the f-measure is 0.99 which is high value for such system. Also, all these contributions lead to decrease time and effort to identify the loanwords in efficient and accurate way. Moreover, researchers do not need to have knowledge in the recipient and the source languages. In addition, this method can be generalized to any two languages using the same steps followed in obtaining Turkish loanwords from Arabic.
MirrorAlign: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning

Word alignment is essential for the downstream cross-lingual language understanding and generation tasks.Recently, the performance of the neural word alignment models (Garg et al., 2019; Ding et al., 2019;Zenkel et al., 2020) has exceeded that of statistical models.However, they heavily rely on sophisticated translation models.In this study, we propose a super lightweight unsupervised word alignment model named MirrorAlign, in which a bidirectional symmetric attention trained with a contrastive learning objective is introduced, and an agreement loss is employed to bind the attention maps, such that the alignments follow mirror-like symmetry hypothesis.Experimental results on several public benchmarks demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in word alignment while significantly reducing the training and decoding time on average.Further ablation analysis and case studies show the superiority of our proposed MirrorAlign.Notably, we recognize our model as a pioneer attempt to unify bilingual word embedding and word alignments.Encouragingly, our approach achieves 16.4× speedup against GIZA++, and 50× parameter compression compared with the Transformerbased alignment methods.We release our code to facilitate the community 1 .
Deep Siamese Network with Co-channel and Cr-Spatial Attention for Object Tracking

Siamese trackers with offline training strategies have recently drawn great attention because of their balanced accuracy and speed. However, some limitations still remain to overcome, i.e., trackers cannot robustly discriminate target from similar background so far. In this paper, we propose a novel real-time co-channel and spatial attention based deeper Siamese network (DCANet). Our approach aims at dealing with some challenging situations like appearance variations, similar distractors, etc. Different from replacing the backbone network Alexnet with VGG16 directly, we modified the structure of VGG16 which has no fully connective layer and padding operation. In addition, co-channel and spatial attention mechanisms were applied to our method to enhance feature representation capability. Channel attention and spatial attention were proposed towards computer vision problems before. However, considered the special structure of siamese network, we designed Co-channel attention module which helps to emphasize the important areas in the two branches simultaneously. When we directly add spatial attention to our tracker, the tracking effect falls. However with a crop operation placed after spatial attention our tracker can tracking better. We perform extensive experiments on several benchmark datasets, including OTB-2013, OTB-2015, VOT-2017, LaSOT and GOT-10k, which demonstrate that our DCANet gains a competitive tracking performance, with a running speed of more than 60 frames per second.
Comments on “Domain Alignment Embedding Network for Sketch Face Recognition”

Guo <i>et al.</i> proposed a method for automated face-sketch recognition in their paper &#x201C;Domain Alignment Embedding Network for Sketch Face Recognition&#x201D;, where it is deemed to be superior to several other methods published in literature. However, the employed evaluation methodology contains several critical flaws, such that definitive conclusions with regards to performance comparisons between the methods considered cannot be made. Moreover, erroneous results are reported for the works published in literature given the employed evaluation framework, potentially compromising the accuracy of future works that use the results published by Guo <i>et al.</i> Discussions on these and other observations are given in this manuscript.
Ethical Considerations for Low-resourced Machine Translation

This paper considers some ethical implications of machine translation for low-resourced languages.I use Armenian as a case study and investigate specific needs for and concerns arising from the creation and deployment of improved machine translation between English and Armenian.To do this, I conduct stakeholder interviews and construct Value Scenarios (Nathan et al., 2007) from the themes that emerge.These scenarios illustrate some of the potential harms that low-resourced language communities may face due to the deployment of improved machine translation systems.Based on these scenarios, I recommend 1) collaborating with stakeholders in order to create more useful and reliable machine translation tools, and 2) determining which other forms of language technology should be developed alongside efforts to improve machine translation in order to mitigate harms rendered to vulnerable language communities.Both of these goals require treating low-resourced machine translation as a language-specific, rather than language-agnostic, task.
Concurrent Skill Composition Using Ensemble of Primitive Skills

One of the key characteristics of an open-ended cumulative learning agent is that it should use the knowledge gained from prior learning to solve future tasks. That characteristic is especially essential in robotics, as learning every perception-action skill from scratch is not only time-consuming but may not always be feasible. In the case of reinforcement learning, this learned knowledge is called a policy. The lifelong learning agent should treat the policies of learned tasks as building blocks to solve those future tasks. One of the categorizations of tasks is based on its composition, ranging from primitive tasks to compound tasks that are either a sequential or concurrent combination of primitive tasks. Thus, the agent needs to be able to combine the policies of the primitive tasks to solve compound tasks, which are then added to its knowledge base. Inspired by modular neural networks, we propose an approach to compose policies for compound tasks that are concurrent combinations of disjoint tasks. Further, we hypothesize that learning in a specialized environment leads to more efficient learning; hence, we create scaffolded environments for the robot to learn primitive skills for our mobile robot-based experiments. We then show how the agent can combine those primitive skills to learn solutions for compound tasks. That reduces the overall training time of multiple skills and creates a versatile agent that can mix and match the skills.
Impact of Longer Viewing Distance to Virtual Screen upon Mouse-Manipulation Performance

This manuscript employs an HMD-based virtual display environment and looks carefully into impacts of long viewing distances to a virtual screen upon mouse manipulation performance by including conditions of viewing distances at smaller intervals. At the moment of writing this manuscript, the experiment is being in progress and this poster reports the latest results. The results show that the speed of mouse manipulation would increase with a certain amount of error, and physical fatigue as well as psychological one would get worse for longer viewing distances.
A Comprehensive Source Code Plagiarism Detection Software

Source code plagiarism is a simple to finish task, yet exceptionally hard to identify without legitimate device support. Different source code recognition frameworks have been created to help distinguish source code plagiarism. Those frameworks need to perceive various lexical and underlying source code adjustments. For instance, by some primary adjustments (e.g., alteration of control structures, adjustment of data structures or underlying overhaul of source code), the source code can be changed so that it nearly looks real. The greater part of the current source code comparability recognition frameworks can be befuddled when these primary alterations have been applied to the first source code. To be viewed as powerful, a source code similitude discovery framework should address these issues. Existing source code plagiarism detection systems that have been most widely used system worldwide are JPlag. There are a few disadvantages of JPlag which have been accounted for. The vast majority of those disadvantages are outcome of the strategy by which JPlag changes over the source code into token strings JPlag. It addresses all variable types using same token, i.e., string and int will be assigned same token “Vardef,” which results in false positive. To overcome it, in our approach we have assigned different tokens to variables of different type, i.e., string and char will be assigned with same token, whereas string and int array will have different tokens. We have undertaken this project to address them, and we planned and built up the source code similarity detection framework for plagiarism identification. To show that the proposed framework has the ideal adequacy, we played out a notable conventionalism test. The proposed framework showed promising outcomes when contrasted with the JPlag framework in distinguishing source code likeness when different lexical or underlying changes are applied to the copied code.
A Novel Approach of Clustering Documents: Minimizing Computational Complexities in Accessing Database Systems

This study addresses the real-time issue of managing an academic program's documents in a university environment. In practice, document classification from a corpus is challenging when the dataset size is large, and the complexity increases if to meet some specific document management requirements. This study presents a practical approach to grouping documents based on a content similarity measure. The approach analyzes the state-of-the-art clustering algorithms performance, considers Hamiltonian graph properties and a distance function. The distance function measures (1) the content similarity between the documents and (2) the distances between the produced clusters. The proposed algorithm improves clusters’ quality by applying Hamiltonian graph properties. One of the significant characteristics of the proposed function is that it determines document types from the corpus. Hence, this does not require the initial assumption of cluster number before the algorithm execution. This approach omits the arbitrary primordial option of k-centroids of the k-means algorithm, reduces computational complexities, and overcomes some limitations of commonly practicing clustering algorithms. The proposed approach enables an effective way of document organization opportunities to the information systems developers when designing document management systems.
Establishment and Analysis of Multi-Scale and Multi-Label 3D Seismic Facies Reservoir/Lithology Characteristics

Summary Based on the similarity between face recognition and seismic facies recognition, we propose a new method of multi-scale multi-label seismic facies database extraction and establishment. Compared with the traditional one-dimensional and two-dimensional sample extraction methods, three-dimensional samples can contain more lithological structure information, and there is an innovation of unbalanced random add-on strategy based on time-space add-on method and label-shuffling balancing strategy, which takes into account the unity of dimensionality of the network training set and fairness of training. The related theory draws on face recognition results, apply it to the samples of 3 d seismic facies analysis and seismic attribute extraction, the results show that certain correlation, results proved that the method can automatically extract, and has certain application value, as a kind of effective machine learning sample tag libraries, can be applied to the geophysics related research areas.
SocioFillmore: A Tool for Discovering Perspectives

SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event.Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics, and implemented using the LOME frame semantic parser.We describe SOCIOFILLMORE's development and functionalities, show how non-NLP researchers can easily interact with the tool, and present some example case studies which are already incorporated in the system, together with the kind of analysis that can be visualised.
A Novel Design of Symmetric Daub-4 Wavelet Filter Bank for Image Analysis

The orthogonal wavelet filter banks (FBs) consume large resources for hardware implementation due to their irrational coefficients. This results in reduction in speed of operation, huge memory requirement and increased power dissipation. In order to solve these issues, a low complex symmetric Daub-4 (8-tap) orthogonal wavelet FB is proposed in this brief. This is achieved by slightly altering the perfect reconstruction (PR) condition to make proposed FB symmetric and to obtain dyadic filter coefficients. The proposed wavelet FB is multiplier free and achieves considerably reduction in hardware and dynamic power consumption by reducing number of adders and shifters. This is verified by implementing the proposed wavelet FB on Kintex-7 field programmable gate array (FPGA) using vivado 2019.2 software from Xilinx. The proposed FB is tested for its suitability in applications such as image compression and medical image retrieval. Simulations results prove that the designed wavelet FB gives better performances in terms of PSNR for image compression and retrieval accuracy (ARP, ARR) for medical image retrieval on the benchmark images as compared to state-of-art methods.
ICHTML 2022 – Education under attack

This is an introductory text to a collection of papers from the ICHTML 2022: 3rd International Conference on History, Theory and Methodology of Learning, which held in Kryvyi Rih State Pedagogical University, Kryvyi Rih, Ukraine, on the May 16-17, 2022. It consists of short introduction, conference review and some observations about the event and its future.