Reviewerly/exHarmony huggingface dataset:
TOWARDS THE AUTOMATIC ONTOLOGY GENERATION AND ALIGNMENT OF BIM AND GIS DATA FORMATS

Abstract. Establishing semantic interoperability between BIM and GIS is vital for geospatial information exchange. Semantic web have a natural ability to provide seamless semantic representation and integration among the heterogeneous domains like BIM and GIS through employing ontology. Ontology models can be defined (or generated) using domain-data representations and further aligned across other ontologies by the semantic similarity of their entities - introducing cross-domain ontologies to achieve interoperability of heterogeneous information. However, due to extensive semantic features and complex alignment (mapping) relations between BIM and GIS data formats, many approaches are far from generating semantically-rich ontologies and perform effective alignment to address geospatial interoperability. This study highlights the fundamental perspectives to be addressed for BIM and GIS interoperability and proposes a comprehensive conceptual framework for automatic ontology generation followed by ontology alignment of open-standards for BIM and GIS data formats. It presents an approach based on transformation patterns to automatically generate ontology models, and semantic-based and structure-based alignment techniques to form cross-domain ontology. Proposed two-phase framework provides ontology model generation for input XML schemas (i.e. of IFC and CityGML formats), and illustrates alignment technique to potentially develop a cross-domain ontology. The study concludes anticipated results of cross-domain ontology can provides future perspectives in knowledge-discovery applications and seamless information exchange for BIM and GIS.
A Cost-Efficient Approximate Dynamic Ranged Multiplication and Approximation-Aware Training on Convolutional Neural Networks

This paper proposes a low-cost approximate dynamic ranged multiplier and describes its use during the training process on convolutional neural networks (CNNs). It has been noted that the approximate multiplier can be used in the convolution of CNN’s forward path. However, in CNN inference on a post-training quantization with a pre-trained model, erroneous convolution output from highly approximate multipliers significantly degrades performance. On the other hand, with the CNN model based on an approximate multiplier, the approximation-aware training process can optimize its learnable parameters, producing better classification results considering the approximate hardware. We analyze the error distribution of the approximate dynamic ranged multiplication and characterize it in order to find the most suitable approximate multiplier design. Considering the effects of normalizing the biased convolution outputs, a low standard deviation of relative errors with respect to the multiplication outputs leads to negligible accuracy drop. Based on these facts, the hardware costs of the proposed multiplier can be further reduced by adopting the partial products’ inaccurate compression, truncated input fraction, and reduced-width multiplication output. When the proposed approximate multiplier is applied to the residual convolutional neural networks for the CIFAR-100 and Tiny-ImageNet datasets, the accuracy drops of the approximation-aware training results are negligible compared to those using 32-bit floating-point CNNs.
Digital Competence Improvement by Lithuanian School Teachers During Covid-19

Distance teaching and learning in Lithuanian schools during COVID-19 pandemic was a booster for teacher digital competence development. The paper analysis the experiences of Lithuanian school teachers to reveal the challenges schools and teachers faced while switching from traditional face to face teaching and learning to distance education. The research revealed existence of different level teacher digital competences at a school and uneven competence development in each school. The analysis of experiences of Lithuanian school teachers through the lens of DigCompEdu framework revealed that professional development competence was the background for other competence areas to develop. The need for digital resources for teaching online contributed to the teacher digital competence development of this area. Digital assessment was indicated as the main challenge that remained unsolved in many cases. Research found that technological peculiarities were easier to learn and showed the need to develop teacher social and didactical competences which are necessary for distance teaching. Lack of activities to empower learners and develop their digital competences was recorded. Teachers themselves acknowledged great improvement of their digital competence.
Understanding Robustness of Transformers for Image Classification

Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (ViT) have matched or even surpassed ResNets for image classification. However, details of the Transformer architecture -- such as the use of non-overlapping patches -- lead one to wonder whether these networks are as robust. In this paper, we perform an extensive study of a variety of different measures of robustness of ViT models and compare the findings to ResNet baselines. We investigate robustness to input perturbations as well as robustness to model perturbations. We find that when pre-trained with a sufficient amount of data, ViT models are at least as robust as the ResNet counterparts on a broad range of perturbations. We also find that Transformers are robust to the removal of almost any single layer, and that while activations from later layers are highly correlated with each other, they nevertheless play an important role in classification.
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability

Digitalization leads to data transparency for production systems that we can benefit from with data-driven analysis methods like neural networks. For example, automated anomaly detection enables saving resources and optimizing the production. We study using rarely occurring information about labeled anomalies into Variational Autoencoder neural network structures to overcome information deficits of supervised and unsupervised approaches. This method outperforms all other models in terms of accuracy, precision, and recall. We evaluate the following methods: Principal Component Analysis, Isolation Forest, Classifying Neural Networks, and Variational Autoencoders on seven time series datasets to find the best performing detection methods. We extend this idea to include more infrequently occurring meta information about production processes. This use of sparse labels, both of anomalies or production data, allows to harness any additional information available for increasing anomaly detection performance.
The Implementation of School Literacy Movement by Using Storybook in Elementary School

Literacy is one way to build and develop children's thinking skills that are important for students. However, students' literacy habits are still low. Therefore, schools as one of the three educational centers should implement a literacy culture to improve children's thinking skills. This study aims to examine information about the application of literacy culture in schools and to apply literacy using children's stories for students. This study uses a qualitative descriptive approach with a survey method. The subjects of this study were elementary school students. Data collection techniques through observation, interviews, and documentation. Data analysis through the stages of data reduction, data presentation, and concluding. The results showed that literacy activities in schools had been implemented with literacy fifteen minutes before the lesson started and used every corner of the school as a reading corner, while the implementation of literacy using storybooks was done by inviting students to read books with various story titles. Based on these results, it can be concluded that elementary schools already have a good literacy program and the application of literacy using storybooks can invite students to retell the story content and the moral values of the story. This research implies that schools should optimize their role as one of the three educational centers to build and develop literacy habits for elementary school students.
Taking Things Personally: Third Person to First Person Rephrasing

The recent advancement of digital assistant technologies has opened new possibilities in the experiences they can provide.One of them is the ability to converse with a persona, e.g., celebrities, famous fictional characters, etc.This experience requires that the replies are answered from the point of view of the persona, i.e., the first person.Since the facts about characters are typically found expressed in the third person, there is a need to rephrase them to the first person in order for the assistant not to break character and the experience to remain immersive.However, the automatic solution to such a problem is largely unexplored by the community.In this work, we present a new task for NLP: third person to first person rephrasing.We define the task and analyze its major challenges.We create and publish a novel dataset with 3493 humanannotated pairs of celebrity facts in the third person with their rephrased sentence in the first person.Moreover, we propose a transformerbased pipeline that correctly rephrases 92.8% of sentences compared to 76.2% rephrased by a rule-based baseline system.
Load Balancing Optimization For Green Cloud Environment Using Effective Scheduling

A solution of the green cloud is not only to save the consumption of energy but expressively reduce operational costs. The main objective is to make the comprehensive computing influence of a vast collection of resources offered to a single application. High consumption of energy is the main concern in green cloud computing because of high computations happened in tightly connected data centers that need managed resources and smooth operations.  The research is implemented on green cloud computing based on the scheduling process for energy conservation which is the main concern. The groups deal with the high efficient arrangement in which every industry is required with high uniformity, scalability, and effective performance over different cloud computing scenarios. This research work deals with the efficient performance to achieve efficient load balancing and the optimization process for high energy efficiency which will be the key feature of our proposed system. The proposed work deals with the hybrid scheduling process i.e. priority-based weighted round-robin and minimum completion time to reduce the energy consumption in green cloud systems. The performance will be evaluated for low error rates and achieve high energy efficiency to balance loads of the requests.
On brackets in translation (or how to elaborate in brackets)

This paper presents findings on the use of brackets in original texts and translations based on the Linnaeus University English-German-Swedish corpus (LEGS). The results show that in originals, brackets are the most frequent in English and the least in Swedish. Translations usually contain more brackets than originals. There are two reasons for this. First, most brackets are retained, and secondly, many are added. Added brackets mostly contain short synonyms facilitating target-reader comprehension. English translators introduce the most changes (additions, omissions, downgrades and upgrades), and Swedish ones the least. Brackets tend to fulfil content-oriented rather than interpersonal functions. When brackets are replaced by other punctuations marks in translations, these tend to be commas or no punctuation marks at all. German originals have a stronger preference for bracketing phrases than clauses compared to English and Swedish. These German phrasal brackets are often expanded into clauses in translations.
Operational Due Diligence on Cryptocurrency and Digital Asset Funds

This article analyzes three key trends that have emerged among institutional investors performing operational due diligence (ODD) on cryptocurrency and digital asset funds. The first trend is enhanced crypto-specific ODD specialization as compared to other alternative asset classes. The second trend is an institutional focus on crypto custody arrangements. The third trend is a rapid movement toward integrating ODD and investigative due diligence processes in the crypto space.
Amazon Biobank - A community-based genetic database

In regions like the Amazon Rainforest, there is much unexplored biodiversity data that could potentially be used to promote innovative biotechnology developments. Building a biobank with such genetic data is, however, a challenge. One reason is that existing repositories (e.g., NCBI) lack clear incentives for collaboration. Aiming to tackle this issue, and promote a biodiversity-based economy in the Amazon region, in this work we present a prototype for the Amazon Biobank, a community-based genetic database. Leveraging blockchain, smart contracts, and peer-to-peer technologies, we build a collaborative and highly scalable repository. It also enables monetary incentives for users who insert, store, process, validate and share DNA data.
Autonomous Trajectory Planning and Control of Anti-Radiation Loitering Munitions under Uncertain Conditions

As an autonomous system, an anti-radiation loitering munition (LM) experiences uncertainty in both a priori and sensed information during loitering because it is difficult to accurately know target radar information in advance, and the sensing performance of the seeker is affected by disturbance and errors. If, as it does in the state of the art, uncertainties are ignored and the LM travels its planned route, its battle effectiveness will be severely restricted. To tackle this problem, this paper studies the method of autonomous planning and control of loitering routes using limited a priori information of target radar and real-time sensing results. We establish a motion and sensing model based on the characteristics of anti-radiation LMs and use particle filtering to iteratively infer the target radar information. Based on model predictive control, we select a loitering path to minimize the uncertainty of the target information, so as to achieve trajectory planning control that is conducive to the acquisition of target radar information. Simulation results show that the proposed method can effectively complete the autonomous trajectory planning and control of anti-radiation LMs under uncertain conditions.
Illation of Video Visual Relation Detection Based on Graph Neural Network

Visual relation detection task is the bridge between semantic text and image information, and it can better express the content of images or video through the relation triple <subject, predicate, object>. This significant research can be applied to image question answering, video subtitles and other directions Using the video as input to the task of visual relationship detection receives less attention. Therefore, we propose an algorithm based on the graph convolution neural network and multi-hypothesis tree to realize video relationship prediction. Video visual relationship detection algorithm is divided into three steps: Firstly, the motion trajectories of the subject and object in the input video clip are generated; Secondly, a VRGE network module based on graph convolution neural network is proposed to predict the relationship between objects in the video clip; Finally, the relationship triplets are generated through the multi-hypothesis fusion algorithm (MHF) and the visual relationship. We have verified our method on the benchmark ImageNet-VidVRD dataset. The experimental results show that our proposed method can achieve a satisfactory accuracy of 29.05% and recall of 10.18% for visual relation detection.
Organization Of Educational Process For Foreign Students During The Pandemic Covid-19

The pandemic COVID-19 had direct impact on the economies of each country and on the world economy as a whole.There was sharp decrease in social activity due to the closure of institutions and travel bans.Education was not left untouched either.Universities began to massively declare the closure and shift to online studying.Many foreign students were never able to return to the usual regime of studying.The relevance of the topic of the article is determined by the need to analyze the implementation of the educational process in the remote format for foreign students at introducing quarantine measures.The authors used theoretical (content analysis of sociological articles on the implementation of educational programs during pandemic COVID-19, generalization) and empirical methods (descriptive method, included observation).During the study there were considered publications of foreign and domestic scientists and authors, summarized own practice at Industrial University of Tyumen.On the basis of the analysis of foreign and Russian experience of organizing remote training format there were identified the main directions of changes in the work of universities and the difficulties associated with them.
The Effectiveness of Video Scribe Sparkol on EFL Students’ Grammar Ability

Grammar is one of the most important aspects as it integrates with four language skills, and it enhances the learners’ fluency. However, some students still get difficulty in understanding the grammar patterns and difficulty in composing grammatical sentences. One of the methods that can be implemented in teaching grammar is Video Scribe Sparkol. This study intends to determine whether or not the employment of Video Scribe Sparkol influences students' grammatical skills. This quantitative-experimental research involved the investigation of two classes; experimental and control. The experimental class was given treatment by using Video Scribe Sparkol, while the control class was taught conventionally (without using any media). The experimental class's SPSS data indicated that a pre-test average score was 68.15, and the post-test average score was 83.23. It indicated a highly significant improvement in the experimental class's score. Meanwhile, the average score of the control class's pre-test was 70.96, and their post-test average score was 78.09. As the significance sig. (2 tailed) of the Independent Sample t-test was 0.027 &lt; 0.05, which means that the null hypothesis (H0) is rejected. Thus, there is a significant difference between the average score of the experimental and the control class. The results revealed that Video Scribe has a significant impact on learners’ grammar ability, and it can motivate and engage them in grammar activities due to interesting images, animations, and audiovisuals on its features. Thus, further researchers need to investigate the students’ motivation toward this method.
Adaptive Neuro-Fuzzy Inference System For Forecasting Traffic Volume(Case Study Of National Road Km 41 Kamal)

The effect of the development of an infrastructure such as shopping centers, settlements, and so on is one study that is generally considered in the governance of a region. Typically, this construction has a major traffic impact. Combined with the effects of population growth, which is constantly growing every year, the flow of traffic is increasingly congested. This is because many of these people choose to own personal vehicles. Tremendous congestion would result from a road capacity that is not proportional to vehicle growth. A traffic analysis was conducted using road traffic volume data to prevent this. This research will attempt to survey a road on National road KM 41 Kamal and use the neuro fuzzy method to forecast traffic volume. The amount of traffic that will be studied is only motorcycles. The results show that with an error percentage of 16.0793%, neuro fuzzy can predict motorcycle traffic volume. It can be inferred from this that Neuro Fuzzy can forecast traffic volume on a road quite well.
Creative Methodologies: Designing a Framework for Teacher Reflection and Authorship

This research has as its main objective to create, test and improve methodologies and artefacts that help teachers to reflect to transform their practices, making them more creative. Actions are expected that go beyond envisioned planning, but that the teacher is able to concretize, evaluate and continuously improve his/her pedagogical actions. This paper describes the creation of a framework that aims to promote reflection on creative methodologies by teachers, helping them to make explicit the creative movements they already make highlighting their authorial moments, their limits, challenges and difficulties, as well as getting to know new creative possibilities.The data used for the initial construction of this framework was collected in two online courses on Design of Pedagogical Strategies, in which the participating teachers had to describe their teaching practices. From the qualitative analysis of this data, we mapped the challenges and strains in the construction of pedagogical strategies, as well as the fashions and trends that appeared in the discourses. An analysis of formative needs was made and a categorization was created based on a theoretical reflection and critical analysis of the trends presented in the teachers’ data, emphasizing the importance of teachers’ authorial and creative stances in their methodological construction. In this article, we will describe the mapped data, the categorization and the theoretical foundations that supported the construction of this framework. We will also describe the categories and the first validation phase already carried out as well as the next steps for a large-scale application.
The Effectiveness of Psychodrama Group Therapy on Assertiveness, Unconditional Acceptance, and the Spirit of Gratitude among Physically Disabled Individuals: A Quasi-Experimental Study

The Effectiveness of Psychodrama Group Therapy on Assertiveness, Unconditional Acceptance, and the Spirit of Gratitude among Physically Disabled Individuals: A Quasi-Experimental Study
Análisis bibliométrico sobre la producción científica del trabajo social digital con Scopus y bibliometrix

It is presented a systematic review focused on the Working Memory (WM), in this paper had been presented diverse theories, investigations and implications according to the development, disorders and learning process in working memory. The papers revised were obtained from databases between 2015-2020, the result of this research ended in a document that categorizes and classifies the information related with the keywords established. Findings showed that Baddeley and Hicth’s multicomponential theory is the most transcendent, however it was not found clear and verifiable&nbsp;&nbsp; information. Besides, the educational field has given the importance of Working Memory in learning, knowing that this process can be affected by stress, or low curricular adaptation to students with special educational needs.
Weaving a Storytelling Tapestry Using Computational Thinking

Computational thinking is a way of formulating a problem so that a computer can find a solution. It involves looking for patterns within multiple sets of data and using algorithmic thinking to find solutions. In this way, it becomes a critical literacy for future-ready youth. In this paper the authors share partial results from a National Forum on Computational Thinking funded by the Institute of Museum and Library Services, explaining computational thinking, exploring cross-cultural connections of the story sequence, and sharing how computational thinking can be used to create and analyze a story within the context of this story sequence.