Reviewerly/exHarmony huggingface dataset:
Demand elasticities of Bitcoin and Ethereum

In this paper we analyze dynamic demand elasticity for Bitcoin and Ethereum in terms of price, transaction fees, and energy usage. We find that while both BTC and ETH have significantly positive price elasticities, transaction fee elasticity is negative and positive for BTC and ETH respectively, indicating differences in potential uses for these cryptocurrencies.
A lightweight approach to two-person interaction classification in sparse image sequences

A lightweight neural network-based approach to two-person interaction classification in image sequences, based on human skeletons detected in sparse video frames, is proposed.The idea is to use an ensemble of pose classifiers ("experts"), where every expert is trained on different time-indexed snapshots of an interaction.Thus, the expertise of "weak" classifiers is distributed over the time duration of an interaction.The overall classification result is a weighted combination of all the pose experts.Important element of proposed solution is the refinement of skeleton data, based on a merging-of-joints procedure.This allows the generation of reliable features being passed to the artificial neural network.This is the key to our lightweight solution, as ANN resources, needed for feature space transformation, can be significantly limited.Our network model was trained and tested on the interaction subset of the well-known NTU RGB+D dataset, although only 2D skeleton information is used, typical in video analysis.The test results show comparable performance of our method with some of the best so far reported STMand CNN-based classifiers for this dataset, when they process sparse frame sequences, like we did.The recently proposed multistream Graph CNNs have shown superior results but only when processing dense frame sequences.Considering the dominating processing time and resources needed for skeleton estimation in every frame of the sequence, the key to real-time interaction recognition is to limit the number of processed frames.
Evaluating M-Learning Activities Using SAMR Model and Students' Writing Skills

Evaluating M-Learning Activities Using SAMR Model and Students' Writing SkillsThis study examines (i) the use of mobile technology (Padlet, Animoto, and Sway) in teaching English to non-English department students by evaluating the M-learning activities using the SAMR model and (ii) students’ writing competence using the CEFR model. The subjects of this study were 152 second-semester students of the primary education department. In this qualitative descriptive research, data were collected through questionnaires, observation, and documentation. The results show that: (i) Padlet is categorized to substitution, augmentation, and modification classification, while Animoto and Sway are categorized to substitution classification; and (ii) the percentages for students’ writing ability levels were 60.52% for basic user A1, 31.57% for basic user A2, and 7.89% for independent user B1 after they joined the class using various applications.&#x0D; Keywords: SAMR model, Padlet, Animoto, Sway, writing skill, CEFR
Analysis of the functionality of voice and video communication systems

The article contains a comparison of the functionality and qualitative verification of voice and video communication systems, through tests of voice message shift in time, at different data rates, tests of system use cases and the study of the popularity of applications in various environments and age groups, thanks to an electronic survey . The systems examined were: Microsoft Teams, Zoom, Signal, WhatsApp, Cisco Webex Meetings. The following hypothesis was made: Voice and video communication systems are very popular systems among users, and their functional capabilities are similar.
Decentralised and Predictive System for Efficient Agri-Transactions Through Blockchain Technology

Agriculture is an art, a craftsmanship and a scientific way of cultivation, growth and maintenance of edible crops and livestock.Majority of the current farming communities do not have prior knowledge of predicting the suitable crop for their soil and climatic conditions.Difficulty in raising the initial investment for crop cultivation is also one of the serious concerns of these communities.The difficulties faced by the civic agriculture, the insecure monetary transactions, along with the concerns related to the financial process have been identified and listed.The paper proposes a feasible solution by predicting the appropriate crops that could be grown in a specific scenario or environmental conditions using the machine-learning model of Support Vector Classifier and provides data related to quality yields using Fuzzy Decision Merkle Tree (FDMT) Regressor.Additionally, a transparent and secure fund transfer mechanism is provided using Ethereum blockchain-based technology.The proposed model implements a secured, translucent and tamper-resistant digital platform for the farming communities to host their products.A fortified consensus can be formed between the farmer and the investor bounded with a rating mechanism to build the credibility of both the farmer and the investor 7 based on the prior knowledge obtained in the Agri-market.
Process-guidance improves predictive performance of neural networks for carbon turnover in ecosystems

Despite deep-learning being state-of-the-art for data-driven model predictions, it has not yet found frequent application in ecology. Given the low sample size typical in many environmental research fields, the default choice for the modelling of ecosystems and its functions remain process-based models. The process understanding coded in these models complements the sparse data and neural networks can detect hidden dynamics even in noisy data. Embedding the process model in the neural network adds information to learn from, improving interpretability and predictive performance of the combined model towards the data-only neural networks and the mechanism-only process model. At the example of carbon fluxes in forest ecosystems, we compare different approaches of guiding a neural network towards process model theory. Evaluation of the results under four classical prediction scenarios supports decision-making on the appropriate choice of a process-guided neural network.
A Competent Hybrid Digital Image Watermarking Technique Based on Daubechies Wavelet and Block Bitmap Modification

Over many years, in order to provide powerful techniques for protecting digital copyrights, digital watermarking techniques have been developed. This research focuses on proposing an efficient blind hybrid digital image watermarking technique based on the transformation of Daubechies wavelet (DW) and Block Bitmap modification (BBM). DW represents an effective multi-determination frequency domain for including the watermarks. The BBM is used to afford an enhanced capability of embedding and minimize distortion. Two layers of security have been added to the proposed technique for protecting digital images from theft by using the logistic chaotic mapping to select the position of the blocks for the embedding process and Lorenz chaotic mapping for scrambling the watermark image. In the experiments, high values of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are obtained, and all the obtained results illustrate that the presented technique is highly imperceptible, secure, and robust.
Dual Data Augmentation Method for Data-Deficient and Occluded Instance Segmentation

Instance segmentation is applied widely in image editing, image analysis and autonomous driving, etc. However, insufficient data and occlusion are common problems in practical application. DeepSportRadar Instance Segmentation challenge has focused on these problems. The goal of DeepSportRadar challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court. And the main characteristics of this challenge are there is a high level of occlusions between players and the amount of data is quite limited. In order to address these problems, we designed a Dual Data Augmentation(DDA) method including an offline data augmentation(ODA) strategy to tackle the data-deficient problem, and an online specific copy-paste(OS-CP) strategy to address the occlusion issue. We demonstrate the applicability proposed method on DeepSportRadar Instance Segmentation challenge. The segmentation model applied is Hybrid Task Cascade based detector on the Swin-Large-based CBNetV2 backbone. Experimental results demonstrate that proposed method can achieve a competitive result on the DeepSportRadar challenge, with [email protected]:0.95 on the challenge set.
Process-oriented documentation of user requirements for analytical applications - challenges, state of the art and evaluation of a service-based configuration approach

Abstract4In recent years, the integration of process design in conjunction with the use of analytical applications to provide information tailored to user requirements to support operational process activities (e.g., Operational BI) has become increasingly widespread.In analytical software development/implementation projects, the insufficient involvement of analytical end users with their process context and the resulting unclear requirements/expected analytical software functions are still one of the main reasons for analytical project failure.Embedded in a Design Science Research Process, this paper shows the shortcomings of existing approaches, tools and models (1.BPMN process model extensions, 2. configurators in analytical applications, 3. models used in analytical implementation projects) for the documentation/conceptual configuration of analytical requirements.As a second part, this paper presents the evaluation results of a new process-oriented and service-based configuration approach for analytical applications, whose practicability, usefulness and acceptance were evaluated in expert reviews and in analytical development projects.
Analyzing longitudinal Data in Knowledge Graphs utilizing shrinking pseudo-triangles

This paper aims to analyze longitudinal data, serial data related to different time points, in knowledge graphs.Knowledge graphs play a central role for linking different data.While multiple layers for data from different sources are considered, there is only very limited research on longitudinal data in knowledge graphs.However, knowledge graphs are widely used in big data integration, especially for connecting data from different domains.Few studies have investigated the questions how multiple layers and time points within graphs impact methods and algorithms developed for single-purpose networks.This manuscript investigates the impact of a modeling of longitudinal data in multiple layers on retrieval algorithms.In particular, (a) we propose a first draft of a generic model for longitudinal data in multi-layer knowledge graphs, (b) we develop an experimental environment to evaluate a generic retrieval algorithm on random graphs inspired by computational social sciences.We present a knowledge graph generated on German job advertisements comprising data from different sources, both structured and unstructured, on data between 2011 and 2021.The data is linked using text mining and natural language processing methods.We further (c) present two different shrinking techniques for structured and unstructured layers in knowledge based on graph structures like triangles and pseudo-triangles.The presented approach (d) shows that on the one hand, the initial research questions, on the other hand the graph structures and topology have a great impact on the structures and efficiency for additional data stored.Although the experimental analysis of random graphs allows us to make some basic observations we will (e) make suggestions for additional research on particular graph structures that have a great impact on the analysis of knowledge graph structures.
About Classifiers Quality Assessment: Balanced Accuracy Curve (BAC) as an alternative for ROC and PR Curve

In this work, we propose a new parameter to study the effectiveness of classifiers -the AUC (area under curve) of the balanced accuracy curve (BAC) on data with different balance degrees -we compare its effectiveness with the popular AUC parameters for the ROC and PR curve.We use a global kNN classifier with typical metrics to verify the utility of the new parameter.BAC, ROC and PR curves generate similar results, the advantage of BAC is its simplicity of implementation and ease of interpretation of results.
Software Implementation of the Optimal Temporal Intuitionistic Fuzzy Algorithm for Franchisee Selection

The selection of the most suitable franchisee applicant in an uncertain environment in a particular moment of time is a key decision for a franchisor and the success of a franchising business.In this work, for the first time, we describe a problem for choosing the optimal candidate for the franchise chain and algorithm for a solution in terms of temporal intuitionistic fuzzy pairs and index matrices as a means for data analysis in uncertain conditions over time.We also use our software utility to demonstrate the proposed algorithm and to apply the decision support approach to a franchisee selection for the largest fast food restaurant chain in Bulgaria.
BiodivBank: designing a global repository and portal for structured biodiversity data

Our goal is to mobilize global species abundance and assemblage information, via a dedicated and openly accessible data repository and web portal. Preservation of raw observational data is a complement to the modelled geographic projections that are the focus of related projects such as the EBV Data Portal, which provides access to EBV (Essential Biodiversity Variables) raster datasets. Key to this effort is an emphasis on the use of standardized terms to ensure interoperability with other services, as well as annotation of (meta)data fields and values to aid discoverability and create a product that adheres to the FAIR data principles, requesting data to be Findable, Accessible, Interoperable, and Reusable (Wilkinson et al. 2016). For example, retrieval and storage of globally unique, persistent and resolvable identifiers for core aspects of the data such as taxon names and geospatial locations can serve not only to underpin more advanced search functionality for portal users, but also facilitate future efforts to link open data within online knowledge networks. However addressing social challenges may be equally important in determining the success of the portal. Where do we fit within the biodiversity data landscape and how can we incentivize researchers to submit datasets? What is our data policy? How should we retain provenance and ensure visibility for data providers? Sometimes the choice of a technical solution can itself lower social barriers, for example by providing an intuitive graphical interface to ease upload of the detailed methodological metadata characteristic of these event-based datasets, or by blurring geographic coordinates in order to reduce the risk that threatened species are located. We will present our plans for the web platform currently under construction, and welcome comment on them, to ensure that the portal meets the needs of a diverse range of potential stakeholders.
Application of Random Sampling in the Concept-Dependent Granulation Method

Professor Zadeh in his works proposed the idea of grouping similar objects on the basis of certain similarity measures, thus initiating the paradigm of granular computing.He made the assumption that similar objects may have similar decisions.This natural assumption, operates in other scientific methodologies, e.g.methods based on k nearest neighbours, in reasoning by analogy and in rough set theory.The above assumption implies the existence of grouped information nodes (granules) and has potential applications in reducing the size of decision systems.The hypothesis has guided" the creation of granulation techniques based on the use of rough inclusions (introduced by Polkowski and Skowron) -according to the scheme proposed by Polkowski.In their work, the possibility of a large reduction of the size of decision systems while maintaining the classification efficiency was verified in experimental works.In this paper, we investigate the possibility of using random sampling in the approximation of decision systems -as part of dealing with Big Data sets.We use concept-dependent granulation as a reference approximation method.Experiments on selected real-world data have shown a common regularity that gives a hint on how to apply random sampling for fast and effective size reduction of decision systems.
Website-Based Sales Reporting Information System with the Laravel Framework (Case Study of Pramana Agency)

The rapid development of technology has a big role in facilitating all human activities. Technology was created to speed up work and make it more efficient. One form of existing technology is information systems. The operating system is a system that exists within an organization, intending to meet data processing needs, supporting operations, managerial and strategic in an organization and agency by providing reports to certain parties. One of the companies engaged in trading, namely Praman Agency, is in dire need of information system technology that provides sales data recap functions and sales profit reports. Activities in the company are currently still recorded manually in an Excel file, even though sales are made through marketplaces such as Shopee and Lazada. By looking at the picture of the workflow, in reality, the idea arose to create a Website-Based Sales Reporting Information System with the Laravel Framework (Case Study of Pramana Agency). An information system is needed by this company as well as other companies that run a similar business model, to facilitate sales reporting so that their business can be measured. This system also makes it easier for business owners to view daily sales reports so they can determine marketing strategies for the future.
Spatio-temporal categorization for first-person-view videos using a convolutional variational autoencoder and Gaussian processes

In this study, HcVGH, a method that learns spatio-temporal categories by segmenting first-person-view (FPV) videos captured by mobile robots, is proposed. Humans perceive continuous high-dimensional information by dividing and categorizing it into significant segments. This unsupervised segmentation capability is considered important for mobile robots to learn spatial knowledge. The proposed HcVGH combines a convolutional variational autoencoder (cVAE) with HVGH, a past method, which follows the hierarchical Dirichlet process-variational autoencoder-Gaussian process-hidden semi-Markov model comprising deep generative and statistical models. In the experiment, FPV videos of an agent were used in a simulated maze environment. FPV videos contain spatial information, and spatial knowledge can be learned by segmenting them. Using the FPV-video dataset, the segmentation performance of the proposed model was compared with previous models: HVGH and hierarchical recurrent state space model. The average segmentation F-measure achieved by HcVGH was 0.77; therefore, HcVGH outperformed the baseline methods. Furthermore, the experimental results showed that the parameters that represent the movability of the maze environment can be learned.
FreesoundVR: soundscape composition in virtual reality using online sound repositories

Abstract The intersection between sound and music computing and Virtual Reality (VR) has grown significantly over the past decades, amounting to an established area of research today. However, still scarce research has been conducted on the development of specific tools for sound design and composition. In this paper, we investigate a new way of exploring online sound repositories to retrieve sounds to be used in soundscape composition, which leverages the VR medium. Specifically, we created a VR system that allows users to search, download, and explore Freesound content in an immersive manner, as well as to use it for soundscape composition practices via a virtual digital audio workstation (DAW). The tags associated to a sound in the repository were converted into virtual objects and environments, which the user could navigate while listening to the sound. We conducted a user study with 16 composers where the developed system was compared against a conventional counterpart comprising the Freesound web version and the Audacity DAW. Overall, quantitative and qualitative results did not indicate a clear and generalized preference for a system over the other. The usability of the two systems along with their offered creativity support, cognitive workload and emotional impact were deemed to be at a comparable level. Nevertheless, the full potential of VR in creating novel compositional experiences also clearly emerged. Our study shows that VR is an effective medium to support users’ creativity during the process of exploring and selecting sounds from an online repository as well as for composing a soundscape.
Trajectory Planning using Transfer Learning and Wireless Sensor Network for Target Search Mobile Robot

Trajectory planning is the core technology in the field of mobile robot research. However, the traditional trajectory planning algorithm has the problems of low accuracy and high complexity. Therefore, we propose a trajectory planning algorithm for target search mobile robot using transfer learning (TL) and wireless sensor network. First, the mobile robot motion model is constructed and converted into a linear motion model; Second, the robot kinematic constraints such as velocity, acceleration and position are analyzed; Then, the ultrasonic sensor is constructed to sense the robot's self and environmental information, and the path planning is performed by the combination of global planning and local planning. Finally, a combination of global planning and local planning is used for path planning, and the knowledge is learned from global planning based on TL, and the relevant factors of the global planning model are applied to local planning as the initial motion state factors of the robot to realize the trajectory planning of the target-search mobile robot. The results show that the fitting of the proposed algorithm and the optimal trajectory planning results is high, the reward value of robot training is higher, the trajectory planning velocity curve is smoother, and the space complexity of the proposed algorithm is much lower than other algorithms, the highest is only 23%, and the accuracy of trajectory planning is as high as 95.6%, which has practical applicability.
On foresight functions of rhetorical invention in acts of counselling

Counselling acts consist in indicating useful activities, remedying human deficiencies. Counselling acts are guided by practical cognition. Since counselling activities are oriented towards the future, their crucial element is foresight. It is cognitive reflection foreseeing the implementation of counselling acts. Counselling acts are actualised in rhetorical and communicational context which is associated with persuasive delivery of counselling content. Belonging to the rhetorical canon, invention disposes of factors that influence the justifying formulation of advisory content. Invention factors introduce the anticipatory determinations and concretisations of counselling content. At rhetorical level, inventive methodology plays anticipatory functions which are convergent with tasks of foresight in practical cognition.