Reviewerly/exHarmony huggingface dataset:
Experimental Evaluation of Four Intermediate Filters to Improve the Motion Field Estimation

For the last 40 years, optical flow (OF) estimation has been challenging the computer vision community. OF is the apparent motion of the pixels in two consecutive images. Variational models are the most used technique to estimate OF, stating an OF estimation error energy model. The minimization of this energy performs an iterative coarse-to-fine approach. In each iteration, the OF is processed with an intermediate filter to eliminate outliers. In the literature, there are studies related to the number of warpings, the number of considered scales, and their effect on the optical flow estimation. However, in the literature, there is no work that studies in depth the effect of different intermediate filters on the optical flow estimation performance. That fact motivates us to compare the effect of different filters in optical flow estimation. Taking an OF model, which is robust to large displacements and illumination changes, we tested it with four different filters: Bilateral filter (BF), Median filter (MF), Weighted median filter (WMF), and a new filter called a Balanced median filter (BMF). This BMF is a weighted sum of the Median filter and the Bilateral filter (BF) using an adaptive weight map that balances the optical flow estimation error due to the median filter or the Bilateral median filter. We assessed these three filters in the state-of-the-art MPI-Sintel training set. That contains realistic synthetic scenes presenting blur, fog, illumination changes, and sequences with significant displacement. Our experimental evaluation shows that the Balanced median filter performs better than the other two filters. When we validated our results on the MPI-Sintel benchmark webpage, we observed that our model performs better than classical methods such as Horn-Schunck, TVL1, and LDOF. Besides, it performs similar to current optical flow estimation methods such as GeoFlow, CPNFlow, and FLOW_UFO.
Note on Distributivity of Different String Operations Over Language Sets

Duplication is an operation on strings, where a substring of a given string is duplicated and introduced in the same position to produce a new string. Strings over an alphabet can be subjected to more operations like reverse-duplication, pseudo-duplication, etc. In this paper, we study the nature of the distribution of duplication, reverse-duplication and pseudo-duplication operations over the set operations like union, intersection and complement. We also consider other operations like shuffle, insertion and deletion to study the nature of duplication distribution (duplication, reverse-duplication and pseudo-duplication).
Robust, practical and comprehensive analysis of soft compression image coding algorithms for big data

With the advancement of intelligent vision algorithms and devices, image reprocessing and secondary propagation are becoming increasingly prevalent. A large number of similar images are being produced rapidly and widely, resulting in the homogeneity and similarity of images. Moreover, it brings new challenges to compression systems, which need to exploit the potential of deep features and side information of images. However, traditional methods are incompetent for this issue. Soft compression is a novel data-driven image coding algorithm with superior performance. Compared with existing paradigms, it has distinctive characteristics: from hard to soft, from pixels to shapes, and from fixed to random. Soft compression may hold promise for human-centric/data-centric intelligent systems, making them efficient and reliable and finding potential in the metaverse and digital twins, etc. In this paper, we present a comprehensive and practical analysis of soft compression, revealing the functional role of each component in the system.
Syntax-Aware Transformer for Sentence Classification

Sentence classification is a significant task in natural language processing (NLP) and is applied in many fields. The syntactic and semantic properties of words and phrases often determine the success of sentence classification. Previous approaches based on sequential modeling mainly ignored the explicit syntactic structures in a sentence. In this paper, we propose a Syntax-Aware Transformer (SA-Trans), which integrates syntactic information in the transformer and obtains sentence embeddings by combining syntactic and semantic information. We evaluate our SA-Trans on four benchmark classification datasets (i.e., AG’News, DBpedia, ARP, ARF), and the experimental results manifest that our SA-Trans model achieves competitive performance compared to the baseline models. Finally, the case study further demonstrates the importance of syntactic information for the classification task.
Investing in Cryptocurrency Mining on the Example of Bitcoin

The development of scientific and technological progress contributed to the formation of the so-called digital economy. The transformations affected, among other things, the monetary, financial and investment spheres. The emergence of bitcoin and other cryptocurrencies has expanded the list of investment assets. Today, not only independent investment is relevant, but also digital investment solutions are to be taken into account. One of the modern types of investments is investments in cryptocurrency mining. This article examines investments in the mining of one of the most famous cryptocurrencies - bitcoin. The difference between digital and cryptocurrency is clarified, the main sources of cryptocurrency are considered, the main attention is paid to mining. The study determines the profitability of bitcoin mining and compares the profitability of investing in bitcoin mining with the profitability of investing a similar amount of resources in a bank deposit.
The Use of SDR Transceivers in Systems for Searching Covert Radiodevices

The paper deals with the construction and design of devices operating by the method of resonance-reflectometric location to discover covert radio devices. The main stages of designing a prototype device operating by the method of resonance-reflectometric location are described. The materials of the analysis of modern types of SDR receivers and their main characteristics are presented. The choice of the SDR transceiver type based on the DDC technology (direct down conversion with frequency) is substantiated, in which the task to digitize the signal from the antenna is performed by the FTP with a high sampling rate. The results of the prototype device studies based on the SDR transceivers to search for covert radio devices are presented. The experimental results confirmed that the characteristics of modern SDR transceivers such as speed, sensitivity, and operating frequency bandwidth is fully consistent with the requirements for the design of resonant-reflectometer locator.
CoLM<sup>2</sup>S: Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Abstract Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently. However, there are still two challenges. First, most of the real‐word system are multiple relations, where entities are linked by different types of relations, and each relation is a view of the graph network. Second, the rich multi‐scale information (structure‐level and feature‐level) of the graph network can be seen as self‐supervised signals, which are not fully exploited. A novel co ntrastive self‐supervised representation l earning framework on attributed m ultiplex graph networks with m ulti‐ s cale (named CoLM 2 S ) information is presented in this study. It mainly contains two components: intra‐relation contrast learning and inter‐relation contrastive learning. Specifically, the co ntrastive self‐supervised representation l earning framework on attributed single‐layer graph networks with m ulti‐ s cale information (CoLMS) framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level self‐supervised signals is introduced first. The structure‐level information includes the edge structure and sub‐graph structure, and the feature‐level information represents the output of different graph convolutional layer. Second, according to the consensus assumption among inter‐relations, the CoLM 2 S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding. The proposed method can fully distil the graph information. Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods, and it outperforms existing competitive baselines.
Accuracy analysis of farmer behaviour based on big data and efficient video transmission: A convolutional neural network approach

Abstract The behaviour analysis of farmers is very important to gradually improve the sustainable production capacity of the land and promote the economics reasonable increasement. How to effectively extract video features and identify human behaviour is a current research hotspot. In this paper, the authors propose a convolutional neural network model based on the deformable convolution including a residual module and an attention module. The model fuses and encodes video spatial features on the time axis and extracts semantic information. The authors combine the attention mechanism to extract local and global features in the video, which are used to calculate spatial features and micro‐motion features, respectively. Temporal attention mechanism fuses each micro‐motion feature of video to extract higher‐level semantic features of motion. Next, the authors describe their proposed model based on deep learning. First, the authors introduce a video frame sampling method based on the TRN algorithm. Second, they design a feature extraction and fusion model based on the ResNet model and the attention mechanism that extract local and global features in videos. Finally, the authors classify and identify the extracted video features through the classifier. The authors’ approach shows comparable performance on publicly available datasets.
Scrum in Strongly Hierarchical Organizations: A Literature Review

This work aims to present a literature review of the application of Scrum in highly hierarchical organizations. The author initially defined three main bibliographic bases in Computer Science in the search strategy applied to this research, which resulted in 369 articles found. The application of exclusion criteria specified in the study resulted in twelve articles described in this paper. In addition to the twelve articles identified and analyzed through the literature review, this paper also presented other relevant articles about the topic and three previous articles from the author. Despite the apparent contradiction between agile teams with horizontal interaction between their members and organizations based on command and control structures, it is clear that compatibility in the application of agile methods in these strongly hierarchical organizations is possible.
Experience in Implementing the Scrum Framework in Incubated Companies

This article presents the experience of implementing the Scrum framework in incubated companies in the Information Technology field. The main goal of this study was to demystify the idea that a senior team is required for the application and good performance of Scrum. Another goal was to verify the time needed for companies to start using the framework and to identify the time it takes for teams to achieve stability in the project. In addition, we observed the velocity rates, if there was an increase in velocity during the project execution, and we also inspected the differences between what was planned and what was performed. In terms of methodology, this is a case study performed by market professionals.
Applying Agile Management on Communities of Practice and Startups: A Survey

The work in communities has been expanding since the ascensus of technology-based companies, today called startups. Whether created informally or having a supporting company behind, it’s hard to find any company that does not have at least a internal discussion group on the topic or support external initiatives related to topics and practices of interest. Today, events, formal and exclusive groups and meetups exist within this collaborative environment. This work aims to understand the motivations that these communities have to adopt Agile Methods in their management and how beneficial this can become for the management of knowledge transfer. 68 respondents stated that safe learning environments make the best positioned companies in their ecosystems and allow that new products are developed in a more collaborative way, even if unofficially, it can generate financial savings and learnings journeys.
Tackling online hate speech from a European perspective: Potentials and challenges of inter-legality

The contribution delves into some main implications of the current soft and hard legal framework related to the Internet governance for tackling online hate speech, from the perspective of legal and social actors based in the European Union. Given the dynamic constellation characterised by centripetal trends towards UN-fostered international governance, Council of Europe and EU soft and hard legal instruments, co-existing with centrifugal forces of national legislations, the article explores how inter-legality may contribute tackling online hate speech in today’s fast changing and complex legal scenario. Hence, due to the lack of a universally recognised definition of hate speech and a global regulation of online communication, inter-legality may be operationalised in still unexplored places – that is, not only by judges but by lawmakers, independent authorities on communication, and even platforms.
User Interest Identification with Social Media Information using Natural Language and Meta-Heuristic Technique

As the number of Internet users and social networking apps has grown in recent years, interest-based recommendation systems have been more commonly used in practice. Given the vast quantity of data available from LinkedIn and Twitter, as well as the expanding number of users, it was critical to create a real-time framework for recommending and monitoring relevant tweets or posts based on the user's interests. Using association rules, the interests of social network users can be uncovered. A considerable number of association rules extracted from social networks were found to be mostly dependent on coverage requirements. After finding patterns of frequent and non-frequent patterns, a large number of non-frequent terms were eliminated in association rule mining. In order to reduce the complexity of the association rule mining process, the more relevant terms are selected by the Hybridized Competitive Swarm Optimizer and Gravitational Search Algorithm (CSO-GSA), which is utilised for association rule generation and classification using deep learning techniques. In this research, numerous relevant rules for human interest are identified. The numerical outcome of the proposed strategy is compared with existing state-of-the-art techniques. The proposed CSO with GSA outperforms the existing techniques.
Can You Spot a Scam? Measuring and Improving Scam Identification Ability

The recent expansion of digital financial products leads to severe consumer protection issues such as fraud and scams. As these potentially decrease trust in digital services, especially in developing countries, avoiding victimization has become an important policy objective. In an online experiment, we first investigate how well individuals in Kenya identify phone scams using a novel measure of scam identification ability. We then test the effectiveness of scam education, a commonly used approach by banks and institutions for fraud and scam prevention. We find that common tips on how to spot scams do not significantly improve individuals’ scam identification ability, i.e., the distinction of scams from genuine messages. This null effect is driven by an increase in correctly identified scams and a decrease in correctly identified genuine messages. We interpret this as an increase in caution. In addition, we find suggestive evidence that genuine messages which contain scam-like features are more likely to be misclassified, highlighting the importance of a careful design of official communication.
Human-Aware Robot Task Planning with Robot Execution Time Estimation

When robots work with humans for a collaborative task, they need to plan their actions while taking humans’ actions into account. The state-of-the-art optimization-based human-aware task planner plans robot actions by prioritizing the actions that are parallel to the human's actions. However, the limitation of this approach is that the robot execution time for each action, which is an important parameter for the optimization problem, is fixed. In this paper, we investigate how online robot execution time estimation can increase the time efficiency of such a human-aware task planner. We propose a direct time estimation method and an analytical time estimation method. An integrated task and trajectory planning framework is also presented. Experiments show that online robot execution time estimation increases the time efficiency of the collaborative task. Moreover, the results of the direct time estimation method are more accurate than those of the analytical method.
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical Systems

Time series anomaly detection is an important process for system monitoring and model switching, among other applications in cyber-physical systems. In this document we present a fast subspace method for time series anomaly detection, with a relatively low computational cost, that has been designed for anomaly detection in real sensor signals corresponding to dynamical systems. We also present some general results corresponding to the theoretical foundations of our method, together with a prototypical algorithm for time series anomaly detection. Some numerical examples corresponding to applications of the prototypical algorithm are presented, and some computational tools based on the theory and algorithms presented in this paper, are provided.
A Global Lexical Database (GLED) for Computational Historical Linguistics

This work presents a lexical database with cognate annotation and phonological alignment for over 6,500 documented language varieties. The database includes per-family and global phylogenetic resources and offers a pre-computed global tree for language variety distance from normalized trees obtained with Bayesian Markov Chain Monte Carlo (MCMC) inference. Lexical data is provided in a single tabular file for convenience of usage, and resources are built adhering to best practices and state-of-the-art algorithms for historical linguistics. The database is a convenient source for research prototypes, method development, and analysis bootstrap. All resources are freely available for download for all interested researchers.
An extended approach to appraise electricity distribution and carbon footprint of bitcoin in a smart city

A nation cannot sustain a highly productive and efficient population without smart cities. Due to their significant reliance on digital technologies, these cities require a high level of cybercrime protection. Cryptocurrencies have gained significant attention due to their secure and reliable infrastructure. The decentralised cryptocurrency operates in a trust-less environment known as the blockchain, where each network participant has a ledger copy of all transactions. Blockchain technology employs a proven consensus mechanism without requiring establishment of a central authority. But the consensus mechanism requires miner to solve a cryptographic problem by generating random hashes until one of them matches the desired one. This procedure is energy-intensive, and when thousands of miners repeat it to verify a single transaction, a substantial amount of electricity is consumed. Moreover, electricity produces a significant amount of carbon footprint. Patch methodology utilises the data of all hashes created per year and the efficiency of mining hardware over a 10-year period to calculate the Bitcoins energy consumption. Due to a large number of unknown and uncertain factors involved, it is difficult to precisely calculate a single value for electricity consumption and carbon footprint as reported by Patch methodology. The proposed method extends the Patch methodology by adding a maximum and minimum limit to the hardware efficiency as well as the sources of power generation, which can help refine estimates of electricity consumption and carbon emissions for a more accurate picture. Using the proposed methodology, it was estimated that Bitcoin consumed between 38.495 and 120.72 terawatt hours of electricity in 2021 and released between 2.12 and 45.37 million metric tonnes of carbon dioxide. To address the issue of excessive energy consumption and carbon emissions, a significant number of individual miners and mining pools are relocating to energy-intensive regions, such as aluminium mining sites that rely on hydroelectricity for energy generation.
An analysis of DOOM level generation using Generative Adversarial Networks

Generative Adversarial Networks (GANs) learn models of data distributions that can be employed to generate synthetic data with similar characteristics. In this paper, we analyze how GANs can create levels for the iconic first-person-shooter Doom. We designed a framework to train GANs to extract regularities from human-designed levels and trained them using more than a thousand levels, taken from the most extensive online library of Doom content. We trained two GAN models: an unconditional one using only visual information about the levels; a conditional one integrating the same visual information with features capturing high-level structures of the levels. We evaluated the two models by comparing the levels they generated against the human-designed levels used for training. First, we compared the levels using topological metrics inspired by the ones used in robotics showing that the conditional model produces levels more similar to the human-designed ones. Next, we compared the levels using the high-level structural features used for the conditional network, showing that the generated levels are similar to human-designed ones when considering features describing the spatial layout. Finally, we analyzed how much the generation of levels in the conditional network can be controlled using the input features. Our results show that some input features (like the ones related to the number of rooms and the size of the walkable area) influence the generation process. In contrast, the remaining features appear to be ineffective in this respect.