Reviewerly/exHarmony huggingface dataset:
GeoImage Workflow Editing Resources: GIWER

While various open-source and commercial image-processing software exist, and they have high-level image-processing skills, there is no flexibility to assemble custom workflows where the experience of the evaluator can be built into the system. Therefore we have developed the Giwer (acronym for GeoImage Workflow Editing Resources, pronounce gaiver) open-source software package for handling, processing and analyzing drone images with the possibility of flexible workflow creation. Giwer has many image-processing functions that can handle traditional RGB, greyscale and hyperspectral images and apply them for their interpretation for any number of images. Giwer has further useful capabilities to organize images and their metadata (EXIF) into a database to navigate thousands of images. Users can compile their own workflows based on the available image-processing functions. Thus the created workflow involves users’ knowledge and experiences. The workflows process any number of images at once. This approach can accelerate the performance if you have a working algorithm group.
A censorious interpretation of cyber theft and its footprints

Cybercrime is a type of crime that steals information from an owner and uses it for criminal purposes. There are other types of theft related to this crime, including identity theft, data theft, and deep fakes. This study provides a detailed analysis of cybercrime. The world has become more advanced in communication, especially after the invention of the internet, which provides a feasible environment for online dealings such as e-shopping, internet banking, and online payments. Although progress has been made to enhance innovation and development, there is a high risk of cybercrime worldwide. It has had a significant impact on digital platforms, adding both positives and negatives to global networking systems. Internet technology has been used by a few people for criminal activities, such as unauthorized access to networks and scams. These criminal activities or offences related to the internet, referred to as cybercrimes, are discussed in this paper.
A Deriving Quantitative Similarities to Computer Programs and Determining Copyright Infringement in South Korea

Determining whether there is a substantial similarity, one of the factors for recognizing copyright infringement, is a hard work. For computer program works, judging the substantial similarity is even more difficult, because professional ability is required to measure. In Korea, this problem is solved through the expert testimony system in the trial process. In trial practice, quantitative similarity is mainly used as a method for determining substantial similarity. However, there are several limitations in two methods taken here, namely the use of the expert testimony system and the use of substantial similarity, and an appropriate response is required to overcome this limitation.
Application of machine learning methods for anomaly detection in internet advertising

This research deals certain with issues regarding downloading data from the Internet, i.e., Internet page advertising, and certain mechanisms to take care of the integrity of the data that is put into the dedicated processing context afterwards. The work also relates to e-commerce, as some advertising scenarios provide high error rates with pricing, which may be unacceptable in various scenarios, such as renting or selling a home. This paper presents a brief overview of the outlier detection methods and machine learning-based classifiers that are used to determine the number of anomalies in the analyzed dataset. This work contributes to the operation of organizations that deal with data accuracy and integrity, such as home rental or selling agencies.
The Study on Technology Acceptance in Healthcare Business Operation

The COVID-19 global pandemic demonstrates that the healthcare industry has a lot of potential and regularly meets a high level of customer demand. The expansion and construction of new hospitals and clinics, as well as new cure discoveries, indicate that this business will continue to expand in the future. Despite the fact that this market has many demands, some healthcare service providers still utilize traditional management practices and are behind in terms of incorporating technology into their business operations. This issue has likewise received little attention from academic studies. On the other side, more and more healthcare providers are learning to incorporate technology into their practices and encouraging business owners to educate themselves on technologies that they believe will help them run their businesses more efficiently and quickly. The purpose of this research is to delve deeply into the phenomenon. Its goal is to observe the interactions of actors in the healthcare business operations. Actor-network theory (ANT), which is used to connect actors throughout the process, is being utilized to explore this occurrence. The application of theory in the framework of this study explores and establishes the nature of the interaction between human and nonhuman agents. This study employs a qualitative methodology, and the researcher completes the fieldwork using an ethnographic case study approach. The researcher worked in the healthcare industry for about 6 months. Observations are made about the procedures and networks that connect one actor to another. The observations’ data are sorted into themes, which are presented in this study. This study contributes to the use of ANT in the business of controlling the supply of healthcare services to consumers. According to the study’s conclusions, digital learning is absolutely required to assist the workers in this company with the firm’s everyday operations.
Evaluating Domain-Independent Small Talk Conversations to Improve Clinical Communication Interaction for Human and Machine

Conversation as trivial as “small talk” has a significant impact on human relationships, including in clinical care environments. Evidence suggests that small talk can improve health and clinical outcomes. This study aims to analyze the characteristics of domain-independent small talk collected from a sample of conversations. We reviewed the impact of personality factors of the conversation and present our findings on specific factors that are correlated with it. In addition, we discuss the possibility of integrating small talk into our existing ontology-driven dialogue systems, which can assist machines in engaging with and building trust with patients in the clinical environments. Future direction will involve implementing the ontology model and integrating it with a health dialogue engine to test the effectiveness of automated small talk.
Evaluating Multimodal Behavior Schemas with VoxWorld

The ability to understand and model human-object interactions is becoming increasingly important in advancing the field of human-computer interaction (HCI). To maintain more effective dialogue, embodied agents must utilize situated reasoning - the ability to ground objects in a shared context and understand their roles in the conversation [35]. In this paper, we argue that developing a unified multimodal annotation schema for human actions, in addition to gesture and speech, is a crucial next step towards this goal. We develop a new approach for visualizing such schemas, such as Gesture AMR [5] and VoxML [33], by simulating their output with VoxWorld [21] in the context of a collaborative problem-solving task. We discuss the implications of this method, including proposing a novel testing paradigm using the generated simulation to validate these annotations for their accuracy and completeness.
Bivariate Time Series Analysis of Rainfall Pattern in Selected States in Nigeria

The issues concerning global rainfall distribution and warming/climate change cannot be over emphasized since it affects virtually every part of live. The study used rainfall pattern in two states of Nigeria. Data on the monthly rainfall distribution in Imo and Rivers state, for a period of 37 years was examined. The result showed a continuous increase in the pattern of rainfall for a period of thirty seven years within the period under study. However the pattern was inconsistence for the remaining years with some kind of fluctuations. The monthly mean series plots showed a clear presence of trend with the peak period being September annually in both series with coefficient of determination- R square value of 83% and 86% for Imo and Rivers series respectively. The yearly mean series plots showed a clear irregular variation over the years in both series. The irregularity in the pattern of rainfall calls for serious commitment in joining the force for climate change abatement process. Pearson correlation coefficient between the two series is 0.80 (substantial correlation), which is the same result with cross correlation between the two rainfall series (0.79988 approx. 0.80). The series reveal the following characteristics: high correlations appear seasonality of order 12 in the monthly mean plots (every September), irregular variation and trend curve which is quadratic trend. ARIMAX model with independent variable was employed to identify the best bivariate time series model for prediction purpose. The result reveal an increase in Rivers series (Y t ) would tend to a linear combination of some increase of the preceding Imo series (I t ) values which is vise visa. SARIMAX (1, 2) and SARIMAX (2, 1) models are identified as the best model with respect to Rivers on Imo series rainfall; via Imo on Rivers series rainfall.
Protocol-Based Interactive Debugging for Domain-Specific Languages.

Interactive debuggers are established tools used by developers to understand programs and localize faults.They are equally valuable in the context of model-driven development, when working on executable behavioral models.However, development costs of interactive debuggers for Domain-Specific Languages (DSLs) can be significant.In order to mitigate these costs, several reusable DSL-agnostic debugging solutions have been proposed.We argue that the applicability of these solutions is limited by being tied to a fixed set of debugging services, a specific language engineering approach, or a particular user interface.In this paper, we present a novel approach to provide interactive debugging services for executable DSLs through a reusable generic architecture.We propose a protocol allowing a generic interactive debugger to communicate with heterogeneous DSL runtimes, both for controlling the execution and for configuring the debugger with domain-specific breakpoints.The proposed debugger can itself be controlled using a reinterpretation of the Debug Adapter Protocol (DAP), for an effortless integration in existing Integrated Development Environments (IDEs) that support it.Using a prototype implementation based on JSON-RPC and two heterogeneous DSL runtimes, we show that our approach provides an off-the-shelf reusable interactive debugger that supports meaningful domain-specific breakpoints, and that can be used with minimal effort within an IDE such as Visual Studio Code.
A Bayesian Dual-Network Clustering Approach for Selecting Data and Parameter Granularities

While there are well-established methods for model selection (e.g., BIC, marginal likelihood), they generally condition on an a priori selected data (e.g., SKU-level data) and parameter granularity (e.g., brand-level parameters). That is, researchers think they are doing model selection, but what they are really doing is model selection conditional on their choices of data and parameter granularities. In this research, we propose a Bayesian dual-network clustering method as a novel way to make these two decisions simultaneously. To accomplish this, the method represents data and parameters as two separate networks with nodes being the unit of analysis (e.g., SKUs). The method then (a) clusters the two networks using a covariate-driven distance function which allows for a high degree of interpretability and (b) infers the data and parameter granularities that offer the best in-sample fit, akin to standard model selection methods. We apply our method to SKU-level demand analysis. The results show that the choices of data and parameter granularities based on our method as compared to extant approaches (e.g., latent class analysis) impact the demand elasticities and the optimal pricing of SKUs. We conclude by highlighting the generalizability of our framework to a broad array of marketing problems.
The Fourth Industrial Revolution in Africa: A Conclusion

This chapter concludes the book Exploring the Development Implications of Smart Technologies in Africa: The Fourth Industrial Revolution in Africa. Positing the importance of 4IR technologies for various sectors of society, this chapter draws from the various findings and conclusions made by the various contributions in the book and argues that the future of African development lies in its ability to rapidly adapt and adopt new technologies. This chapter, however, acknowledges that the rate of technological adoption in Africa is slower due to the scarcity of financial resources to access these technologies. This chapter recommends that developed countries, most of which managed to develop at the expense of Africa due to slave trade and colonialism, need to take responsibility and commit to help Africa to meet its technological needs.
Post-Independence Sustainable Development in Africa and Policy Proposals to Meet the Sustainable Development Goals

The most powerful instrument available for achieving sustainable development is economic expansion. The reduction of poverty and the enhancement of living standards are both attainable goals for nations in the process of economic development. Research that encompasses multiple countries, and case studies conducted inside individual countries demonstrate that quick and sustained economic growth is necessary if we are to make faster headway toward achieving our objectives, particularly sustainable development goals. The chapter concentrated on the post-independence sustainable development measures in Africa using facts and data. The chapter also commented on the ideas that the African states might follow to accomplish Sustainable Development Goals. The systematic literature review was used for this chapter’s focus. The chapter was successful in providing a range of development indicators for Africa, and it wrapped up with an in-depth examination of the numerous policy options that could help Africa achieve the Sustainable Development Goals (SDGs).
Dynamic Label Smoothing and Semantic Transport for Unsupervised Domain Adaptation on Object Recognition

The application of domain adaptation techniques has emerged as a valuable approach for reducing the cost of data annotation in object recognition domains. Despite its usefulness, domain adaptation is often impeded by domain shift issues that can lead to suboptimal performance. To address this challenge, previous works have attempted to align the global distribution across two domains. However, this approach may not be adequate for handling misalignment near classifier boundaries, which can cause a bias towards the source domain. In this paper, we introduce a novel label-smoothing strategy and semantic transport optimization method for unsupervised domain adaptation. Our approach leverages a memory bank for dynamic smooth rate learning and employs a semantic alignment optimization that treats it as an optimal transport problem. We also integrate class proportions into the optimization to enhance the discriminative ability of target features. To further improve the performance, we incorporate these two strategies into adversarial-based adaptation methods. We conduct comprehensive experiments on three common benchmarks to evaluate the performance of our method. Our results show that our proposed approach achieves competitive performance compared to existing methods. We make our codes publicly available at https://github.com/feifei-cv/DLST.
E-Booster: A Field-Programmable Gate Array-Based Accelerator for Secure Tree Boosting Using Additively Homomorphic Encryption

Tree boosting is a widely used machine learning model in many financial fields. Additively homomorphic encryption is an important cryptographic tool used for secure tree boosting in the setting of federated learning. However, homomorphic encryption includes computationally expensive operations. Current frameworks for secure tree boosting are extremely slow. In this article, we propose E-Booster, a novel accelerator for the training of secure tree boosting. E-Booster can fully exploit algorithmic superiority and architectural optimization to achieve unprecedented performance, and to address the obstacle in deploying additively homomorphic encryption in industrial applications. E-Booster has been implemented on an Intel Agilex field-programmable gate array and evaluated on four public datasets. It achieves a 5.1–7.8-times speedup over a CPU with 32 threads for secure tree boosting. To the best of our knowledge, E-Booster is the first additively homomorphic encryption accelerator that can be applied to industrial secure tree boosting.
Effect of risk attitude on cryptocurrency adoption for compensation and spending

Purpose This study aims to investigate the effect of risk tolerance on the individual choice of adopting Bitcoin in the form of making and receiving payment and receiving compensation. Design/methodology/approach The study uses data collected from an anonymous survey of 225 undergraduate and graduate students to measure their risk attitude using the general risk-taking propensity scale proposed by Zhang et al. (2018) and the risk-taking index, proposed by Nicholson et al. (2018). After controlling for a variety of personal traits, the study uses logistic regression to identify the predicted probabilities and marginal effects on individual choice of adopting Bitcoin. Findings The findings of this study suggest that individuals with a higher risk-seeking attitude are more likely to choose to receive payment for goods they sell in Bitcoin and more likely to choose to receive a portion of their compensation in cryptocurrency. Individuals in the higher-income groups are more likely to adopt Bitcoin 46% and 65% than their lower 14% and 45% and middle income 4% and 18% counterparts. While there was no statistically significant difference between males and females in adopting Bitcoin, respondents between the age of 26 and 29 were more likely to adopt Bitcoin. The effect on receiving gold was slightly smaller but highly comparable to that of receiving Bitcoin, which highlights a similar perception of risk toward the Bitcoin and gold. Originality/value The study uses a new data set collected by surveying 225 individuals and two different risk measurements to identify the relationship between perceived risk and Bitcoin adoption.
Optimal plane curves of degree q − 1 over a finite field

Let q≥5 be a prime power. In this note, we prove that if a plane curve X of degree q−1 defined over Fq without Fq-linear components attains the Sziklai upper bound (d−1)q+1=(q−1)2 for the number of its Fq-rational points, then X is projectively equivalent over Fq to the curve C(α,β,γ):αXq−1+βYq−1+γZq−1=0 for some α,β,γ∈Fq⁎ such that α+β+γ=0. This completes the classification of curves that are extremal with respect to the Sziklai bound. Also, since the Sziklai bound is equal to the Stöhr-Voloch's bound for plane curves of degree q−1, our main result classifies the Fq-Frobenius extremal classical nonsingular plane curves of degree q−1.
A multiple head selection joint entity-relation extraction model

In the entity extraction task, there are some complex extraction problems, such as nested entity, entity boundary recognition, context ambiguity, and multi-instance entity recognition. Entity nesting is an important challenge in relational extraction. The main reason of entity nesting problem is that the boundary information between entities is not clear. In order to solve the entity nesting problem at the fragment level, while preserving the relationship between fragments with the same characteristics and improving efficiency, we proposed a brand new fragment annotation method. On the basis of traditional fragment annotation method, combined with pointer annotation method, we designed an annotation method of "ergodic enumeration + group mapping". On the basis of this method, an entity extraction model is designed: Span-Extraction Based Entity Extraction Model (LMA). Our model underwent a series of validations in the English data sets New York Times(NYT) and WEBNLG, showing significant improvements over the baseline model F1. It can effectively alleviate the above problems.
Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled nodes, which may not be accessible in real-world applications. To this end, we present a novel unsupervised graph neural network model with Cluster-aware Self-training and Refining ( CLEAR ). Specifically, in the proposed CLEAR model, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments. To avoid degenerate solutions of clustering, we formulate the graph clustering problem as an optimal transport problem and leverage a balanced clustering strategy. Moreover, we observe that graphs often contain inter-class edges, which mislead the GNN model to aggregate noisy information from neighborhood nodes. Therefore, we propose to refine the graph topology by strengthening intra-class edges and reducing node connections between different classes based on cluster labels, which better preserves cluster structures in the embedding space. We conduct comprehensive experiments on two benchmark tasks using real-world datasets. The results demonstrate the superior performance of the proposed model over baseline methods. Notably, our model gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts.
INTERPRETIVE INNOVATIONS OF THE OPERA IMAGE IN THE CREATIVE DIALOGUE M. CALLAS - L. VISCONTI

In the article, on the example of the performing arts of Maria Callas, it is shown that the so-called personification of the musical images of the opera, their visualization and their transition from the world of “pure music” to the stage space occurs through the phenomenon of combining melody and poetic text. It is logical to assume that such a change in the “proposed circumstances” in the opera in relation to the drama entails a transformation or, at least, an adjustment in the mechanism for bringing the actor's imagination to the state necessary for stage creativity. The research presents a singer-actor as part of the operatic text.