Reviewerly/exHarmony huggingface dataset:
An Interpretable Authorship Attribution Algorithm Based on Distance-Related Characterizations of Tokens

Natural Language Processing has focused its efforts in sentiment analysis, token categorization, topic identification, translation, authorship attribution and many other relevant and useful tasks. In this contribution, we describe an algorithm able to characterize texts in a feature space from which, among other tasks, the authorship attribution problem can be tackled. Although several deep learning architectures have shown good results, the solution they offer is usually hard to interpret and the explanation about the attribution is opaque. In our algorithm, each token is characterized in terms of both, the number of tokens that separate it from its previous appearance within the text, and the number of words that separate it from the last novel token. A novel token is an element that appears for the first time in the context of the analyzed writing. Following the proposed approach, we analyzed hundreds of texts from dozens of writers. The embeddings created by our proposal allows classifiers to correctly attribute the authorship of a novel text, as shown by several tests. Our proposal achieves identification metrics similar, and in some cases, better than state-of-the-art models. Equally relevant, our method is interpretable and shows a far lower computational complexity than deep learning architectures as Large Language Models.
VR as a metaleptic possible world of global citizenship embodiment: a cognitive stylistic approach

Abstract Bringing together narrative elements, virtual affordances, and participants’ embodied interactions, virtual reality (VR) movies instantiate new narrative techniques by offering an immersive experience. This study examines virtual narrative beyond mere interactional engagement and extends the phenomenon to include worlding, metaleptic embodiment, and instantiated possible selves. It aims at exploring VR narrative as idiosyncratic cognitive processes, with a special focus on the notions of empathy and emotional involvement as significant elements contributing to this peculiar interactional and cognitive experience. A cognitive stylistic approach is adopted to explain the functional ability of VR technology in transporting participants to alternate worlds and in making them experience a kind of self-transformation. The immersively metaleptic discourse of Baba Yaga is examined as engaging participants in a quest of how to act as morally and socially empathetic and responsible citizens—global citizens. Baba Yaga narrative deploys the narrative discourses of flashbacks, facework, doubly deictic ‘you’, performatives, and imperatives along with material processes to situate participants in a virtual space of actions and doings and hence encourage them to configure their desired self(ves) across different immersive interactions. The global citizen is embodied in the interactive narrative of Baba Yaga, through invoking various storyworld possible selves (SPSs): the feeling self, the responsible self, and the moral self, which encompasses climate activist self and interculturally aware self who manages to get rid of its own cultural biases as the narrative proceeds. Embodied in these selves, participants transform the virtual world into possible worlds of their own passion, agency, choices, hopes, and desires.
Large Sentiment Dictionary of Russian Words

Sentiment analysis is a widely studied area of computational linguistics. The main tool for sentiment analysis of texts are dictionaries with positive/negative ratings of words. Hundreds of such dictionaries have been compiled for dozens of world languages. The largest English dictionary contains up to 500 thousand words. By contrast, the largest dictionary of the Russian language contains less than 50 thousand words. We compiled a large Russian dictionary with positive/negative ratings of approximately 2 million word forms. To do this, we applied the technique of machine extrapolation of valence ratings contained in 6 existing dictionaries obtained by the survey method. Pretrained fasttext vectors were used as input to the neural network sentiment predictor. Multiple trained models allowed us for cross–validation of sentiment estimations. The obtained Spearmen's correlation coefficient between the human ratings and their machine estimates is up to 0.835.
FuzzyDeepNets based feature extraction for classification of mammograms

Breast cancer is one of the most aggressive tumors that claims the lives of women each year. Radiologists recommend mammography to detect cancer at the early stages. Masses, micro-calcifications, and distortion in mammography indicate breast cancer. This paper proposes FuzzyDeepNets for extracting the features and the Hanman transform classifier for the classification of mammograms. In this work, mammograms are categorized based on abnormality present, type of abnormality, and the characteristics of the abnormality present. FuzzyDeepNets allows us to skip the layers thereby reducing the computational complexity of the deep learning architectures. Principal component analysis helps in reducing the dimensionality of the selected features. The results achieved using proposed method on publicly available mini-MIAS, DDSM, INbreast and private database surpasses the results of the state-of-the-art techniques used for comparison. Results of the proposed method are clinically relevant as they are validated by expert radiologists.
Mitigating Persistence of Open-Source Vulnerabilities in Maven Ecosystem

Vulnerabilities from third-party libraries (TPLs) have been unveiled to threaten the Maven ecosystem in the long term. Despite patches being released promptly after vulnerabilities are disclosed, the libraries and applications in the community still use the vulnerable versions, which makes the vulnerabilities persistent in the Maven ecosystem (e.g., the notorious Log4Shell still greatly influences the Maven ecosystem nowadays from 2021). Both academic and industrial researchers have proposed user-oriented standards and solutions to address vulnerabilities, while such solutions fail to tackle the ecosystem-wide persistent vulnerabilities because it requires a collective effort from the community to timely adopt patches without introducing breaking issues. To seek an ecosystem-wide solution, we first carried out an empirical study to examine the prevalence of persistent vulnerabilities in the Maven ecosystem. Then, we identified affected libraries for alerts by implementing an algorithm monitoring downstream dependents of vulnerabilities based on an up-to-date dependency graph. Based on them, we further quantitatively revealed that patches blocked by upstream libraries caused the persistence of vulnerabilities. After reviewing the drawbacks of existing countermeasures, to address them, we proposed a solution for range restoration (Ranger) to automatically restore the compatible and secure version ranges of dependencies for downstream dependents. The automatic restoration requires no manual effort from the community, and the code-centric compatibility assurance ensures smooth upgrades to patched versions. Moreover, Ranger along with the ecosystem monitoring can timely alert developers of blocking libraries and suggest flexible version ranges to rapidly unblock patch versions. By evaluation, Ranger could restore 75.64% of ranges which automatically remediated 90.32% of vulnerable downstream projects.
Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?

Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i.e, the fact that software is released in a shape not as good as it should be, e.g, in terms of functionality, reliability, or maintainability. This paper empirically investigates the extent to which technical debt can be automatically paid back by neural-based generative models, and in particular models exploiting different strategies for pre-training and fine-tuning. We start by extracting a dateset of 5,039 Self-Admitted Technical Debt (SATD) removals from 595 open-source projects. SATD refers to technical debt instances documented (e.g, via code comments) by developers. We use this dataset to experiment with seven different generative deep learning (DL) model configurations. Specifically, we compare transformers pre-trained and fine-tuned with different combinations of training objectives, including the fixing of generic code changes, SATD removals, and SATD-comment prompt tuning. Also, we investigate the applicability in this context of a recently-available Large Language Model (LLM)-based chat bot. Results of our study indicate that the automated repayment of SATD is a challenging task, with the best model we experimented with able to automatically fix ∼2% to 8% of test instances, depending on the number of attempts it is allowed to make. Given the limited size of the fine-tuning dataset (∼5k instances), the model's pre-training plays a fundamental role in boosting performance. Also, the ability to remove SATD steadily drops if the comment documenting the SATD is not provided as input to the model. Finally, we found general-purpose LLMs to not be a competitive approach for addressing SATD.
On the Evaluation of Neural Code Translation: Taxonomy and Benchmark

In recent years, neural code translation has gained increasing attention. While most of the research focuses on improving model architectures and training processes, we notice that the evaluation process and benchmark for code translation models are severely limited: they primarily treat source code as natural languages and provide a holistic accuracy score while disregarding the full spectrum of model capabilities across different translation types and complexity. In this paper, we present a comprehensive investigation of four state-of-the-art models and analyze in-depth the advantages and limitations of three existing benchmarks. Based on the empirical results, we develop a taxonomy that categorizes code translation tasks into four primary types according to their complexity and knowledge dependence: token level (type 1), syntactic level (type 2), library level (type 3), and algorithm level (type 4). We then conduct a thorough analysis of how existing approaches perform across these four categories. Our findings indicate that while state-of-the-art code translation models excel in type-1 and type-2 translations, they struggle with knowledge-dependent ones such as type-3 and type-4. Existing benchmarks are biased towards trivial translations, such as keyword mapping. To overcome these limitations, we construct G-TransEval, a new benchmark by manually curating type-3 and type-4 translation pairs and unit test cases. Results on our new benchmark suggest that G-TransEval can exhibit more comprehensive and finer-grained capability of code translation models and thus provide a more rigorous evaluation. Our studies also provide more insightful findings and suggestions for future research, such as building type-3 and type-4 training data and ensembling multiple pretraining approaches.
Multitarget Distributed Tracking With Probability Hypothesis Density in Large-Scale Sensor Networks

In this article, multitarget distributed tracking problems in large-scale sensor networks consisting of local fusion centers (LFCs) and sensor nodes (SNs) are considered. Each SN transmits measurements to its superior LFC, which runs a local probability hypothesis density filter having Gaussian mixture representation (GM-PHD) using measurements from subordinate SNs. A framework based on random finite set (RFS) is developed, incorporating an improved GM-PHD filter and an improved geometric average (GA) fusion rule. Regarding the local GM-PHD filter, a modified adaptive birth model using presegmented measurements and an SN-related clutter-based update step are proposed to handle the measurement origin uncertainty and the cardinality overestimation issues. Regarding the proposed distributed fusion algorithm that fuses posteriors among LFCs with the proposed GM-PHD filters, a hybrid fusion rule is proposed to compensate the GA miss detection by arithmetic average (AA) fusion. The boundedness of the proposed algorithm is derived. Finally, simulations illustrate the effectiveness of the proposed algorithm.
Co-designing conversational agents: A comprehensive review and recommendations for best practices

This paper presents a comprehensive review of fifty-two studies co-designing conversational agents (CAs). Its objectives are to synthesise prior CA co-design efforts and provide actionable recommendations for future endeavours in CA co-design. The review systematically evaluates studies' methodological and contextual aspects, revealing trends and limitations. These insights converge into practical recommendations for co-designing CAs, including (1) selecting the most suitable design technique aligned with desired CA outcomes, (2) advocating continuous stakeholder involvement throughout the design process, and (3) emphasising the elicitation and embodiment of stakeholder values to ensure CA designs align with their perspectives. This paper contributes to standardising and enhancing co-design practices, promising to improve the quality of outcomes in the case of CAs while benefiting stakeholders and users.
Underwater visual feature matching based on attenuation invariance

Abstract Underwater visual feature matching plays an important role in the localization and autonomous motion of underwater robots. However, feature descriptors are prone to change in degraded underwater images due to light attenuation, which leads to image color distortion, blur, and low illumination. Although the recent developments in deep learning have improved the performance of feature descriptors, the degradation of underwater images and the lack of underwater datasets have hindered the development of underwater visual feature matching and localization techniques. Herein, we propose an underwater visual feature matching method based on attenuation invariance. Our approach involves two main components. First, we study the attenuation differences between images to generate feature descriptors that are invariant to attenuation, thereby improving the accuracy of underwater visual feature matching. Second, we construct two datasets: the multiple water type (MWT) dataset, which contains over 30000 underwater images, and the underwater image feature descriptor (UIFD) evaluation dataset. These datasets are developed to compensate for the lack of evaluation datasets for underwater visual feature matching. The code and datasets are available at https://github.com/Sun-Jinghao/UIFD .
Advances in the Optimization of Vehicular Traffic in Smart Cities: Integration of Blockchain and Computer Vision for Sustainable Mobility

The growing adoption of Artificial Intelligence of Things technologies in smart cities generates significant transformations to address urban challenges and move towards sustainability. This article analyzes the economic, social, and environmental impacts of Artificial Intelligence of Things in urban environments, focusing on a case study on optimizing vehicular traffic. The research methodology is based on a comprehensive analysis of academic literature and government sources, followed by the creation of a simulated city model. This framework implemented a vehicle-traffic optimization system integrating artificial intelligence algorithms, computer vision, and blockchain technology. The results obtained in this case study are highly encouraging: artificial intelligence algorithms processed real-time data from security cameras and traffic lights, resulting in a notable 20% reduction in traffic congestion during peak hours. Furthermore, implementing blockchain technology guarantees the security and immutability of traffic data, strengthening trust in the system and promoting sustainability in urban environments. These results highlight the importance of combining advanced technologies to effectively address modern cities’ complex challenges and move towards more sustainable and livable cities.
High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies

Abstract High-throughput digital pathology offers considerable advantages over traditional semiquantitative and manual methods of counting pathology. We used brain tissue from 5 clinical-pathologic cohort studies of aging; the Religious Orders Study, the Rush Memory and Aging Project, the Minority Aging Research Study, the African American Clinical Core, and the Latino Core to (1) develop a workflow management system for digital pathology processes, (2) optimize digital algorithms to quantify Alzheimer disease (AD) pathology, and (3) harmonize data statistically. Data from digital algorithms for the quantification of β-amyloid (Aβ, n = 413) whole slide images and tau-tangles (n = 639) were highly correlated with manual pathology data (r = 0.83 to 0.94). Measures were robust and reproducible across different magnifications and repeated scans. Digital measures for Aβ and tau-tangles across multiple brain regions reproduced established patterns of correlations, even when samples were stratified by clinical diagnosis. Finally, we harmonized newly generated digital measures with historical measures across multiple large autopsy-based studies. We describe a multidisciplinary approach to develop a digital pathology pipeline that reproducibly identifies AD neuropathologies, Aβ load, and tau-tangles. Digital pathology is a powerful tool that can overcome critical challenges associated with traditional microscopy methods.
A New Particle Swarm Optimisation Based Memetic Procedure for Fuzzy J-K Flop Neural Networks Learning

Fuzzy Flip-Flop Neural Networks are a type of neural network that combines the strengths of fuzzy logic and recurrent neural networks. In this article, a new method of training the Fuzzy Flip-Flop Neural Network is described. This combines classic particle swarm optimisation procedure with selected evolutionary procedures. Among these are mutation or crossing. The proposed algorithm is tested both on the example of regression of a simple function and then on benchmark data used for classification. In most test cases, the memetic procedure has been shown to provide fast convergence, high accuracy, and robustness, making it a popular choice for the learning and optimisation of Fuzzy Flip-Flop Neural Networks.
Defining the Metaverse with challenges and opportunities in the business environment

This paper defines the Metaverse in the business environment using a holistic lens, highlighting opportunities and challenges. The research is based on a systematic literature review (SLR) that collects definitions, opportunities and challenges of the Metaverse from the latest marketing and management research streams. The research results highlight the emergence of the Metaverse as a technology that facilitates communication, actor interaction and knowledge dissemination, both for the competitive advantage of companies and for the social interaction of people in society.
A Convergence Study of the Possibilistic One Means Algorithm

The possibilistic one means (P1M) algorithm finds one single possibilistic cluster in a given data set. Several clusters can be found by sequential runs of P1M, which has been shown to outperform fuzzy clustering for data sets with large numbers of clusters. Several papers have theoretically proven the convergence of possibilistic clustering. Here, we do not prove convergence but analyze the convergence behavior of P1M. Artificial reference data sets containing possibilistic distributions (which for one-dimensional data correspond to Cauchy distributions) are generated. Experiments with such reference data sets indicate that (1) the number of P1M update steps linearly increases with the distance of the initial cluster center estimates, but for large distances converges to one single step, and (2) P1M will accurately find one of the cluster centers if the cluster centers are separate by at least five times the cluster radius. These findings are very useful to predict the performance of P1M clustering and whether for a given data set we can expect good estimates of the cluster centers.
Towards Reliable Uncertainty Quantification and High Precision with General Type-2 Fuzzy Systems

Deep learning models have been successfully developed to solve complex problems with the main focus on high precision. Yet, accurately assessing uncertainty and prediction is essential for making informed decisions, especially in high-risk tasks. In this paper, we present a step towards learning reliable uncertainty quantification and high precision performance via α- plane based General Type-2 Fuzzy Logic Systems (GT2-FLSs). To balance between accuracy and uncertainty quantification, we propose a novel composite loss function consisting of an accuracy-focused and uncertainty-focused loss term that exploits the parameters of the Secondary Membership Functions (SMFs). For the uncertainty-focused term, we use only the type-reduced set of α <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> = 0 plane of the GT2-FLS, i.e. the size of the SMFs, which does not contribute to the output calculation directly. In the accuracy-focused part, we present two options for the error terms. One uses the aggregated output while the other uses only the output α <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> = 1 plane of the GT2-FLS. In both terms, we make the SMF shape parameters responsible for learning pointwise prediction. We present statistical comparisons and demonstrate that the learned GT2-FLSs generate reliable prediction intervals while also resulting in high-precision performance. The results show the potential of the proposed approach for GT2-FLS as a promising solution for making reliable predictions in real-world applications.
Shifting from Web2 to Web3: The adaptive creator experiences on blockchain-based video-sharing and streaming platforms

Blockchains have inspired imaginaries of a new iteration of the internet, hailed as Web3, where the power of centralized platform companies would be limited and the ownership of personal data and content could be retained by their individual owners and creators. Web3 is expected to facilitate the emergence of novel protocols and platforms that enable decentralized coordination of data and digital assets. This article examines critically the experiences and imaginaries of creators working on two blockchain-based video-sharing platforms: Theta.tv and Odysee. Building on the studies of creator culture and institutionalist blockchain economics and based on open-ended interviews with the early adopters of these platforms, the paper investigates how the creators experience these decentralized social media applications in terms of their processes of governance, community creation, and career development. We show how the affordances of blockchains and creator expectations can result in further convergence of community management and career-building functions potentially benefiting creators. We also show that the new wave of decentralization, against optimistic blockchain visions, has not yet led to the distributed ‘ownership’ of social media networks. Rather, while blockchains seem to have increased creator autonomy and added career opportunities, novel forms of platform governance and power have also introduced new perceptions of precarity among creators.