Reviewerly/exHarmony huggingface dataset:
Explanatory Pluralism in Explainable AI

The increasingly widespread application of AI models motivates increased demand for explanations from a variety of stakeholders. However, this demand is ambiguous because there are many types of 'explanation' with different evaluative criteria. In the spirit of pluralism, I chart a taxonomy of types of explanation and the associated XAI methods that can address them. When we look to expose the inner mechanisms of AI models, we develop Diagnostic-explanations. When we seek to render model output understandable, we produce Explication-explanations. When we wish to form stable generalizations of our models, we produce Expectation-explanations. Finally, when we want to justify the usage of a model, we produce Role-explanations that situate models within their social context. The motivation for such a pluralistic view stems from a consideration of causes as manipulable relationships and the different types of explanations as identifying the relevant points in AI systems we can intervene upon to affect our desired changes. This paper reduces the ambiguity in use of the word 'explanation' in the field of XAI, allowing practitioners and stakeholders a useful template for avoiding equivocation and evaluating XAI methods and putative explanations.
Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles

This paper investigates whether computer usage profiles comprised of process-, network-, mouse-, and keystroke-related events are unique and consistent over time in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication. We collected ecologically-valid computer usage profiles from 31 MS Windows 10 computer users over 8 weeks and submitted this data to comprehensive machine learning analysis involving a diverse set of online and offline classifiers. We found that: (i) profiles were mostly consistent over the 8-week data collection period, with most (83.9%) repeating computer usage habits on a daily basis; (ii) computer usage profiling has the potential to uniquely characterize computer users (with a maximum F-score of 99.90%); (iii) network-related events were the most relevant features to accurately recognize profiles (95.69% of the top features distinguishing users were network-related); and (iv) binary models were the most well-suited for profile recognition, with better results achieved in the online setting compared to the offline setting (maximum F-score of 99.90% vs. 95.50%).
SpeakNav

Many navigation applications take natural language speech as input, which avoids users typing in words and thus improves traffic safety. However, navigation applications often fail to understand a user's free-form description of a route. In addition, they only support input of a specific source or destination, which does not enable users to specify additional route requirements. We propose a SpeakNav framework that enables users to describe intended routes via speech and then recommends appropriate routes. Specifically, we propose a novel Route Template based Bidirectional Encoder Representation from Transformers (RT-BERT) model that supports the understanding of natural language route descriptions. The model enables extraction of information of intended POI keywords and related distances. Then we formalize a template-driven path query that uses the extracted information. To enable efficient query processing, we develop a hybrid label index for computing network distances between POIs, and we propose a branch-and-bound algorithm along with a pivot reverse B-tree (PB-tree) index. Experiments with real and synthetic data indicate that RT-BERT offers high accuracy and that the proposed algorithm is capable of outperforming baseline algorithms.
Quality-Diversity Meta-Evolution: Customizing Behavior Spaces to a Meta-Objective

Quality-diversity (QD) algorithms evolve behaviorally diverse and high-performing solutions. To illuminate the elite solutions for a space of behaviors, QD algorithms require the definition of a suitable behavior space. If the behavior space is high-dimensional, a suitable dimensionality reduction technique is required to maintain a limited number of behavioral niches. While current methodologies for automated behavior spaces focus on changing the geometry of the behavior space or on unsupervised learning of its key features, there remains a need for customizing behavioral diversity to a particular meta-objective specified by the end user. In the newly emerging framework of QD Meta-Evolution, or QD-Meta for short, one evolves a population of QD algorithms, each with different algorithmic and representational characteristics, to optimize the algorithms and their resulting archives to a user-defined meta-objective. Despite promising results compared to traditional QD algorithms, QD-Meta has yet to be compared to state-of-the-art behavior space automation methods, such as centroidal voronoi tessellations multidimensional archive of phenotypic elites (CVT-MAP-Elites) and autonomous robots realizing their abilities (AURORAs). This article performs an empirical study of QD-Meta on function optimization and multilegged robot locomotion benchmarks. Results demonstrate that QD-Meta archives provide improved average performance and faster adaptation to a priori unknown changes to the environment when compared to CVT-MAP-Elites and AURORA. A qualitative analysis shows how the resulting archives are tailored to the meta-objectives provided by the end user.
Orenburg State University in the Conditions of Transformation

The article presents the key points of the development strategy of Orenburg State University with a focus on the criteria of the Strategic Academic Leadership Program “Priority 2030”. Transformations of popular models of university development in the historical logic are considered, actual models in an international and regional scale are highlighted. The article makes the case for transition from the adaptation strategy of the university development to the strategy of a driver of positive changes in the Orenburg Region. The resources of the OSU leadership strategy and tactical solutions for the implementation of the OSU mission – “Development of territory and human capital” in various types and scales of influence are determined.
Blockchain-Based Secure Method for Tiger Detection Using Machine Learning

The field of computer vision is encountering an extraordinary jump forward improvement today. This targets giving a thorough review of the ongoing advancement on computer vision calculations and their related equipment usage. Specifically, the noticeable accomplishments in computer vision errands, for example, picture characterization, object identification, and picture division, brought by profound learning methods are featured. Then again, survey of procedures for executing and upgrading profound learning put together by computer vision calculations with respect to Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA), and other new ages of equipment quickening agents is introduced to encourage ongoing or potentially energy-productive activities. At long last, a few promising bearings for future examination are introduced to propel further advancement in the field. The capacity to convey remote sensor networks in far-off territories is making new occasions to screen jeopardized species and their current circumstance so as to guarantee their insurance and security, and trustworthiness is the fundamental issue in Internet of Things (IoT)-based organization climate in which capture attempt free made sure about correspondence is required. The blockchain innovation is one of the indisputable and tip-top procedures that can be used for the joining of security with remote frameworks. This part gives the definite contextual investigation on blockchain about IoT climate for checking and securing the natural life creatures like tigers.
Cyber-Security Training Evaluation Metrics

Cyber-security training has evolved into an imperative need, aiming to provide cyber-security professionals with the knowledge and skills required to confront cyber-attacks that are increasing in number and sophistication. Training activities are typically associated with evaluation means, aimed to assess the extent to which the trainee has acquired the knowledge and skills whose development is targeted by the training programme, while cyber-security awareness and skill level evaluation means may be used to support additional security-related aspects of organizations. In this paper, we review trainee performance assessment metrics in cyber-security training, aiming to assist designers of cyber-security training activities to identify the most prominent trainee performance assessment means for their training programmes, while additional research directions involving cyber-security training evaluation metrics are also identified.
Weighted holoentropy-based features with optimised deep belief network for automatic sentiment analysis: reviewing product tweets

In this paper, a novel sentiment analysis model is implemented, which consists of six stages: (i) Pre-processing, (ii) Keyword extraction and its sentiment categorisation, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Initially, the Mongodb documented tweets are subjected to pre-processing that includes steps such as stop word removal, stemming, and blank space removal. Accordingly, from the pre-processed tweets, the keywords are extracted. Based on the extracted keywords, the prevailing semantic words are extracted after classifying the sentimental keywords. Further, the evaluation of the semantic similarity score with the keywords takes place. Also, it exploits joint holoentropy and cross holoentropy. Here, the extraction of weighted holoentropy features is the main contribution, where a weight function is multiplied by the holoentropy features. To improve the performance of classification, a constant term is used for calculating weight function. It is tuned or optimised in such a way that the accuracy of the proposed method is better. The optimisation strategy uses the hybrid model that merges Particle Swarm Optimisation (PSO) into Whale Optimisation Algorithm (WOA). Hence, the proposed algorithm is named as Swarm Velocity-based WOA (SV-WOA). Finally, the analysis is done to prove the efficiency of the proposed model.
Convex covariate clustering for classification

Clustering, like covariate selection for classification, is an important step to compress and interpret the data. However, clustering of covariates is often performed independently of the classification step, which can lead to undesirable clustering results that harm interpretability and compression rate. Therefore, we propose a method that can cluster covariates while taking into account class label information of samples. We formulate the problem as a convex optimization problem which uses both, a-priori similarity information between covariates, and information from class-labeled samples. Like ordinary convex clustering [1], the proposed method offers a unique global minima making it insensitive to initialization. In order to solve the convex problem, we propose a specialized alternating direction method of multipliers (ADMM), which scales up to several thousands of variables. Furthermore, in order to circumvent computationally expensive cross-validation, we propose a model selection criterion based on approximating the marginal likelihood. Experiments on synthetic and real data confirm the usefulness of the proposed clustering method and the selection criterion.
An approach for knowledge acquisition from a survey data by conducting Bayesian network modeling, adopting the robust coplot method

This study proposes a methodological approach for extracting useful knowledge from survey data by performing Bayesian network (BN) modeling and adopting the robust coplot analysis results as prior knowledge about association patterns hidden in the data. By addressing the issue of BN construction when the expert knowledge is limited/not available, this proposed approach facilitates the modeling of large data sets describing numerously observed and latent variables. By answering the question of which node(s)/link(s) should be retained or discarded from a BN, we aim to determine a compact model of variables while considering the desired properties of data. The proposed method steps are explained on real data extracted from Turkey Demographic and Health Survey. First, a BN structure is created, which is based solely on the judgment of the analyst. Then the coplot results are employed to update the BN structure and the model parameters are updated using the updated structure and data. Loss scores of the BNs are used to ensure the success of the updated BN that inherits knowledge from coplot.
Complexity of a Fragment of Infinitary Action Logic with Exponential via Non-well-founded Proofs

Infinitary action logic (\(\mathbf {ACT}_\omega \)) can be viewed as an extension of the multiplicative-additive Lambek calculus (\(\mathbf {MALC}\)) with iteration (Kleene star) governed by an omega-rule (Buszkowski, Palka 2007). An alternative formulation utilizes non-well-founded proofs instead of the omega-rule (Das, Pous 2017). Another unary operation commonly added to \(\mathbf {MALC}\) is the exponential. We consider a system which has both Kleene star and the exponential. In general, this system is of a very high complexity level: it is \(\varPi ^1_1\)-complete (Kuznetsov, Speranski 2020), while \(\mathbf {ACT}_\omega \) itself is \(\varPi ^0_1\)-complete. As a reasonable intermediate logic, we consider the fragment where Kleene star is not allowed to appear in the scope of the exponential. For this fragment we manage to construct a formulation based on non-well-founded proofs, with an easily checkable correctness criterion. Using this formulation, we prove that this fragment indeed has strictly intermediate complexity, namely, we prove a \(\varPi ^0_2\) lower bound and a \(\varDelta ^1_1\) upper bound. We also prove a negative result that this fragment does not enjoy Palka’s *-elimination property, which would have given a \(\varPi ^0_2\) upper bound as well.
Semantic oscillations of supporting music of the ancient cult as the foundation of metamodernism elements in the works of contemporary opera directors

In recent decade, modernism as one of the varieties of post-postmodernism, draws interest of the researchers. It is suggested that modern culture has transgressed the situation of postmodernism, gravitating towards conceptual and semantic sustainability. As a language of self-description of the new era, the foundations of metamodernism are reflected in various forms of art. The opera house, overcoming the inertia of conservatism, perceives these trends, refracting them in a characteristic oscillation between the extreme semantic poles, which formed during the period of antiquity. The subject of this research is the correlation between the ancient musical heritage and metamodernistic trends in the modern opera theater. It is determined that translation of the principles of musical score of the ancient cult within the framework of the works of modern opera directors implies the characteristic to the supporting music of the cult forms of Ancient Greece and Rome oscillation between the poles of monophony &amp;ndash; polyphony, instrumentality of accompaniment &amp;ndash; a cappella, improvisational &amp;ndash; preset of musical pieces. Paradigmatic assimilation of the fluctuations inherent to the cult of antiquity, as well as the absence of intentionality in their manifestations (which partially reflects the religious-ecstatic procedurality of art), largely determine the specific attributes of metamodernism, emphasized in the works of R. Castellucci, P. Sellars, R. Wilson, and&amp;nbsp;D. Chernyakov.
Improving Digital Competences of Students-Bachelors of the Field of Study 38.03.02 “Management”

The paper summarizes the experience of introducing digital open educational resources into medical higher school. The article examines business communication competence requirements of the Federal State Educational Standard of Higher Education 3++ for specialty 38.03.02 “Management”, analyzes a new universal competence students should acquire, provides examples of academic curriculum implementation. The authors broaden the curriculum of the discipline “Business Communications” to include digital open educational resources enhancing the formation of students’ digital competences, and herein lies scientific originality of the study. The research findings are as follows: the authors update the curriculum of the discipline “Business Communications” to include digital open educational resources promoting the formation of students’ digital competences.
ICN PATTA: ICN Privacy Attack Through Traffic Analysis

PATTA is the first privacy attack based on network traffic analysis in Information-Centric Networking. PATTA aims to automatically identify the category of requested content by sniffing the communication towards the first hop router. PATTA applies text processing and machine learning techniques to content names in content-oriented architectures. We evaluate PATTA in a simulated network, achieving an accuracy in determining a real-time content category equal to 96%.
Language-Conditioned Region Proposal and Retrieval Network for Referring Expression Comprehension

Referring expression comprehension (REC) is a multi-modal task that aims to localize target regions in images according to language descriptions. Existing methods can be concluded into two categories, proposal-based methods and proposal-free methods. Proposal-based methods first detect all candidate objects in the image and then retrieve the target among those objects based on the language description, while proposal-free methods directly locate the region based on the language without any region proposals. However, the proposal-based methods suffer from separate region proposal networks that actually do not suit this task well, and the proposal-free methods are not able to perform fine-grained visual-language alignments to yield higher precision. To overcome the above drawbacks, we propose a language-conditioned region proposal and retrieval network that first detects those regions only related to the language and then retrieves the target region by compositional reasoning on the language. Specifically, the proposed network consists of a language-conditioned region proposal network (LC-RPN) to detect those language-related regions, and a language-conditioned region retrieval network (LC-RRN) to perform region retrieval with a full understanding of the language. A pre-training mechanism is proposed to teach our model knowledge about language decomposing and vision-language alignment. Experimental results demonstrate that our proposed method achieves leading performance with high inference speed on RefCOCO, RefCOCO+, and RefCOCOg benchmarks.
A Crypto-Token Based Charging Incentivization Scheme for Sustainable Light Electric Vehicle Sharing

The ecological impact of shared light electric vehicles (LEV) such as kick scooters is still widely discussed. Especially the fact that the vehicles and batteries are collected using diesel vans in order to charge empty batteries with electricity of unclear origin is perceived as unsustainable. A better option could be to let the users charge the vehicles themselves whenever it is necessary. For this, a decentralized, flexible and easy to install network of off-grid solar charging stations could bring renewable electricity where it is needed without sacrificing the convenience of a free float sharing system. Since the charging stations are powered by solar energy the most efficient way to utilize them would be to charge the vehicles when the sun is shining. In order to make users charge the vehicle it is necessary to provide some form of benefit for them doing so. This could be either a discount or free rides. A particularly robust and well-established mechanism is controlling incentives via means of blockchain-based crypto-tokens. This paper demonstrates a crypto-token based scheme for incentivizing users to charge sharing vehicles during times of considerable solar irradiation in order to contribute to more sustainable mobility services.
X-BaD: A Flexible Tool for Explanation-Based Bias Detection

As widely known, machine learning has been thriving during the last two decades on the strength of two key factors: significant and continuous improvements in hardware performance and the possibility to produce large datasets through automated procedures. However, it has been shown that datasets often contain biases that can significantly affect the performance and resilience of machine learning models, e.g., when deployed to realize functionality for cyber-physical systems. For this reason, a lot of research has been devoted to methodologies and tools for detecting biases in the dataset.This paper presents X-BaD, a tool for bias detection designed to inject and discover biases in a neural network. It is implemented as an open-source Python library that extends the Spectral Relevance Analysis methodology. It allows data reusability and user customization by parameter configurations, and offers built-in functions to inject artificial biases into popular image datasets such as CIFAR-10, Pascal VOC, and ImageNet, for test purposes. This tool is compatible and extensible with features that are commonly used in machine learning frameworks, such as PyTorch and Pytorch Lightning datasets and models, Captum attributions, and Sci-kit Learn clustering algorithms and clustering performance evaluation methods. It also includes functions to interpret and assess the processed data. A set of experiments is finally presented to evaluate the effectiveness of the proposed tool.
CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network

Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patient’s information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost.