Reviewerly/exHarmony huggingface dataset:
VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism

Abstract The process of forming, expressing, and updating beliefs from data plays a critical role in data‐driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief‐driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief‐driven visualization. In this paper, we synthesize a design space for belief‐driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief‐driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
The Nature-Based Solutions Case-Based System: A hybrid expert system

Deriving knowledge and learning from past experiences is essential for the successful adoption of Nature-Based Solutions (NBS) as novel integrative solutions that involve many uncertainties. Past experiences in implementing NBS have been collected in a number of repositories; however, it is a major challenge to derive knowledge from the huge amount of information provided by these repositories. This calls for information systems that can facilitate the knowledge extraction process. This paper introduces the NBS Case-Based System (NBS-CBS), an expert system that uses a hybrid architecture to derive information and recommendations from an NBS experience repository. The NBS-CBS combines a 'black-box' artificial neural networks model with a 'white-box' case-based reasoning model to deliver an intelligent, adaptive, and explainable system. Experts have tested this system to assess its functionality and accuracy. Accordingly, the NBS-CBS appears to provide inspirational recommendations and information for the NBS planning and design process.
Evaluation of Techno-economic Using Decision Making Trial and Evaluation Laboratory (DEMATEL) Method

Techno economic means feasibility of project requirement and optimized technology means selection. Techno-economic means existing market and technology is an analysis of selection of technology in project design in demand capacity techno-economic max existing. Chamoli, uttarakhand, India electrical and cooking energy to meet the requirements, integrated renewable energy on the development of the system a techno-economic study has been carried out on paper. Locally available renewable energy resources and exploration capacity of area requirements an attempt has been made to assess. In this from analysis Decision making trial and evaluation laboratory (DEMATEL) complex system elements of the cause-and-effect chain a great one for identification considered proper. This is interdependence between factors assessing relationships and through visual structural modeling what matters deals with identifying. Alternative: air conditioner, color television, washing machine, water heater, electric cooker. Evaluation preference: air conditioner, color television, washing machine, water heater, electric cooker. The result it is seen that Air conditioner is got the first rank where as is the Water heater is having the lowest rank. As a result, the air conditioner gets the first rank, while the water heater gets the lowest rank.
‘妈妈, I miss you ’: Emotional multilingual practices in transnational families

Aims: This study explores how home language(s) are used in bilingual family members’ daily digital or non-digital interactions to demonstrate their emotions and affections for each other. The study investigates linguistic features and situated-language use of home language(s) in emotional negotiations between family members. In addition, it explores how multimodalities and languages are mixed to contribute to the interactional dynamics underlying ‘family talk’ and how children and adults position themselves emotionally in moment-to-moment interaction of family life. Methodology: This study involves six families from two transnational communities (Chinese and Polish) and it employs an ethnographic research design for data collection. Data and analysis: Data sources include recordings of mealtime conversations, daily WhatsApp/WeChat texts, and digital-mediated family talk recorded by the families themselves. Affective repertoire is used as an analytical framework. Findings and conclusions: The following five key features are identified in online or offline family talks regarding expression of emotionality: emojis, terms of endearment, diminutives, declaration of love and situated emotive language use. Originality: Unlike recent studies of emotions in bi-multilingual families, this study does not rely on surveys, memoirs, or interviews. Few studies have investigated how family members present their emotional expression in their daily communications, what role home/heritage language(s) actually play in communicating their emotional needs, and how these emotional expressions are manifested in their lived experiences and socialisation practices. Significance and implications: The findings contribute to FLP literature by looking into how emotions are involved in the practices of family life and the construction of familyness. Emotions, as a key factor in FLP, are directly related to parents’ perceptions of and investment in their heritage language. Although these perceptions are linked to the powerful societal languages that often represent educational possibilities, when it comes to emotional expressions, the primacy is given to the first language/heritage language.
Comparing the latent space of generative models

Abstract Different encodings of datapoints in the latent space of latent-vector generative models may result in more or less effective and disentangled characterizations of the different explanatory factors of variation behind the data. Many works have been recently devoted to the exploration of the latent space of specific models, mostly focused on the study of how features are disentangled and of how trajectories producing desired alterations of data in the visible space can be found. In this work we address the more general problem of comparing the latent spaces of different models, looking for transformations between them. We confined the investigation to the familiar and largely investigated case of generative models for the data manifold of human faces. The surprising, preliminary result reported in this article is that (provided models have not been taught or explicitly conceived to act differently) a simple linear mapping is enough to pass from a latent space to another while preserving most of the information. This is full of consequences for representation learning, potentially paving the way to the transformation of editing trajectories from one space to another, or the adaptation of disentanglement techniques between different generative domains.
Calculation of the Meniscus Shape Formed under Gravitational Force by Solving the Young–Laplace Differential Equation Using the Bézier Curve Method

This work presents a method to calculate the meniscus shape by solving the differential equation based on the Young-Laplace equation. More specifically, the differential equation is solved by applying the cubic Bézier curve. A complicated nonlinear differential equation is solved using the Bézier control points and the least-squares method while maintaining computational simplicity. The results show all of the expected features of the meniscus under the gravitational force. A brief discussion is also made on the effect of the errors on the results. The method is further validated by its agreement with the numerical solutions reported in the existing literature.
PISA: Pixel skipping-based attentional black-box adversarial attack

The studies on black-box and evolutionary algorithm-based adversarial attacks have become increasingly popular due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs). However, the performance of these emerging attacks is negatively impacted when fooling DNNs tailored for high-resolution images. One of the explanations is that they usually focus on attacking the entire image, regardless of its spatial semantic information, and thereby encounter the notorious curse of dimensionality. To this end, we propose a pixel skipping and evolutionary algorithm-based attentional black-box adversarial attack, termed PISA. In PISA, only one of every two neighboring pixels in the salient region is recognized as the target by leveraging the attention map and pixel skipping, such that the dimension of the black-box attack reduces. After that, PISA allows the embedding of an arbitrary multiobjective evolutionary algorithm, which is employed to traverse the reduced pixels and finally generates effective perturbations that are imperceptible by human vision. Extensive experimental results have demonstrated that the proposed PISA is more competitive in attacking high-resolution images than existing black-box and evolutionary algorithm-based attacks.
Multicopy observables for the detection of optically nonclassical states

Distinguishing quantum states that admit a classical counterpart from those that exhibit nonclassicality has long been a central issue in quantum optics. Finding an implementable criterion certifying optical nonclassicality (i.e, the incompatibility with a statistical mixture of coherent states) is of major importance as it often is a prerequisite to quantum information processes. A hierarchy of conditions for detecting whether a quantum state exhibits optical nonclassicality can be written based on some matrices of moments of the optical field [Phys. Rev. A 72, 043808 (2005)]. Here, we design optical nonclassicality observables that act on several replicas of a quantum state and whose expectation value coincides with the determinant of these matrices, hence providing witnesses of optical nonclassicality that overcome the need for state tomography. These multicopy observables are used to construct a family of physically implementable schemes involving linear optical operations and photon number detectors.
An exploratory study on the handwritten allographic features of multi-ethnic population with different educational backgrounds

Ethnicity, native and/or foreign language knowledge, as well as the learned writing systems potentially influence the development of an individual’s handwriting. The unique education system consisting of National schools, Chinese-medium vernacular schools, Tamil-medium vernacular schools, and Islamic religious schools in Malaysia may have established specific characteristic handwritten allographic features that deserve investigation within the intelligence context. This study was aimed to explore handwritten allographic features of handwriting samples from 120 subjects (30 writers from four different educational backgrounds mentioned above). Characteristic features which could be attributed to the study groups were statistically analyzed and identified. In this study, thirteen allographic features, including letters "A", "B", "D", "H", "p", "T", "t", "w", "X" and "x", were found to be discriminative. Such information could serve to indicate the primary education system undergone by a writer; enabling the comparison of different handwriting profiles and allowing characterization of writers to a specific group of people.
YoungDeafDesign: Participatory design with young Deaf children

It is common in HCI research to involve children in the design of their own technology. However, no design methods exist to design with young Deaf children. To address this gap, I have created YoungDeafDesign, a design method for designing with young Deaf children. YoungDeafDesign was originally intended to be a design method for working with young Deaf children as equal design partners, according to Druin's levels of involvement (Druin, 2002) . However, in YoungDeafDesign as it is presented here, the children's involvement level falls between design partner and informant, as the communication gap between hearing designers and young Deaf children prevents the dialogue which is required for working with children as design partners. The YoungDeafDesign method addresses children's youth, language level, individual Deafness and cultural Deafness, enabling adult, hearing designers to design technologies with and for this unique group of children. YoungDeafDesign will be described in this paper through the lens of patterns and themes which are common in designing with children (Korte, 2020) , and which were evaluated for applicability to designing with young Deaf children through a series of twenty-five design sessions conducted with young Deaf children (3–5 years), staff members from the children's preschool programme in the role of sign language interpreter or support assistant; and at times, the children's parents. This led to a synthesis of the relevant aspects of existing methods into a new design method for designing with young Deaf children. The existence of YoungDeafDesign will enable the creation of technologies to assist young Deaf children and their families in ways which address young Deaf children's needs and abilities.
Financial development and educational quality in Sub-Saharan Africa

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Moving-target travelling salesman problem for a helicopter patrolling suspicious boats in antipiracy escort operations

Using the realistic background of antipiracy escort operations in the Gulf of Aden, this study presents a moving-target travelling salesman problem (MT-TSP) of a helicopter patrolling suspicious boats (moving targets). Since the targets in the MT-TSP move over time, the real-time locations of the convoy, helicopter and suspicious boats were analysed. Mathematical formulations for the MT-TSP were proposed with the goal of minimising the cumulative threat value of all targets. Owing to the highly nonlinear and dynamic MT-TSP, we proposed a hybrid genetic algorithm and simulated annealing heuristic (HGA-SA) for solving the model. Numerical experiments were proposed to test the performance of the proposed approach. Under the circumstances of instances with 20 to 50 targets, the quality of the solution, computation time and stability of HGA-SA were considerably improved compared to GA and SA, proving the superiority and effectiveness of HGA-SA in solving the MT-TSP model.
Long-term prediction of multiple types of time-varying network traffic using chunk-based ensemble learning

With the development of networking technologies, global Internet traffic is constantly increasing. Moreover, various traffic types associated with a variety of network services and applications co-exist in the network, having diverse volume and seasonality patterns. The knowledge of future patterns of these heterogeneous traffic types is beneficial for the network operators, as it enables proper adjustment of available network resources in near real-time, and allow performing periodical network reconfigurations. In this paper, we propose a chunk-based ensemble learning method for a long-term prediction of multiple network traffic types. Our developed prediction method combines two popular machine learning (ML) approaches: chunk-based learning on data streams and ensemble learning. The proposed method does not require large volumes of training data and is resilient to changes in traffic characteristics. Finally, we create a custom metric called allocation outside blocking threshold (AOBT), which links the problem of network traffic prediction to bandwidth blocking probability in dynamic traffic routing. We compare the performance of our online method to a number of baselines, using the root mean square percentage error (RMSPE) and the proposed AOBT metrics. According to conducted experiments, the proposed streaming approach outperforms the reference ones in RMSPE and AOBT. Depending on the metric and model, chunk-based ensemble learning achieves 5%–34% lower prediction errors across traffic types. However, the choice of a specific model depends on the investigated traffic type and applied metric.
Privacy preserving IoT-based crowd-sensing network with comparable homomorphic encryption and its application in combating COVID19

IoT-based crowd-sensing network, which aims to achieve data collection and task allocation to mobile users, become more and more popular in recent years. This data collected by IoT devices may be private and directly transmission of these data maybe incur privacy leakage. With the help of homomorphic encryption (HE), which supports the additive and/or multiplicative operations over the encrypted data, privacy preserving crowd-sensing network is now possible. Until now several such secure data aggregation schemes based on HE have been proposed. In many cases, ciphertext comparison is an important step for further secure data processing. However efficient ciphertext comparison is not supported by most such schemes. In this paper, aiming at enabling ciphertext comparison among multiple users in crowd-sensing network, with Lagrange's interpolation technique we propose comparable homomorphic encryption (CompHE) schemes. We also prove our schemes' security, and the performance analysis show our schemes are practical. We also discuss the applications of our IoT based crowd-sensing network with comparable homomorphic encryption for combatting COVID19, including the first example of privacy preserving close contact determination based on the spatial distance, and the second example of privacy preserving social distance controlling based on the spatial difference of lockdown zones, controlled zones and precautionary zones. From the analysis we see our IoT based crowd-sensing network can be used for contact tracing without worrying about the privacy leakage. Compared with the existing CompHE schemes, our proposals can be collusion resistance or secure in the semi-honest model while the previous schemes cannot achieve this easily. Our schemes only need 4 or 5 modular exponentiation when implementing the most important comparison algorithm, which are better than the existing closely related scheme with advantage of 50% or 37.5%.
An International Federal Hyperledger Fabric Verification Framework for Digital COVID-19 Vaccine Passport

The COVID-19 virus has been spreading worldwide on a large scale since 2019, and the most effective way to prevent COVID-19 is to vaccinate. In order to prove that vaccination has been administered to allow access to different areas, paper vaccine passports are produced. However, paper vaccine passport records are vulnerable to counterfeiting or abuse. Previous research has suggested that issuing certificates digitally is an easier way to verify them. This study used the consortium blockchain based on Hyperledger Fabric to upload the digital vaccine passport (DVP) to the blockchain network. In order to enable collaboration across multiple systems, networks, and organizations in different trust realms. Federated Identity Management is considered a promising approach to facilitate secure resource sharing between collaborating partners. Therefore, the international federal identity management architecture proposed in this study enables inspectors in any country to verify the authenticity of the DVP of incoming passengers using the consortium blockchain. Through practical construction, the international federal Hyperledger verification framework for the DVP proposed in this study has shown the feasibility of issuing a global DVP in safety analysis and efficacy testing.
A Model of Pixel and Superpixel Clustering for Object Detection

The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error means the total squared error. An ambiguous image is described as a non-hierarchical structure but is represented as an ordered superposition of object hierarchies, each containing at least one optimal approximation in g0 = 1, 2,..., etc., colors. For the selected hierarchy of pixel clusters, the objects-of-interest are detected as the pixel clusters of optimal approximations, or as their parts, or unions. The paper develops the known idea in cluster analysis of the joint application of Ward's and K-means methods. At the same time, it is proposed to modernize each of these methods and supplement them with a third method of splitting/merging pixel clusters. This is useful for cluster analysis of big data described by a convex dependence of the optimal approximation error on the cluster number and also for adjustable object detection in digital image processing, using the optimal hierarchical pixel clustering, which is treated as an alternative to the modern informally defined "semantic" segmentation.
Artificial Intelligence Implementation to Counteract Cybercrimes Against Children in Pakistan

Increased internet usage also enhances cyberattacks, particularly in a developing country like Pakistan. These cybercrimes are common against children, demanding the implementation of automated systems and models to detect and counteract these crimes. By keeping in view the growing importance of AI-enabled cybersecurity systems, this article provides insights regarding implementing the relevant systems and models to counteract cybercrimes against children in Pakistan. The researchers reviewed current studies witnessing the performance, reliability, and results of AI implementation in different countries to relate their possible potential further to detect, takedown, and trace online violence against children effectively. Findings showed that AI-enabled software, i.e., Spotlight, SomeBuddy, Google AI Tool, etc., and models such as DAPHNE, iCOP toolkit, and PrevBOT can identify and takedown any indecent activity against children. Besides, detecting the perpetrators' URLs, domains, and personal emails can further help the children resume internet usage in a healthy online environment. Thus, it is concluded that the internet technology is also creating vectors for abuse and exploitation against children. Harnessing powerful technology, i.e., AI, to analyze and manage the data can also enrich investigative functions. Towards this, local government and law enforcement agencies should resort to suggested tools that may identify even keywords and images. Further, the researchers have provided policy recommendations and discussed the limitations accordingly.