Reviewerly/exHarmony huggingface dataset:
Investigating the Influence of Cute Aesthetics in Community Crowdsourcing

Abstract This paper investigates the influence of cute aesthetics design in community crowdsourcing. Specifically, we propose that cute aesthetics design is a viable approach to appeal to the crowd and motivate everyday participation in community crowdsourcing as prior works indicate that cute aesthetics can invoke positive emotion. We conducted a comparative study on two sets of interface designs on a community crowdsourcing app where respondents evaluated and assessed if cute aesthetics interface design has effects on app usage intention. Data were collected from 103 respondents and results indicate that cute aesthetics design appeal to younger female adults. Findings contribute to a better understanding of the conceptualization of aesthetic‐usability in community crowdsourcing.
The Community-Based Learning Approach for Designing School Curriculums in Early Childhood Education with an Application to Promote a 21st Century Learning

The mobile phone applications currently adopts and increasingly applies in education. The applications are widely used to promote and support learning in many levels and contexts. In this research, the 19 sophomore students who enrolled in a school curriculum in early childhood education course at Suan Dusit University in Thailand were expected to have the ability to design the school curriculum in early childhood education according to the early childhood school curriculum compositions with Roomle 3D & AR room planner application after using the learning approach of community-based integration and the use of application. The Roomle 3D & AR room planner application was used as a tool to draw floor plans, furnish and decorate the early childhood classroom and school. This study also aims to compare the academic achievement and to study the students' learning satisfaction in the school curriculum in early childhood education course before and after using the learning approach of community-based integration and the use of application. The statistics used in this research were mean, standard deviation and percentage. The results showed the students' ability to design the school curriculum in early childhood education according to the early childhood school curriculum compositions with Roomle 3D & AR room planner application. After using the learning approach, the students had an average score of 14.89 and a standard deviation of 2.08 which is 74.47% higher than before. The students' learning satisfaction was also at the highest level with the mean of 4.77 and the standard deviation of 0.40. Thus, it can be concluded that the community-based integration and the use of application can be used in promoting student's learning and increasing their knowledge towards the subject learning.
Quantum walks in polycyclic aromatic hydrocarbons

Aromaticity is a well-known phenomenon in both physics and chemistry, and is responsible for many unique chemical and physical properties of aromatic molecules. The primary feature contributing to the stability of polycyclic aromatic hydrocarbons is the delocalised $\pi$-electron clouds in the $2p_z$ orbitals of each of the $N$ carbon atoms. While it is known that electrons delocalize among the hybridized $sp^2$ orbitals, this paper proposes quantum walk as the mechanism by which the delocalization occurs, and also obtains how the functional chemical structures of these molecules arise naturally out of such a construction. We present results of computations performed for some benzoid polycyclic aromatic hydrocarbons in this regard, and show that the quantum walk-based approach does correctly predict the reactive sites and stability order of the molecules considered.
A Multi Attack Argumentation Framework

This paper presents a novel abstract argumentation framework, called Multi-Attack Argumentation Framework (MAAF), which supports different types of attacks. The introduction of types gives rise to a new family of non-standard semantics which can support applications that classical approaches cannot, while also allowing classical semantics as a special case. The main novelty of the proposed semantics is the discrimination among two different roles that attacks play, namely an attack as a generator of conflicts, and an attack as a means to defend an argument. These two roles have traditionally been considered together in the argumentation literature. Allowing some attack types to serve one of those roles only, gives rise to the different semantics presented here.
A Deep Learning Approach For Bangla Image Captioning System

Naturalness and generalization are the two challenges while generating automated Image Captioning through a System. There is a lack of research to focus on these challenges for Image Captioning in the Bangla language. Furthermore, the lexical resources for Image Captioning in Bangla are not adequate. An effort has been made in this research to contribute to experimenting and analyzing the results of Image Captioning for Bangla Language using deep learning on a novel dataset “Ovro1.1” which comprises 2000 images along with a caption describing that image. Images on Bangla culture, lifestyle, festivals, etc. are recorded with captions in this dataset which makes the training more immersive for the Bangla language. Two neural networks are used in this model where a Convolutional Neural Network (CNN) is used for extracting the features of the images into a vector representation, whereas the vector representation is trained by a Recurrent Neural Network (RNN) for generating text output as its caption. This is an encoder-decoder model architecture where the CNN acts as an encoder and the RNN acts as a decoder. Inception V4 architecture is used as the CNN model for the encoder. Long Short-Term Memory (LSTM) Cells are used in decoding. The model is trained with the existing “BanglaLekha ImageCaption” dataset along with our developed dataset “Ovro1.1” and an evaluation has been done on the results. The model gives a BLEU score of 0.4224 and gives decent logical captioning for the images. However, the captions are constrained within 50 words and the model does not work well with conceptual arts, cartoons, and in recognizing special persons or famous places.
Female images in the novel “Three Comrades” by Erich Maria Remarque

This article analyzes female images in the novel &amp;ldquo;Arch of Triumph&amp;rdquo; by Erich Maria Remarque. The research is based on the theory of N. Pavlovich that each image has several similar invariants ascending to the uniform archetype. A hypothesis is advanced that the characters in the works of E. M. Remarque can be divided into two groups: 1) associated with the generally literary archetypes; 2) collective living images. The article also examines female images who have a plot-driven and artistic meaning in the text, although do not belong to the indicated time period. The image of Patricia Holman in the novel is associated with the mythological archetype, which is reflected in the description of her appearance, behavior, and corresponding events. It is noted that all central female images in the novels by E. M. Remarque have are intertwined with the mystical characters from myths and legends. The scientific novelty consists in tracing the evolution in description of the images of public female figures in comparison with another novel &amp;ndash; &amp;ldquo;Arch of Triumph&amp;rdquo;. The author also examines another narrative parallel between the two novels: similar stories of two families lived in a particular historical time. The conclusion is made that the storyline of many novels by Erich Maria Remarque is based on a certain pattern. The author attempted to reconstruct the aforementioned pattern, which has largely proven the assumptions put forward in the earlier research.
A Trusted User Integrity-Based Privilege Access Control (UIPAC) for Secured Clouds

The cloud is a virtual domain that offers a wide range of storage areas for domain users in the storage cloud. Clients will actually want to get to information and retrieve the assistance rapidly and without any problem. Protection and security are the main issues in the cloud platform. The main facets of security are availability, confidentiality, and integrity. An enormous number of clients keeps their information classified in the cloud for security reasons. Therefore, security plays an important role in cloud computing, which is used to protect user confidential data in the cloud. User information should not be hijacked by an unknown user, so client authentication is very important. In this work, we propose a new cryptographic RBAC model, called User Integrity-based Privilege Access Control (UIPAC) with master key algorithm (MKA) and privilege authentication policy algorithm (PAPA) to help different security features, including client identification, permission policy to the authorized client. The need for cloud storage security cannot be efficiently warranted; many users are unenthusiastic to transfer their vital information to the cloud for storage, making it really difficult to source cloud storage. At the moment, guaranteeing the secrecy of client data and forestalling unapproved access is the way to taking care of the security issues of cloud storage; a lot of encryption privilege access authentication has been conducted. The vast majority of existing authentication control schemes for enciphering data in cloud storage do not assist dynamic refreshing of access control strategies and computational overheads are high. The in-depth structure of our scheme including system setup, create and delete users and their roles, granting the privileges for the authorized user can read and write the file. This work mainly focus on increasing the integrity and authentication of confidential data over the cloud.
About the Concept “Learning” in Russian-language Academic Discourse

Being one of the basic didactic terms, “learning” is frequently translated by Russianspeaking researchers as either “obuchenie” or “uchenie”. It seems strange, as these two words are not synonyms in Russian. Moreover, in Russian conventional didactics they denote two different phenomena: “obuchenie” refers to the entire educational process consisting of both the teacher’s and the learner’s activities while “uchenie” corresponds to the latter one. Successful international academic communication presupposes that any terms accepted by English-speaking professionals should be adapted to the national didactic concepts of this or that country. Taking this requirement into account, the author concludes that the only possible equivalent of “learning” in Russian is “uchenie”, not “obuchenie”, while the phrase “the shift from teaching to learning” and the term “student-centred learning” should be translated as “sdvig / perekhod ot prepodavaniya k ucheniyu” and “studentotsentrirovannoe uchenie” respectively. The author comes to the conclusion that student-centred learning (SCL) opposed to student-centred teaching can be considered the highest point in the development of student-centred approach currently prevailing in European countries. SCL as a new type of learning means that the student becomes an active participant in educational process having his (her) own needs and interests in the focus of attention. The substitution of the word “uchenie” by the incorrect variant “obuchenie” existing in the Russian academic discourse nowadays prevents Russian-speaking researchers from seeing clearly the essence of SCL.
Elliptical Ordinal Embedding

Ordinal embedding aims at finding a low dimensional representation of objects from a set of constraints of the form ”item j is closer to item i than item k”. Typically, each object is mapped onto a point vector in a low dimensional metric space. We argue that mapping to a density instead of a point vector provides some interesting advantages, including an inherent reflection of the uncertainty about the representation itself and its relative location in the space. Indeed, in this paper, we propose to embed each object as a Gaussian distribution. We investigate the ability of these embeddings to capture the underlying structure of the data while satisfying the constraints, and explore properties of the representation. Experiments on synthetic and real-world datasets showcase the advantages of our approach. In addition, we illustrate the merit of modelling uncertainty, which enriches the visual perception of the mapped objects in the space.
Automatic Hanging Point Learning from Random Shape Generation and Physical Function Validation

The purpose of this paper is the robotic hanging manipulation of an object of various shapes that is not limited to a specific category. To achieve this, we propose a method that allows the estimator to learn many different shapes with hanging points without any manual annotation. A random shape generator using GAN solves the limitation of the number of 3D models and can handle objects of various shapes. In addition, hanging is repeated in the dynamics simulation, and hanging points are automatically generated. A large amount of training data is generated by rendering random-textured objects with hanging points in the random simulation environment. A deep neural network trained with these data was able to estimate hanging points of an unknown category object in the real world and achieved hanging manipulation by a robot.
Leveraging Lexical Common-Sense Knowledge for Boosting Bayesian Modeling

Recent research has shown that, since the ultimate goal of Bayesian perspective is to infer a posterior distribution, it is demonstrably more direct to impose domain knowledge directly on the posterior distribution. This paper presents a model by imposing lexical common-sense knowledge as constraints on the posterior distribution, under the conventional regularized Bayesian framework. We then improve the latent topic modeling with help of the aforementioned model, and experimental results show that, combining lexical common-sense knowledge and Bayesian modeling, is beneficial for prediction.
Bit Reduction based Audio Steganography Algorithm

Today, the development of data hiding techniques for information security or confidential communication is a subject of great demand and interest. The main purpose of most studies is to develop imperceptible hiding techniques and to hide more information with as little distortion as possible. Similarly, the main purpose of the presented study is to hide an image inside an audio file with less distortion. For this purpose, a bit reduction-based approach is used. The difference between the original audio file and the bit-reduced audio file can be classified as insignificant mostly. By adding the small changes made on this difference to the reduced audio file and saving it while preserving the original bit depth, the audio file containing the hidden image (stego-audio) is obtained. At the stage of data extracting, bit reduction is performed on the stego-audio first. The difference between stego-audio and reduced stego-audio includes the hidden image. As a result of this study, relatively higher bits can be hidden inside an audio file with very low distortion. The application results show that the developed algorithm is successful in terms of SNR and PSNR. The MATLAB codes of the developed application and the cover audio are available in the link: https://bit.ly/31Nynfe
Children's Participation in the Design of Digital Artifacts

In user-centered design processes of digital artifacts, representatives of the target users are invited to participate, because they have knowledge of the problems to be solved by these artifacts. However, generally, the inclusion of these potential users into the process does not entail genuine participation. The aim in these cases is obtaining information that these users can provide for designers to make decisions about the product. When these potential users are children, participation restrictions are often even greater, because children are often seen as unable to make valid contributions. Participation with power balance between designers and representatives of the target users in decision-making in design projects is one of the ethical motivations of Participatory Design (PD), and much has been studied about the inclusion of children in projects that apply PD. However, PD projects could further benefit from theories on child participation. Aiming to contribute to bridge this gap, this paper presents a discussion relating child participation theories to participatory design processes of digital artifacts.
The SMART4ALL High Performance Computing Infrastructure: Sharing high-end hardware resources via cloud-based microservices

The goal of the SMART4ALL EU funded project is to build capacity amongst European stakeholders via the development of self-sustained, cross-border experiments that transfer knowledge and technology between academia and industry. It targets Customized Low Energy Computing (CLEC) in the Cyber-Physical (CPS) and the Internet of Things (IoT) domains. The vision of the project will be mainly realized through funded (via open calls) Pathfinder Application Experiments (PAEs) that will enable the transformation of academic knowledge into products.It is important to mention that SMART4ALL sets forward the concept of marketplace which is offered as a service (Marketplace-as-a-Servtce or MaaS) that acts as one-stopsmart-stop by offering tools, HPC services, and platforms to the PAE grantees. The purpose of this paper is to present the SMART4ALL High-Performance Computing (HPC) infrastructure and services realized according to the Hardware-as-a-Service paradigm. The SMART4A11 HPC infrastructure includes CPU, Memory, GPU, storage as well as FPGA resources connected with high-speed links.
Minimal Rare Pattern-Based Outlier Detection Approach For Uncertain Data Streams Under Monotonic Constraints

Abstract Existing association-based outlier detection approaches were proposed to seek for potential outliers from huge full set of uncertain data streams ($UDS$), but could not effectively process the small scale of $UDS$ that satisfies preset constraints; thus, they were time consuming. To solve this problem, this paper proposes a novel minimal rare pattern-based outlier detection approach, namely Constrained Minimal Rare Pattern-based Outlier Detection (CMRP-OD), to discover outliers from small sets of $UDS$ that satisfy the user-preset succinct or convertible monotonic constraints. First, two concepts of ‘maximal probability’ and ‘support cap’ are proposed to compress the scale of extensible patterns, and then the matrix is designed to store the information of each valid pattern to reduce the scanning times of $UDS$, thus decreasing the time consumption. Second, more factors that can influence the determination of outlier are considered in the design of deviation indices, thus increasing the detection accuracy. Extensive experiments show that compared with the state-of-the-art approaches, CMRP-OD approach has at least 10% improvement on detection accuracy, and its time cost is also almost reduced half.
Hybrid Reasoning Network for Video-based Commonsense Captioning

The task of video-based commonsense captioning aims to generate event-wise captions and meanwhile provide multiple commonsense descriptions (e.g., attribute, effect and intention) about the underlying event in the video. Prior works explore the commonsense captions by using separate networks for different commonsense types, which is time-consuming and lacks mining the interaction of different commonsense. In this paper, we propose a Hybrid Reasoning Network (HybridNet) to endow the neural networks with the capability of semantic-level reasoning and word-level reasoning. Firstly, we develop multi-commonsense learning for semantic-level reasoning by jointly training different commonsense types in a unified network, which encourages the interaction between the clues of multiple commonsense descriptions, event-wise captions and videos. Then, there are two steps to achieve the word-level reasoning: (1) a memory module records the history predicted sequence from the previous generation processes; (2) a memory-routed multi-head attention (MMHA) module updates the word-level attention maps by incorporating the history information from the memory module into the transformer decoder for word-level reasoning. Moreover, the multimodal features are used to make full use of diverse knowledge for commonsense reasoning. Experiments and abundant analysis on the large-scale Video-to-Commonsense benchmark show that our HybridNet achieves state-of-the-art performance compared with other methods.
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control

Designing adaptable control laws that can transfer between different robots is a challenge because of kinematic and dynamic differences, as well as in scenarios where external sensors are used. In this work, we empirically investigate a neural networks ability to approximate the Jacobian matrix for an application in Cartesian control schemes. Specifically, we are interested in approximating the kinematic Jacobian, which arises from kinematic equations mapping a manipulator’s joint angles to the end-effector’s location. We propose two different approaches to learn the kinematic Jacobian. The first method arises from visual servoing where we learn the kinematic Jacobian as an approximate linear system of equations from the k-nearest neighbors for a desired joint configuration. The second, motivated by forward models in machine learning, learns the kinematic behavior directly and calculates the Jacobian by differentiating the learned neural kinematics model. Simulation experimental results show that both methods achieve better performance than alternative data-driven methods for control, provide closer approximations to the proper kinematics Jacobian matrix, and on average produce better-conditioned Jacobian matrices. Real-world experiments were conducted on a Kinova Gen-3 lightweight robotic manipulator, which includes an uncalibrated visual servoing experiment, a practical application of our methods, as well as a 7-DOF point-to-point task highlighting that our methods are applicable on real robotic manipulators.
A HIE-BERT Model for Diagnosis Assistant Based on Chinese Obstetric EMRs

In recent years, language models have been widely applied to various natural language processing tasks. The pre-trained language model BERT(Bidirectional Encoder Representation from Transformers), proposed by Devlin et al. [14], has achieved a good performance in different tasks, and it only requires simple fine-tuning to adapt to different tasks. In this paper, the BERT model is used as a baseline, and various information enhancement strategies are adopted to enhance its representation. For any Obstetric EMR, it can be divided into textual information and numerical information. According to the importance of the diagnosis, the textual information in EMRs can be further divided into indiscriminate information, basic information, and key information. Therefore, it is essential to enhance the model representation with the hierarchical information to improve the performance of diagnosis. The model BERT, proposed by Devlin et al. [14], which takes all the sequence information as input, takes no account of the differentiated processing of hierarchical information, and ignores the importance of numerical information. In this paper, indiscriminate information processing and key diagnostic text information fusion are utilized to process the hierarchical information, moreover the textual feature enhancement and numerical feature fusion are performed through the enhanced-layer. A novel Hierarchical Information Enhanced BERT (HIE-BERT) model is proposed for the diagnosis assistant. The evaluation results using the AAPD dataset and the dataset with more than 2,0000 EMRs show that our model has improved performance compared with the basic BERT model. The main contributions of this paper are summarized as follows: