Reviewerly/exHarmony huggingface dataset:
PyRCN: A toolbox for exploration and application of Reservoir Computing Networks

Reservoir Computing Networks (RCNs) belong to a group of machine learning techniques that project the input space non-linearly into a high-dimensional feature space, where the underlying task can be solved linearly. Popular variants of RCNs are capable of solving complex tasks equivalently to widely used deep neural networks, but with a substantially simpler training paradigm based on linear regression. In this paper, we show how to uniformly describe RCNs with small and clearly defined building blocks, and we introduce the Python toolbox PyRCN (Python Reservoir Computing Networks) for optimizing, training and analyzing RCNs on arbitrarily large datasets. The tool is based on widely-used scientific packages and complies with the scikit-learn interface specification. It provides a platform for educational and exploratory analyses of RCNs, as well as a framework to apply RCNs on complex tasks including sequence processing. With a small number of building blocks, the framework allows the implementation of numerous different RCN architectures. We provide code examples on how to set up RCNs for time series prediction and for sequence classification tasks. PyRCN is around ten times faster than reference toolboxes on a benchmark task while requiring substantially less boilerplate code.
Deep embeddings and logistic regression for rapid active learning in histopathological images

Recognizing different tissue components is one of the most fundamental and essential works in digital pathology. Current methods are often based on convolutional neural networks (CNNs), which need numerous annotated samples for training. Creating large-scale histopathological datasets is labor-intensive, where interactive data annotation is a potential solution. We propose DELR (Deep Embedding-based Logistic Regression) to enable rapid model training and inference for histopathological image analysis. DELR utilizes a pretrained CNN to encode images as compact embeddings with low computational cost. The embeddings are then used to train a Logistic Regression model efficiently. We implemented DELR in an active learning framework, and validated it on three histopathological problems (binary, 4-category, and 8-category classification challenge for lung, breast, and colorectal cancer, respectively). We also investigated the influence of active learning strategy and type of the encoder. On all the three datasets, DELR can achieve an area under curve (AUC) metric higher than 0.95 with only 100 image patches per class. Although its AUC is slightly lower than a fine-tuned CNN counterpart, DELR can be 536, 316, and 1481 times faster after pre-encoding. Moreover, DELR is proved to be compatible with a variety of active learning strategies and encoders. DELR can achieve comparable accuracy to CNN with rapid running speed. These advantages make it a potential solution for real-time interactive data annotation.
Generic Digital Equity Model in Education: Mobile-Assisted Personalized Learning (MAPL) through e-Modules

This study addresses the challenges most learners face in Third World and developing countries concerning education accessibility in an emergency. On the basis of the shortcomings found in a review of past studies, this scoping review introduces adapted model mobile-assisted personalized learning (MAPL), which focused on full distance learning using the personalized learning (PL) concept. This concept is rarely used in the classrooms of Third World and developing countries. MAPL is technology-integrated and customized PL but it does not depend on artificial intelligence. This model bridges the digital divide that hinders learners in accessing education by providing flexibility, regardless of weak internet reception or low bandwidth, among other hindrances, in Third World or developing countries. Learners in these countries inevitably opt for mobile devices as their preferred learning tool. MAPL is necessary and can aid underprivileged learners who are highly dependent on mobile devices. Rethinking and reforming current teaching practices are required. In this study, a pool of 60 articles from 2011 to 2021 was qualitatively synthesized. Among the articles, 29 focused on PL, 15 on mobile learning, and 16 on the potentials of MAPL. The findings indicate that MAPL could be a viable solution for achieving equity in education for every learner during full-fledged distance learning.
Combining Blockchains, Smart Contracts, and Complex Sensors Management Platform for Hyper-Connected SmartCities: An IoT Data Marketplace Use Case

In this paper, we demonstrate the multiple points of innovation when combining blockchain technology with Internet of Things (IoT) and security frameworks. The deployment and use of IoT device networks in smart city environments has produced an enormous amount of data. The fact that those data are possessed by multiple sources that use independent systems for data collection, storage, and use impedes the exploitation of their value. Blockchains, as distributed ledgers, can be used for addressing the development of a universal system for data collection and distribution. Smart contracts can be used to automate all the processes of such a network, while at the same time, blockchain and the InterPlanetary File System (IPFS) protect sensitive data through anonymity and distributed storage. An innovative and open IoT blockchain market of applications, data, and services is proposed that: (i) provides the framework upon which objects and people can exchange value in form of virtual currencies, for assets (data and services) received; (ii) defines the motivation incentives according to social and business context for humans and smart objects to interact. The specific marketplace is piloted through a cross-border trial between Santander and Fujisawa, in the context of the M-Sec project, validating thus the interoperability, efficiency, and data protection principles.
Multi-label Pattern Image Retrieval via Attention Mechanism Driven Graph Convolutional Network

Pattern images are artificially designed images which are discriminative in aspects of elements, styles, arrangements and so on. Pattern images are widely used in fields like textile, clothing, art, fashion and graphic design. With the growth of image numbers, pattern image retrieval has great potential in commercial applications and industrial production. However, most of existing content-based image retrieval works mainly focus on describing simple attributes with clear conceptual boundaries, which are not suitable for pattern image retrieval. It is difficult to accurately represent and retrieve pattern images which include complex details and multiple elements. Therefore, in this paper, we collect a new pattern image dataset with multiple labels per image for the pattern image retrieval task. To extract discriminative semantic features of multi-label pattern images and construct high-level topology relationships between features, we further propose an Attention Mechanism Driven Graph Convolutional Network (AMD-GCN). Different layers of the multi-semantic attention module activate regions of interest corresponding to multiple labels, respectively. By embedding the learned labels from attention module into the graph convolutional network, which can capture the dependency of labels on the graph manifold, the AMD-GCN builds an end-to-end framework to extract high-level semantic features with label semantics and inner relationships for retrieval. Experiments on the pattern image dataset show that the proposed method highlights the relevant semantic regions of multiple labels, and achieves higher accuracy than state-of-the-art image retrieval methods.
Semantic similarity controllers: On the trade-off between accuracy and interpretability

In recent times, we have seen an explosion in the number of new solutions to address the problem of semantic similarity. In this context, solutions of a neuronal nature seem to obtain the best results. However, there are some problems related to their low interpretability as well as the large number of resources needed for their training. In this work, we focus on the data-driven approach for the design of semantic similarity controllers. The goal is to offer the human operator a set of solutions in the form of a Pareto front that allows choosing the configuration that best suits a specific use case. To do that, we have explored the use of multi-objective evolutionary algorithms that can help find break-even points for the problem of accuracy versus interpretability.
Using Shallow and Deep Learning to Automatically Detect Hate Motivated by Gender and Sexual Orientation on Twitter in Spanish

The increasing phenomenon of “cyberhate” is concerning because of the potential social implications of this form of verbal violence, which is aimed at already-stigmatized social groups. According to information collected by the Ministry of the Interior of Spain, the category of sexual orientation and gender identity is subject to the third-highest number of registered hate crimes, ranking behind racism/xenophobia and ideology. However, most of the existing computational approaches to online hate detection simultaneously attempt to address all types of discrimination, leading to weaker prototype performances. These approaches focus on other reasons for hate—primarily racism and xenophobia—and usually focus on English messages. Furthermore, few detection models have used manually generated databases as a training corpus. Using supervised machine learning techniques, the present research sought to overcome these limitations by developing and evaluating an automatic detector of hate speech motivated by gender and sexual orientation. The focus was Spanish-language posts on Twitter. For this purpose, eight predictive models were developed from an ad hoc generated training corpus, using shallow modeling and deep learning. The evaluation metrics showed that the deep learning algorithm performed significantly better than the shallow modeling algorithms, and logistic regression yielded the best performance of the shallow algorithms.
An Emoji Is Worth a Thousand Words

Face emojis are regularly used in reviews on online shopping sites to add richness and/or levity to the text. However, few researchers have investigated the use of face emojis in consumer reviews. This paper examined the effects of the number of face emojis (none, one, and three) in user-generated reviews (UGRs) on trustworthiness and purchase intention. They found that the number of face emojis in UGRs has a profound impact on consumers. A greater number of face emojis correlates with a greater degree of review trustworthiness. However, the effect of emojis on purchase intention changes depending on the situation. Additionally, when there is a profile picture, consumers pay more attention to it, and the positive effect due to face emojis disappears. Moreover, the gift giver-recipient relationship moderates the effect of face emojis on user decisions. The findings of our work have theoretical and managerial implications with respect to providing a new means of understanding consumer perceptions of products, from which business can benefit by improving sales and policies.
The Impact of Cybersecurity Practices on Cyberattack Damage: The Perspective of Small Enterprises in Saudi Arabia

Small and medium-sized enterprises represent the majority of enterprises globally and yet have some difficulties in understanding the impact that cybersecurity threats could have on their businesses and the damage they could do to their assets. This study aims to measure the effectiveness of security practices at small-sized enterprises in Saudi Arabia in the event of a cybersecurity attack. Our paper is among the first research papers to measure the effectiveness of cybersecurity practices and the threat posed by cybersecurity breaches among small enterprises in the event of cybersecurity attacks. A total of 282 respondents participated, all of them representing small-sized enterprises in Saudi Arabia. The study applies multiple regression tests to analyze the effectiveness of 12 cybersecurity practices in three aspects: financial damage, loss of sensitive data, and restoration time, at small enterprises. The findings indicate that having an inspection team and a recovery plan may limit the financial damage caused by cybersecurity attacks on small enterprises. The results also show that cybersecurity awareness, knowledge of cybersecurity damage, and professionals' salaries were related to the loss of sensitive data. Furthermore, the results indicate that contact with cybersecurity authorities and having an inspection team have statistically significant effects on restoration time.
An Expanded Heterogeneous Particle Swarm Optimization Based on Adaptive Inertia Weight

In this study, a modified version of multiswarm particle swarm optimization algorithm (MsPSO) is proposed. However, the classical MsPSO algorithm causes premature stagnation due to the limitation of particle diversity; as a result, it is simple to slip into a local optimum. To overcome the above feebleness, this work presents a heterogeneous multiswarm PSO algorithm based on adaptive inertia weight strategies called (A-MsPSO). The MsPSO’s main advantages are that it is simple to use and that there are few settings to alter. In the MsPSO method, the inertia weight is a key parameter affecting considerably convergence, exploration, and exploitation. In this manuscript, an adaptive inertia weight is adopted to ameliorate the global search ability of the classical MsPSO algorithm. Its performance is based on exploration, which is defined as an algorithm’s capacity to search through a variety of search spaces. It also aids in determining the best ideal capability for searching a small region and determining the candidate answer. If a swarm discovers a global best location during iterations, the inertia weight is increased, and exploration in that direction is enhanced. The standard tests and indications provided in the specialized literature are used to show the efficiency of the proposed algorithm. Furthermore, findings of comparisons between A-MsPSO and six other common PSO algorithms show that our proposal has a highly promising performance for handling various types of optimization problems, leading to both greater solution accuracy and more efficient solution times.
An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resourcescarce domain.Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL.However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots.To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL.ESD formulates FSSL as a span-level matching problem between test query and supporting instances.Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution.Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the nested and noisy tagging scenarios.Our code is available at https://github.com/Wangpeiyi9979/ESD.
FPGA Implementation of Protected Compact AES S–Box Using CQCG for Embedded Applications

In this work, we obtain an area proficient composite field arithmetic Advanced Encryption Standard (AES) Substitution (S) byte and its inverse logic design. The size of this design is calculated by the number of gates used for hardware implementation. Most of the existing AES Substitution box hardware implementation uses separate Substitution byte and its inverse hardware structures. But we implement the both in the same module and a control signal is used to select the substitution byte for encryption operation and its inverse for the decryption operation. By comparing the gate utilization of the previous AES S–Box implementation, we reduced the gate utilization up to 5% that is we take only 78 EX-OR gates and 36 AND gates for implementing the both Substitution byte and its inverse. While implementing an AES algorithm in circuitry or programming, it is liable to be detected by hackers using any one of the side channel attacks. Data to be added with a random bit sequence to prevent from the above mentioned side channel attacks.
Visuo-haptic Illusions for Motor Skill Acquisition in Virtual Reality

In this article we investigate the potential of using visuo-haptic illusions in Virtual Reality environment to learn motor skills in a real environment. We report on an empirical study where 20 participants perform a multi-object pick-and-place task. The results show that although users do not perform the same motion trajectories in the virtual and real environments, skills acquired in VR augmented with visuo-haptic illusions can be successfully reused in a real environment: There is a high amount of skill transfer (78.5%), similar to the one obtained in an optimal real training environment (82.4%); Finally, participants did not notice the illusion and were enthusiastic about the VR environment. Our findings invite designers and researchers to consider visuo-haptic illusions to help operators to learn motor skills in a cost-effective environment.
Developers perception of peer code review in research software development

Background: Research software is software developed by and/or used by researchers, across a wide variety of domains, to perform their research. Because of the complexity of research software, developers cannot conduct exhaustive testing. As a result, researchers have lower confidence in the correctness of the output of the software. Peer code review, a standard software engineering practice, has helped address this problem in other types of software. Aims: Peer code review is less prevalent in research software than it is in other types of software. In addition, the literature does not contain any studies about the use of peer code review in research software. Therefore, through analyzing developers perceptions, the goal of this work is to understand the current practice of peer code review in the development of research software, identify challenges and barriers associated with peer code review in research software, and present approaches to improve the peer code review in research software. Method: We conducted interviews and a community survey of research software developers to collect information about their current peer code review practices, difficulties they face, and how they address those difficulties. Results: We received 84 unique responses from the interviews and surveys. The results show that while research software teams review a large amount of their code, they lack formal process, proper organization, and adequate people to perform the reviews. Conclusions: Use of peer code review is promising for improving the quality of research software and thereby improving the trustworthiness of the underlying research results. In addition, by using peer code review, research software developers produce more readable and understandable code, which will be easier to maintain.
Coastal Waste Detection Based on Deep Convolutional Neural Networks

Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm's mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.
Application of Digital Mining Facing Information Fusion Technology in the Field of National Costume Culture Design

In order to improve the design effect of minority clothing, according to the needs of minority clothing design, this paper uses data mining and Internet of Things technologies to construct an intelligent ethnic clothing design system and builds an intelligent clothing design system that meets customer needs based on the idea of human-computer interaction. In data processing, this paper uses the constraint spectrum clustering algorithm to take the Laplacian matrix and the constraint matrix as input and finally outputs a clustering indicator vector to improve the data processing effect of minority clothing design. Finally, this paper verifies the performance of the system designed in this paper through experiments. From the experimental research, it can be known that the minority clothing design system based on the Internet of Things and data mining constructed in this paper has a certain effect and can effectively improve the minority clothing design effect.
Modern aesthetic education and its further directions

The relevance of the research topic lies in the fact that in the field of modern education there are numerous discussions and study of such an aspect as updating the education methods. The leading task of educational policy is to ensure a high-quality level of education at all stages of education – from preschool to professional. The above also applies to arts education, as it is part of the general education system. The purpose of the study is to study the issue of modern art education, to analyses further possible directions in which it can develop. The main results that can be highlighted in the analysis of aesthetic education at the present time: education in the field of creativity is underestimated in the learning process, although it is an important aspect of the development of a particular individual; artistic and aesthetic education in the context of modern educational activities is not a primary task of society current principles embedded in education provide for the dominance of authoritarian models of regulation art education is insufficiently equipped with a material, technical and personnel base within the framework of the general educational process.