Reviewerly/exHarmony huggingface dataset:
Autobiographical Design for Emotional Durability through Digital Transformable Fashion and Textiles

To promote a resilient user-product relationship for sustainable fashion, design methods for emotional durability are required. Digitally transformable fashion design can be seen as a practical approach that enables dynamic, sensory, experiential, and emotional interaction. Literature shows that features of transformable fashion and textiles, such as versatility, perceived quality, biomorphic forms, and aesthetics, can induce emotional durability in users. However, mainstream works are conducted from function-oriented and technology-led perspectives, neglecting the significance of fashion design as a creative and affective role. To fill the gap, we present exhaustive accounts of two autobiographical design projects as case studies: Pneum-Muscle, a body-worn pneumatic wearable, and E-coral, an artistic interactive textile installation. We utilised the first-person soma design method to facilitate the iterative design and unfold the emotional connection between the user and the materials. We contribute technology-embedded fashion design strategies to inspire novice fashion designers, which involve dynamic draping, ambiguous cutting, and sewing technique-based pneumatic systems. Design guidelines generated can shed new light on the artistic use of technologies, somatic design, and the emotionally durable design approach.
Measurement and Analysis of Tourism Website User Experience Using Usability Techniques

A tourist destination is a geographical area within one or more administrative areas in which there are tourist attractions, public facilities, tourism facilities, accessibility, and communities that are interrelated and complement the realization of tourism. A website is a form of communication media that can provide certain information, which can be accessed by many people anytime and anywhere. The Regional Government (pemda) of Pekanbaru City has provided tourism information services in the form of tourist destinations and tourism activities on the website. Based on the results of preliminary observations made on the tourism website of the Pekanbaru City government, it was found that the tourism information provided was not up to date, and there were still many tourist objects in Pekanbaru that had not been published, so the information delivery process was not optimal and ineffective. The effectiveness of using the website must pay attention to usability of its use. Usability is a quality level of a system that is easy to learn, easy to use, and encourages users to use the system as a tool in completing tasks. Usability can be measured based on the quality of the user experience when interacting with the system operated by the user himself. Usability measurement on this tourism website is carried out using the System Usability Scale (SUS) measurement tool. Based on the measurement results using SUS, a score of 65.87 was obtained, where 10 of the statement items were declared valid and reliable. The score obtained shows that in terms of user satisfaction, it is stated to be included in the Marginal High category, predicate D with the adjective range Ok. This shows that the user's subjective assessment illustrates that the Pekanbaru City government's tourism website is less effective, efficient, and satisfying for users.
Teaching EFL With Immersive Virtual Reality Technologies: A Comparison With the Conventional Listening Method

This paper investigates the impact of different immersive Virtual Reality (iVR) technological approaches in teaching and learning English as a Foreign Language (EFL). Specifically, this paper explores the passive iVR and interactive iVR in a real authentic learning context as didactic possibilities compared to the conventional method of listening, consisting of audio-only listening exercises. The study was conducted using university students of B1 level EFL classes. The dependent variables considered in the study were Knowledge Retention, Presence, User Satisfaction, Cybersickness, and Preferred Technology. Results indicated that users showed significant satisfaction and preference for using this technology for learning, revealing enjoyment and motivation which are vital factors when learning a foreign language. However, no significant differences were found between learning via traditional listening exercises or the virtual system. Correlation tests were conducted between the questionnaire subscales to understand better which elements can influence learning. The study concludes that using iVR-based learning tools to learn a foreign language as an alternative to audio listening can only produce a broader positive impact and greater motivation. The results also suggest that iVR can be a valuable tool in the education field for knowledge transfer and motivation when learning foreign languages.
Lärares (digitala) kompetens före, under och efter covid-19: Skärvor av förståelse från skolor i Sverige

This study chrono-logically investigates teachers’ professional knowledge in relation to the digitally ‘boosted’ educational landscape caused by the Covid-19-pandemic. The aim is to describe how teachers employ their competences under extreme digitized circumstances by reading these through the lens of ordinary, to greater extent analogically organized schooling. The study is inspired by action research and data is collected in schools by five secondary and upper secondary teachers between March 19 and April 2, 2020. The analysis highlights qualitative aspects of teachers’ (digital) competence when teaching is affected by digital “interferences” in its corporeal and material framing, a dissolved spatiality and “truncated” senses/sensuousness. Employing different dimensions of knowledge in terms of intellectus and ratio, the study argues that subjective, emotional and relational processes of teachers’ digital competence need to be prioritized in contrast to the easily measurable aspects that tend to overrun the discussions about educational digitalization and its knowledge in society.
MSA-Net: Multiscale spatial attention network for medical image segmentation

Edge accuracy and positional accuracy are the two goals pursued by medical image segmentation. In clinical medicine diagnosis and research, these two goals enable medical image segmentation techniques to help in the effective determination of lesions and lesion analysis. At present, U-Net has become the most important network in the field of image segmentation, and the technologies used in various achievements are derived from its architecture, which also proves from practice that the network structure proposed by U-Net is effective. We have found in a large number of experiments that classical networks indeed show good performance in the field of medical segmentation, but there are still some deficiencies in edge determination and network robustness, especially in the face of blurred edges, the processing results often fail to achieve the expected results. In order to be able to locate segmentation targets and achieve effective determination of blurred edges, a Multiscale Spatial Attention Network (MSA-Net) is proposed as in Fig. 1. In MSA-Net, the Multiscale Pyramid Attention Block (MPAB) is created to enhance the capture of high-level semantic information. In addition, the network uses ASPP, which not only expands the network’s field of view, but also captures richer feature information. In the decoding phase, the Feature Fusion Block (FFB) is created to enable better focus on different dimensional information features and to enhance the feature fusion process. To demonstrate the effectiveness of the network, we validate the performance of MSA-Net on four datasets (ISIC2016, DSB2018, JSRT, GlaS) in three different categories. Compared with mainstream networks, MSA-Net shows better results in detail features, target localization, and edge processing. Finally, we also demonstrate the effectiveness of the MSA-Net architecture through ablation experiments.
Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.
Can Genetic Algorithms Be Used for Real-Time Obstacle Avoidance for LiDAR-Equipped Mobile Robots?

Despite significant progress in robot hardware, the number of mobile robots deployed in public spaces remains low. One of the challenges hindering a wider deployment is that even if a robot can build a map of the environment, for instance through the use of LiDAR sensors, it also needs to calculate, in real time, a smooth trajectory that avoids both static and mobile obstacles. Considering this scenario, in this paper we investigate whether genetic algorithms can play a role in real-time obstacle avoidance. Historically, the typical use of genetic algorithms was in offline optimization. To investigate whether an online, real-time deployment is possible, we create a family of algorithms called GAVO that combines genetic algorithms with the velocity obstacle model. Through a series of experiments, we show that a carefully chosen chromosome representation and parametrization can achieve real-time performance on the obstacle avoidance problem.
Explainability of deep learning models in medical video analysis: a survey

Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine and have been performing significantly better in comparison to other traditional machine learning methods. However, the black-box nature of deep neural networks has restricted their use in real-world applications, especially in healthcare. Therefore, explainability of the machine learning models, which focuses on providing of the comprehensible explanations of model outputs, may affect the possibility of adoption of such models in clinical use. There are various studies reviewing approaches to explainability in multiple domains. This article provides a review of the current approaches and applications of explainable deep learning for a specific area of medical data analysis-medical video processing tasks. The article introduces the field of explainable AI and summarizes the most important requirements for explainability in medical applications. Subsequently, we provide an overview of existing methods, evaluation metrics and focus more on those that can be applied to analytical tasks involving the processing of video data in the medical domain. Finally we identify some of the open research issues in the analysed area.
Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data

&lt;abstract&gt; &lt;p&gt;Colorectal cancer (CRC) is one of the most popular cancers among both men and women, with increasing incidence. The enhanced analytical load data from the pathology laboratory, integrated with described intra- and inter-variabilities through the calculation of biomarkers, has prompted the quest for robust machine-based approaches in combination with routine practice. In histopathology, deep learning (DL) techniques have been applied at large due to their potential for supporting the analysis and forecasting of medically appropriate molecular phenotypes and microsatellite instability. Considering this background, the current research work presents a metaheuristics technique with deep convolutional neural network-based colorectal cancer classification based on histopathological imaging data (MDCNN-C3HI). The presented MDCNN-C3HI technique majorly examines the histopathological images for the classification of colorectal cancer (CRC). At the initial stage, the MDCNN-C3HI technique applies a bilateral filtering approach to get rid of the noise. Then, the proposed MDCNN-C3HI technique uses an enhanced capsule network with the Adam optimizer for the extraction of feature vectors. For CRC classification, the MDCNN-C3HI technique uses a DL modified neural network classifier, whereas the tunicate swarm algorithm is used to fine-tune its hyperparameters. To demonstrate the enhanced performance of the proposed MDCNN-C3HI technique on CRC classification, a wide range of experiments was conducted. The outcomes from the extensive experimentation procedure confirmed the superior performance of the proposed MDCNN-C3HI technique over other existing techniques, achieving a maximum accuracy of 99.45%, a sensitivity of 99.45% and a specificity of 99.45%.&lt;/p&gt; &lt;/abstract&gt;
Similarity Learning on Big Data: A Case Study

The current article aims to analyze student performance using some similarity measures.The analysis will result in a classification of the student based on how they usually take their lunch.Throughout the processes, we define some notions of similarity measures and finally select some measures to evaluate various data types of attributes.The Nearest-Neighbor approach is used for classification, with the K-Nearest-Neighbor (KNN) algorithm.At last we compare the performance on three data types: numerical, categorical and mixed data.Finally, the result is tested and validated using the Python programming language.
A Novel Framework of Network Packet Loss Detection Using Random Forest Algorithm over Support Vector Machine Learning Algorithms to Improve Accuracy

The objective of this paper is to employ a novel framework of network packet loss detection with increased accuracy rate using Novel Random Forest classifier compared to a Support Vector Machine (SVM) Classifier. Materials & Procedures: The data set used in this study utilizes the publicly available Kaggle network traffic data set and UCI machine learning repositories. The sample size of network packet loss detection with improved accuracy rate sample size was 50 (Groups 1 and 2 each had 25 participants), and the computation was performed using a G-power of 0.8, with alpha and beta values of 0.05 and 0.2 and a 95% confidence interval. A network packet loss detection with improved accuracy rate is performed by Random Forest (RF) whereas multiple samples <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{N}=10\mathrm{A})$</tex> SVM were a number of samples <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{N}=10)$</tex> Results: The Novel Random Forest classifier has 93.56% higher accuracy rates in comparison to the accuracy rate of SVM is 89.23%. There exists a statistical significance difference between two groups <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{p}=0.0231;\mathrm{p} &lt; 0.05)$</tex> with confidence interval 95%. Conclusion: Novel Random Forest when compared, a classifier offers more accurate results. to SVM for network packet loss detection with improved accuracy rate.
Onto-oriented Information Systems for Teaching Physics and Technical Disciplines by STEM-environment

Therefore, a significant problem of our time is the substantiation of the scientific approach to the construction of an innovative educational environment with the use of STEM technologies in the teaching of physics and technical disciplines. Research hypothesis: introduction of a scientifically based onto-oriented information system of teaching physics and technical disciplines using the requirements of STEM education will help ensure the quality of the educational process of higher education institutions of technical orientation and stimulate the development of STEM skills in applicants.&#x0D; The purpose of the research is to substantiate the principles of an onto-oriented information system for teaching physics and technical disciplines using STEM-environment and its approbation in the process of training specialists in the speciality 272 «Aviation Transport». The object of research is the process of modelling the onto-oriented information system of teaching physics and technical disciplines using the requirements of STEM education. The subject of the research is to improve the process of professional training of specialists in technical specialities based on integrative and interdisciplinarity. The authors have identified and substantiated the onto-oriented information system of teaching physics and technical disciplines using STEM-environment in institutions of higher education of technical profile; it is established that the implementation of the proposed onto-oriented information system of teaching physics and technical disciplines with the use of STEM-technologies.
Exploration of Vehicle Target Detection Method Based on Lightweight YOLOv5 Fusion Background Modeling

Due to the explosive increase per capita in vehicle ownership in China brought about by the continuous development of the economy and society, many negative impacts have arisen, making it necessary to establish the smart city system that has rapidly developing vehicle detection technology as its data acquisition system. This paper proposes a lightweight detection model based on an improved version of YOLOv5 to address the problem of missed and false detections caused by occlusion during rush hour vehicle detection in surveillance videos. The proposed model replaces the BottleneckCSP structure with the Ghostnet structure and prunes the network model to speed up inference. Additionally, the Coordinate Attention Mechanism is introduced to enhance the network’s feature extraction and improve its detection and recognition ability. Distance-IoU Non-Maximum Suppression replaces Non-Maximum Suppression to address the issue of false detection and omission when detecting congested targets. Lastly, the combination of the five-frame differential method with VIBE and MD-SILBP operators is used to enhance the model’s feature extraction capabilities for vehicle contours. The experimental results show that the proposed model outperforms the original model in terms of the number of parameters, inference ability, and accuracy when applied to both the expanded UA-DETRAC and a self-built dataset. Thus, this method has significant industrial value in intelligent traffic systems and can effectively improve vehicle detection indicators in traffic monitoring scenarios.
Data-driven quantum approximate optimization algorithm for power systems

Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.
Strengthening the scientific record

Science is a social process. Discoveries—even by supposed “lone” geniuses—do not become knowledge until the findings are shared with the scientific community, to be vetted, challenged, and expanded on. Since the birth of the digital age, this discourse has become much more apparent, as communication outlets such as social media, blogs, and websites such as PubPeer and Retraction Watch allow the scientific community to discuss new findings. Further, the availability of greater amounts of data that support findings allows peers to redo key analyses to confirm or report discrepancies. This week, Science is announcing two changes—one to accelerate the conversation about papers and another to ease deposition of supporting data—that will facilitate the evaluation of research findings across the wide scientific enterprise.
Virtual Art Therapy: Application of Michelangelo Effect to Neurorehabilitation of Patients with Stroke

In neurorehabilitation, some studies reported the effective use of art therapy for reducing psychological disorders and for enhancing physical functions and cognitive abilities. Neuroaesthetical studies showed that seeing an art masterpiece can spontaneously elicit a widespread brain arousal, also involving motor networks. To combine contemplative and performative benefits of art therapy protocols, we have developed an immersive virtual reality system, giving subjects the illusion that they are able to paint a copy of famous artistic paintings. We previously observed that during this virtual task, subjects perceived less fatigue and performed more accurate movements than when they were asked to color the virtual canvas. We named this upshot the Michelangelo effect. The aim of this study was to test the rehabilitative efficacy of our system. Ten patients with stroke in the subacute phase were enrolled and trained for one month with virtual art therapy (VAT) and physiotherapy. Their data were compared with those of ten patients matched for pathology, age and clinical parameters, trained only with conventional therapy for the same amount of time. The VAT group showed a significantly higher improvements in the Barthel Index score, a measure of independency in activities of daily living (66 ± 33% vs. 31 ± 28%, p = 0.021), and in pinching strength (66 ± 39% vs. 18 ± 33%, p = 0.008), with respect to the group treated with conventional rehabilitation.
Weakly Supervised Image Dehazing Using Generative Adversarial Networks

With the increase in air pollution, the quality of captured images has degraded to a large extent. This makes removing haze a prerequisite for a lot of scientific applications like monitoring traffic in real-time, celestial observation, and different sorts of tracking systems. Few methods like prior based have already been proposed and have demonstrated some success. However, they are not truly applicable in real-life applications because of the artifacts they produce in the outputs. Along with this, there is a dearth of paired images of hazy and cleared images; due to this, their testing capabilities are not completely tested. We have attempted to merge the advantages of the learning-based and prior-based models. Our model comprises three steps; first, it uses gamma correction to restore visibility; second, it takes advantage of adversarial learning to improve image realness; and lastly, a fusion block is used to blend different dehazing outputs. Extensive experimentations prove that our model outperforms most state-of-the-art dehazing models and produces visually pleasing dehazed images.