Reviewerly/exHarmony huggingface dataset:
InContext: Futuring User-Experience Design Tools

Technology is changing, which means the design processes supporting it must also change. Digital tools for user experience and interaction design are vital in enabling designers to create appropriate, enjoyable and functional human-computer experiences, and so will necessarily evolve alongside our technological development. This workshop aims to support the futuring of user experience and user interfaces, and will engage with stakeholders, practicing designers, researchers, students and educators in order to understand better the needs for next-generation design tools. We will envisage new forms of design tools that encourage best practice, for example, linking representations, analysis tools, just-in-time evidence, physicality, experience, and crucially, put context at the centre of design.
Information Processing Capacity in the Rat Auditory Cortex

The rich dynamics of spatio-temporal activities in the neural system could be considered a computational resource. In this work, we attempted to quantify the capacity of information processing in the rat auditory cortex by applying a reservoir computing framework. Click-evoked multi-unit activities in the auditory cortex of rats were measured and considered as a reservoir, in which information processing capacity (IPC) was quantified. IPC was then compared with the performances of two benchmark tasks; shift register and logical calculation (AND, OR and XOR). Consequently, we found the first and second order IPC in the auditory cortex, which were positively correlated with the score of the benchmark tasks. These results suggest that IPC provides a new strategy to investigate sensory information processing in the brain.
Max-Plus Algebra-Based Morphological Wavelet Transform Watermarking for Mobile Devices Using Distributed Data Embedding Scheme

In this paper, we report on the high image quality, large data capacity, and low computational cost of a distributed data embedding watermarking scheme that uses a Max-plus algebra-based Morphological wavelet Transform (MMT). Previously, we evaluated the basic MMT watermarking scheme for image quality and robustness against image compression attacks defined by the Information Hiding and its Criteria (IHC). The proposed distributed data embedding scheme focuses on middle frequency signal-groups after the MMT decomposition process for embedding watermarks. During the experiments for evaluating scheme capability, we processed 12 benchmark images using the proposed watermarking scheme. The proposed method records to increase the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM) values. For example, the PSNR value of the middle frequency is improved by 6 dB (24 to 30 dB) higher than that of the high-frequency value. The proposed scheme can keep 30 dB for embedding 12 bits of the watermark in the transformed image of all benchmark images. The SSIM value of the middle frequency is about 1.15 (0.88 to 0.98) times higher than the high-frequency value. Thus, all benchmark images of the SSIM values achieve up to 0.5 when the watermark data of 24-bit are embedded using the proposed distributed scheme. The processing performance on the mobile processor is approximately equal to previous studies despite better image quality. The distributed data embedding scheme indicates the possibility of obtaining practical results compared with related studies.
Integrated room temperature single-photon source for quantum key distribution: publisher’s note

This publisher's note contains a correction to Opt. Lett.47, 1673 (2022)10.1364/OL.454450.
Digital Divide in Education Field of India – A Way to Eliminate

Education became highly technology-driven after this 2019 pandemic. This situation has widened new pathways in various dimensions in the life of every educators and learners. In today’s technology-driven world, teachers may no longer be the sole keepers of knowledge in the classroom else The chalkboard, books, homework, and teacher-classroom culture has shifted, making way for online classrooms and personalized way of learning to include customized learning. The use of simulations, models, graphic calculators, animation, 3D images, and video clips on smart screens bring to life the virtual classroom experience making it much more interactive and exciting. In spite of the government’s push towards a Digital India, there is a wide gap in Internet usage in urban and rural India. While Internet penetration in urban India is about 64.85 per cent, it’s just 20.26 per cent in rural areas.1The report done by Internet and Mobile Association of India (IAMAI) on the growth rate of internet and mobile users is critically low. It has been over a year since schools across the country were forced to shut in the wake of the corona virus pandemic. The pandemic has brought to fore the digital divide in the country and made education a privilege only a few can afford. This article is based on the review of available primary and secondary literature and authors have tried to understand the issues related to digital divide, the important role of media teachers to bridging the gap of digital divide among rural college going young students. Key words: Digital Divide, Rural, College Students, Literature Review.
Components and Changing Patterns of Cyber Security

In today’s computerized world, new risks emerge every hour of every day. Connecting to the Internet opens up the possibility of a hacker targeting every organization. Cybercrime can shatter any organization if it has no Cyber security. Because Cybercrime is becoming big business and it risks the cyberspace of organizations and governments globally. It can create Monetary and reputational risks if organizations don’t have an appropriate cybersecurity plan. Every industry has its own terminology, and the cyber world is no different. While built on technological foundations that we all know – computers, the internet, smartphones, and similar- as you delve deeper into the subject you start to encounter acronyms and technical concepts that you may not be familiar with. And, if we’re all to communicate on the subject of cybersecurity-across all sectors of government, business, industry, and academia – then it can help to familiarize yourself with the nomenclature associated with this diverse and compelling subject.
Sentence Formation Using NLP on the Basis of American Sign Language

Abstract: Natural language processing at its core is a method to understand, process and utilize human language that helps in the development of different tools. One such field where this tool can be used for is sign language which is the primary method of communication for the impaired which usually requires a translator to interpret the meaning for those who do not have the knowledge. This paper aims to propose a method that can translate recognized signed words from ASL into proper grammatically correct English sentences with the use of different NLP techniques and parsing it into a Lexicalized Tree Adjoining Grammar (LTAG) using LALR parser. This approach uses a LTAG which is a lexicon that is organized on grammar and vocabulary of the English language and connects in a group of trees. The output matrix of words from the sign recognition is used as an input for the Parts of Speech (POS) tagger that will be parsed into the grammar tree giving a proper English sentence which will be verified by using Language Tool to check the grammar of the final sentence. Keywords: Sentence Formation, American Sign Language, Natural Language Processing, Sign Language recognition, Grammar mapping
The use of assistive technological tools in people with depression arising from the Covid Pandemic

Depression is a disease that affects approximately 60 million people in the world each year, affecting around 11.5 million Brazilians, which presents in the individual severe anxiety disorders, which may evolve into cases of suicide, ranking among the 20 major causes of death/ year. As a result of Covid-19, approximately 90% of cases of depression in Brazil in the last year increased and people with anxiety crisis and symptoms of acute stress doubled in 2020, and this increases concerns regarding the social isolation that extends. This study aimed to investigate the resources of assistive technologies applied to depressive cases with potential application as treatment mechanisms in the context of the COVID-19 pandemic. This is a study with an exploratory objective, a quantitative approach and a bibliographic procedure and descriptors databases. The study evaluated the presence of 306 articles, being used as a filter the interval between 5 and 10 years of publication. The tools used to depression acquired during the pandemic were also discussed, where the results contributed to broader reflections on the challenges imposed by the pathology. Some interesting results were obtained with the application of Tec. Assistive (AT) depressed patients by Covid-19, highlighting: remote care, electroconvulsive therapy, Deprexis, in addition to art therapy and music therapy, the latter being calls for integrative and complementary practices in health (PICs) with application encouraged by SUS showing great relevance and effectiveness in the treatment. The research carried out has shown the potential of ED tools to address the effects of depression in people with Covid-19. This study encourages the development of new research and the development of instruments that are more specific to this need in order to minimize the problems arising from this disease.
From Captions to Explanations: A Multimodal Transformer-based Architecture for Natural Language Explanation Generation

The growing importance of the Explainable Artificial Intelligence (XAI) field has led to the proposal of several methods for producing visual heatmaps of the classification decisions of deep learning models. However, visual explanations are not sufficient because different end-users have different backgrounds and preferences. Natural language explanations (NLEs) are inherently understandable by humans and, thus, can complement visual explanations. Therefore, we introduce a novel architecture based on multimodal Transformers to enable the generation of NLEs for image classification tasks. Contrary to the current literature, which models NLE generation as a supervised image captioning problem, we propose to learn to generate these textual explanations without their direct supervision, by starting from image captions and evolving to classification-relevant text. Preliminary experiments on a novel dataset where there is a clear demarcation between captions and NLEs show the potential of the approach and shed light on how it can be improved.
On Heuristics for Parsing-based Verification of Hierarchical Plans with a Goal Task

Verification of hierarchical plans deals with the problem if a given action sequence is a valid hierarchical plan – the action sequence can be obtained by decomposing a particular (goal) task using given decomposition methods. The existing parsing-based verification approach greedily composes actions until it obtains the goal task. Greediness implies that this approach alsogenerates tasks unrelated to the goal task. In this paper, we study the use of heuristics when creating new tasks. We also ask whether the prior knowledge of the goal task improves efficiency.
Rapid deployment of smartphone‐based augmented reality tools for field and online education in structural biology

Structural biology education commonly employs molecular visualization soft- ware, such as PyMol, RasMol, and VMD, to allow students to appreciate structure-function relationships in biomolecules. In on-ground, classroom- based education, these programs are commonly used on University-owned devices with software preinstalled. Remote education typically involves the use of student-owned devices, which complicates the use of such software, owing to the fact that (a) student devices have differing configurations (e.g., Windows vs MacOS) and processing power, and (b) not all student devices are suitable for use with such software. Smartphones are near-ubiquitous devices, with smartphone ownership exceeding personal computer ownership, according to a recent survey. Here, we show the use of a smartphone-based augmented real- ity app, Augment, in a structural biology classroom exercise, which students installed independently without IT support. Post-lab attitudinal survey results indicate positive student experiences with this app. Based on our experiences, we suggest that smartphone-based molecular visualization software, such as that used in this exercise, is a powerful educational tool that is particularly well-suited for use in remote education.
On differences between the Russian scientific and artistic idiostyles in the usage frequency of transition words

The article proposes a method for characterisation of an author’s idiostyle that relies on the estimated usage frequency of transition words or phrases (TW). The whole of Russian scientific idiostyle can be treated as a combination of usage frequencies of transition words that defines scientific texts in Russian. It is typical of Russian scientific idiostyle to feature high usage frequency of TW that denote the cause-and-effect relationship and summarise and specify information, whereas TW for describing comparison, location and certain aspects of time relationship are used infrequently.
Project 80: a reproducible, student‐driven framework for creating multimedia educational resources from primary literature

Natural science education resources traditionally teach only codified theory. While theory education is crucial, much of academic science takes place via scrutinizing contested scientific discourse. Due to such resources' content complexity, high school students are rarely exposed to current, debatable, and relevant science. In response, we introduce Project80: a systemic, student-run protocol to synthesize the latest primary literature in a sub-field into approachable, produced multimedia educational content. The protocol is run by a team of 7 students over the course of 1 month. Students running the protocol consume complex scientific literature, distill relevant data and findings, and synthesize a culminating product of audiovisual content to supplement existing biology and chemistry pedagogy. The system runs independently with limited faculty involvement. Our analysis indicates that the multimedia content created by this protocol will be relevant to roughly 30 courses locally at our institution and will have further extensions in secondary education beyond.
Best Practices and Student Preferences for Implementing iPads into an Occupational Therapy Anatomy Laboratory

Multimodal approaches in teaching anatomy have been shown to improve student performance (Johnson, 2012). Most studies regarding best practices using technology to supplement teaching and learning in anatomy have been conducted in medical and undergraduate settings (Chakraborty, 2018; Sugand, 2010; Tam, 2020). There is little data that outlines the best practices for implementing iPads into occupational therapy (OT) anatomy courses (Meyer, 2016). Here, we aim to determine best practices for the implementation and student preferences of iPads for first year OT students in an anatomy laboratory at Samuel Merritt University (SMU). Our objective is to formulate a curriculum template on how to implement iPads in gross anatomy laboratory experiences. This study is significant as it will add to the body of literature that is lacking on the best practices for implementing iPads into an OT anatomy course and will establish best practices for enhancing student engagement in the anatomy laboratory. Best practices were documented for the implementation of thirteen iPads and eBooks into an OT anatomy lab course that met twice a week. OT students completed pre-and post- surveys that included five-point Likert scale and free text questions. The survey asked students to rate their comfort level and preferences for learning with iPads. Survey results displayed that 75% of students were comfortable using iPads, 37% of students strongly agreed that iPad applications improved their grade in the lab, and 43.75% believed that iPad applications helped with their understanding of cadavers. A working document on the best practices for implementing iPads into an anatomy laboratory space was also created. Best practices for implementing iPads and eBooks into an anatomy laboratory include setting up an orientation to the iPads and eBooks, assigning page numbers for eBooks, providing students with instructions on how to utilize the iPads outside of the eBooks, and how to use Poll Everywhere quizzes during laboratory sessions. The survey results display that OT students agree that iPad applications are of benefit to their understanding of cadaveric anatomy. This study also supports the need for a document that outlines the best practices for implementing iPads into an anatomy lab, specifically for OT students. Our results indicate that iPad technology aids students in learning cadaveric anatomy and can be incorporated as a supplemental resource to teaching cadaver-based anatomy for OT students. Initial implementation of the iPads into the anatomy laboratory displayed a need for a document that outlined best practices that is absent from the literature. A working document for best practices can be disseminated to other anatomy faculty for use in their own anatomy laboratory courses. Future work and next steps include determining how students' performance is impacted by the eBooks via tracking Poll Everywhere quiz scores and how students are actively using eBooks in the anatomy laboratory space and off-campus by tracking eBook usage.
Effective Face Mask Detection by Deep ConvNeuralNets Learning for Covid-19 Prevention

In Recent years, COVID-19 is the buzzword in our society, since it is too much dangerous, believed as it is originated from China from the place of Wuhan in December 2019. This Disease is spreading from humans to humans through droplets and airborne. A methodology has to be developed recognize whether the people are wearing mask or not. Therefore, this paper proposes a framework to recognize the mask. Based on the features proposes a machine learning basis system which recognizes the mask from the inputted image. Existing system Only detecting the person who is not using a mask apart from that system will not predict whether the used mask safe or not. A better deep learning framework which predicts the people with mask or not so it will be helping the society is discussed and apart from that this, it extends the mean of finding what type of mask they are wearing also predicts the efficiency of mask so user can protect themselves from the dreadful corona virus.
Naming Performativity on Twitter

This article examines naming as a discursive performance deployed by antiracist feminists in Brazil. I analyze tweets referencing the names of three Black Brazilian women intellectuals: Marielle Franco, Lélia González and Djamila Ribeiro, seeking to unearth the way in which their names help to build counterpublic spaces of resistance involving notions of citizenship, belonging and democracy. Using platform studies and Critical Technocultural Discourse Analysis as a theoretical frame, I perform different operations of computational textual analysis to map most frequent users referenced and hashtags used, as well as most relevant topics. I conclude that naming grants a powerful role in building counterpublics’ identities, helping to constitute alternative intellectual traditions in Brazil. Linking a social media post with a name to a collective mobilization serves to establish and maintain cultural identity, combining ephemerality with a continuing legacy.
Classification of data in erp systems using decision tree algorithms

ERP systems has become indispensable for business intelligence, varying from small enterprises to multinational corporations. Its significance as a hub for enterprise data analysis will develop exponentially throughout the next years. In terms of the recent developments in machine learning and artificial intelligence, using intelligent classification techniques for data analysis will hereby be the supreme exciting area. As the extensive usage of ERP systems increase, great volume of business information gets generated and managing such large information automatically becomes a challenging task. The accurate classification of data also becomes an additional multifarious task. It is an essential part for operative ERP applications and classical machine learning problems. This paper presents an overview of machine learning principles using decision trees to classify data in ERP systems, which can greatly benefit legacy systems, not powered with machine learning tools.
Autonomous Pulse Control for Quantum Transducers with Deep Reinforcement Learning

Quantum transducers are the back-bone technology and enabler for the Quantum Internet. We created a Deep Reinforcment Learning control framework to overcome current, low conversion efficiencies, bringing quantum transducers towards practical use.
A Novel Hybrid Extreme Learning Machine-Whale Optimization Algorithm for Bearing Fault Diagnosis

In order to avoid fatalities and ensure safe operation, a good and accurate diagnosis method is required. A diagnosis method based on extreme learning machine (ELM) has attracted much attention and the ELM method had been applied in various field of study. The advantages of the ELM method which are rapid learning rate, better generalization performance and ease of implementation makes the ELM method suitable to be used in various field including fault diagnosis fields. However, the performance of the ELM method becomes inefficient due to incorrect selection of neurons number and randomness of input weight and hidden layer bias. Hence, this paper aims to propose a novel hybrid fault diagnosis method based on ELM and whale optimization algorithm (WOA), known as ELM-WOA for bearing fault diagnosis. Four different types of bearing datasets from Case Western Reserve University Bearing Data Centre were used in this paper in order to present the performance of the proposed method. Based on the result, the performance of the proposed method was able to surpass the performance of the conventional ELM.
The effect of storytelling assisted by pop-up media to improve language development skills and attitude of opinion

This study aims to find out the effect of storytelling assisted by pop-up media to improve language skills and attitude of opinion in children aged 5-6 years. This study uses a quantitative pre-experimental research design with one group pre-test-post-test design without a control class. This research is conducted in TK Laboratorium UM, Malang City; with the population of the students of Class B. The data analysis used in this study includes normality test, homogeneity test, and hypothesis test. According to research and data analysis that has been carried out and the results obtained in this study, it can be concluded that there is a significant effect between storytelling assisted by pop-up media to improve language skills and the attitude of opinion in early childhood.