Reviewerly/exHarmony huggingface dataset:
Single image dehazing by dark channel prior and luminance adjustment

This paper presents a new and simple algorithm to eliminate haze from a single image using YIQ colour space and Dark Channel Prior (DCP). The suggested method consists of two parts. The first one is enhancement by adjusting contrast and lightness using contrast-limited adaptive histogram equalization and gamma transform. The second part involves applying DCP on a hazy image using the RGB colour model to achieve a haze-free image. The resulting image is then converted into YIQ, and the chrominance components (I and Q) are extracted and combined with the enhanced luminance component (Y) to achieve an enhancement haze-free image. The suggested method is applied to many outdoor images, and the quality of the resulting images is compared with that of images from other methods, by using several measures of quality. Analysing the results explain that the suggested method has a higher quality value than other methods.
Comparative Study of AutoML Approach, Conventional Ensemble Learning Method, and KNearest Oracle-AutoML Model for Predicting Student Dropouts in Sub-Saharan African Countries

Student dropout in secondary schools is a major issue in developing countries, particularly in Sub-Saharan Africa. Sub-Saharan African countries had the highest dropout rate (37.5%), followed by South Asia (15.5%), the Middle East (11%), East Asia (9.5%), Latin America (7%), and Central Asia (3.5%). Various initiatives such as the big results now initiatives, no child left behind, and secondary education development programme as well as machine learning prediction models have been used to reduce the severity of the problem in Sub-Saharan countries. The ongoing dropout problem, particularly in secondary schools is ascribed to improper root cause identification and the absence of formal procedures that can be used to estimate the severity of the issue. This study has compared the AutoML model, ensemble learning approach, and KNORA-AutoML to predict student dropout problems. The KNORA-AutoML model scored 97% of accuracy, precision = 71%, and AUC = 87% when compared to the conventional ensemble of optimized ML models with accuracy = 96%, precision = 70%, and AUC = 78%. KNORA-AutoML model performance increased by 0.6% accuracy, 0.8% precision, and 8.7% AUC. An optimized model draws a lot of attention to the findings related to student dropout rates in developing countries.
Bi‐level deep mutual learning assisted multi‐task network for occluded person re‐identification

An occluded person re-identification (ReID) approach is presented by constructing a Bi-level deep Mutual learning assisted Multi-task network (BMM), where the holistic and occluded person ReID tasks are treated as two related but not identical tasks. This is inspired by the human perception characteristic that there exist both similarities and differences when human views a holistic image and the occluded one. Specifically, a multi-task network with two branches is designed, where the convolutional neural network based feature representation part shares the weights by two tasks for commonality extraction, while the following output layers have respective weights for difference representation. Furthermore, as the non-occluded regions convey discriminative information, a bi-level mutual learning strategy is proposed and applied mutually on two branches to obtain more effective information from the non-occluded regions in the occluded images for better identity recognition. This is achieved by both feature-level and output-level mutual loss functions. Extensive experiments prove the advantages of the BMM for person ReID.
Effectuation, causation, and machine learning in co-creating entrepreneurial opportunities

In creating innovative entrepreneurial opportunities, entrepreneurs are characterized by limited information processing capabilities and local search routines that are immanent to humans. Machine learning (ML) offers the opportunity to overcome these limitations while reshaping the innovation process. It is indisputable that ML alone cannot realize entrepreneurial opportunities, but that close collaboration with the human entrepreneur is required. While entrepreneurs usually act according to the principles of effectuation logic in situations of high uncertainty and according to the principles of causation logic in situations of risk, it remains unclear how co-creation with ML affects the entrepreneur's decision-making behavior. By contrasting the functionalities of four different ML paradigms with the principles of the two decision logics, it is shown that supervised ML supports causation logic, while unsupervised and reinforcement ML support effectuation logic in their approach. As a fourth, semi-supervised ML is classified somewhere between effectuation and causation. However, in relation to the situational context of different types of uncertainty, ML may also prove limiting for effectuation by transitioning to causation in the medium-term.
Writer responsibility across sections of research articles: Recycling of directional determinants by English and Iranian academics

Motivated by the assumption that the recycling of directional determinants (DDs) (the research purposes, hypotheses, questions) across English research articles (RAs) is evidence of the writer's responsibility, this study sought to investigate any potential differences between English and Iranian researchers in the recycling of DDs throughout RA sections. To this end, 600 empirical RAs representing six soft science disciplines from 2006 to 2018 were chosen, 300 of which were authored by English L1 scholars and another 300 by Iranian researchers. The quantitative analysis revealed similarities between the two groups of scholars, with the DD recycling appearing more frequently in the Discussion section than in other sections, and in Economics than in other disciplines. In the qualitative phase, the two groups of authors' common rationales for the DD recycling, including editorial policy, RA length, English academic writing conventions, and reader guidance were determined. However, only two reasons were identified in novice Iranian researchers' responses: redundancy and English academic writing conventions. This implies that EAP writing course designers need to provide more explicit teaching materials to help novice non-English L1 researchers find out how the DD recycling in each RA section directs the writing of that section and contributes to text coherence.
Privacy and Security Challenges in the Era of the COVID-19 Pandemic

The outbreak of the COVID-19 pandemic has resulted in social, economic, and healthcare disruption unprecedently worldwide. It has brought practically everything to a halt. Moreover, it has dramatically accelerated the adoption of digital technology resulting in a growing number of incidences of cybercrime, including social engineering to access sensitive information. Furthermore, with the reduced amount of common physical labor, some digital surveillance and security systems were proposed for keeping the trace of transmission. Nevertheless, various automatic associated tracking implementations and the lack of smartphone usage among people bring privacy and security concerns. So, this chapter mainly discusses digital security and privacy violations with their protection measures and the recently developed digital surveillance systems after COVID-19. Furthermore, this chapter will pave the way to understand the futuristic tools and aspects that could be worthwhile in tackling digital privacy and security challenges in managing such infectious diseases.
Principles of corpus querying: A discussion note

Abstract Nowadays, it is quite common in linguistics to base research on data instead of introspection. There are countless corpora – both raw and linguistically annotated – available to us which provide essential data needed. Corpora are large in most cases, ranging from several million words to some billion words in size, clearly not suitable to investigate word by word by close reading. Basically, there are two ways to retrieve data from them: (1) through a query interface or (2) directly by automatic text processing. Here we present principles on how to soundly and effectively collect linguistic data from corpora by querying i.e. without knowledge of programming to directly manipulate the data. What is worth thinking about, which tools to use, what to do by default and how to solve problematic cases. In sum, how to obtain correct and complete data from corpora to do linguistic research.
Light Field GAN-based View Synthesis using full 4D information

Light Field (LF) technology offers a truly immersive experience having the potential to revolutionize entertainment, training, education, virtual and augmented reality, gaming, autonomous driving, and digital health. However, one of the main issues when working with LF is the amount of data needed to create a mesmerizing experience with realistic disparity, smooth motion parallax between views. In this paper, we introduce a learning based LF angular super-resolution approach for efficient view synthesis of novel virtual images. This is achieved by taking four corner views and then generating up to five in-between views. Our generative adversarial network approach uses LF spatial and angular information to ensure smooth disparity between the generated and original views. We consider plenoptic, synthetic LF content and camera array implementations which support different baseline settings. Experimental results show that our proposed method outperforms state-of-the-art light field view synthesis techniques, offering novel generated views with high visual quality.
Formulation and Data-Driven Optimization for Maximizing the Photovoltaic Power with Tilt Angle Adjustment

This paper reports on how the trade-off between the incident solar irradiance and conversion efficiency of a photovoltaic panel affects its power production. A neural network was developed through statistical analysis and a data-driven approach to accurately calculate the photovoltaic panel’s power output. Although the incident beam irradiance at a specified location directly relates to the tilt angle, the diffusion irradiance and energy conversion efficiency are nonlinearly dependent on a number of operating parameters, including cell temperature, wind speed, humidity, etc. A mathematical model was implemented to examine and cross-validate the physics of the neural network. Through simulation and comparison of the optimized results for different time horizons, it was found that hourly optimization can increase the energy generated from the photovoltaic panel by up to 42.07%. Additionally, compared to the base scenario, annually, monthly, and hourly optimization can result in 9.7%, 12.74%, and 24.78% more power, respectively. This study confirms the data-driven approach is an effective tool for optimizing solar power. It recommends adjusting the tilt angle of photovoltaic panels hourly, during the daily operation of maximizing the energy output and reducing solar costs.
Novel Scheme for Robust Confusion Component Selection Based on Pythagorean Fuzzy Set

The substitution box, often known as an S-box, is a nonlinear component that is a part of several block ciphers.Its purpose is to protect cryptographic algorithms from a variety of cryptanalytic assaults.A Multi-Criteria Decision Making (MCDM) problem has a complex selection procedure because of having many options and criteria to choose from.Because of this, statistical methods are necessary to assess the performance score of each S-box and decide which option is the best one available based on this score.Using the Pythagorean Fuzzy-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, the major objective of this investigation is to select the optimal S-box to be implemented from a pool of twelve key choices.With the help of the Pythagorean fuzzy set (PFS), the purpose of this article is to evaluate whether this nonlinear component is suitable for use in a variety of encryption applications.In this article, we have considered various characteristics of S-boxes, including nonlinearity, algebraic degree, strict avalanche criterion (SAC), absolute indicator, bit independent criterion (BIC), sum of square indicator, algebraic immunity, transparency order, robustness to differential cryptanalysis, composite algebraic immunity, signal to noise ratio-differential power attack (SNR-DPA), and confusion coefficient variance on some standard S-boxes that are Advanced Encryption Following this, the findings of the investigation are changed into Pythagorean fuzzy numbers in the shape of a matrix.This matrix is then subjected to an analysis using the TOPSIS method, which is dependent on the Pythagorean fuzzy set, to rank the most suitable S-box for use in encryption applications.
Modern Hopfield Networks for graph embedding

The network embedding task is to represent a node in a network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding method from a new perspective, which leverages Modern Hopfield Networks (MHN) for associative learning. Our network learns associations between the content of each node and that node's neighbors. These associations serve as memories in the MHN. The recurrent dynamics of the network make it possible to recover the masked node, given that node's neighbors. Our proposed method is evaluated on different benchmark datasets for downstream tasks such as node classification, link prediction, and graph coarsening. The results show competitive performance compared to the common matrix factorization techniques and deep learning based methods.
Auggie: Encouraging Effortful Communication through Handcrafted Digital Experiences

Digital communication is often brisk and automated. From auto-completed messages to "likes," research has shown that such lightweight interactions can affect perceptions of authenticity and closeness. On the other hand, effort in relationships can forge emotional bonds by conveying a sense of caring and is essential in building and maintaining relationships. To explore effortful communication, we designed and evaluated Auggie, an iOS app that encourages partners to create digitally handcrafted Augmented Reality (AR) experiences for each other. Auggie is centered around crafting a 3D character with photos, animated movements, drawings, and audio for someone else. We conducted a two-week-long field study with 30 participants (15 pairs), who used Auggie with their partners remotely. Our qualitative findings show that Auggie participants engaged in meaningful effort through the handcrafting process, and felt closer to their partners, although the tool may not be appropriate in all situations. We discuss design implications and future directions for systems that encourage effortful communication.
"A Reasonable Life"

Past CSCW work has examined the role of temporal rhythms in cooperative work and has identified alignment work--the work required to bring dissonant rhythms into alignment--as an important aspect of large-scale collaboration. We ask instead how individual workers interact with temporal rhythms to sustain the conditions that make their work possible--not aligning rhythms, but attuning them. This paper draws on interviews with farmer-knowledge workers, people who engage with both farm work (the work of growing food or raising animals for food, on a commercial or non-commercial basis) and computer-based knowledge work. We identify three ways that farmer-knowledge workers interact with natural and structural rhythms to construct sustainable work-lives: anchoring (tying oneself to a particular rhythm to create accountability and structure), decoupling (loosening or cutting ties with a rhythm to create flexibility), and gap-filling (interweaving complementary rhythms to create balance). Together, these practices constitute attunement work.
Counting to be Counted: Anganwadi Workers and Digital Infrastructures of Ambivalent Care

Data collection on the population is a key mode of public health management in the Global South. This information is seen as a means to improve health metrics through welfare programs. In this study, we examine the changes brought about by an ICT-based Real-Time Monitoring System to the infrastructure of a welfare program and the nature of work of Anganwadi workers in India. Anganwadi workers, traditionally serving as daycare providers and community health workers, are increasingly being asked to serve primarily as data collectors for the new digital system. We ask the question 'cui bono?' to this system by drawing attention to the precarity of Anganwadi workers whose care-work is standardized through this app for 'efficient' monitoring by the Indian state but remains contingent on their relationship with the local community and ability to mobilize resources on the ground. Using auto-ethnographic and interview methods, we find that Anganwadi workers are caught between conflicting demands of state bureaucracy and the situated nature of their care work resulting in forms of ambivalent care. We find that the real-time monitoring apps intended to collect data for efficient delivery of state services end up serving the state's need for performing care through data rhetorics produced at the expense of the professional and personal well-being of the workers, and arguably the communities they serve.
There is No App for That: Manifestations of the Digital Divides During COVID-19 School Closures in India

The COVID-19 induced lockdowns forced people to shift several activities, including education, online. However, in the context of online schooling, the Digital Divides have magnified and perpetuated existing inequities in the education system and in society. Through a qualitative study with 48 participants across four stakeholder groups we find that students in under-funded government schools in India largely have not been able to access online classes because of a lack of devices, poor quality of Internet access, unreliable data networks and expensive data plans. We also document attempts by teachers and non-profit workers to use mass media broadcast technologies to work around the issue of digital access, highlighting the importance of a human infrastructure to build resilience during a disruptive event. Socioeconomic factors have also forced several students to drop out of schools and into taking up jobs to support their families. We document the importance of enabling environments and economic safety nets at home for online education to succeed. We present some focus points for researchers and policy makers working in the space of digital divide and education to build more resilient systems through Digital Welfarism.
An Efficient Video Encryption Algorithm Based on the Pseudorandom Number Generator of Zipf Distribution

In this study, a pioneer selective video encryption (PSVE) algorithm is proposed based on the pseudorandom number generator (PRNG) of the Zipf distribution (Z-PRNG). It is a general algorithm with high efficiency and security. The encryption process is completely separable from the video coding process. In the PSVE algorithm, Z-PRNG is designed based on the 3D SCL-HMC hyperchaotic map. Firstly, encapsulated byte sequence payloads (EBSPs) are extracted from the video bitstream. Secondly, random numbers of the Zipf distribution are generated by Z-PRNG, and they are used to randomly select encrypted data from each EBSP. Lastly, the extracted data are encrypted by AES-CTR to obtain the encrypted video. Compared with existing algorithms, the encryption position is more flexible, and the key space is further enhanced. The high efficiency video coding (HEVC) video and the advanced video coding (AVC) video are taken as examples to test the PSVE algorithm. The analysis results show that the proposed scheme can effectively resist common attacks, and its time complexity is much less than most existing algorithms.
Warm Solutions: Centering Nurse Contributions in Medical Making

Making medical devices in healthcare settings engages practitioners in organizational innovation. Nurses improvise physical workarounds at the bedside in response to patient needs. Yet nurse-led problem-solving is rarely centralized in an emerging innovation ecosystem through medical making. We interviewed medical makers in six healthcare makerspaces to understand factors for nurse inclusion in the medical making ecosystem. Findings from 16 multi-stakeholder interviews with 6 facilitators and 10 nurses in the USA, reveal insights into nurse-led problem-solving with and without the use of physical prototyping (making) in formal innovation spaces with maker technologies. We report how a nurse's capacity for making is practice-driven to address in-patient discomfort, repair their own practice, and update standardized workflows. Most nurses iterate on low-tech solutions facing barriers to formal collaboration when they attempt to scale up. Their technical capabilities extend from innovation-centered resources (e.g., lab spaces, technologies), often with complete reliance on facilitators who have limited authority in the medical system. We contribute to themes around practice-based innovation, participation in technology design, and articulation work for collaborative innovation. From nurse makers' experiences, we discuss how nurse participation can be supported in healthcare technology design.
Semantic space captioner: generating image captions step by step

Image captioning is a popular research direction at the intersection of machine vision and natural language processing. Most of the existing image captioning methods adopt an encoder–decoder-like structure in which the image is encoded and fed into a decoder to generate a paragraph describing the image content. Although the existing methods have achieved great results in describing natural images, there is still much room for improvement in describing details. We propose the semantic space captioner model to introduce the concept of dense captioning into image captioning using contrastive language-image pretraining as an encoder for text and images. Dense captions are generated for image regions and are used as an extra semantic space for decoding to enhance the final caption. According to the experimental results, our model outperforms existing methods in generalizing image details and is able to generate diverse and meaningful captions. It also performs well on the MSCOCO dataset-related metrics scores.
Attention-Based Sentiment Region Importance and Relationship Analysis for Image Sentiment Recognition

Image sentiment recognition has attracted considerable attention from academia and industry due to the increasing tendency of expressing opinions via images and videos online. Previous studies focus on multilevel representation from global and local views to improve recognition performance. However, it is insufficient to research the importance and relationship of visual regions for image sentiment recognition. This paper proposes an attention-based sentiment region importance and relationship (ASRIR) analysis method, including important attention and relation attention for image sentiment recognition. First, we extract spatial region features using a multilevel pyramid network from the image. Second, we design important attention to exploring the sentiment semantic-related regions and relation attention to investigating the relationship between regions. In order to release the excessive concentration of attention, we employ a unimodal function as the objective function for regularization. Finally, the region features weighted by the attention mechanism are fused and input into a fully connected layer for classification. Extensive experiments on various commonly used image sentiment datasets demonstrate that our proposed method outperforms the state-of-the-art approaches.