Reviewerly/exHarmony huggingface dataset:
Exploring Surgeons’, Nurses’, and Patients’ Information Seeking Behavior on Medical Innovations

To explore information seeking behavior on medical innovations.While autologous and alloplastic options for breast reconstruction are well established, it is the advent of the combination of 3D printing technology and the biocompatible nature of a highly porous biodegradable implants that offers new treatment options for the future. While this type of prosthesis is not yet clinically available understanding how patients, surgeons, and nurses take up new medical innovations is of critical importance for efficient healthcare provision.Using the largest ever combined sample of breast cancer patients (n = 689), specialist surgeons (n = 53), and breast care nurses (n = 101), we explore participants preference for a new surgical treatment concept rooted in 3D printed and biodegradable implant technologies in the context of breast reconstruction.We find that patients overwhelmingly favor information from a successful patient of the proposed new technology when considering transitioning. Surgeons and nurses instead favor regulatory body advice, peer-reviewed journals, and witnessing the procedure performed (either in person or online). But while 1 in 4 nurses nominated talking to a successful patient as an information source, not a single surgeon chose the same. Our multinomial logit analysis exploring patient preference (controlling for individual differences) showed statistically significant results for both the type of surgical treatment and choice to undergo reconstruction. Women who underwent a type of mastectomy procedure (compared with lumpectomy patients) were more likely to choose a former patient than a surgeon for seeking information relating to a new breast implant technology. Further, women who chose to undergo a reconstruction procedure, compared with those who did not, where more likely to prefer a surgeon for information relating to a new breast implant technology, rather than a successful patient. For medical professionals, we find no statistically significant relationship between medical professionals' preference and their age, nor the number of other medical professionals they work with daily, nor the average number of breast procedures performed in their practice on a weekly basis.As our findings show large variation exists (both within our patient group and compared with medical professionals) in where individuals favor information on new medical innovations, future behavioral research is warranted.
Survey on Implicit Feedbacks Extraction based on Yelp Dataset using Collaborative Filtering

In e-commerce websites, associated micro-blogs, and business social media, users provide online feedback demonstrating their preferences for different items. These studies are usually found in textual comments, reviews, geo-tagged photos, and other contextual data and account for essential user preferences. Several factories have recently utilized review texts and the amount of information associated with them, such as review words, review subjects, and review moods. They also employed social photographs and other contextual information to improve collaborative filtering recommender systems based on ratings. These efforts employ review texts, geo-tagged photographs, and other contextual information to determine user preferences. This study gives a targeted survey of the most recent studies that mix review texts, photographs, and other contextual information and explores how these metadata and visual information are used to solve some of the most critical topics in Algorithms for collaborative filtering.
EXTERNAL EVALUATION OF SELF-DIRECTED LEARNING SKILLS: ONLINE EDUCATION

Self-isolation measures implemented during the Covid-19 pandemic resulted in a quick transition to online instruction. As a result, teachers and students must master online learning tools and technologies in educational institutions. In addition, the problems with online education are frequently reported in scholarly sources. Many participants in the educational process faced difficulties during this period. In our opinion, many difficulties in online education were caused by insufficient training of self- directed learning skills in students and corresponding skills in teachers. At the same time, this group of universal learning skills is a mandatory part of education in Russian schools. The skills of self-organization and self-control are included in the Russian federal state educational standards requirements. A considerable number of scientific publications are devoted to the formation of assessment skills in school children. At the same time, the problem of training self-directed learning skills in online classes has not been sufficiently studied. This article examines the results of an independent assessment of self-directed learning skills and training in online classes for both primary school students and primary school teachers. As a result of the study, the following skills were identified as important for teaching primary school online learners: Planning, Self-Monitoring, Self-assessment, and selecting effective approaches for online learning. Empirical data were collected as part of the study. From this, the following points can be made: Primary school teachers are not prepared for online teaching; time demands on teachers have increased due to the specifics of teacher self-monitoring under the new conditions. Consequently, students need to learn the following skills during online teaching: self-monitoring, use of automated assessment and monitoring tools, self- and peer-assessment in the digital educational environment, and planning. The empirical data obtained show the importance of training self-monitoring skills for online teaching. Therefore, the proposed recommendations for improving teacher training education programs in pedagogical universities and in the teacher professional development system have practical relevance and can be implemented through appropriate educational programs.
HARNESSING HUMAN-CENTRED DESIGN APPROACHES TO BUILD A CHATBOT FOR PEOPLE LIVING WITH HYPERTENSION

Objective: To describe how human-centered design was used to create an artificial intelligence (AI) enabled chatbot for people living with hypertension. Design and method: The project focused on technology augmentation and testing to strengthen the mDoc’s CompleteHealthTM platform (an omnichannel platform that provides self-management techniques digitally to people with chronic diseases) to enable us to drive scale in providing self-care support. We leveraged Human-Centered Design principles to guide our pathway based on inspiration, ideation, and implementation to design a chatbot called “Kem’’, aimed to provide real-time response to members living with hypertension. In the inspiration phase, our clinical and tech teams hypothesized that 80% of our growing members’ questions on hypertension could be answered by Kem at scale. In the ideation phase, we leveraged our members to understand and develop the conversation structure and conducted extensive research on how to provide real-time responses to as many members as possible. Kem’s prototype was developed on Botmock (a tool used for designing and fleshing out conversational design flow) with a persona designed to function as a health coach to help bridge the gap in knowledge of health topics, guide, and answer mDoc members. After a series of iterations based on users’ feedback, during the implementation phase, Kem was integrated into a chat platform, Telegram, and was tested with a larger group of users. Results: By applying human-centered design and integrating user feedback into the design and creation of a chatbot to answer questions around hypertension, we were able to create a tool that will help members increase their knowledge of the prevention and management of hypertension. Conclusions: Co-creation should occur at every step of the journey when designing for members’ needs and behaviors. The users’ expressed excitement about Kem and the design process revealed that we needed to be more intentional about making the conversation with the bot interesting, user-friendly, and less technical. As the implementation phase continues and more members use Kem, we will be able to educate them about the prevention and management of hypertension.
Case Studies on Anti-Social Media Laws in African Countries

Social media is now integral to communication in society, and formal and informal communication is widely accepted on social media. The growing popularity of social media has caused several African countries to enact laws to regulate it for different reasons. This chapter provides case studies of anti-social media laws in ten African countries to show that the legislation often arises out of the political climate in the legislating country, often resulting in stifling social media and, with it, freedom of expression, speech, and privacy.
ABCinML: Anticipatory Bias Correction in Machine Learning Applications

The idealization of a static machine-learned model, trained once and deployed forever, is not practical. As input distributions change over time, the model will not only lose accuracy, any constraints to reduce bias against a protected class may fail to work as intended. Thus, researchers have begun to explore ways to maintain algorithmic fairness over time. One line of work focuses on dynamic learning: retraining after each batch, and the other on robust learning which tries to make algorithms robust against all possible future changes. Dynamic learning seeks to reduce biases soon after they have occurred and robust learning often yields (overly) conservative models. We propose an anticipatory dynamic learning approach for correcting the algorithm to mitigate bias before it occurs. Specifically, we make use of anticipations regarding the relative distributions of population subgroups (e.g., relative ratios of male and female applicants) in the next cycle to identify the right parameters for an importance weighing fairness approach. Results from experiments over multiple real-world datasets suggest that this approach has promise for anticipatory bias correction.
Construction of Accounting Technology Block Analysis System Under the Background of Big Data Cloud Computing

In order to explore the application and development methods of blockchain technology in the accounting and internal control sharing platform, the article analyzes the core concept of blockchain technology, expounds the advantages of the distributed accounting model, and takes the accounting and internal control sharing platform of public institutions as the blueprint. This article applies to specific business types, analyzes the feasibility of the integration of blockchain technology and sales activity accounting, and builds a sales accounting system from the perspective of technology, business, and financial integration, and broadens and deepens the application scenarios of new technologies. Provide reference for the transformation of traditional financial management mode.
Prostate Cancer Classification Using Artificial Neural Networks

Prostate cancer is one of the major causes of cancer death in men in the Western world. One in 6 men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs [Computer-aided detection of prostate cancer in MRI. IEEE , May 2014]. The artificial neural network (ANN) is used to classify and extract the feature of the magnetic resonance imaging (MRI) scan. This classification is computer-based and has the advantage that it enriches the patient from the doctor and undergoes the different diagnostic techniques that may appear to be stressful and painful. A computer-aided classification set of MRI scans of the prostate subjects these images to a set of preprocessing and image enhancement giving an accurate classification to improve the properties of the image such as noise filtering to improve the contrast of the image and then using segmentation to separate the tumor region and feature extraction; this step can be done by using a set of rolls (explained below). This feature is used to input data to ANN. It classifies the given data set into benign or malignant (prostate cancer). The system succeeded in classifying malignant tumors with 92.4%, increasing decision-making and providing particular treatment. The computerized method detects the tumor in its early stage; thus, good classification is an essential part. The first stage is to extract features from MRI using principal component analysis (computer aided diagnosis), and the second stage is to train the probabilistic neural network (ANN) for classification. The computer aided diagnosis method gives existing attributes a new set of calculated parameters.
ATQMB: Design of an augmented trust enabled QoS aware MAC model with intelligent blockchain sharding

Enhancing security in wireless networks compromises quality of service (QoS) parameters, which reduces network scalability. In order to improve network security without compromising on QoS performance, redundant calculations must be reduced, which requires design of a performance tuning machine learning layer. Most of the research work done in this area of machine learning model design, focuses on reducing these redundancies via use of low complexity models, light weight mechanisms for trust evaluation, or simplifying key-exchange & other cryptographic processes. But these reductions in complexity also introduce security vulnerabilities into the system, for instance, using a light-weight trust model introduces trojan horse like attacks, while, using a low complexity cryptographic mechanism facilitates cryptanalysis. In order to reduce the computational complexity needed during security patch deployments, and improve overall quality of service (QoS) for wireless networks, this text proposes an augmented trust enabled QoS aware MAC model with intelligent blockchain sharding. The model works in 4 phases, wherein, initially an augmented trust model is implemented, which assists in improving QoS of network during routing. This is followed by design of a medium access control (MAC) model, which assists in further improving network QoS via an incremental learning layer that reduces packet contention, link quality issues, etc. Due to use of augmented trust layer, and incremental learning MAC model, various privacy & security loopholes are added to the network, which are handled via use of a proof-of-stake (PoS) consensus based blockchain model. The PoS model is very secure, has high traceability, but is moderately scalable, because of exponential relationship between number of blocks & computational complexity of mining. In order to reduce this complexity, and maintain a high QoS performance, this text proposes an intelligent blockchain sharding model that divides the underlying PoS blockchain into smaller shards, and manages each shard using tree-based hash range search model.
Exploring diversity perceptions in a community through a Q and A chatbot

While diversity has become a debated issue in design, very little research exists on positive use-cases for diversity beyond scholarly criticism. The current work addresses this gap through the case of a diversity-aware chatbot, exploring what benefits a diversity-aware chatbot could bring to people and how do people interpret diversity when being presented with it. In this paper, we motivate a Q&A chatbot as a technology probe and deploy it in two student communities within a study. During the study, we collected contextual data on people's expectations and perceptions when presented with diversity during the study. Our key findings show that people seek out others with shared niche interests, or their search is driven by exploration and inspiration when presented with diversity. Although interacting with chatbots is limited, participants found the engagement novel and interesting to motivate future research.
A Brief Survey on Segmentation and Classification Techniques for Face Recognition

Face detection, registration, and identification has become a fascinating subject for researchers. The huge interest in the subject originates from the need to improve accuracy of real-time applications. Numerous techniques have been recognised and presented in recent years. Acquiring data (database), pre-processing, segmentation, feature extraction, and classification are the five stages of the whole system. This survey's goal is to provide an overview of facial recognition techniques. The survey needs to take into account research papers published between 2018 and 2021 from a number of different databases. The paper provides an overview of the work performed so far to increase the efficiency of face recognition methods using hybrid methods depend on level-set based segmentation methods, support vector machine (SVM), and random forest methods.
A Unified Code Review Automation for Large-scale Industry with Diverse Development Environments

Code Review is an essential activity to ensure the quality of the software in the development process. Code Review Automation with various analyses can reduce human efforts of code review activities. However, it is a challenge to automate the code review process for large-scale companies such as Samsung Electronics due to their complex development environments: many kinds of products, various sizes of software, different version control systems, and diverse code review systems. In this work, we show how we automated the code review process for those intricate environments, and share some lessons learned during two years of operation. Our unified code review automation system, Code Review Bot, is designed to process review requests holistically regardless of such environments. Our findings provide practical evidence that our system motivates developers in Samsung Electronics to improve code quality.
Madness to the methods: Speculating approaches to study and nurture children's designer and Maker identities

As our relationship with digital technology radically changes during this pandemic, it becomes imperative to reimagine new ways of interactions and collaborations. It is also important for children grow from passive consumers of digital technology to active designers and Makers. Typical research approaches for inquiring or probing children's digital technology identities include interview-, reflection-, and hands-on creative-types of methods. But how will these methods fare in a future that is online or hybrid? In this paper, we present the outcomes of a workshop that employed critical design fiction with Child-Computer Interaction experts to speculate on how such methods can be applied in the future, in online or limited access scenarios, to study children's designer and Maker identities. We focused on approaches that are empowering, albeit provocative. We call for researchers working with children to reconsider and expand their methods repertoire to keep in tune with the changing times.
Design and Fabricate of Reconnaissance Robots for Nuclear Power Plants that Underwent Accidents

This paper presents a robot developed for reconnaissance at a nuclear power plant that had an accident. When an accident occurs, such as the Fukushima Daiichi Nuclear Power Plant disaster, rubble is scattered on the premises, increasing the radiation dose. Consequently, it is nearly impossible for humans to enter the plant and assess the situation. Therefore, a robot that performs reconnaissance is required. In this study, we designed and fabricated a reconnaissance robot to be used in a nuclear power plant that has undergone an accident. The inaccessibility of the scattered on-site rubble and high radiation dose can be addressed by employing an airship-type design that does not use electronic devices.
Significance of Blockchain in Banking and Insurance

The chapter focuses on robustness of blockchain technology for recording a chain of transactions and maintenance of data. The initial sections of the chapter explain basic concepts like cryptography, double-entry ledger, cryptographic hashing, etc. that have been used in the development of blockchain technology. The authors explain the functioning of blockchain and its advantages in terms of time- stamped, immutable records. Blockchain represents a chain of transactions in the form of blocks that are connected through a hash unique to each block. Blockchain offers solutions to many real-world problems like recording the transactions and maintaining the records in a decentralized manner making the system efficient and yet ensuring transparency and safety. In subsequent sections, the authors discuss several use cases of blockchain in insurance and banking. The important applications include fraud detection and risk prevention, decentralized insurance and cheaper premiums, reinsurance, cross-border payments, trade finance, and compliance.
What Does the Future Hold for Requirements Engineers?

As my five-year cycle as shepherd of the IEEE Software “Requirements” department column nears its conclusion, it feels appropriate to offer a confession: I am not a requirements engineer.
Remote Meter Reading based on Lightweight Edge Devices

With the Industrial Internet of Things springs up, it is necessary to provide a remote and automatic meter reading solution for traditional enterprises and factories to avoid huge labor costs during production. However, traditional object detection solutions cannot be deployed on the current lightweight edge devices due to the high computational complexity of deep neural networks. In this paper, we propose a remote meter reading system, named EdgeMeter, which can provide a robust and real-time meter reading solution for lightweight edge devices. To ensure real-time and high-precision performance, we propose a lightweight feature point matching strategy to amortize the high latency of object detection, and we perform perspective correction to minimize the influence of meter orientations. To further improve the robustness of the system, we design a customized auxiliary device to eliminate the interference of complex outdoor environments. Real-world experiment results show that EdgeMeter achieves the meter reading error within 2.2° with a latency of less than 42 ms.
Revolutionary Epistemology

The value of acknowledging limitations within existent theories of political and social challenge is where the promise of ignorance studies lies. The term was deliberately chosen as broad, non-pejorative phrase allowing for wide variety of studies dealing with unknowns, uncertainties and knowledge gaps – good or bad – to avoid one-sided analyses from narrow disciplinary perspectives, and to urge scholars to examine how disciplinary silos can compound ignorance. The problem of epistemic privilege and epistemic disadvantage is particularly acute in light of the COVID-19 pandemic, which has resuscitated long-standing questions about the rights and duties of private individuals in societies that are intractably interdependent. More empirical research is needed and underway that seeks to understand why some people are hesitant to trust the evidence-base for new vaccines while other groups, even those who insist that benefits far outweigh known risks, are wary of imposed vaccine mandates. The chapter also presents an overview on the key concepts discussed in this book.