Reviewerly/exHarmony huggingface dataset:
A two-dimensional sharding model for access control and data privilege management of blockchain

This paper presents a method to manage private data stored on a blockchain. With our method, the blockchain’s features for log transparency and tamper-resistance are maintained, even though the data is only available to authorized users. The most relevant work so far randomly selects nodes to store the decryption key shares of a threshold cryptosystem for some data which are not maintained in the system. They provide the decryption keys to the data requester via on-chain methods. This is for guaranteeing the availability and distributing the incentives. If the system maintains the data and wants to achieve the same guarantees, it has to post the data to the blockchain. This will make the blockchain oversized and the work impractical. This paper shows that nodes in our method may provide data to the requester directly without posing to the blockchain while guaranteeing availability and that the incentives be fairly distributed. Furthermore, each data request incurs a tiny size of transactions. We achieve so by implementing a two-dimensional sharding model, where nodes are randomly assigned to shards. Data is arithmetically compressed and then split into pieces. Each data piece is stored by a node in a first dimension shard. Without getting all the pieces, the data cannot be successfully decompressed. Each node in the first dimension shard is monitored by a second dimension shard. We propose designs that empower the corresponding second dimension shard for evaluating whether the first dimension node has provided the correct data piece to the data requester. This waives the need for placing the data into transactions and being witnessed by all. In case when a first dimension node fails, its data will be recovered by the corresponding second dimension shard.
Technology in Hotel Sector

This chapter presents the discussion on the technology applied in the hotel sector in the new norm for hotels’ operation around the world. Various studies have been conducted to report the latest hotel’s technology trends and innovation to sustain competitive advantage. Therefore, this chapter offers light on the recent transformation, development, as well as the application of various technological advancement, to replace human labor in the hotel sector. Further, this chapter presents the application of these technologies in several departments in the hotel sector. This chapter offers a significant view on the strategies that hotels are implementing in using technology to transform their communication medium, financial transaction, food and beverage operations, marketing, and promotional tools, as well as the use of Artificial Intelligence (AI) in hotel’s service delivery to replace human labor. The impact of the technology application in the hotel sector is also discussed at the end of this chapter.
Undergraduate Students’ Attitudes Towards Remote Learning During COVID-19 Pandemic: A Case Study from the UAE

The sudden closure of learning institutions due to the unprecedented COVID-19 pandemic has impacted education all over the world. With remote learning playing an increasingly important role in teaching during the pandemic, it is crucial to identify the variables that influence students’ behaviors in using online education. Framed within the Technology Acceptance Model, this study examined undergraduate students’ behavioral intention toward their remote learning experience at a federal higher education institution in the UAE. A random sample of 216 undergraduate students responded to an online survey. The results suggest that Perceived Ease of Use (PEU) and Perceived Usefulness (PU) positively impacted undergraduate students’ acceptance of remote learning. In addition, data analysis revealed no significant difference between male and female students’ attitudes towards remote learning. The results of this study are important to inform future efforts in facilitating institutional readiness for online education.
Best Practices to Promote Data Utility and Reuse by the Non-Traditional Model Organism Community

The dramatic increase in accessibility to sequencing technologies has opened new avenues into studying different processes, cells, and animal models. In the amphibian models used for regeneration research, these new datasets have uncovered a variety of information about what genes define the regenerating limb as well as how genes and cells change over the course of regeneration. The accumulation of data from these studies undoubtedly increases our understanding of regeneration. Throughout these studies, it is important to consider how data can be made most useful not only for the primary study but also for reuse within the scientific community. This chapter will focus on best practices for data collection and handling as well as principles to promote access and reuse of big datasets. However, the deposition and thorough description of data of all sizes generated for a publication (e.g., images, fcs files, etc.) can also be done following this generic workflow. The aim is to lower hurdles for reuse, access, and re-evaluation of data which will in turn increase the utility of these datasets and accelerate scientific progress.
NLSP: A novel lattice‐based secure primitive for privacy‐preserving smart grid communications

Summary As the new generation of power scheme, smart grid is proposed to overcome the shortcomings of traditional systems, such as low efficiency and reliability. In this article, a novel lattice‐based secure primitive for privacy‐preserving smart grid communications is proposed, which has the remarkable characteristics, such as scalable multi‐dimensional fine‐grained power data structure and differential privacy security. First, combining with the lattice‐based data encryption technology, while effectively resisting quantum attacks, the method of simultaneous processing of multi‐dimensional data is innovated. Second, through combining the additive homomorphism of the lattice‐based cryptosystem and the Chinese remainder theorem, the data aggregation mechanism that can directly perform homomorphic operations on compressed ciphertext is constructed. Thanks to the above innovative design ideas, the proposed scheme not only significantly improves the efficiency of data communication and processing, greatly reduces the computational cost of the intermediate entity, but also realizes the data confidentiality and information privacy. Finally, observing the decentralized topology of communication nodes in the typical cyber‐physical system of smart grid, the localized differential privacy technology is leveraged to optimize and balance the utility, security, and efficiency of differential privacy. Extensive performance evaluations are conducted to illustrate that the proposed scheme outperforms the state‐of‐the‐art similar schemes in terms of computation complexity and communication cost.
Optimized multi‐objective Q‐learning with enhanced beetle swarm optimization based scientific workflows scheduling on cloud computing environment

Summary Cloud technology has raised significant prominence providing a unique market economic approach for resolving large‐scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on‐demand to meet a variety of user QoS standards. Due to the large number of users and tasks, it is important to achieve efficient planning of tasks submitted by users. One of the most important and difficult nondeterministic polynomial‐hard challenges in cloud technology is task scheduling. To overcome the problem, in this article, optimized multi‐objective Q‐learning with EBSO‐based task scheduling on the cloud is proposed. Q‐learning is one of the machine learning algorithms that can solve these types of problems. The proposed approach is divided into two processes: task prioritization and resource selection. In this article, initially, the tasks are prioritized by using the Q‐learning algorithm. After the prioritization, the tasks are assigned to resources by using the enhanced beetle swarm optimization (EBSO) algorithm. To achieve this, the multi‐objective function is designed based on makespan, cost, and resource utilization. The effectiveness of the suggested method is evaluated using a variety of criteria such as makespan, resource utilization, and cost.
Text-Guided Customizable Image Synthesis and Manipulation

Due to the high flexibility and conformity to people’s usage habits, text description has been widely used in image synthesis research recently and has achieved many encouraging results. However, the text can only determine the basic content of the generated image and cannot determine the specific shape of the synthesized object, which leads to poor practicability. More importantly, the current text-to-image synthesis research cannot use new text descriptions to further modify the synthesis result. To solve these problems, this paper proposes a text-guided customizable image synthesis and manipulation method. The proposed method synthesizes the corresponding image based on the text and contour information at first. It then modifies the synthesized content based on the new text to obtain a satisfactory result. The text and contour information in the proposed method determine the specific content and object shape of the desired composite image, respectively. Aside from that, the input text, contour, and subsequent new text for content modification can be manually input, which significantly improves the artificial controllability in the image synthesis process, making the entire method superior to other methods in flexibility and practicability. Experimental results on the Caltech-UCSD Birds-200-2011 (CUB) and Microsoft Common Objects in Context (MS COCO) datasets demonstrate our proposed method’s feasibility and versatility.
Device for the Fall Detection in Older Adults Through Neural Networks

For an elderly adult, a fall can be fatality by the fact that his bones are fragile and propense to fracture. The present device is intended to detect a change of posture prior to a fall by using a sensor composed of an accelerometer and a triaxial gyroscope. The signal processing used multiple classification neural networks with hyper-parameters of five hidden layers and 500 interactions, training and validation showed an accuracy of 83% respectively. In the results, the device detects fall postures efficiently and with a minimum margin of error.
Breast Cancer Detection Algorithm Using Ensemble Learning

There are certain parameters in the human body that may be indicators of the presence of breast cancer, these can be assessed with different algorithms such as the Support-Vector Machine (SVM), the Naïve Bayes Algorithm (BA) and Artificial Neural Networks (ANN) to determine whether the laboratory tests are positive or not. Machine Learning (ML) has gained more uses across fields as it proposes a cost-effective classifier with versatility to be developed for any type of application, such as early breast cancer detection. This paper shows an ensemble of the algorithms previously mentioned that, based on a database consisting of 8 characteristics, can provide a high accuracy result. The highest F1 Score obtained was 78.261% from the BA, followed by the ANN’s score of 77.273% and a 72.34% from the SVM, resulting in a compositive F1 Score of 80.851%. All the data used on this article was trained using supervised machine learning techniques and variables of interest for breast cancer proliferation.
QUESTION-FORMATTED IN CLASSROOM INTERACTION TO ATTRACT PRIMARY SCHOOL STUDENT ATTENTION IN ONLINE CLASSROOM

Online learning is increasingly used around the world due to covid-19. Therefore, this research aims to investigate on conversation analysis that focuses on how the teacher’s question which can engage students’ interaction in classroom. It is known that it is very complex in engaging students ‘in online class, especially first grade primary students. While prior studies regarding online class interaction has not been scrutinized in detail regarding how teacher’s question and students’ responses. Therefore, this study eager to analyzes the teacher and students’ conversation in online class. This study employed qualitative method. The data collected from a series of classroom zoom video. In addition, the data analysis has three stages. First, reduction the utterance, it is based on the question formatted types. Second, coding the data and analyzed the data based on question formatted types. The third is concluding in order to find in the questions-formatted types in online classroom. The types are preserve the sequential implication, slightly alter the sequential implications, following student’s question, and deviant case. The result illustrates in this research is slightly later the sequential implications as the only type which occurred in first grade primary school online classes. It occurred because in online classes it is very demanding to engage student’s attention in online classroom. It is therefore teacher gave questions and pointed directly to students. Consequently, this study implicates on how the pedagogical insight in teaching online learning.
Hybrid renewable energy resources accuracy, techniques adopted, and the future scope abetted by the patent landscape – a conspicuous review

Abstract Renewables always attract everything than other thrust research areas owing to the global warming issue caused by using the non-renewables therein which should be mitigated at the earliest possible time. The easiest and safest alternative is employing renewable energy resources for the generation of energy that is completely sustainable. Many countries started insisting to seek alternatives trying to terminate to the fullest extent. Even though all these measures are taken, it is very hard to mitigate the utilization of non-renewables, owing to efficacy lag in renewables. The simplest solution for this problem is hybrid usage of renewables to enhance the efficiency. This article took this as a factor, gathered data for hybrid RERs technique with its accuracy range for easy selection of techniques by the stakeholders, trending technology being adopted, and future scope abetted by the patent landscape.
FootUI: Designing and Detecting Foot Gestures to Assist People with Upper Body Motor Impairments to Use Smartphones on the Bed

Some people with upper body motor impairments but sound lower limbs usually use feet to interact with smartphones. However, touching the touchscreen with big toes is tiring, inefficient and easy to mistouch. Targeting at this pain point, we propose FootUI, which leverages the phone camera to track users’ feet and translates the foot gestures to smartphone operations. This technique enables users to interact with smartphones while reclining on the bed and improves the comfort of users. In this paper, we present the design and evaluation of the foot gestures through user studies as well as the development and evaluation of FootUI. Results show that toes-based foot gestures are not only less perceivable but also uncomfortable. FootUI avoid the use of toes-based gestures and is proved to be an easy, efficient and interesting input technique for people with upper body motor impairments but sound lower limbs.
Applying Technology in a Hybrid-Fashion to Create Dementia-Inclusive Community Spaces

While there is currently no cure for dementia, socially prescribing activities can enhance wellbeing for people living with dementia (PLWD). However, accessibility to such activities can be hindered by location, being unaware of activities, scheduling issues, or lack of interest in the specific activity. The COVID-19 pandemic further complicated accessibility and changed how coordinators of such programs thought about technology. This PhD research explores how technology can improve accessibility to community spaces and programs for PLWD and their informal carers. The first study involved understanding how technology used for social programs changed during the pandemic and developing guidelines for how program coordinators and technologists can adapt and develop technology for disseminating social programs. The current study aims to discover what elements may be necessary for spaces and programs to feel accessible for PLWD and how these elements can be translated or manifested through technology.
Socially Connecting Adults with Intellectual Disabilities Through Inclusive Co-Design of Tangible and Visual Technology

People with intellectual disabilities experience difficulties developing social connections with others as it heavily relies on verbal and written communication. Co-designing tangible and visual technology with people with intellectual disabilities can create new inclusive opportunities to connect with others and alternative modes of communication. This paper discusses a three stage research plan that has been created to explore such technologies, and provides findings from completing the first year of this research. This paper presents a broad research plan, what has been completed, and opportunities for novel research in the form of new technologies and design frameworks.
Flexible Activity Tracking for Older Adults Using Mobility Aids — An Exploratory Study on Automatically Identifying Movement Modality

Wearable activity trackers are inaccessible to older adults who use mobility aids (e.g., walker, wheelchair), because the accuracy of trackers drops considerably for such movement modalities (MMs). As an initial step to address this problem, we implemented and tested a minimum distance classifier to automatically identify the used MM out of seven modalities, including movement with or without a mobility aid, and no movement. Depending on the test setup, our classifier achieves accuracies between 82 % and 100 %. These findings can be leveraged in future work to combine the classifier with algorithms tailored to each mobility aid to make activity trackers accessible to users with limited mobility.
Personalizable Alternative Mouse and Keyboard Interface for People with Motor Disabilities of Neuromuscular Origin

People with motor disabilities of neuromuscular origin often struggle with operating the computer through a commercially available mouse and QWERTY keyboard. This work presents an alternative mouse and keyboard interface, which is personalizable to meet the individual needs of the target population to allow a more efficient use of the computer. The regular/commercially available mouse is therefor replaced by a spectacle frame equipped with pressure sensors for mouse click activation and an inertial measurement unit for mouse cursor control through head movements. The alternative keyboard is based around a ten button keyboard, with multiple letters assigned to each key. The users can then chose the desired word from a list of word suggestions that is compiled based on the keyboard input. Prior experiments, the users went through a software guided calibration procedure to experimentally determine individual thresholds and parameters. The system was tested by two nondisabled participants through typing and click tests, followed by a questionnaire to give feedback on the system.
Understanding Design Preferences for Sensory-Sensitive Earcons with Neurodivergent Individuals

Earcons are a critical auditory modality for those who perceive information best through sound. Yet earcons can trigger sensory sensitivities with neurodivergent individuals, causing pain or discomfort and creating barriers to information access. They must be carefully designed with neurodivergent representation in the design process to minimize the harm they impose. To address these challenges, we conduct a study on Twitter, a social media platform with frequent earcons, to understand how to design sensory-sensitive earcons for neurodivergent individuals. We present the results of our qualitative interviews with nine neurodivergent Twitter users, uncovering six key themes for designing sensory-sensitive earcons. Based on our findings, we offer a set of novel guidelines for practitioners to design sensory-sensitive earcons for accessibility.
ProAesthetics: Changing How We View Prosthetic Function

There are multiple perspectives that must be considered while designing a limb: The engineering requirements, the visual and aesthetic appeal, the needs and wants of the client, and how these designs impact users physically, socially, and mentally. Historically, the main focus of design has been on the engineering requirements while neglecting the individual's needs or desires for aesthetics which are seen as secondary concerns. However, these aspects still hold important roles in how the user views and connects with their own limb. In order to better understand the impact that aesthetic design has on the individual, this exploratory case study aimed to create custom-designed prosthetic limb covers for lower limb amputees. This poster includes the findings of the research and design process, including related works, the interview, design, and prototyping processes, as well as issues that occurred during the project and how they were either overcome or could be addressed in the future.
Co-designing the automation of Theatre Touch Tours

This paper presents the five-step co-design process of a prototype system for the automation of Touch Tours (TTs) service in theatre performances.
Learning ocean circulation models with reservoir computing

Two elementary models of ocean circulation, the well-known double-gyre stream function model and a single-layer quasi-geostrophic (QG) basin model, are used to generate flow data that sample a range of possible dynamical behavior for particular flow parameters. A reservoir computing (RC) machine learning algorithm then learns these models from the stream function time series. In the case of the QG model, a system of partial differential equations with three physically relevant dimensionless parameters is solved, including Munk- and Stommel-type solutions. The effectiveness of a RC approach to learning these ocean circulation models is evident from its ability to capture the characteristics of these ocean circulation models with limited data including predictive forecasts. Further assessment of the accuracy and usefulness of the RC approach is conducted by evaluating the role of both physical and numerical parameters and by comparison with particle trajectories and with well-established quantitative assessments, including finite-time Lyapunov exponents and proper orthogonal decomposition. The results show the capability of the methods outlined in this article to be applied to key research problems on ocean transport, such as predictive modeling or control.