Reviewerly/exHarmony huggingface dataset:
NikshayChain: A Blockchain-Based Proposal for Tuberculosis Data Management in India

A recent development in the Internet of Things (IoT) has accelerated the application of IoT-based solutions in healthcare. Next-Gen networks and IoT, supported by the development of technologies such as Artificial Intelligence (AI) and blockchain, have propelled the growth of e-health applications. However, there are some unique challenges in the widespread acceptance of IoT in healthcare. Safe storage, transfer, authorized access control, and the privacy and security aspects of patient data management are crucial barriers to the widespread adoption of IoT in healthcare. This makes it necessary to identify current issues in the various health data management systems to develop novel healthcare solutions. As a case study, this work considers a scheme launched by the Government of India for tuberculosis care called Nikshay Poshan Yojana (NPY). It is a web-based Direct Benefit Transfer scheme to provide a nutritional incentive of INR 500/- per month to all tuberculosis patients. The main objective of this work is to identify the current implementation challenges of the NPY scheme from patient and healthcare stakeholder perspectives and proposes a blockchain-based architecture called NikshayChain for sharing patient medical reports and bank details among several healthcare stakeholders within or across Indian cities. The proposed architecture accelerates healthcare stakeholder productivity by reducing workload and overall costs while ensuring effective data management. This architecture can significantly improve medical care, incentive transfer, and data verification, propelling the use of e-health applications.
Total Differential Photometric Mesh Refinement with Self-Adapted Mesh Denoising

Variational mesh refinement is a crucial step in multiview 3D reconstruction. Existing algorithms either focus on recovering mesh details or focus on suppressing noise. Approaches with consideration of both are lacking. To address this limitation, we proposed a new variational mesh refinement method named total differential mesh refinement (TDR), which mainly included two improvements. First, the traditional partial-differential photo-consistency gradient used in the variational mesh refinement method was replaced by the proposed total-differential photo-consistency gradient. With consideration of the photo-consistency correlation between adjacent pixels, our method can make photo-consistency achieve a more effective convergence than traditional approaches. Second, we introduced the bilateral normal filter with a novel self-adaptive mesh denoising strategy into the variational mesh refinement. This strategy maintains a balance between detail preservation and effective denoising via the zero-normalized cross-correlation (ZNCC) map. Various experiments demonstrated that our method is superior to traditional variational mesh refinement approaches in both accuracy and denoising effect. Moreover, compared with the mesh generated by open-source and commercial software (Context Capture), our meshes are more detailed, regular, and smooth.
Innovative Design and Kinematic Characteristics Analysis of Floating Mobile Chassis of Inspection Robot

In view of the problems that exist in the working plane of the inspection robot equipped with precision instruments that cannot always maintain a stable state when moving on a complex road surface, a floating mobile chassis was designed based on the Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) theory, and the floating suspension device was also optimized based on the substance field. The kinematic model of the floating mobile chassis was established, and the obstacle-surmounting analysis has been carried out on complex road conditions such as the boss and trench. The dynamic model and mobile performance evaluation model of the obstacle crossing wheel are established. The prototype of the non-floating mobile chassis and the prototype of the floating mobile chassis were respectively established in ADAMS, and the motion comparison simulation analysis of boss, trench crossing and complex road conditions were also carried out. The results showed that the floating mobile chassis has strong adaptive performance, and the stability of the working plane can always be maintained when crossing obstacles.
Navigation of Multiple Disk-Shaped Robots with Independent Goals within Obstacle-Cluttered Environments

In this work, we propose a hybrid control scheme to address the navigation problem for a team of disk-shaped robotic platforms operating within an obstacle-cluttered planar workspace. Given an initial and a desired configuration of the system, we devise a hierarchical cell decomposition methodology which is able to determine which regions of the configuration space need to be further subdivided at each iteration, thus avoiding redundant cell expansions. Furthermore, given a sequence of free configuration space cells with an arbitrary connectedness and shape, we employ harmonic transformations and harmonic potential fields to accomplish safe transitions between adjacent cells, thus ensuring almost-global convergence to the desired configuration. Finally, we present the comparative simulation results that demonstrate the efficacy of the proposed control scheme and its superiority in terms of complexity while yielding a satisfactory performance without incorporating optimization in the selection of the paths.
Techno-Economic Analysis of Electricity Generation by Photovoltaic Power Plants Equipped with Trackers in Iran

The use of solar trackers can help increase the production and time period of electricity generation in photovoltaic power plants. Different types of trackers in terms of rotation mechanisms and sun tracking systems have been used in these types of power plants in recent years. In this article, a comparison has made between the electricity produced of fixed and tracking structures in a number of power plants which are located in different cities of Iran but in similar geographical locations. Following this, software modeling is used to evaluate various sun tracking scenarios in the design of sample power plants. Finally, a techno-economic analysis has been made to evaluate the decision-making regarding the construction of such power plants in Iran using this technology. Using east-west detectors has had a positive effect on increasing the production of power plants, especially in summer. Due to the higher initial costs of using this technology in the power plants, as well as the higher maintenance costs, the economization of the power plants’ business plan is extremely dependent on other economic conditions governing the project. Using trackers alone cannot lead to a better situation in a project’s lifetime. From the national point of view, if domestic companies can produce east-west single-axis tracking technology at an acceptable cost and provide related services in the long term, it would be beneficial for the power grid. One of the attractive proposals for the power plants under construction will be the use of this technology, because of additional electricity production in peak hours in summer. Annual experimental data from different tracking/fixed PV power plant have been used for the first time in this techno-economic investigations, they are validated with the simulation process, and the results have been predicted for a wide range of tracking scenarios. Techno-economic analyses of this type of power plant are an essential need for policy makers and investors in Iran’s energy market.
Assessment of Soil Degradation and Hazards of Some Heavy Metals, Using Remote Sensing and GIS Techniques in the Northern Part of the Nile Delta, Egypt

Soil degradation and pollution is one of the main problems threatening the sustainable development of agriculture. This study used remote sensing and geographic information system (GIS) techniques to assess the risks of soil degradation and the risks of heavy metals in some soils north of the Nile Delta. The study area suffers from salinity, alkalinity, and water logging, so a spatial degradation model was used. Relying on landsat ETM+ images and the digital elevation model (DEM), it was possible to produce a geomorphological map, and it showed that the studied area consists of two landscapes, i.e., flood plain and lacustrine plain. The results indicated that salinization, alkalization, compaction, and water logging were the main types of soil degradation in the studied area. The spatial land degradation model showed that 16.61% of soils were affected by low degrees of degradation, 74.03% were affected by moderate degrees, and 9.36% were affected by high degrees of degradation. The studied area was affected by chemical degradation risks between low and high at 90.62% and 9.37%, respectively, while the physical degradation risks varied between low, moderate, high, and very high with percentages of 9.37%, 41.53%, 40.14%, and 8.93%, respectively. The environmental risks of heavy metals were assessed in the studied area using pollution indices including, the enrichment factor (EF), the pollution load index (PLI), and the potential ecological risk index (PER).
Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.
Spatio-Temporal Forecasting of Global Horizontal Irradiance Using Bayesian Inference

Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the research on solar irradiance forecasting has been based on a single-site analysis. It is crucial to explore multisite modeling to capture variations in weather conditions between various sites, thereby producing a more robust model. In this research, we propose the use of spatial regression coupled with Gaussian Process Regression (GP Spatial) and the GP Autoregressive Spatial model (GP-AR Spatial) for the prediction of GHI using data from seven radiometric stations from South Africa and one from Namibia. The results of the proposed methods were compared with a benchmark model, the Linear Spatial Temporal Regression (LSTR) model. Five validation sets each comprised of three stations were chosen. For each validation set, the remaining five stations were used for training. Based on root mean square error, the GP model gave the most accurate forecasts across the validation sets. These results were confirmed by the statistical significance tests using the Giacommini–White test. In terms of coverage probability, there was a 100% coverage on three validation sets and the other two had 97% and 99%. The GP model dominated the other two models. One of the study’s contributions is using standardized forecasts and including a nonlinear trend covariate, which improved the accuracy of the forecasts. The forecasts were combined using a monotone composite quantile regression neural network and a quantile generalized additive model. This modeling framework could be useful to power utility companies in making informed decisions when planning power grid management, including large-scale solar power integration onto the power grid.
Imbalanced classification for protein subcellular localization with multilabel oversampling

Subcellular localization of human proteins is essential to comprehend their functions and roles in physiological processes, which in turn helps in diagnostic and prognostic studies of pathological conditions and impacts clinical decision-making. Since proteins reside at multiple locations at the same time and few subcellular locations host far more proteins than other locations, the computational task for their subcellular localization is to train a multilabel classifier while handling data imbalance. In imbalanced data, minority classes are underrepresented, thus leading to a heavy bias towards the majority classes and the degradation of predictive capability for the minority classes. Furthermore, data imbalance in multilabel settings is an even more complex problem due to the coexistence of majority and minority classes.Our studies reveal that based on the extent of concurrence of majority and minority classes, oversampling of minority samples through appropriate data augmentation techniques holds promising scope for boosting the classification performance for the minority classes. We measured the magnitude of data imbalance per class and the concurrence of majority and minority classes in the dataset. Based on the obtained values, we identified minority and medium classes, and a new oversampling method is proposed that includes non-linear mixup, geometric and colour transformations for data augmentation and a sampling approach to prepare minibatches. Performance evaluation on the Human Protein Atlas Kaggle challenge dataset shows that the proposed method is capable of achieving better predictions for minority classes than existing methods.Data used in this study are available at https://www.kaggle.com/competitions/human-protein-atlas-image-classification/data. Source code is available at https://github.com/priyarana/Protein-subcellular-localisation-method.Supplementary data are available at Bioinformatics online.
Transformers for Imbalanced Baggage Threat Recognition

Baggage screening for identifying concealed threat items has become inevitable for maintaining public security at high-risk locations, including airports and border checkpoints. However, manual screening needs both expertise and experience, in addition to being cumbersome and prone to errors, encouraging researchers to invest in developing autonomous baggage screening systems. However, these approaches based on CNNs prioritize localized interactions due to their solid inductive bias, restricting their ability to model object-level and image-wide context. Hence, in this paper, we explore Transformers for baggage threat recognition to exploit their ability to model global features to capture concealed threat items within cluttered and tightly packed baggage scans and thereby learn enhanced representations to identify the abnormal scans. Further, the property of visual transformers to prioritize shape over textural information render them a suitable candidate for threat recognition from baggage scans since they lack texture and have low contrast. We also explore the potential of visual transformers in heavily imbalanced settings. Further, we have also implemented a weakly supervised localization approach to identify the input regions contributing to the abnormality classification. The proposed approach surpasses the state-of-art methods achieving 0.979 on Compass-XP, and 0.873 on SIXray, in terms of F1 score.
General stabilizer approach for constructing highly entangled graph states

Highly entangled multipartite states such as $k$-uniform ($k$-UNI) and absolutely maximally entangled (AME) states serve as critical resources in quantum networking and other quantum information applications. However, there does not yet exist a complete classification of such states, and much remains unknown about their entanglement structure. Here we substantially broaden the class of known $k$-UNI and AME states by introducing a method for explicitly constructing such states that combines classical error correcting codes and qudit graph states. This method in fact constitutes a general recipe for obtaining multipartitite entangled states from classical codes. Furthermore, we show that at least for a large subset of the class of $k$-UNI states that we present, the states are inequivalent under stochastic local operations and classical communication. This subset is defined by an iterative procedure for constructing a hierarchy of $k$-UNI graph states.
Poster: Towards Edge-Intelligent Wearable for early Drowning Detection

This research aims to develop a wearable monitoring system that can help in monitoring respiratory distress. Drowning-related accidents are 3rd leading cause of unintentional death. Among the victims most of them are children and world statistics state that there are 4000 fatalities per day in children due to drowning. In this research project, we propose a wearable system that can help in monitoring the heart rate, oxygen level, and body temperature variability, along with the accelerometer. Our proposed framework is designed as an anklet that can help detect drowning incidents early.
Comparing VR and Desktop 360 Video Museum Tours

We investigate the user experience of taking a remote museum tour with 360 video technologies. We compare the experience of viewing a 360 video feed on a laptop screen vs. a 360 virtual reality (VR) video experienced through a head-mounted display (HMD). Our salient findings from a user study (n = 10) highlight that HMD VR provides a better immersion and sense of control for users. However, the HMD VR user experience suffers from the lack of personal contact, such as eye contact with the guide, discontinuities in the visual presentation, and missing multimodal contextual cues. The research contributes to the design of remote tourism services.
Can you please cover both the "smart" and the "home"? Exploring expectations on smart homes considering changing needs

The number of connected devices in-home has increased rapidly, and connected solutions in smart homes do not yet cover needs of all inhabitants. We explored future interfaces that blend seamlessly with the home interior in co-creation sessions with people from diverse co-living situations. The results contribute to acknowledging a home’s complexity considering shared households as places where many needs meet and possibly contradict each other. Based on findings from three workshop sessions with a total of 12 participants, we present two themes of relevance when designing for smart homes. Covering both, a smart home can adapt to users’ needs and become valuable members of multi-people households while maintaining the characteristics of a home for all individuals.
Practical anonymization for data streams: z-anonymity and relation with k-anonymity

With the advent of big data and the emergence of data markets, preserving individuals’ privacy has become of utmost importance. The classical response to this need is anonymization, i.e., sanitizing the information that, directly or indirectly, can allow users’ re-identification. Among the various approaches, k-anonymity provides a simple and easy-to-understand protection. However, k-anonymity is challenging to achieve in a continuous stream of data and scales poorly when the number of attributes becomes high. In this paper, we study a novel anonymization property called z-anonymity that we explicitly design to deal with data streams, i.e., where the decision to publish a given attribute (atomic information) is made in real time. The idea at the base of z-anonymity is to release such attribute about a user only if at least z−1 other users have exposed the same attribute in a past time window. Depending on the value of z, the output stream results k-anonymized with a certain probability. To this end, we present a probabilistic model to map the z-anonymity into the k-anonymity property. The model is not only helpful in studying the z-anonymity property, but also general enough to evaluate the probability of achieving k-anonymity in data streams, resulting in a generic contribution.
Teachers' Attitude Scale Towards Metaverse Use: A Scale Development Study

The metaverse, or virtual universe, is a hypothetical iteration of the internet that supports persistent online 3D virtual environments through traditional personal computers as well as virtual and augmented reality devices. The "metaverse", which has been on the agenda in recent days, had a great impact both in Turkey and in the world. In other words, a virtual world, represented by a metaverse avatar, where users can shop, socialize, participate in leisure activities and learn. The concept of metaverse, which came to the fore with billions of dollars of investment plans by companies such as Facebook, Microsoft, Roblox and Epic, is seen as the next stage in the development of the internet. It is possible to access this virtual reality universe using VR headsets, augmented reality (AR), smart watches and smart glasses. With the increasing popularity of online learning, educators, trainers, institutions are looking for ways to make distance learning more interesting and interactive. There are 4 important ways that the metadata store can help with this; creating an engaging and life-like online classroom, encourage communication, supporting immersive learning and enriching gamification. Due to the effect of the pandemic on digitalization, many different models have been developed in education methods and three-dimensional models have been focused on, where students' readiness levels and interaction with each other are higher. Metaverse is one of these methods. In this sense, it is important to get opinions from teachers and students, who are the most important stakeholders of education, in the use of metaverse technologies, which make a quick introduction to learning and teaching processes, in educational environments. It is important to determine the readiness levels of students and teachers, especially for the use of metaverse technologies in teaching environments. In the light of all these, this study was based on evaluating the teachers' attitudes towards metaverse technologies by considering the teacher dimension. It is aimed to present a valid and reliable scale that can be developed for the relevant subject. The validity and reliability studies of the scale were carried out on a total of 301 computers teachers working as permanent teachers in a city in Türkiye. The three-factor structure (perceived benefit, readiness and satisfaction) of the scale with an eigenvalue greater than 1 explains 78.42% of the total variance. A scale whose validity and reliability have been proven according to the results of EFA and CFA has been added to the literature. Moreover it is foreseen that the research will be important since it is the first scale developed in Türkiye on the metaverse.
Quantum Master Equation for a Lossy Josephson Traveling-Wave Parametric Amplifier

In this paper, we present a theoretical framework for investigating losses and thermal fluctuations in a Josephson traveling-wave parametric amplifier (JTWPA). Our model is based on a discrete-mode Hamiltonian which includes a four-wave-mixing process and system-reservoir interactions. From this Hamiltonian, we derive a quantum master equation that describes the lossy Josephson-junction-embedded transmission line. The resulting equation of motion for the reduced density operator is applied for evaluating the average photon number in the signal mode. We present an analytic solution for the case of a dispersionless transmission line, where the phase-modulation effects can be neglected, and evaluate the photon number spectrum of a JTWPA structure with parameters from the literature. The quantum master equation is especially advantageous for a direct treatment of the expectation values of the photon number, in comparison to other strategies.