Reviewerly/exHarmony huggingface dataset:
Applying blockchain technology to ensure compliance with sustainability standards in the PPE multi-tier supply chain

Because of the Covid-19 pandemic, urgent surging demand for healthcare products such as personal protective equipment (PPE) has caused significant challenges for multi-tier supply chain management. Although a given firm may predominantly focus on an arms-length solution by targeting the first-tier supplier, the firm can still struggle with extended multi-tier suppliers it cannot choose which use unsustainable practices. One key goal is compliance across various dimensions with production, environmental and labour standards across the multi-tier supply chain, a goal that blockchain technology can be applied to manage. Based on a comprehensive literature review, this research develops a system architecture of blockchain-based multi-tier sustainable supply chain management in the PPE industry designed to identify and coordinate standards in production and social and environmental sustainability in multi-tier PPE supply chains. The architecture was validated by theoretical basis, expert opinions and technical solutions. We conclude with managerial implications for implementing blockchain technology to advance sustainable multi-tier supply chain practices.
Deep Learning Approaches Based on Transformer Architectures for Image Captioning Tasks

This paper focuses on visual attention, a state-of-the-art approach for image captioning tasks within the computer vision research area.We study the impact that different hyperparemeter configurations on an encoder-decoder visual attention architecture in terms of efficiency.Results show that the correct selection of both the cost function and the gradient-based optimizer can significantly impact the captioning results.Our system considers the cross-entropy, Kullback-Leibler divergence, mean squared error, and negative log-likelihood loss functions; the adaptive momentum (Adam), AdamW, RMSprop, stochastic gradient descent, and Adadelta optimizers.Experimentation shows that a combination of cross-entropy with Adam is the best alternative returning a Top-5 accuracy value of 73.092 and a BLEU-4 value of 20.10.Furthermore, a comparative analysis of alternative convolutional architectures demonstrated their performance as an encoder.Our results show that ResNext-101 stands out with a Top-5 accuracy of 73.128 and a BLEU-4 of 19.80; positioning itself as the best option when looking for the optimum captioning quality.However, MobileNetV3 proved to be a much more compact alternative with 2,971,952 parameters and 0.23 Giga fixed-point Multiply-Accumulate operations per Second (GMACS).Consequently, MobileNetV3 offers a competitive output quality at the cost of lower computational performance, supported by values of 19.50 and 72.928 for the BLEU-4 and Top-5 accuracy, respectively.Finally, when testing vision transformer (ViT), and data-efficient image transformer (DeiT) models to replace the convolutional component of the architecture, DeiT achieved an improvement over ViT, obtaining a value of 34.44 in the BLEU-4 metric.
Protecting Personal Healthcare Record Using Blockchain &amp; Federated Learning Technologies

For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.
Fake Image Detection in Twitter using Flood Fill Algorithm and Deep Neural Networks

Background:One of the primary concerns in online social networks is fake image or fake colorized image diffusion, which allows anyone to add, remove, and alter the images.Purpose:Till now it is not possible to develop a model or a technique that can accurately recognize or categorize images as fake or genuine.Method:In this paper we are trying to utilize the flood fill algorithm to In this paper we are trying to utilize the flood fill algorithm to highlight the forged object in the image and a Deep Learning based solution is proposed to detect whether the image is fake or genuine. We collect twitter dataset, which is given as input to the deep learning models and train them to identify whether the image is fake or genuine.Result:Experimental evaluations showed that the proposed framework can detect the fake images that have been diffused in Twitter with 96&#x0025; accuracy.
Application of choosing by advantages to determine the optimal site for solar power plants

Abstract Solar energy is a critical component of the energy development strategy. The site selection for solar power plants has a significant impact on the cost of energy production. A favorable situation would result in significant cost savings and increased electricity generation efficiency. California is located in the southwest region of the United States of America and is blessed with an abundance of sunlight. In recent years, the state's economy and population have expanded quickly, resulting in an increased need for power. This study examines the south of California as a possibly well-suited site for the constructing large solar power plants to meet the local electricity needs. To begin, this article imposed some limits on the selection of three potential sites for constructing solar power plants (S1, S2, and S3). Then, a systematic approach for solar power plant site selection was presented, focusing on five major factors (economic, technological, social, geographical, and environmental). This is the first time that the choosing by advantages (CBA) method has been used to determine the optimal sites for solar power plant construction, with the possible sites ranked as S2 &gt; S1 &gt; S3. The results were then compared with traditional methods such as the multi-criteria decision-making method. The findings of this study suggest that the CBA method not only streamlines the solar power plant site selection process but also closely aligns with the objectives and desires of the investors.
Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management

Healthcare professionals, patients, and other stakeholders have been storing medical prescriptions and other relevant reports electronically. These reports contain the personal information of the patients, which is sensitive data. Therefore, there exists a need to store these records in a decentralized model (using IPFS and Ethereum decentralized application) to provide data and identity protection. Many patients recurrently visit doctors and undergo treatments while receiving different prescriptions and reports. In case of an emergency, the doctors and attendants may need and benefit from the patients' medical history. However, they are unable to go through medical history and a wide range of previous reports and prescriptions due to time constraints. In this paper, we propose an AI-assisted blockchain-based framework in which the stored medical records (handwritten prescriptions, printed prescriptions, and printed reports) are stored and processed using various AI techniques like optical character recognition (OCR) to form a single patient medical history report. The report concisely presents only the crucial information for convenience and perusal and is stored securely over a decentralized blockchain network for later use.
Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and Cr values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems.
Sympathy for the digital: Influence of synthetic voice on affinity, social presence and empathy for photorealistic virtual humans

In this paper, we investigate the effect of a realism mismatch in the voice and appearance of a photorealistic virtual character in both immersive and screen-mediated virtual contexts. While many studies have investigated voice attributes for robots, not much is known about the effect voice naturalness has on the perception of realistic virtual characters. We conducted the first experiment in Virtual Reality (VR) with over two hundred participants investigating the mismatch between realistic appearance and unrealistic voice on the feeling of presence, and the emotional response of the user to the character expressing a strong negative emotion. We predicted that the mismatched voice would lower social presence and cause users to have a negative emotional reaction and feelings of discomfort towards the character. We found that the concern for the virtual character was indeed altered by the unnatural voice, though interestingly it did not affect social presence. The second experiment was conducted with a view towards heightening the appearance realism of the same character for the same scenarios, with an additional lower level of voice realism employed to strengthen the mismatch of perceptual cues. While voice type did not appear to impact reports of empathic responses towards the character, there was an observed effect of voice realism on reported social presence, which was not detected in the first study. There were also significant results on affinity and voice trait measurements that provide evidence in support of perceptual mismatch theories of the Uncanny Valley.
A Survey of Deep Learning on Mobile Devices: Applications, Optimizations, Challenges, and Research Opportunities

Deep learning (DL) has demonstrated great performance in various applications on powerful computers and servers. Recently, with the advancement of more powerful mobile devices (e.g., smartphones and touch pads), researchers are seeking DL solutions that could be deployed on mobile devices. Compared to traditional DL solutions using cloud servers, deploying DL on mobile devices have unique advantages in data privacy, communication overhead, and system cost. This article provides a comprehensive survey for the current studies of adopting and deploying DL on mobile devices. Specifically, we summarize and compare the state-of-the-art DL techniques on mobile devices in various application domains involving vision, speech/speaker recognition, human activity recognition, transportation mode detection, and security. We generalize an optimization pipeline for bringing DL to mobile devices, including model-oriented optimization mechanisms (e.g., pruning and quantization) and nonmodel-oriented optimization mechanisms (e.g., software accelerator and hardware design). Moreover, we summarize popular DL libraries regarding their support to state-of-the-art models (software) and processors (hardware). Based on our summarization, we further provide insights into potential research opportunities for developing DL for mobile devices.
3D human motion prediction: A survey

3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.
An integrated forecasting system based on knee-based multi-objective optimization for solar radiation interval forecasting

In the context of global carbon neutrality, solar energy deserves more attention as a clean energy source. Accurate solar radiation forecasting techniques can provide favorable theoretical support for the siting and management of solar power plants. This study aims to establish an integrated interval forecasting system to obtain better solar radiation interval forecasting results. In the proposed system, a feature extraction method based on mutual information is used to reduce the dimension of correlation variables. Then, a dual-channel input structure is constructed with the dimensionally reduced variables and lagged terms of solar radiation. A hybrid kernel relevance vector machine is developed to construct the forecasting intervals of solar radiation. In the hybrid kernel, a newly developed knee-based multi-objective jellyfish search is used to determine the weight of different kernel functions. According to the experiment results based on three American solar sites, it is demonstrated that the proposed system can obtain superior solar radiation forecasting intervals which provide higher interval coverage rate and narrower interval width. Through the optimization of the weight of the kernel function, we can obtain a forecasting interval that is more balanced between the forecasting coverage and the width of the forecasting intervals.
Towards a better understanding of annotation tools for medical imaging: a survey

Medical imaging refers to several different technologies that are used to view the human body to diagnose, monitor, or treat medical conditions. It requires significant expertise to efficiently and correctly interpret the images generated by each of these technologies, which among others include radiography, ultrasound, and magnetic resonance imaging. Deep learning and machine learning techniques provide different solutions for medical image interpretation including those associated with detection and diagnosis. Despite the huge success of deep learning algorithms in image analysis, training algorithms to reach human-level performance in these tasks depends on the availability of large amounts of high-quality training data, including high-quality annotations to serve as ground-truth. Different annotation tools have been developed to assist with the annotation process. In this survey, we present the currently available annotation tools for medical imaging, including descriptions of graphical user interfaces (GUI) and supporting instruments. The main contribution of this study is to provide an intensive review of the popular annotation tools and show their successful usage in annotating medical imaging dataset to guide researchers in this area.
Tripartite Quantum Correlations under Power‐Law and Random Telegraph Noises: Collective Effects of Markovian and Non‐Markovian Classical Fields

Abstract The collective effects of Markovian and non‐Markovian classical fields on the dynamics of entanglement, coherence, and quantum state mixing in a three‐qubit mixed entangled state are examined. Three independent local fields are considered to be influenced by the decoherence effects of two different mixed noisy models: Markovian maximized and non‐Markovian maximized local random configurations. Power‐law noise characterizes two classical environments in the first case, whereas random telegraph noise governs the third. Random telegraph noise rules two classical environments in the latter example, whereas power‐law noise governs the third. In classical fields, non‐Markovian effects are more robust and have a stronger character than Markovian effects, yet both are driven by relevant noises. Non‐Markovianity vanishes at the upper bound of the Markovian noise parameters. Markovian effects are found to be vulnerable to the environments non‐Markovianity and longer memory features. The strength of Markovianity and non‐Markovianity is only moderately affected by the type of environment, but noise parameter optimization has a significant impact. Initial and final limits are provided for the restriction and relaxation in Markovianity and non‐Markovianity of the classical fields for the actual deployment of non‐local protocols.
Multi-criteria decision making for different concentrated solar thermal power technologies

Solar thermal power technologies are promising renewable energy sources that could replace the conventional fossil fuel power plants. Parabolic trough collector (PTC), linear Fresnel reflector (LFR), solar power tower (SPT), and Solar power dish (SPD) are the commercial concentrated solar thermal power technologies. Technical, economical, and environmental aspects are varied from one technology to another. Deciding the best solar thermal power technology is quite important for the commercial applications. In order to decide the best technology, based on 22 criterions including the technical (9), economical (2), and environmental (11) aspects, different multi-criteria decision making (MCDM) models were applied. The main parameters of the MCDM model that determine the rating of alternatives include the choice of the method of normalization of the matrix of decisions, the choice of the method for evaluating the weights of the criteria and the choice of the method of aggregation of attributes when determining the indicator of the effectiveness of alternatives. Three different groups of MCDM methods are defined as a method for aggregating attributes in determining the performance indicator of alternatives: based on a weighted sum approach (WSM, WPM, WAPRAS), based on the concept of distance or distance from the desired object (TOPSIS, GRA, VIKOR), and based on the approach of aggregating local ratings based on setting the preference function (PROMETHEE-II). According to the results of all models, alternative A4 that represents the solar power dish technology is the best selection among the different solar technologies. A4 is winner (first rank I) for about 65% of considered cases followed by A3 that represents solar power tower technology. A3 is winner (first rank I) for about 35% of considered cases.
Reductions in sickness with repeated exposure to HMD-based virtual reality appear to be game-specific

Abstract While head-mounted display (HMD) based gaming is often limited by cybersickness, research suggests that repeated exposure to virtual reality (VR) can reduce the severity of these symptoms. This study was therefore aimed at: (1) examining the exposure conditions required to reduce cybersickness during HMD VR; and (2) learning whether such reductions generalise from one HMD VR game to another. Our participants played two commercially-available HMD VR video games over two consecutive days. Their first exposure to HMD VR on both days was always to a 15-min virtual rollercoaster ride. On Day 1, half of our participants also played a virtual climbing game for 15-min, while the rest of them finished testing early. Participants in the latter group were only exposed to the climbing game late on Day 2. We found that sickness was significantly reduced for our participants on their second exposure to the virtual rollercoaster. However, sickness to the rollercoaster on Day 2 was unaffected by whether they had played the climbing game on Day 1. Sickness during virtual climbing was also unaffected by group differences in exposure to the virtual rollercoaster. This convergent evidence suggested that the reductions in cybersickness produced by repeated exposure to HMD VR were game- specific . While these benefits did not generalise to the second game, two 15-min exposures to the same HMD VR game was sufficient to significantly reduce cybersickness in this study.
Spatial distribution based on optimal interpolation techniques and assessment of contamination risk for toxic metals in the surface soil

The condition of the soil environment is critical for human health and agricultural sustainability. As a result, the environmental and ecological issues impacting the soils throughout the world are receiving more attention. This research focuses on local site-specific studies in Cerrito Blanco, Matehuala municipality, San Luis Potosi, Mexico, and describes different types of GIS interpolation techniques, multivariate statistical analysis, and various contamination indices to investigate the relationship between predictive accuracy, levels of contamination risk, and soil toxic metal elements variation. Inductively coupled plasma optical emission spectroscopy (ICP-EOS) used to test 39 digested surface soil samples for significant toxic metals (Ag, Cd, Co, Cr, Li, and Ni) after suitable dilution with deionised water. According to the results, we found that only the mean value of cadmium (Cd) exceeded the permissible standard value. After evaluating the four types of interpolation techniques, the Inverse Distance Weighting (IDW) was determined to be the optimal interpolation model for assessing the spatial distribution patterns of toxic metal concentration in the research area. The calculated contamination risk indices showed no significant high contamination risk due to soil-borne toxic metals. These results provide a comprehensive analysis of the impact of past mining activities on toxic metal concentrations in non-cultivated surface soil.