Reviewerly/exHarmony huggingface dataset:
An Improved Method for Broiler Weight Estimation Integrating Multi-Feature with Gradient Boosting Decision Tree

Broiler weighing is an indispensable part in the broiler farming industry. Camera-based weighing systems have the potential of weighing a wider variety of broilers, which would avoid a costly platform weigher. However, most existing computer vision-based methods for broiler body weight estimation are not mature enough and focus on dealing with young broilers. Basically, the estimation error increases with the age of the broiler, as it is difficult to segment broiler instances due to the increasing density and great weight imbalance. To address the problem, this paper presents a framework for estimating the weight of broilers of great day age. First, the proposed framework uses Mask R-CNN (an instance segmentation network) to segment an ensemble of depth images, with each of which captured by 3D camera (RealSense D435i). Once the images of single or multiple instance broiler chickens are segmented, the extended artificial features and the learned features extracted by Customized Resnet50 (C-Resnet50) will be fused in a feature fusion module. Finally, the fused features are adopted to estimate the body weight of each instance broiler chicken by using Gradient Boosting Decision Tree (GBDT). Therefore, the proposed framework can effectively gain the instance broiler among many depth images of multiple broilers in the visual field despite the complex background. Meanwhile, the fusion of multi-feature can depict the target broiler more precisely, so that a simple model can also achieve excellent performance in estimating the body weight of the broiler. The extensive experimental results obtained from a large-scale depth image dataset of 63-day-old broilers demonstrate that the proposed framework has significantly increased the accuracy and robustness of weight estimation, thus achieving more superior performance compared with the state-of-the-art methods.
Efficient Fragile Privacy-Preserving Audio Watermarking Using Homomorphic Encryption

Audio has become increasingly important in modern social communication and mobile Internet. In cloud computing, practical cloud-based applications should achieve high computation efficiency and secure data protection simultaneously. In this paper, we study the application of an efficient encrypted audio fragile watermarking using homomorphic encryption and the batching technique SIMD in cloud computing. We firstly implement the algorithm of Haar wavelet transform in the encrypted domain (BS-HWT) using the batching technique SIMD with the fully homomorphic encryption scheme CKKS. By performing BS-HWT on the encrypted audio, we transform the encrypted audio signal into the encrypted frequency-domain coefficients. The encrypted fragile watermark is then embedded into the encrypted discrete wavelet transform domain. Our experimental results show that the proposed watermarking scheme is highly efficient and sensitive to common audio attacks. We also present the proposed scheme has a good ability of tamper localization.
Citizen-Centric Smart Planning: Case Study San Diego, California

San Diego is the second-largest in California State and the eighth-largest city in the United States. From a small border town, San Diego has grown to a thriving metropolis of nearly 1.4 million people (2019). San Diego smart city planning began with its first Comprehensive Plan as early as 1908 by John Nolen. The city has a unique history of well-planned development with active citizen participation. Over the years, the city's economic base has expanded from tourism and defense to include high technology research, manufacturing, and international trade. This chapter looks at the journey undertaken by the city with strategies, goals, and developments in transforming the city of San Diego into a world-class Smart City. Many innovative concepts and technologies are adopted and deployed for improving the resident's quality of life. Smart projects are undertaken in the city to improve public mobility and provide real-time data to help in better decision-making are discussed.
A Rational Hierarchical Quantum State Sharing Protocol

In this paper, we propose a rational hierarchical quantum state sharing protocol. First, the dealer shares an arbitrary two-particle entangled state with m high-power players and n low-power players through (m + n + 1)-particle cluster states. The high-power players and low-players have different authorities to reconstruct the quantum state. In detail, when a high-power player reconstructs the quantum state, he needs the help of the other high-power players and any low-power players; when a low-power player reconstructs the quantum, he needs the help of all the players. Then, in order to guarantee the fairness of the protocol, we make the players with different powers have the same possibility to be the player David who can reconstruct the quantum state shared by the dealer Alice, which means David is elected by all the rational players. Second, in the process of reconstructing the quantum state, when David is a high-power player, he does not need the help of all the low-power players. Under this circumstance, we analyze the game process and solve the bargaining equilibrium between David and the low-power players selected by David based on the Rubenstein bargaining model with incomplete information. Finally, our protocol achieves security, fairness, correctness, and strict Nash equilibrium, and conforms to the actual scenario.
Emergency Distance Learning at the University: Features of Applied Technologies

Purpose: The purpose of this work is to study the technologies used and distance learning opportunities of university students in pandemic conditions. Design/methodology/approach: The conceptual framework of the research was the model of technology adoption, the theory of social learning and the theory of diffusion of innovation. It was shown that the satisfaction of its participants is an important factor in the adoption of information and communication technologies in the educational process. In order to assess the experience of implementing emergency distance learning at the university, a situational analysis method was used. Findings: The work discloses the content of distance learning. Its connection with e-learning and development of information and communication technologies environment is shown. Comparative characteristics of distance learning and emergency distance learning were carried out. The case of the Russian university on introductions of the emergency distance learning is given during COVID-19 pandemic. Features of emergency implementation of Internet platforms in educational process are shown. Originality/value: The presented research of the features of the introduction of emergency distance learning in pandemic conditions allows assessing the prospects of using distance learning technologies at universities and the loyalty to them of the main participants in the educational process.
Predicting Heart Failure Onset in the General Population Using a Novel Datamining Artificial Intelligence Method

Background: We established an artificial intelligence (AI) method for identifying clinical factor combinations that predict cardiovascular events in patients with heart failure (HF). The present study aimed to identify combinations of clinical, medical, physical, and habitual factors that predict HF onset in the general population using a novel limitless-arity multiple-testing procedure (LAMP). In addition, we determined if an increase in the number of predictive combinations that apply to an individual increases the probability of developing HF.Methods: We recruited Japanese subjects without HF who received health check-ups in 2010. The subjects were followed annually for 4 years. Using 32,547 subjects, LAMP was performed to identify combinations of fewer than four factors that could potentially predict the onset of HF. The ability of the method to predict the probability of HF onset based on the number of matching predictive combinations was determined in 275,658 subjects. Findings: We identified 549 combinations that linked to the onset of HF. We classified the 275,658 subjects into six groups having 0, 1–50, 51–100, 101–150, 151–200 or 201–250 predictive combinations for the onset of HF. As the number of predictive combinations increased, the probability of HF progressively increased.Interpretation: We identified combinations of clinical parameters that predict HF onset in the general population. An increased number of matching predictive combinations for the onset of HF increased the probability of HF onset in the general population. This quantitative AI method predicts the probability of new-onset HF.Funding Information: This research was funded by Grants-in-Aid from the Japan Agency for Medical Research and Development.Declaration of Interests: Y.M., T.H., J.K., and T.W. have nothing to disclose. H.F. reports grants from Kitano Kadetto, outside the submitted work. M.K. reports grants from Japanese government, grants from Japan Heart Foundation, grants from Japan Cardiovascular Research Foundation, personal fees from Daiichi-sankyo, personal fees from Viatris, grants and personal fees from Ono, grants from Novartis, grants and personal fees from Tanabemitubishi, grants from Takeda, grants and personal fees from Astra Zeneca, grants and personal fees from Boehringer-ingelheim, grants from Kowa, personal fees from Otsuka, personal fees from Eli Lilly outside the submitted work. Ethics Approval Statement: On the basis of the Japanese Clinical Research Guidelines, the committee decided that patient informed consent was not essential for inclusion in this study because of the retrospective observational nature of the study. Instead, JMDC made a public announcement in accordance with the ethics committee’s request and the Japanese Clinical Research Guidelines. The study was performed following the principles of the Declaration of Helsinki and the Japanese Ethical Guidelines for Clinical Research.
Analysis of Text Classification Using Machine Learning and Deep Learning

This paper analyzes the performance of text classification using machine learning and deep learning models. The data is preprocessed, followed by text preprocessing. Natural language processing requires the conversion of text data to numerical vectors before they are passed to machine learning or deep learning models. Bag of words and Term Frequency and Inverse Document Frequency (TFIDF) techniques are used for converting text to a numeric vector. The machine learning models demonstrated are Naïve Bayes Classifier and Logistic Regression. Here, the Bag of words is used with Naïve Bayes Classifier, and TFIDF is used with the logistic regression model. The deep learning models demonstrated here are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The data set used consists of 1786 instances. Train test ratio used is 80:20. The performance of text classification models depends upon the dataset and how well its features are preprocessed as well as the selection of train test data. In this paper, four different algorithms are applied to the same dataset to analyze the performance of text classification.
AAGAN: Accuracy-Aware Generative Adversarial Network for Supervised Tasks

Owing to their great success in unsupervised tasks, generative adversarial networks (GANs) are widely adopted for supervised (conditional) image-generation tasks such as in-painting. Unfortunately, designing an objective function using GANs is not trivial. For supervised tasks, the generator trained only with GAN loss does not yield the corresponding target for the given input because GAN is trained to match the data distribution, not to find the exact answer. Therefore, the loss function of a generator is often formulated by linearly combining supervised loss and GAN loss as similar to multi-task learning, expecting that each loss’s weakness is complemented. Contrary to expectation, both losses cause a conflict in practice due to different optimum of each loss, yielding low objective and subjective image qualities. To address this problem, we empirically investigated the conflict caused by using a conventional GAN with pixel-wise losses; we then propose a novel (relativistic) accuracy-aware discriminator. Based on the proposed discriminator, we developed an accuracy-aware GAN (AAGAN) and proved its optimality under an ideal assumption. We then propose a relativistic accuracy-aware GAN (RAAGAN) by considering practical assumptions. Experimental results on supervised tasks demonstrated that the proposed schemes alleviated the competition between losses and outperformed conventional GANs in terms of both objective and subjective qualities.
Surrounding Environment Detection of an Intelligent Wheelchair Using Improved Convolutional Neural Networks

Electric wheelchairs, as mobile auxiliary equipment, are more intelligent by applying image processing, deep learning technique to improve the quality of life and self-independence of the disabled/elder people. In this paper, we have proposed an approach for object/person detection base on improved Yolov3 and image processing. We develop the dataset for training process. Our wheelchair is equipped a camera to detect the object on its path such as: pillar, bin, stair, people, door. The wheelchair experiment with person is implemented in our campus corridor. The results proved that our wheelchair system has ability to detect and recognize the trained object/people in the surroundings with a very high average accuracy.
Sigmoid Local Pattern for Robust Car and Pedestrian Detection

In this paper, we propose a solution that can solve the problems of detecting cars and pedestrians under extremely difficult conditions, providing higher object detection and recognition rate. By analyzing the existing encoding-base feature method, we found that they do not consider the relationship between each channel when encoding the local region. The encoder was used to extract the information in the local region by considering the feature for each channel, separately. The fusion feature is combined by only considering the linear relationship between RGB input features and local features. This combination is not good enough because it is difficult to estimate the linear parameters during the running time. Therefore, it is necessary to introduce an efficient feature-based method that can help to resolve the current limitations of the existing methods. After analyzing the performance of the existing local pattern, we found that we can encode the local region by tolerating the relationship between the center features and neighbor features using an idea of a sigmoid activation function in the deep learning model. The proposed sigmoid-based method achieved the accuracy of 78% under extremely difficult conditions while the existing yolov5-based method achieved 65% under extremely difficult conditions.
Create More Than One Design: A Case Study of Applying Ethnographic Approach to Explore the Potential Information Application in Senior Karaoke Community

In the commercial world, design ethnographers look into people's behaviors and thoughts to discover the broad patterns of everyday experiences that are specifically relevant for the conception, design and development of new products and services. An understanding of the social and cultural contexts generated through an ethnographic approach usually contributes to better product or service designs. However, compared to the classic ethnographic study, the design ethnography usually has a short duration and a quick investigation of the field sites without many details. Also, since the application of technology or potential technology is central to many modern design ethnography studies, those studies had a technology application target in the first place of researchers arriving at the field sites. In this paper, we argued for a more "classic" way of doing design ethnography, which means appropriating the open attitudes and immersive engagements with the field with a longer design ethnography research period than the common quick-and-dirty ethnography. This approach makes it possible to get an in-depth understanding of the field for better design that addresses the actual needs of users in their everyday lives. This paper reported a nine-month design ethnography study with a senior karaoke community, and the researchers kept their attitude open while collecting qualitative data. The research findings indicated the importance of researchers' immersion with the community and being natural to the data collection. Applying this ethnographic design approach in field study revealed some hidden patterns of social phenomena and inspired various perspectives of product designs.
Getting Stuck in the Status Quo Ante: Evidence from the Egyptian Economy

In this study, for the first time, to our knowledge, we use the propensity score matching algorithm to estimate the probability to remain 'stuck in the status-quo ante' across generations in Egypt. We use repeated cross-sectional data relative to a 20-year period from 1998 to 2018 to build transition matrices of intergenerational occupational mobility. The findings of the econometric analysis hint at a low degree of occupational mobility, with children of fathers in the agricultural sector or holding a blue- or white-collar job remaining linked to the profession of their fathers in most cases and experiencing only rarely upward mobility from agricultural to blue- and white-collar jobs.
An Enhanced Haze Removal: Using DCP and Enriched-Invariant Features

Removal of atmospheric turbulence from an image is a task that has been under study for some time now. When a picture is captured, it is corrupted due to the suspended light particles in the air. Fog-free or haze-free images are needed for better visualization and image information retrieval. This paper reports different mist evacuation calculations on how haze is taken out and improved perceivability. The defogging procedure we have further utilized is the dark channel prior algorithm framework which has proved to be a better option in obtaining the features from a hazed image. We have used the Laplacian-based gamma correction for the haze thickness estimation, which refines the generated transmission map. The next phase utilizes the White Patch Retinex technique that restores the image’s white balance for visibility restoration. This paper has introduced an additional step of identifying and enhancing the critical features in the resulting dehazed image using the SIFT descriptors. Analysis exhibits that the current methodological analysis creates dehazed outcomes that have an improved ocular calibre with sharpened and contrast features compared to the simple frameworks available.
Shifting to Smart Conferences: Author Perceptions of Presenting Virtually at the 2021 Associated Schools of Construction Conference

The COVID-19 pandemic caused significant disruption to academic institutions as well as the conferences associated with those disciplines. The Associated Schools of Construction (ASC) was among those affected and was forced to cancel their 2020 conference and shift their 2021 conference to a Smart Conference. This study collected feedback from the authors to gauge their perceptions of presenting in a virtual format to determine if the ASC should continue to integrate smart options for future events post-pandemic. The results indicated that the smart option for presenting conference proceedings papers was well received, and that almost 60% of the respondents indicated that if a smart option was available in the future, they would be more inclined to submit a paper for publication. These results indicate that an opportunity exists for the ASC to broaden its engagement with faculty members and further its mission to advance construction education by embracing Smart Conference techniques at future events.
Optimizing Convolutional Neural Networks Architecture Using a Modified Particle Swarm Optimization for Image Classification

Although the Convolutional Neural Network (CNN) has been shown to be effective in solving image classification tasks, its architecture is often difficult to design (i.e., much tuning is required for optimization) due to the large number of hyperparameters that require careful human intervention to determine their optimal values. In this paper, we propose an approach to create the best CNN design and reduce human intervention during this process. The approach uses an optimization algorithm derived from the particle swarm without velocity (PSWV) equation to significantly reduce the number of iterations to determine the optimal CNN hyperparameters and architecture for a given dataset. A novel strategy for updating a particle swarm without velocity using particle swarm optimization (PSO) with CNNs was developed. In this approach, we adopted the variable-length coding strategy to represent the particles in the population. We proposed an algorithm for a suitable process that allows fast convergence to the best architecture. The proposed algorithm was studied and compared with several recent algorithms in the literature. Experiments on eight benchmark datasets for classification tasks have demonstrated the efficiency and practicality of our approach in finding the best CNN architecture with less human intervention.
Certificateless Identity Management and Authentication Scheme Based on Blockchain Technology

Abstract Identity management and authentication in cyberspace is crucial for all forms of remote communication. The traditional authentication technology has great security risks due to its central third-party structure, such as single point of failure, malicious server attacks and so on. The emergence of blockchain technology provides a new way of thinking to solve this problem. This paper focuses on the identity management and authentication scheme based on blockchain technology. Using the decentralized, open and transparent characteristics of blockchain to make up for the shortcomings of traditional identity management and authentication mechanisms. In this paper, we analyze the BIDaaS [1] identity management and authentication scheme proposed by Jong-Hyouk and point out the obvious shortcomings of the scheme, such as suffer impersonating attack simply, virtual identities are not unique. We combine the specificity of biological characteristics to implement a unique virtual identity on the chain and improve the off-chain identity authentication process using a certificateless scheme to build a reasonable and secure identity management and authentication scheme, which realizes two-way authentication and session key agreement. The analysis shows that the scheme has a high level of safety.
An Audio Data-Driven Roadway Digital Twin and its Underlying Framework for Digitized Transportation Construction Environment

With the increasing need to build and maintain transportation systems, project managers are required to manage multiple construction projects simultaneously. To improve the current condition, this study proposes an audio and GPS data-driven roadway digital twin framework for a digitized transportation construction environment. The authors investigated the latent characteristics of a digital twin for construction projects that formed the fundamental principles for establishing a digitized work environment. Active construction activities were identified in real-time using a deep neural network-based audio classification which served as the primary method for seamlessly linking the physical and the virtual worlds. With obtaining real-time information inherent in the digital twin concept, this study establishes a fundamental framework for developing digital twin prototypes that can provide autonomous insight into a worksite's real-time and future conditions. Audio-based activity identification and GPS-based tracking allowed the estimation of real-time progress made in roadway construction activities. The field experiments conducted in this study show the system’s capability to provide critical information and real-time simulation data for analyzing and predicting future work logistics and possible bottlenecks.