Reviewerly/exHarmony huggingface dataset:
Smart healthcare digital transformation during the Covid-19 pandemic

The Covid-19 pandemic is revolutionizing our lives, forcing us to change our habits and reshaping our relationship with the healthcare system. Digitalization plays a crucial role, allowing for timely and aseptic connections that improve the efficiency of many healthcare processes. It shortens the supply chain—especially the last mile, from the hospital to the patient—and boosts the value chain that coalesces around value cocreating stakeholders, starting from the traditionally neglected patients. The digital healthcare value chain starts from the Internet of Things and big data that are then stored in the cloud, interpreted with artificial intelligence patterns, and validated with blockchains. This chapter analyzes in an interdisciplinary way some practical applications of healthcare digital transformation that—thanks to networking digitalization—can become “smart,” improving the quality of care and long-term socioeconomic sustainability.
Research Advanced in the Face Recognition

Face recognition has always been a research hotspot in the field of computer vision, whose basic task is to recognize the identity of the face in the image. With the rapid development of computer technology, both the accuracy and speed of face recognition have achieved great breakthroughs, and face recognition technology has been widely used in the field such as cyber security, artificial intelligence and so on. In this paper, based on detailed literature research and analysis, we present a comprehensive review of the research work on face recognition technology. Specifically, we first analyze the difficulty of face recognition task from the human face feature extraction, which due to the variations within and between classes. We then introduce the main frameworks of face recognition from geometric feature-based method, template based method and model-based method. We further compare the performance of different algorithms on different common datasets. Finally, we give an outlook on the future research directions of face recognition.
Investment study of gold and bitcoin based on time series forecasting models

Since investment products are subject to many influences, there is a great deal of uncertainty about their direction. Therefore, people try to predict their price trends by various methods to develop their own investment strategies. We predict the movement of gold and bitcoin by building mathematical models and develop investment strategies to profit. We build a price prediction model for bitcoin and gold. Then, based on the price prediction model, we built an investment model that considers investment commissions, returns and risks to maximize investment returns. Finally, we evaluated the model and proved that the model provides the best investment strategy. Using this model, with an initial capital of $1,000, a gold trading rate of 1%, and a bitcoin trading rate of 2%, our mathematical model achieves an annual asset appreciation of over 250% for a final return of $103,191 over a five-year period starting on September 11, 2016 and ending on September 10, 2021.
Elucidating Cryptocurrency with Trading Dashboard

With the rise of interest in cryptocurrency in the recent decade, an ocean of financial news data has surfaced in articles, tweets, and even Reddit posts. Due to the sheer volume, it is not practical for the casual trader to read through all these news sources manually. However, only going through one or two sources alone may result in receiving biased information, or no useful information at all. With the current rise in cryptocurrency, accurately predicting market trends becomes highly beneficial to the user, providing a major opportunity for lower-income households to have a higher chance of profiting and living a substantially more comfortable lifestyle. In this study, a developer's API key was obtained for three news sources to scrape financial news from. Then, the TensorFlow Keras model and Gensim model's doc2vec NLP tool were utilized to process the data scraped online. The data is then saved as a .model and .sav file, and a website was constructed using the Flask framework. The website is now deployed and is available for all users. However, because the data obtained was too small to be utilized well, only a weak linear model that could give us a correlation between price and news sentiment was able to be constructed. The dashboard passed its functional and UAT tests with 100%, and via the usability test with SUS, the dashboard is considered to be easy to use. In all, the website summarizes the main details and sentiment of the coins and will benefit users who are just being introduced to the cryptocurrency space.
Data research based on ARIMA and Gray Relational Analysis

The New York Times’ word-guessing game Wordle has swept the internet and ignited a Wordle craze on Twitter. In this paper, we build four mathematical models for the Wordle game puzzle report and do further research. First, we used the ARIMA model for prediction, and the Gray Correlation model was used to find the relationship between word attributes and the percentage of hard-mode participants. Next, we resorted to the BP neural network model to forecast the distribution on the number of tries, and the result was (1, 9, 26, 29, 15, 10, 11). Then, we applied the K-means model to categorize all the words based on their difficulty level, and EERIE was found to be in the ”easy” range. In the end, most players are devoted game enthusiasts, and roughly 10 percent of players consistently picked the hard mode. Additionally, most players preferred simpler games for fun and relaxation, and the percentage of hard-mode participants increased among the overall reported number.
Validating Design Choices of a Bio-Inspired Histogram to Support a Shared Practice of Clean Energy at the Workplace

Faced with the challenges presented by climate change, many interfaces presenting feedback on available, consumed or saved energy resources have been proposed to support a change of behavior towards a more environmental one in the field of sustainable HCI. If, for this purpose, bio-inspired systems have been proposed, few of them are presented in a tangible way or as histograms. This, despite encouraging results, as much in terms of awareness of energy consumption as in terms of emotional connection developed with the system, of artistic interfaces aiming at the commitment in an environmental behavior. With the aim of designing a system to support users in a shared practice of shifting energy demand, we present the results of the first steps of our approach. This paper focuses on design choice validation at an early stage of the design and development cycle, before spending time and money on physical prototyping; studying behavior change remains outside the scope of this work. Thus, the results presented concern the effectiveness and UX of static and animated mock-ups of eco-prediction and eco-feedback interfaces in the form of bio-inspired histograms. The results indicate that the efficacy of a bio-inspired histogram compared to a conventional histogram is lower. The second study shows that part of this loss of efficacy can be resolved by contextualizing the data. Also, without being significative, the leaf plant metaphor is found to be more effective and aesthetic than a metaphor closer to a classical histogram inspired by bamboo. We conclude that the leaf plant metaphor can be used for an eco-forecast and eco-feedback interface in the form of a histogram to shift the energy demand of battery-capable devices.
MESAC: Learning to Remove Mismatches via Maximizing the Expected Score of Sample Consensuses

Most learning-based methods require labelling the training data, which is time-consuming and gives rise to wrong labels. To address the labelling issues thoroughly, we propose an unsupervised learning framework to remove mismatches by maximizing the expected score of sample consensuses (MESAC). The proposed MESAC can train various permutation invariant networks (PINs) based on training data with no labels, and has three distinct merits: 1) the framework can train various PINs in an unsupervised mode such that these are immune to wrong labels; 2) the gradients of the expected score are explicitly calculated by a revised score-function estimator, which can avoid gradient explosion; 3) the distribution of matching probabilities is learned from the PIN and precisely modelled by a categorical distribution, which can decrease the sampling times and improve the computational efficiency accordingly. Experiments of testing datasets disclose that mean recall is increased by at most 77% when pure PINs are embedded in MESAC, and mean precision is also improved by 16%. Applications in pose recovery indicate that the success rates of MESAC-integrated PINs outperform the compared methods when training with neither matching labels nor ground truth epipolar geometry (EG) constraints, showing the great potential of MESAC in mismatch removal.
Co-Attention-Based Cross-Stitch Network for Parameter Prediction of Two-Phase Flow

Gas-liquid two-phase flow is pervasive in industrial processes, and accurate measurement of its parameters can effectively improve industrial efficiency. Void fraction, a key parameter in gas-liquid two-phase flow, plays a crucial role in mixture density determination and flow structure analysis. In this paper, a novel multi-task learning method is proposed to predict the gas void fraction. Firstly, we conduct vertical upward gas-liquid two-phase flow experiments to obtain fluid signals relying on the four-sector distributed conductance sensor. Then, we pair the measurement of gas void fraction and its classification and design a Co-Attention based Cross-Stitch Network (CACSNet) to process these two closely related tasks simultaneously. In CACSNet, two parallel embedding branches are adopted to extract task-specific features. There are three feature extractors in each branch to capture in-depth representations of temporal and spatial. To learn an optimal combination of these task-specific features and generate shared representations, we propose the Cross-stitch Embedding with Attention Modules (CEAM), which is incorporated the Channel Co-Attention Module (CCAM) and the Temporal Co-Attention Module (TCAM). Finally, we compare our CACSNet with single-task baseline and other competitive methods, which proves that our framework achieves superior performance in gas void fraction prediction. We also conduct detailed model ablation and parameter analysis to illustrate the efficiency of our proposed structure.
Integration of Blockchain Technology in Tourism Industry: Opportunities and Challenges

Tourism is crucial to people’s everyday life and the global economy. However, despite its significance, the conventional tourism business confronts significant development obstacles. Consequently, incorporating digital technology into the traditional tourism business in order to further cut costs and increase efficiency is regarded as an absolute necessity. As a nascent technology, blockchain has the potential to revolutionize the tourism sector since it provides a secure platform connecting the tourism business and its customers (employees and tourists). Through this endeavor, a comprehensive literature assessment on blockchain technology in smart tourism will be conducted. In addition, the Internet of Things (IoT) and large-scale analytics can be utilized to enhance the tourist experience. It is important to note that during the COVID-19 pandemic, the tourism industry’s issues were more standardized. Therefore, the conventional tourism business should be swiftly modernized. From the perspective of smart tourism, blockchain can serve as a platform that reliably and efficiently connects travelers with tourism products.
Experiments and a User Study for Hierarchical Drawings of Graphs

We present a detailed hierarchical graph drawing technique that is based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Path Based Framework (PBF)</i> . Extensive edge bundling is applied to draw all edges of the graph and the height of the drawing is minimized using compaction. We present experimental results and a user study for hierarchical drawings of graphs that show the usefulness of the new drawing technique. The drawings produced by the new framework are compared to drawings produced by the well known Sugiyama framework in terms of area, number of bends, number of crossings, and execution time. The new algorithm runs very fast and produces drawings that are readable and more efficient in terms of area and number of bends. Furthermore, since there are advantages (and disadvantages) to both frameworks, we performed a user study. The results show that the drawings produced by the new framework are well received in terms of clarity, readability, and usability. Hence, the new technique offers an interesting alternative to drawing hierarchical graphs, and is especially useful in applications where user defined paths are important and need to be highlighted.
Hand Gesture Recognition Pad Using an Array of Inductive Sensors

Gesture recognition is a field of study within human-computer interaction technology and is used in an increasing number of applications. So far, the systems designed for gesture recognition have been able to sense multiple static and dynamic gestures. However, they suffer from some limitations. This study introduces a novel sensing pad system consisting of an array of inductive sensors which can recognize and differentiate specific static hand gestures through machine learning algorithms. It is designed to be a non-contact apparatus where the gestures made by a user can be perceived by the system. It uses five coils, one for each finger, and can sense the fingers that are unfolded while making a particular gesture. Ten volunteer users participated in this study. Ten gestures, numbers 1-10 of the American Sign Language are chosen to be tested upon, 10 times each for every user. The responses from the sensing coils were measured via a data acquisition board and sent to the PC for processing. A total of 1000 responses were recorded and processed using machine learning algorithms which provided an accuracy of 98.7% using 5-fold cross-validation (5F-CV) and 97.3% using leave-one-subject-out CV (LOSO-CV) proving that the system can successfully distinguish hand gestures instantly.
Memory Compiler Performance Prediction using Recurrent Neural Network

Semiconductor chips incorporate a large number of smaller memories. As memories contribute an expected 25 to 40 percentage of the overall performance, power and area of a product, memories should be planned cautiously to meet the current era system requirements. Memories are highly uniform and can be described by approximately ten different parameters. Thus, memories are typically generate by memory compilers, to enhance PPA utilization in memory compilers, A crux task in the design procedure of a chip is to choose optimal memory compiler parameters, which fulfill the one part of the system requirements while on the other part optimize PPA, we proposed training fully connected RNN to predict PPA outputs given to a memory compiler parameterization. We have used Open RAM for the generation of dataset the dataset consists of the parameters which can predict the PPA of the desired memory and model is training for the prepared dataset in python as Open RAM is also developed using python so it is easy to collect data from it. Using an exhaustive search based optimizer RNN framework which generates neural network predictions, In our method, a recurrent neural network model with different designs yielded accuracy up to 98 percent.
One Dimensional Chaotic Map Implementation in FPGA Board for Image Encryption

Internet information transport has always been accompanied by significant security concerns. After and during the epidemic, it has been noticed that as digital transactions increase, so do instances of intrusion and hacking. Consequently, the demand for safe transactions increases. This study proposes a cryptographic method for image encryption and decryption based on chaotic and associated algorithms. In this article one dimensional logistic chaotic map has been implemented in FPGA board for image encryption and addition, we conduct several performance analyses and methods, such as Histogram Analysis, Entropy Analysis, to evaluate the quality of the encryption and confirm that the specified standard has been fulfilled.
Federated Quantum Neural Network with Quantum Teleportation for Resource Optimization in Future Wireless Communication

The following study introduces FT-QNN, a federated and quantum teleportation –based quantum neural network, utilized to optimize resource allocation for future wireless communications. The proposed FT-QNN consists of edge quantum neural networks (QNNs) and a cloud QNN, while quantum teleportation allows the cloud QNN to obtain the outputs of edge QNNs without requiring prior measurements on the output states, allowing the cloud to process the outputs directly as quantum states. As a particular case to demonstrate its applicability for wireless resource allocation, FT-QNN is then employed to optimize transmit power allocation coefficients in a power domain non-orthogonal multiple access (NOMA)-based system, aiming to maximize the achievable sum-rate. FT-QNN yields lower complexity compared to a distributed QNN scheme without quantum teleportation, while the numerical results also demonstrated that the FT-QNN is capable to achieve a similar sum-rate compared to the scheme without quantum teleportation.
Effects of experimental impairments on the security of continuous-variable quantum key distribution

Quantum Key Distribution (QKD) is a cutting-edge communication method that enables secure communication between two parties. Continuous-variable QKD (CV-QKD) is a promising approach to QKD that has several advantages over traditional discrete-variable systems. Despite its potential, CV-QKD systems are highly sensitive to optical and electronic component impairments, which can significantly reduce the secret key rate. In this research, we address this challenge by modeling a CV-QKD system to simulate the impact of individual impairments on the secret key rate. The results show that laser frequency drifts and small imperfections in electro-optical devices such as the beam splitter and the balanced detector have a negative impact on the secret key rate. This provides valuable insights into strategies for optimizing the performance of CV-QKD systems and overcome limitations caused by component impairments. By offering a method to analyze them, the study enables the establishment of quality standards for the components of CV-QKD systems, driving the development of advanced technologies for secure communication in the future.
Multi-dimensional Bayesian network classifiers for partial label ranking

The label ranking problem consists in learning preference models from training datasets labeled with (possibly incomplete) rankings of the class labels. The goal is then to predict a ranking for a given unlabeled instance. This work focuses on a more general interpretation where both the training dataset and the prediction given as output allow tied class labels, i.e., there is no particular preference between them. This problem is known as the partial label ranking problem. This paper tackles the partial label ranking problem by transforming the ranking with ties into a set of discrete variables representing the preference relations (ranked ahead, tied with, and ranked behind) between each pair of class labels. The posterior probabilities for each pair are then used to fill the values of a preference matrix. This preference matrix is the basis for solving the rank aggregation problem required to obtain the output ranking with ties. This paper aims to exploit the resemblance of this problem with multi-label and multi-dimensional classification by studying the use of Bayesian network classifiers to compute the posterior probabilities for the new class structure, i.e., pairs of class labels. In particular, binary relevance with naive Bayes and averaged one-dependence estimators between the new class structure are used to solve the partial label ranking problem. Furthermore, bivariate relationships between all the pairs of class labels are considered. However, the complexity of the model grows significantly, which makes it necessary to reduce the number of allowed bivariate relationships between pairs. Thus, a feature selection method is included to select the more relevant subset of bivariate relationships. The experimental evaluation shows that our proposals are competitive in accuracy with the current instance-based and decision tree induction algorithms. Moreover, they outperform the existing mixture-based probabilistic graphical models, while the algorithms proposed are much faster.
SADENet: A supervised attention delicate enhanced network for subtle person detection

Object detection algorithms have been developed for decades, and one of remaining important challenges is the detection of subtle targets in complex environments. Many convolution neural network (CNN) based methods have been proposed to handle this problem. However, the information pertaining to subtle targets is delicate and can easily be lost or neglected in the upsampling and downsampling stages of conventional CNN. In this paper, we propose a new subtle target detection network, named the supervised attention delicate enhanced network (SADENet). Firstly, two sample vertebras are designed to solve the problem of information loss during upsampling and downsampling. Then, an enhanced context attention module (ECAM) is proposed, which extends the original supervised heatmap by fusing information from different scales. Based on the ECAM and the respective advantages of high-resolution and low-resolution feature maps, two effective sampling attention modules, the semantic fidelity upsampling module and the location enhancement downsampling module, are created to build a bi-directional connection neck structure. The effectiveness of our proposed method is demonstrated in experiments on the TinyPerson dataset, which outperforms many state-of-the-art detectors, especially in the detection of tiny and tiny1 scale targets.
A visual guidance calibration method for out-of-focus cameras based on iterative phase target

Calibration of out-of-focus cameras is crucial for some special 3d reconstruction applications. An essential factor in improving the calibration result is avoiding the solution's singularity which tends to pose the calibration plate at a large inclination. However, the depth of field (DOF) of cameras is limited, especially in the defocused scenes, and the blurred corners will significantly reduce the calibration accuracy. A vision-guided calibration method is proposed, including an optimal pose selection algorithm and an iterative phase-target technology. Compared with other phase-target methods, this method simultaneously optimizes the mathematical properties of the solution and the angular uncertainty of the corners. Compared with the Chessboard-target or Circle-target, this method has a higher calibration accuracy at three different distances that gradually change from focusing to defocusing. In addition, experiments also prove the effectiveness of the two submodules: Gaussian iteration and vision guidance. Finally, its code is available in the URL: https://github.com/SHU-FLYMAN/Visual-Guidence-Calibration.