Reviewerly/exHarmony huggingface dataset:
Human activity recognition for analyzing stress behavior based on Bi-LSTM

Stress is one of the critical health factors that could be detected by Human Activity Recognition (HAR) which consists of physical and mental health. HAR can raise awareness of self-care and prevent critical situations. Recently, HAR used non-invasive wearable physiological sensors. Moreover, deep learning techniques are becoming a significant tool for analyzing health data.In this paper, we propose a human lifelog monitoring model for stress behavior recognition based on deep learning, which analyses stress levels during activity. The proposed approach considers activity and physiological data for recognizing physical activity and stress levels.To tackle these issues, we proposed a model that utilizes hand-crafted feature generation techniques compatible with a Bidirectional Long Short-Term Memory (Bi-LSTM) based method for physical activity and stress level recognition. We have used a dataset called WESAD, collected using wearable sensors for model evaluation. This dataset presented four levels of stress emotion, including baseline, amusement, stress, and meditation.The following results are from the hand-crafted feature approaches compatible with the bidirectional LSTM model. The proposed model achieves an accuracy of 95.6% and an F1-score of 96.6%.The proposed HAR model efficiently recognizes stress levels and contributes to maintaining physical and mental well-being.
Multi-station automatic classification of seismic signatures from the Lascar volcano database

Abstract. This study was aimed to build a multi-station automatic classification system for volcanic seismic signatures such as hybrid, long period, tremor, tectonic, and volcano–tectonic events. This system was based on a probabilistic model made using transfer learning, which has, as the main tool, a pre-trained convolutional network named AlexNet. We designed five experiments using different datasets with data that were real, synthetic, two different combinations of these (combined 1 and combined 2), and a balanced subset without synthetic data. The experiment presented the highest scores when a process of data augmentation was introduced into processing sequence. Thus, the lack of real data in some classes (imbalance) dramatically affected the quality of the results, because the learning step (training) was overfitted to the more numerous classes. To test the model stability with variable inputs, we implemented a k-fold cross-validation procedure. Under this approach, the results reached high predictive performance, considering that only the percentage of recognition of the tectonic events (TC) class was partially affected. The results obtained showed the performance of the probabilistic model, reaching high scores over different test datasets. The most valuable benefit of using this technique was that the use of volcano seismic signals from multiple stations provided a more generalizable model which, in the near future, can be extended to multi-volcano database systems. The impact of this work is significant in the evaluation of hazard and risk by monitoring the dynamic evolution of volcanic centers, which is crucial for understanding the stages in a volcano’s eruptive cycle.
Optimal Circuit Decomposition of Reversible Quantum Gates on IBM Quantum Computers

A critical task in utilizing quantum physics in many application fields is circuit design using reversible quantum gates. Using decomposition techniques enables transformation of unitary matrices into fundamental quantum gates. Any 3x3 reversible quantum gate can be decomposed into single-qubit rotation gates and two qubit CNOT gates. In this chapter, quantum implementations of FRSG1, URG, JTF1 and R gates into CNOT gates and single qubit U3 gates with different optimization levels on a platform provided by IBM have been discussed. FRSG1 and JTF1 gates are important in applications like Stochastic computing, fingerprint authentication system, and parity generation circuits. URG gate is better in terms of number of complex functions and can be utilized to design quantum comparator circuits. R gate plays an important role in inverting and duplicating a signal. In FRSG1, URG, JTF1 and R gates, the implementation count of single qubit gates decreases by 56%, 11%, 71%, and 62%, respectively and the count of two qubit gates reduces by 15%, 26%, 41%, and 5% respectively after optimization.
Quantum Computing and the Qubit

A model of dissimilarity-based study is featured after studies and design. No statistical data are included whatsoever. Diving into quantum computing as the title suggests, introductions to the concept of Qubit are given. Future papers contain the advancement of quantum computing operations, shifting to quantum computers, developing intelligent algorithms for the new age machines (referred to here as quantum machines). The chapter assumes the requirement of knowing the evolution of intelligent machines in the form of an introduction.
Identification of Seismo‐Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier

Abstract We present an algorithm based on Self‐Organizing Maps (SOM) and k‐means clustering to recognize patterns in a continuous 12.5‐year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano‐seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre‐eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes.
Falling Estimation Based on PoseNet Using Camera with Difference Absolute Standard Deviation Value and Average Amplitude Change on Key-Joint

Elderly people need special attention and some of them need to be monitored continuously and in real-time. Fall detection is one of the systems used to monitor the daily life of the elderly. Various devices and methods were developed to monitor the condition of the elderly on daily activity. The system that has been proposed in previous studies uses a number of sensors that are placed on the body. However, this system tends to be high cost, complex installation, and inconvenient to use. Therefore, an alternative system is needed to overcome this problem. The purpose of this study is to developed a fall detection method using PoseNet with pose calculations based on key joins. Testing on larger data sets is needed to verify the proposed method's performance further. The use of cameras can be a solution to monitoring the activities of the elderly. With the image processing method, it is possible to estimate the activities of the elderly. The purpose of this study is to developed a fall detection method using PoseNet with pose calculations based on key joins. This study developed a fall detection method using PoseNet with pose calculations based on key joins. The key-join used is Left and Right Shoulder and is only measured at the y-coordinate. We calculated the difference absolute standard deviation value (DASDV) and average amplitude change (AAC) on the Y-coordinate. From 10 falling and non-falling conditions trials, we obtained 85% and 80% accuracy for AAC and DASDV. The result of this research can be used as a source or comparison for future research.
The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review

Visual Analytics (VA) is a multidisciplinary field that requires various skills including but not limited to data analytics, visualizations, and the corresponding domain knowledge. Recently, many studies proposed creating and using Domain-Specific Languages (DSLs) for VA in order to abstract complexities and assist designers in developing better VAs for different data domains. However, development methods and types of DSLs vary for different applications and objectives. In this study, we conducted a systematic literature review to overview DSL methods and their intended applications for VA systems. Moreover, the review outlines the benefits and limitations of each of these methods. The aim is to provide decision support for both the research and development communities to choose the most compatible approach for their application. We think the communication of this research delivers a broad figure of previous relevant research and assists with the transfer and adaptation of the results to other domains.
A High Throughput BFV-Encryption-Based Secure Comparison Protocol

Secure comparison is a fundamental problem in multiparty computation. There are two different parties, each holding an l-bit integer, denoted by a and b, respectively. The goal of secure comparison is to compute the order relationship between a and b, say (a&gt;b)∈{0,1}, without revealing their inputs to any others. Since previous solutions based on homomorphic encryption need at least Ω(l) encryptions for each l-bit comparison, the total encryption time leads to a computational bottleneck for these protocols. This work presents a fast, semi-honest, secure comparison protocol based on the BFV encryption scheme. With its vector-like plaintext space, the number of required encryptions can be significantly reduced; actually, only six encryptions are needed for each comparison in our protocol. In other words, the proposed protocol can achieve the time complexity O˜(λ+l) for a given security parameter λ. As a result, 4096-bit integers can be securely compared within 12.08 ms, which is 280 times faster than the state-of-the-art homomorphic encryption-based secure comparison protocol. Furthermore, we can compare k pairs of l⋅k−1-bit integers with almost the same execution time as comparing l-bit integers and achieve higher throughput regardless of the compared integer size.
Are you really smiling? Display rules for emojis and the relationship between emotion management and psychological well-being

Display rules specify socially appropriate facial expressions in a given situation. However, managing emotions for such a social adaption sometimes leads to deleterious psychological outcomes. Given that people nowadays rely on emojis to express emotions online, the present study investigated (1) whether display rules exist in emoji communications and (2) how emotion management using emojis is associated with psychological well-being. Prior studies have demonstrated the effects of context on the frequency of emoji use. However, the intensity and type of expression may differ, even if emojis are used at the same frequency. The current study thus investigated whether emotional expressions and the types of emojis used are adjusted to contexts similar to facial displays. As many as 1,289 Japanese participants typed emojis in response to Internet chats and reported the intensity of their emotional expressions. The contexts of the chats varied depending on the target of use, the emotional value of contexts, and private or public settings. The results showed that, similar to facial displays, individuals expressed emotions through emojis more with those closely related, more in positive contexts than in negative contexts, and more in private than in public contexts. When the expressions were intense, individuals used emojis consistent with the emotional value of the context. Upon attenuating the expressions, this study found that individuals tended to use euphemistic emojis and sent smiling emojis in negative contexts to manage the expressions. Moreover, expressing emotions with emojis was associated with subjective well-being, whereas managing emotions with emojis was weakly associated with depressive symptoms. Together, this study indicates the existence of display rules for emojis, calling for future research on the psychological impact of online emotion norms.
Binocular fringe projection profilometry for the metrology of meter-scale optical surfaces

Demand for better quality, larger quantity, and size of astronomical telescopes from visible to radio frequencies is increasing. More rapid, efficient, and adaptable manufacturing processes are needed to support the needs of growing science and engineering communities in these fields. To aid the development and execution of these new processes, a flexible, accurate, and low-cost metrology system is needed. This paper outlines a variety of fringe projection profilometry (FPP) that has demonstrated high accuracy over large areas, making it a critical tool for manufacturing steel molds for forming primary reflectors and shape verification of the reflectors themselves used for radio astronomy.
A Lattice-Based Certificateless Traceable Ring Signature Scheme

A ring signature (RS) scheme enables a group member to sign messages on behalf of its group without revealing the definite signer identify, but this also leads to the abuse of anonymity by malicious signers, which can be prevented by traceable ring signatures (TRS). Up until that point, traceable ring signatures have been secure based on the difficult problem of number-theoretic (discrete logarithms or RSA), but since the advent of quantum computers, traditional traceable ring signatures may no longer be secure. Thus Feng proposed a lattice based TRS, which are resistant to attacks by quantum computers. However, that works did not tackle the certificate management problem. To close this gap, a quantum-resistant certificateless TRS scheme was proposed in the study. To the best of our knowledge, this is the first lattice based certificateless TRS. In detail, a specific TRS scheme was combined with the lattice-based certificateless signature technology to solve the certificate management problem while avoid key escrow problem. Additionally, a better zero-knowledge protocol is used to improve the computational efficiency of the scheme, and by reducing the soundness error of the zero-knowledge protocol, the number of runs of the zero-knowledge protocol is reduced, so that the communication overhead of the scheme is reduced. Under random oracle model, the proposed scheme satisfies tag-linkability, anonymity, exculpability and is secure based on the SIS problem and the DLWE problem. In conclusion, the proposed scheme is more practical and promising in e-voting.
LFSS-KF: lightweight fast real-time security standards with key fusion for surveillance videos

Uses of video surveillance (VS) have exponentially increased using the internet as a platform. Security concerns related to the transmission of videos must be addressed. Even though the majority of current encryption standards are effective, it is computationally demanding and unsuitable for real-time VS applications. Lightweight crypto-encryption standards are necessary to meet real-time needs. This paper provides a thorough analysis of various encryption standards to protect video data and evaluates vulnerability under various attacks. A Lightweight Fast Security Standard (LFSS) is proposed to meet real-time requirements by adopting dual key fusion (KF) approach. Entropy and crypto weights are quantitatively compared with proposed and chosen lightweight ciphers for surveillance videos based on NCPR, UACI and attacks into account. The proposed LFSS method offers a good NCPR of 99.5806 and offers only 40% and 3% average execution time over Secure Internet of things (SIT) and Secure Force (SF) security standards, respectively.
Multimodal biomedical image retrieval and indexing system using handcrafted with deep convolution neural network features

Abstract The advances in biomedical imaging equipment have produced a massive amount of medical images that are generated by the different modalities. Consequently, a huge volume of data has been produced and caused a complex and time-consuming retrieving process of the relevant cases. To resolve this issue, the Content-Based Biomedical Image Retrieval (CBMIR) system is applied to retrieve the related images from the earlier patients’ databases. However, the previous handcrafted features methods that applied the CBMIR model have shown poor performance in many multimodal databases. In this paper, we focus on designing CBMIR technique using Deep Learning (DL) models. We present a new Multimodal Biomedical Image Retrieval and Classification (M-BMIRC) technique for retrieving and classifying the biomedical images from huge databases. The proposed M-BMIRC model involves three dissimilar processes as following: feature extraction, similarity measurement, and classification. It uses an ensemble of handcrafted features from Zernike Moments (ZM) and deep features from Deep Convolutional Neural Networks (DCNN) for feature extraction process. Additionally, the Hausdorff Distance based similarity measure is employed to identify the resemblance between the queried image and the images that exist in the database. Moreover, the classification process gets executed on the retrieval images using the Probabilistic Neural Network (PNN) model, which allocates the class labels of the tested images. Finally, the experimental studies are conducted using two benchmark medical datasets and the results ensure the superior performance of the proposed model in terms of different measures include Average Precision Rate (APR), Average Recall Rate (ARR), F-score, accuracy, and Computation Time (CT).
English Teaching Achievement Prediction by Big Data Analysis under Deep Intervention

Appropriate data analysis technology can make people use the online degree education, obtain the data and information generated in the learning management system, and provide a useful decision basis for optimizing the teaching and management process of online degree education. Data analysis technology can help English teachers better grasp students’ learning situations and progress and optimize management. First, data analysis methods and decision tree algorithms are analyzed. Second, in data mining technology, the C4.5 decision tree method is used to construct an English score prediction model. Through the analysis of English learning-related information such as questionnaires and collected student test score data, the prediction of English teaching performance is analyzed from the perspective of teachers’ in-depth intervention. The survey results are shown as follows: (1) The model is simulated and tested. The model’s prediction accuracy is 98.20%, 99.10%, 99.40%, 98.70%, and 98.90%, higher than the standard accuracy of 97.5%. Additionally, the average response efficiency of the model is 99.42%, which can be used. (2) The failure rate of boys’ final grades is 11%, and the failure rate of female students’ final grades is 10%. There is only a 1% difference in the final grade failure rate between male and female students. The effect of gender on teaching performance is less pronounced. (3) As the number of practice questions increases, the rate of failing grades decreases. Thus, the data suggest that the number of practice questions affects instructional performance. (4) Teachers’ intervention can improve students’ English achievement. Increasing the intensity of the intervention also improves student achievement. Therefore, the follow-up research should increase the number of practice questions and teacher intervention in English teaching. The English teaching achievement prediction suggestion based on big data analysis is put forward, providing a reference for prediction management.
Multi-modal graph reasoning for structured video text extraction

Structured video text information extraction is a crucial part of video understanding for exploring the structured text fields from different category-specific videos such as scores in basketball games or identities in news. Recent natural language models and text detectors have demonstrated state-of-the-art performance in video text detection and recognition. However, understanding text from unstructured video frames is challenging in practice due to a variety of video text and dynamic text layout changes. Limited work has focused on the solutions that efficiently extract structured information from the video text. In this paper, we address this task by modeling a multi-modal attention graph on the video text. Specifically, we encode both the visual and textual features of detected text regions as nodes of the graph; the spatial layout relationship of the text regions is modeled as edges of the graph. The structured information extraction is solved by iteratively propagating text region messages along graph edges and reasoning the structured categories of graph nodes. To promote the representation capacity of the graph, we further introduce a contrastive loss on the visual embeddings of the text regions in a self-supervised manner. In order to roundly evaluate our proposed method as well as boost future research, we release a new dataset collected and annotated from several standard NBA regular seasons and playoff match videos. Experimental results demonstrate the superior performance of the proposed method over several state-of-the-art methods.
AI + precision medicine

This chapter discusses the AI transformation of precision medicine (PrMed) by beginning with the distinction between precision versus personalized medicine, its historical development, and key trends (including genomic matching and umbrella, basket, and N-of-1 trials). It then moves on to clarify the central structural relationship in modern healthcare transformation between public health (PubHealth) and PrMed, to progressively build in this chapter on how AI is advancing (and posing new problems) for PrMed and its relationship with PubHealth (allowing the subsequent chapter's corollary discussion from the perspective of PubHealth). This chapter then considers the strategic and technical changes producing the system trend: AI + healthcare Big Data = PrMed. It then moves on to consider the concrete steps in this transformation including clarifying the clinical and organizational structures of healthcare Big Data (including data oceans), AI analytics, value-based approach to PrMed (including value barriers and countermeasures), and the impact of the above on the future's healthcare system design. It then considers AI-enabled multiomics in PrMed, including translational multiomics, pharmacogenetics, and radiogenomics use cases to eventually build to the value proposal of the emerging future's healthcare system: AI-enabled data science + multiomics = PrMed's future.