Reviewerly/exHarmony huggingface dataset:
Deep Learning-based Head Pose Estimation for Enhancing Nonverbal Communication in Human-Robot Interaction

This study presents an approach for enhancing human-robot interaction by developing a deep learning-based algorithm that recognizes nonverbal communicative information, with a focus on head pose estimation. The head pose estimation is a crucial component of nonverbal communication, and to construct an accurate model for it, both a suitable architecture and a large-scale dataset that reflects diverse real-life conditions are crucial. However, it is challenging to annotate the 3D movement of the facial region based on real images. To address this issue, this study proposes a pipeline for creating a large-scale learning dataset consisting of images, and 3D head poses annotations by using a photo-realistic 3D head model. Based on the dataset collected in this way, a deep learning-based 6D object poses model was trained to directly estimate the head pose from an RGB image. The 6D pose model is based on a convolutional neural network with multi-heads designed to perform facial expression classification, facial region detection, and head pose estimation. Experimental results with a custom test dataset show that the proposed method achieves 3.49 degrees of mean absolute errors of Euler angles.
Bandwidth Control in Computer- Generated Holography

The Fourier transform between the time and frequency signals of light enables highly efficient band usage in optical communications. Analogously, the Fourier transform between the wavefield and wavevector distributions in the optical spatial diffraction is useful to understand the bandwidth considered in the diffraction simulations. In this work, we thoroughly investigate the numerical techniques for controlling the bandwidth in the design of computer-generated holography. The experiments reveal the effects of bandwidth control on both accuracy improvement of diffraction equations and suppression of higher hologram orders. A general formula for controlling the bandwidth directly using the spatial-frequency components is concluded.
Random hand gesture authentication via efficient Temporal Segment Set Network

Biometric authentication technologies are rapidly gaining popularity, and hand gestures are emerging as a promising biometric trait due to their rich physiological and behavioral characteristics. Hand gesture authentication can be categorized as defined hand gesture authentication and random hand gesture authentication. Unlike defined hand gesture authentication, random hand gesture authentication is not constrained to specific hand gesture types, allowing users to perform hand gestures randomly during enrollment and verification, thus more flexible and friendly. However, in random hand gesture authentication, the model needs to extract more generalized physiological and behavioral features from different viewpoints and positions without gesture templates, which is more challenging. In this paper, we present a novel efficient Temporal-Segment-Set-Network (TS2N) that directly extracts both behavioral and physiological features from a single RGB video to further enhance the performance of random hand gesture authentication. Our method adopts a new motion pseudo-modality and leverages a set-based representation to capture behavioral characteristics online. Additionally, we propose a channel-spatial attention mechanism, Contextual Squeeze-and-Excitation Network (CoSEN), to better abstract and understand physiological characteristics by explicitly modeling the channel-spatial interdependence, thereby adaptively recalibrating channel-specific and spatial-specific responses. Extensive experiments on the largest public hand gesture authentication dataset SCUT-DHGA demonstrate TS2N’s superiority against 21 state-of-the-art models in terms of EER (5.707% for full version and 6.664% for lite version) and computational cost (98.9022G for full version and 46.3741G for lite version). The code is available at https://github.com/SCUT-BIP-Lab/TS2N.
Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization

Abstract Background In France an average of 4% of hospitalized patients die during their hospital stay. To aid medical decision making and the attribution of resources, within a few days of admission the identification of patients at high risk of dying in hospital is essential. Methods We used de-identified routine patient data available in the first 2 days of hospitalization in a French University Hospital (between 2016 and 2018) to build models predicting in-hospital mortality (at ≥ 2 and ≤ 30 days after admission). We tested nine different machine learning algorithms with repeated 10-fold cross-validation. Models were trained with 283 variables including age, sex, socio-determinants of health, laboratory test results, procedures (Classification of Medical Acts), medications (Anatomical Therapeutic Chemical code), hospital department/unit and home address (urban, rural etc.). The models were evaluated using various performance metrics. The dataset contained 123,729 admissions, of which the outcome for 3542 was all-cause in-hospital mortality and 120,187 admissions (no death reported within 30 days) were controls. Results The support vector machine, logistic regression and Xgboost algorithms demonstrated high discrimination with a balanced accuracy of 0.81 (95%CI 0.80–0.82), 0.82 (95%CI 0.80–0.83) and 0.83 (95%CI 0.80–0.83) and AUC of 0.90 (95%CI 0.88–0.91), 0.90 (95%CI 0.89–0.91) and 0.90 (95%CI 0.89–0.91) respectively. The most predictive variables for in-hospital mortality in all three models were older age (greater risk), and admission with a confirmed appointment (reduced risk). Conclusion We propose three highly discriminating machine-learning models that could improve clinical and organizational decision making for adult patients at hospital admission.
Single-qubit measurement of two-qubit entanglement in generalized Werner states

Conventional methods of measuring entanglement in a two-qubit photonic mixed state require detection of both qubits. We generalize a recently introduced method which does not require detection of both qubits, by extending it to cover a wider class of entangled states. Specifically, we present a detailed theory that shows how to measure entanglement in a family of two-qubit mixed states, obtained by generalizing Werner states, without detecting one of the qubits. Our method is interferometric and does not require any coincidence measurement or postselection. We also perform a quantitative analysis of anticipated experimental imperfections. We show that the method is resistant to a decrease in the interference visibility resulting from such imperfections.
Surveying the landscape of text summarization with deep learning: A comprehensive review

In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. This is in contrast to traditional NLP approaches, which rely on hand-engineered features and rules to perform NLP tasks. The ability of deep neural networks to learn hierarchical representations of language data, handle variable-length input sequences, and perform well on large datasets makes them well-suited for NLP applications. Driven by the exponential growth of textual data and the increasing demand for condensed, coherent, and informative summaries, text summarization has been a critical research area in the field of NLP. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks. In this survey, we begin with a review of fashionable text summarization tasks in recent years, including extractive, abstractive, multi-document, and so on. Next, we discuss most deep learning-based models and their experimental results on these tasks. The paper also covers datasets and data representation for summarization tasks. Finally, we delve into the opportunities and challenges associated with summarization tasks and their corresponding methodologies, aiming to inspire future research efforts to advance the field further. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific setting. This survey aims to provide a comprehensive review of existing techniques, evaluation methodologies, and practical applications of automatic text summarization.
Optimalisasi Metode Diskusi dalam Interpretasi Puisi “Bila Kutitipkan” Karya Ahamad Mustofa Bisri

The aim of this study is to determine the ability of the 10th-grade students at State Senior High School 1 Sidrap to interpret the poem "Bila Kutitipkan" by Ahmad Mustofa Bisri and the factors influencing students' ability to interpret poetry. The sample for this research consists of 30 students. The research was conducted on September 18, 2023, at State Senior High School 1 Sidrap. The data collection method used was group discussions. The data was analyzed using frequency tables. Based on the research results, it was found that 24 students scored above 75, with a percentage of 80%, and 6 students scored below 75, with a percentage of 20%. Based on the data above, it can be concluded that 10th-grade students at State Senior High School 1 Sidrap are capable of interpreting the poem "Bila Kutitipkan" by Gus Mus. There are three factors influencing students' ability to interpret poetry: the frequency of reading poetry, the enjoyment of reading poetry, and the impact of poetry on students' lives. Based on the research results, out of the 30 students at State Senior High School 1 Pancarijang, 22 of them stated that they often and enjoy reading poetry, and poetry has an impact on their lives, while 8 students stated that they neither enjoy nor often read poetry, and poetry does not have an impact on their lives.
QREChem: quantum resource estimation software for chemistry applications

As quantum hardware continues to improve, more and more application scientists have entered the field of quantum computing. However, even with the rapid improvements in the last few years, quantum devices, especially for quantum chemistry applications, still struggle to perform calculations that classical computers could not calculate. In lieu of being able to perform specific calculations, it is important have a systematic way of estimating the resources necessary to tackle specific problems. Standard arguments about computational complexity provide hope that quantum computers will be useful for problems in quantum chemistry but obscure the true impact of many algorithmic overheads. These overheads will ultimately determine the precise point when quantum computers will perform better than classical computers. We have developed QREChem to provide logical resource estimates for ground state energy estimation in quantum chemistry through a Trotter-based quantum phase estimation approach. QREChem provides resource estimates which include the specific overheads inherent to problems in quantum chemistry by including heuristic estimates of the number of Trotter steps and number of necessary ancilla, allowing for more accurate estimates of the total number of gates. We utilize QREChem to provide logical resource estimates for a variety of small molecules in various basis sets, obtaining estimates in the range of 10 7 –10 15 for total number of T gates. We also determine estimates for the FeMoco molecule and compare all estimates to other resource estimation tools. Finally, we compare the total resources, including hardware and error correction overheads, demonstrating the need for fast error correction cycle times.
Примитивы движения робота в задаче планирования траектории с кинематическими ограничениями

Automatic trajectory planning is an urgent scientific and technical problem, whose solutions are in demand in many fields: unmanned transportation, robotic logistics, social robotics, etc. Often, when planning a trajectory, it is necessary to consider the fact that the agent (robot, unmanned car, etc.) cannot arbitrarily change its orientation while moving, in other words, it is necessary to consider kinematic constraints when planning. One widespread approach to solving this problem is the approach that relies on the construction of a trajectory from prepared parts, motion primitives, each of which satisfies kinematic constraints. Often, the emphasis in the development of methods implementing this approach is on reducing the combinations of choices in planning (heuristic search), with the set of available primitives itself being regarded as externally defined. In this paper, on the contrary, we aim to investigate and analyze the effect of different available motion primitives on the quality of solving the planning problem with a fixed search algorithm. Specifically, we consider 3 different sets of motion primitives for a wheeled robot with differential drive. As a search algorithm, the A* algorithm well known in artificial intelligence and robotics is used. The solution quality is evaluated by 6 metrics, including planning time, length and curvature of the resulting trajectory. Based on the study, conclusions are made about the factors that have the strongest influence on the planning result, and recommendations are given on the construction of motion primitives, the use of which allows to achieve a balance between the speed of the planning algorithm and the quality of the trajectories found.
Privacy-Preserving Machine Learning on Apache Spark

The adoption of third-party machine learning (ML) cloud services is highly dependent on the security guarantees and the performance penalty they incur on workloads for model training and inference. This paper explores security/performance trade-offs for the distributed Apache Spark framework and its ML library. Concretely, we build upon a key insight: in specific deployment settings, one can reveal carefully chosen non-sensitive operations (e.g. statistical calculations). This allows us to considerably improve the performance of privacy-preserving solutions without exposing the protocol to pervasive ML attacks. In more detail, we propose Soteria, a system for distributed privacy-preserving ML that leverages Trusted Execution Environments (e.g. Intel SGX) to run computations over sensitive information in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41% when compared to previous related work. Our protocol is accompanied by a security proof and a discussion regarding resilience against a wide spectrum of ML attacks.
Average Based Length Fuzzy Time Series Data Seasonal untuk Prediksi Volume Impor Migas Indonesia

This study aims to predict the value of Indonesian oil and gas imports using the Fuzzy Time Series method of determining average-based intervals (Average Based Length). The data used in this research is annual periodic data from 2013-2021 which was downloaded from the bps.go.id website. The research results show that the volume value of oil and gas imports in Indonesia in January 2023 will reach 4125,802 thousand tons. This research can be concluded that the accuracy of the estimation is very good, producing a MAPE value of 8.04%. The MAPE value is defined as having very good estimation results if the MAPE value is &lt;10%.
Person Re-identification Based on Fusion of Shared Feature Branches

Based on improvement of the existing person re-identification network model and investigating person re-identification related problems on open datasets, a pedestrian re-identification method that incorporates shared feature branching as well as a fused attention mechanism is proposed. First, the benchmark network framework of pedestrian re-identification method is formed by image pre-processing method, generalized average pooling and loss function together, and BNNeck structure is added to optimize the problem of inconsistent multi-loss objectives before obtaining classification features. Second, the global feature branching structure is proposed to be assisted by shared feature branching, which can effectively assist global feature branching to obtain more distinguishable pedestrian features and the attention network is combined with person re-recognition using a network structure that fuses non-local modules with channel attention to fully utilize the feature information of pedestrian images. Finally, the performance improvement of the benchmark network with shared feature branching and the fused attention mechanism approach is demonstrated by experiments on the re-identification dataset.
Multi-element geochemical analyses on ultrafine soils in Western Australia - Towards establishing abundance ranges in mineral exploration settings

In this study, we present summary statistics for multi-element soil geochemistry across Western Australia based on over 74,000 soil samples using the UltraFine+ ® method that extracts and analyses the clay (&lt;2 µm) fraction of a soil sample. This method is a critical advancement for the detection of mobile element signatures for soil geochemical mineral exploration surveys in cover. However, existing estimates of background metal abundances acquired with other methods and on different sample media do not readily provide context for these analyses as recovery from the fine fraction differs to that of whole-sample analysis. We therefore present herein the geochemical results for 52 elements including precious, base and critical metals, as well as commonly associated pathfinder elements for Western Australian samples analysed during several research projects by the Commonwealth Scientific and Industrial Research Organisation. This dataset is separated by tectonic unit, into the Eastern Goldfields Superterrane and the Youanmi Terrane in the Yilgarn Craton, the Pilbara Craton and Sylvania Inlier, the Gascoyne, Lamboo and Aileron Provinces, and the Bryah and Earaheedy Basins to provide exploration-relevant context in these areas. We discuss some of the general trends observed for twelve of these elements, as well as some considerations for the use of these data in comparison to other geochemical soil surveys and with regards to mineral exploration settings. The samples presented in this study are not evenly distributed across Western Australia and limited information is available to correlate whether lithology at depth is mineralised or barren. However, in the absence of other, systematic datasets using the &lt;2 µm size fraction, these data present a suitable first-pass resource of element abundance ranges in areas of mineral exploration interest using the UltraFine+ ® method in some of the mineral endowed areas of Western Australia. Supplementary material: https://doi.org/10.6084/m9.figshare.c.6919933
Formation of Professional Competence of Students in the Conditions of Remote Form of Education

The article is devoted to the study of the professional competence of students in distance learning conditions. Significant interest in the development of distance education allows us to conclude that the topic of developing the professional competence of students in the conditions and need for independent cognitive activity is in demand. The article examines the main ways of developing students’ professional competence based on cognitive independence — as one of the components of lifelong education in the conditions of distance learning. One of the conditions for the effective functioning of the distance learning system at a university is the preparation of employees and students to work in a virtual educational environment. Students’ mastery of methods of independent cognitive activity is due to the fact that during the training period the foundations of professionalism and competence are laid, and skills of independent professional activity are formed. Based on the results of the study, recommendations were developed for developing the professional competence of students in distance learning.
Educational/Instructional Technology and Learning Sciences

This is the Korean open access guide delivering the fundamental and integral ideas of instructional technology and learning sciences fields.
BELMM: Bayesian model selection and random walk smoothing in time-series clustering

Abstract Motivation Due to advances in measuring technology, many new phenotype, gene expression, and other omics time-course datasets are now commonly available. Cluster analysis may provide useful information about the structure of such data. Results In this work, we propose BELMM (Bayesian Estimation of Latent Mixture Models): a flexible framework for analysing, clustering, and modelling time-series data in a Bayesian setting. The framework is built on mixture modelling: first, the mean curves of the mixture components are assumed to follow random walk smoothing priors. Second, we choose the most plausible model and the number of mixture components using the Reversible-jump Markov chain Monte Carlo. Last, we assign the individual time series into clusters based on the similarity to the cluster-specific trend curves determined by the latent random walk processes. We demonstrate the use of fast and slow implementations of our approach on both simulated and real time-series data using widely available software R, Stan, and CU-MSDSp. Availability and implementation The French mortality dataset is available at http://www.mortality.org, the Drosophila melanogaster embryogenesis gene expression data at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121160. Details on our simulated datasets are available in the Supplementary Material, and R scripts and a detailed tutorial on GitHub at https://github.com/ollisa/BELMM. The software CU-MSDSp is available on GitHub at https://github.com/jtchavisIII/CU-MSDSp.
Symbolic Synthesis of Clifford Circuits and Beyond

Path sums are a convenient symbolic formalism for quantum operations with applications to the simulation, optimization, and verification of quantum protocols.Unlike quantum circuits, path sums are not limited to unitary operations, but can express arbitrary linear ones.Two problems, therefore, naturally arise in the study of path sums: the unitarity problem and the extraction problem.The former is the problem of deciding whether a given path sum represents a unitary operator.The latter is the problem of constructing a quantum circuit, given a path sum promised to represent a unitary operator.In this paper, we show that the unitarity problem is co-NP-hard in general, but that it is in P when restricted to Clifford path sums.We then provide an algorithm to synthesize a Clifford circuit from a unitary Clifford path sum.The circuits produced by our extraction algorithm are of the form C 1 HC 2 , where C 1 and C 2 are Hadamard-free circuits and H is a layer of Hadamard gates.We also provide a heuristic generalization of our extraction algorithm to arbitrary path sums.While this algorithm is not guaranteed to succeed, it often succeeds and typically produces natural looking circuits.Alongside applications to the optimization and decompilation of quantum circuits, we demonstrate the capability of our algorithm by synthesizing the standard quantum Fourier transform directly from a path sum.