Reviewerly/exHarmony huggingface dataset:
Innovative integration of augmented reality and optical surface imaging: A coarse‐to‐precise system for radiotherapy positioning

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses, and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice.To propose a noninvasive radiotherapy positioning system integrating augmented reality (AR) and optical surface, and to evaluate its feasibility in clinical workflow.AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, the AR-based coarse guidance and the optical surface-based precise verification. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association, and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. The point clouds of body surface were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in six degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, (i) quality assurance workflow, (ii) errors of both steps and correlation analysis, (iii) receiver operating characteristic (ROC), (iv) distance characteristics of accuracy, and (v) clinical positioning efficiency.The maximum errors in phantom evaluation were 3.4 ± 2.5 mm in Vrt and 1.4 ± 1.0° in Pitch for the coarse guidance step, while 1.6 ± 0.9 mm in Vrt and 0.6 ± 0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation, respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6 ± 1.1 vs. 1.7 ± 0.8, p < 0.01), Lng (2.3 ± 1.1 vs. 1.4 ± 0.9, p < 0.01), and Rtn (0.8 ± 0.4 vs. 0.6 ± 0.3, p = 0.01) while relatively similar in Lat (1.8 ± 0.9 vs. 1.7 ± 0.8, p = 0.07). The translation displacement range, after coarse guidance step, required for high accuracy of the optical surface component of the integrated system was 0-42 mm, and the average positioning duration of the integrated system was significantly less than that of conventional workflow (355.7 ± 21.7 vs. 387.7 ± 26.6 s, p < 0.01).The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.
Faster exact solution of sparse MaxCut and QUBO problems

Abstract The maximum-cut problem is one of the fundamental problems in combinatorial optimization. With the advent of quantum computers, both the maximum-cut and the equivalent quadratic unconstrained binary optimization problem have experienced much interest in recent years. This article aims to advance the state of the art in the exact solution of both problems—by using mathematical programming techniques. The main focus lies on sparse problem instances, although also dense ones can be solved. We enhance several algorithmic components such as reduction techniques and cutting-plane separation algorithms, and combine them in an exact branch-and-cut solver. Furthermore, we provide a parallel implementation. The new solver is shown to significantly outperform existing state-of-the-art software for sparse maximum-cut and quadratic unconstrained binary optimization instances. Furthermore, we improve the best known bounds for several instances from the 7th DIMACS Challenge and the QPLIB, and solve some of them (for the first time) to optimality.
Deep Learning-based Analysis of Algerian Dialect Dataset Targeted Hate Speech, Offensive Language and Cyberbullying

Toxicity and hate speech on social media platforms can lead to cyber-crime, affecting social life on a personal and community level.Therefore, automatic toxicity and hateful content detection are necessary to enhance web content quality and fight against inappropriate speech spread through social media.This need is also a challenge when comments are posted and written in complex languages, such as Arabic, which is recognised for its difficulties and lack of resources.This paper introduces a new dataset for Algerian dialect toxic text detection, whereby we build an annotated multi-label dataset consisting of 14150 comments extracted from Facebook, YouTube and Twitter, and labelled as hate speech, offensive language and cyberbullying.To assess the practical utility of the created annotated dataset, several tests have been conducted using many classification models of traditional machine learning (ML), namely, Random Forest, Naïve Bayes, Linear Support Vector (SVC), Stochastic Gradient Descent (SGD) and Logistic Regression.Furthermore, several assessments have been conducted using Deep Learning (DL) models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional-LSTM (Bi-LSTM) and Bidirectional-GRU (Bi-GRU).Experimental tests demonstrate the success of the Bi-GRU model, which achieved the highest results for DL classification, with 73.6% Accuracy and 75.8% F1-Score.
Automatic Alignment in Higher-Order Probabilistic Programming Languages

Abstract Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints —relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run—they are aligned . We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC . We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC.
Personalized federated learning for heterogeneous data: A distributed edge clustering approach

&lt;abstract&gt;&lt;p&gt;Federated learning (FL) is a distributed machine learning technique that allows multiple devices (e.g., smartphones and IoT devices) to collaborate in the training of a shared model with each device preserving the privacy of its local data. However, the highly heterogeneous distribution of data among clients in FL can result in poor convergence. In addressing this issue, the concept of personalized federated learning (PFL) has emerged. PFL aims to tackle the effects of non-independent and identically distributed data and statistical heterogeneity and to achieve personalized models with rapid model convergence. One approach is clustering-based PFL, which utilizes group-level client relationships to achieve personalization. However, this method still relies on a centralized approach, whereby the server coordinates all processes. To address these shortcomings, this study introduces a blockchain-enabled distributed edge cluster for PFL (BPFL) that combines the benefits of blockchain and edge computing. Blockchain technology can be used to enhance client privacy and security by recording transactions on immutable distributed ledger networks, thereby improving client selection and clustering. The edge computing system offers reliable storage and computation such that computational processing is locally performed in the edge infrastructure to be closer to clients. Thus, the real-time services and low-latency communication of PFL are improved. However, further work is required to develop a representative dataset for the examination of related types of attacks and defenses for a robust BPFL protocol.&lt;/p&gt;&lt;/abstract&gt;
The Night Shift: Understanding Performance Variability of Cloud Serverless Platforms

Function-as-a-Service is a popular cloud programming model that supports developers by abstracting away most operational concerns with automatic deployment and scaling of applications. Due to the high level of abstraction, developers rely on the cloud platform to offer a consistent service level, as decreased performance leads to higher latency and higher cost given the pay-per-use model. In this paper, we measure performance variability of Google Cloud Functions over multiple months. Our results show that diurnal patterns can lead to performance differences of up to 15%, and that the frequency of unexpected cold starts increases threefold during the start of the week. This behavior can negatively impact researchers that conduct performance studies on cloud platforms and practitioners that run cloud applications.
The Effects of Augmented Reality in a 7th-Grade Science Lesson on Students' Academic Achievement and Motivation

This study examines the effects of a seventh-grade science course supported by augmented reality technologies on students' academic achievement on elements and compounds, their motivation towards the science course, and their motivation toward the augmented reality materials used. The study was conducted with the quasi-experimental design of the quantitative research method. The research sample consists of 50 seventh-grade students, 25 in the experimental group and 25 in the control group, studying in a public school. As data collection tools, academic achievement tests, motivation scale for learning science, and motivation scale for the materials used were utilized. In addition, the collected data were analyzed with the SPSS 24.0 program. As a consequence of the research, it was discovered that augmented reality technologies boosted students' academic success and motivation toward science learning (p0.001). In addition, students' motivation toward augmented reality materials was also found to be high.
A Research Toward Chinese Named Entity Recognition Based on Transfer Learning

Abstract To improve the performance of named entity recognition in the lack of well-annotated entity data, a transfer learning-based Chinese named entity recognition model is proposed in this paper. The specific tasks are as follows: (1) first/, a data transfer method based on entity features is proposed. By calculating the similarity of feature distribution between low resource data and high resource data, the most representative entity features are selected for feature transfer mapping, and the distance of entity distribution between the two domains is calculated to make up the gap between the data of the two domains then model is trained by high resource data. (2) Then, an entity boundary detection method is proposed. This method utilizes the BiLSTM+CRF as the main structure and integrates character boundary information to assist the attention network to improve the model’s ability to recognize entity boundaries. (3) Finally, multiple named entity recognition methods are selected as baseline methods for comparison, and experiments are conducted on several datasets. The results show that the model proposed in this paper improves the accuracy of named entity recognition by 1%, the recall rate by 2%, and the F 1 value by 2% on average in the field with low-resource.
Interactive and Explainable Region-guided Radiology Report Generation

The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://github.com/ttanida/rgrg .
Parallel Multi-Deque Partition Dual-Deque Merge sorting algorithm using OpenMP

Quicksort is an important algorithm that uses the divide and conquer concept, and it can be run to solve any problem. The performance of the algorithm can be improved by implementing this algorithm in parallel. In this paper, the parallel sorting algorithm named the Multi-Deque Partition Dual-Deque Merge Sorting algorithm (MPDMSort) is proposed and run on a shared memory system. This algorithm contains the Multi-Deque Partitioning phase, which is a block-based parallel partitioning algorithm, and the Dual-Deque Merging phase, which is a merging algorithm without compare-and-swap operations and sorts the small data with the sorting function of the standard template library. The OpenMP library, which is an application programming interface used to develop the parallel implementation of this algorithm, is implemented in MPDMSort. Two computers (one with an Intel Xeon Gold 6142 CPU and the other with an Intel Core i7-11700 CPU) running Ubuntu Linux are used in this experiment. The results show that MPDMSort is faster than parallel balanced quicksort and multiway merge sort on the large random distribution data. A speedup of 13.81[Formula: see text] and speedup per thread of 0.86 can be obtained. Thus, developers can use these parallel partitioning and merging algorithms to improve the performance of related algorithms.
Job Satisfaction among Employees of a Higher Educational Institution

A longitudinal study was conducted to determine the level and key determinants of job satisfaction among different categories of employees of a classical university on the example of University of Tyumen. A typology of university employees has been formed depending on the nature of job satisfaction: satisfied with (1) career growth and material component, (2) the meaning of work, fairness and comfort of work, (3) working conditions and equipment of the workplace. The highest overall job satisfaction is characteristic of employees who highly appreciated their satisfaction with the content of the work performed, fairness and comfort of work. &#x0D; Groups of factors that motivate employees to work effectively are identified: reliability and stability, career growth and social capital, managerial potential. The respondents who are most satisfied with their work are those who are motivated by career growth and building up social capital. This group is dominated by representatives of the university management and line managers. It was also found that job satisfaction is higher among those teachers and researchers who expect career changes related to territorial displacement. &#x0D; The results of the study can be useful to managers of university personnel services when developing a set of measures aimed at increasing job satisfaction among representatives of various professional groups, which will increase the prestige of work in the academic sector, retain talented employees and reduce staff turnover.
Peculiarities of the Formation of Educational Trajectories of Russian Students: Assessment and Opportunities

New geopolitical challenges that necessitate the formation of a nationally oriented education system require a multidimensional analysis, socio-cultural understanding and the search for new formats for the organization of higher education in Russia. In the conditions of the post-Bologna reality, Russia needs to create its own model of higher education, which will be able to provide the economy with highly qualified personnel, which will coopt all the best from the classical Soviet system of education and will be able to build on the national traditions, interests of students, their parents, teachers and employers. In this regard, it is relevant to analyze and evaluate the requests of nowadays students for these changes and the nature of those changes. The article is based on the materials of the authors’ sociological research (N=123977 people), conducted in 2022.The problematic area of the research supposed that the revision of the existing predominantly two-level (bachelor and master) structure of higher education requires a transition to the optimal and traditional for Russia model of the specialist program with the introduction of more flexible forms of work, the possibility of changing the educational trajectory in the learning process, the search for new ways of academic mobility and universities cooperation. The research data are compared with the data of similar works of Russian and foreign analysts. An analysis of the opinion of Russian students showed that in the process of learning and gradually entering a specialty, some students need certain changes in the chosen training program made upon admission. The possibility or impossibility of implementing these changes determines their motivation for learning, academic achievements, further educational and professional trajectories, and satisfaction with education in general. Based on the analysis done and the results obtained, the authors propose practical steps to form a new structure of higher education based on the specialist program. They suggest recommendations on the necessary changes in the legislative, organizational, educational and methodological nature related to this process.
Symbiotic-Witch 2: More Efficient Algorithm and Witness Refutation

Abstract The new version of the witness validator Symbiotic-Witch follows more precisely the (fixed version of the) semantics of verification witnesses. This makes the tool more efficient as it can benefit from sink nodes. Further, the tool can now refute a witness. To sum up, Symbiotic-Witch 2 can confirm or refute violation witnesses of reachability safety , memory safety , memory cleanup , and overflow properties of sequential C programs.
Compositional Automata Learning of Synchronous Systems

Abstract Automata learning is a technique to infer an automaton model of a black-box system via queries to the system. In recent years it has found widespread use both in industry and academia, as it enables formal verification when no model is available or it is too complex to create one manually. In this paper we consider the problem of learning the individual components of a black-box synchronous system, assuming we can only query the whole system. We introduce a compositional learning approach in which several learners cooperate, each aiming to learn one of the components. Our experiments show that, in many cases, our approach requires significantly fewer queries than a widely-used non-compositional algorithm such as $$\mathtt {L^*}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> .
Correction of the Solar Azimuth Discontinuity at Sunrise and Sunset

Solar azimuth may become indefinite at sunrise and sunset. This discrepancy is corrected in the present work. The correction concerns application of Fourier–series analysis to the solar paths over a site during days when the problem arises. The result is the derivation of a new curve that is fitted with great accuracy to the daily solar azimuth values, thus bridging the gap of the discontinuity. A demonstration for the solar azimuth correction is given for 3 sites around the world (Athens, Stockholm, and Sydney). The correction can be applied to any solar geometry code; in this work the algorithm of the XRONOS code is selected without (XRONOS.bas, a BASIC programme) and with (XRONOS.m, a MATLAB code) the correction proposed. A corrected expression for the atmospheric refractive index at various altitudes is given in Appendix A as the refraction of solar right is treated in XRONOS.
On Optimal Subarchitectures for Quantum Circuit Mapping

Compiling a high-level quantum circuit down to a low-level description that can be executed on state-of-the-art quantum computers is a crucial part of the software stack for quantum computing. One step in compiling a quantum circuit to some device is quantum circuit mapping, where the circuit is transformed such that it complies with the architecture’s limited qubit connectivity. Because the search space in quantum circuit mapping grows exponentially in the number of qubits, it is desirable to consider as few of the device’s physical qubits as possible in the process. Previous work conjectured that it suffices to consider only subarchitectures of a quantum computer composed of as many qubits as used in the circuit. In this work, we refute this conjecture and establish criteria for judging whether considering larger parts of the architecture might yield better solutions to the mapping problem. We show that determining subarchitectures that are of minimal size, i.e., from which no physical qubit can be removed without losing the optimal mapping solution for some quantum circuit, is a very hard problem. Based on a relaxation of the criteria for optimality, we introduce a relaxed consideration that still maintains optimality for practically relevant quantum circuits. Eventually, this results in two methods for computing near-optimal sets of subarchitectures—providing the basis for efficient quantum circuit mapping solutions. We demonstrate the benefits of this novel method for state-of-the-art quantum computers by IBM, Google, and Rigetti.
Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection

Gaze pointing is the de facto standard to infer attention and interact in 3D environments but is limited by motor and sensor limitations. To circumvent these limitations, we propose a vergence-based motion correlation method to detect visual attention toward very small targets. Smooth depth movements relative to the user are induced on 3D objects, which cause slow vergence eye movements when looked upon. Using the principle of motion correlation, the depth movements of the object and vergence eye movements are matched to determine which object the user is focussing on. In two user studies, we demonstrate how the technique can reliably infer gaze attention on very small targets, systematically explore how different stimulus motions affect attention detection, and show how the technique can be extended to multi-target selection. Finally, we provide example applications using the concept and design guidelines for small target and accuracy-independent attention detection in 3D environments.