Reviewerly/exHarmony huggingface dataset:
Multiple intelligences: Educational and cognitive development with a guiding focus

Development and dissemination of innovative pedagogies continues to be one of the challenges of the 21st century. The visible deficiencies in the educational field have highlighted the need for other types of pedagogies that promote complete student development. Gardner’s theory about multiple intelligences (MIs) has great potential that has not yet been realised in practice in school contexts. With this research we aimed to analyse the relationship between the intelligences that students develop in primary education and the increase in certain cognitive and academic capacities, and to demonstrate that a pedagogy based on Gardner’s theory does more to promote creativity, maturation and school performance than traditional teaching-learning pedagogies. A total of 420 participants from 2 state-funded schools participated in this study (experimental group = EG; control group = CG). The EG (n = 230) was taught using Gardner’s theory and the CG (n = 190) was taught according to traditional pedagogy. There was an association between the intelligences developed by the students and their academic, creative, and maturational levels. Finally, significant differences were found between the EG and CG, with the EG obtaining a higher mean in the variables analysed in favour of the EG. In conclusion, using MIs in classrooms allows for a more mature and creative development and greater academic performance.
Robust Character Labeling in Movie Videos: Data Resources and Self-Supervised Feature Adaptation

Robust face clustering is a vital step in enabling computational understanding of visual character portrayal in media. Face clustering for long-form content is challenging because of variations in appearance and lack of supporting large-scale labeled data. Our work in this paper focuses on two key aspects of this problem: the lack of domain-specific training or benchmark datasets, and adapting face embeddings learned on web images to long-form content, specifically movies. First, we present a dataset of over 169,000 face tracks curated from 240 Hollywood movies with weak labels on whether a pair of face tracks belong to the same or a different character. We propose an offline algorithm based on nearest-neighbor search in the embedding space to mine hard-examples from these tracks. We then investigate triplet-loss and multiview correlation-based methods for adapting face embeddings to hard-examples. Our experimental results highlight the usefulness of weakly labeled data for domain-specific feature adaptation. Overall, we find that multiview correlation-based adaptation yields more discriminative and robust face embeddings. Its performance on downstream face verification and clustering tasks is comparable to that of the state-of-the-art results in this domain. We also present the SAIL-Movie Character Benchmark corpus developed to augment existing benchmarks. It consists of racially diverse actors and provides face-quality labels for subsequent error analysis. We hope that the large-scale datasets developed in this work can further advance automatic character labeling in videos. All resources are available freely at https://sail.usc.edu/~ccmi/multiface.
A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems

Abstract In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world applications. The results reveal that HWW-NM is an attractive optimization approach for optimizing real-life problems.
International research activities and harmonious development of student’s personality

The article discusses the role of universities in developing student’s harmonious personality through their involvement in international educational and scientific events. Experiences in organizing and holding international Eurasian Economic Youth Forum are described, benefits of students’ presenting research papers on international platforms are considered. The analyses of international cooperation between Tashkent State University of Economics and Russian higher educational institutions are provided. Applying theoretical and qualitative research methods allowed to prove the effectiveness of scientific extracurricular activities for improving students’ professional skills, realizing their creative potential and fostering their personality growth. Getting students to participate in international educational and research projects promote effective implementing principles of sustainable development in education. Perspectives on international students’ research experiences are outlined.
Hyper-heuristics: Autonomous Problem Solvers

Algorithm design is a general task for any problem-solving scenario. For Search and Optimization, this task becomes rather challenging due to the immense algorithm design space. Those existing design options are usually traversed to devise algorithms by the human algorithm development experts together with the specialists on the target problem domains. The resulting algorithms are mostly problem-specific as they are unable to solve a different problem than the current target. Unlike the traditionally developed algorithms, Hyper-heuristics are known as problem-independent solvers pursuing the grand goal of generality. Generality, in this context, means that effectively solving different problems with a single algorithm under varying experimental conditions. This generality element is chased by performing a high-level search across the algorithm space differently than the majority of the algorithms directly operating on the solution space. In that respect, by design, a hyper-heuristic can be applied to any problem with a search space of quantifiable solutions. This flexibility coming from their easy-to-use nature has been validated in various academic and real-world applications. The present chapter provides a general overview of hyper-heuristics while discussing their shortcomings and recipes for future hyper-heuristic research.
Two-beam light with simultaneous anticorrelations in photon-number fluctuations and sub-Poissonian statistics

Two twin beams with a shared signal beam and separated idler beams are used together with the photon-number-resolving postselection in the signal beam to arrive at two coupled beams with anticorrelations in photon-number fluctuations. Moreover, the beams exhibit the sub-Poissonian photon-number statistics in their marginal distributions under suitable conditions. The postselected fields with the increasing mean photon numbers are reconstructed from the experimental photocount histograms by the maximum likelihood approach. Also a suitable Gaussian fit of both original twin beams and simulation of the postselection process are applied to arrive at the corresponding photon-number distributions. Their nonclassical properties are analyzed by suitable nonclassicality criteria and quantified by the corresponding nonclassicality depths. Determining the appropriate quasi-distributions of integrated intensities with negative values, the performance of different nonclassicality criteria is judged. Properties of the postselected fields reached both by the used and ideal photon-number-resolved detectors are mutually compared.
Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation

Quality estimation (QE) has recently gained increasing interest as it can predict the quality of machine translation results without a reference translation. QE is an annual shared task at the Conference on Machine Translation (WMT), and most recent studies have applied the multilingual pretrained language model (mPLM) to address this task. Recent studies have focused on the performance improvement of this task using data augmentation with finetuning based on a large-scale mPLM. In this study, we eliminate the effects of data augmentation and conduct a pure performance comparison between various mPLMs. Separate from the recent performance-driven QE research involved in competitions addressing a shared task, we utilize the comparison for sub-tasks from WMT20 and identify an optimal mPLM. Moreover, we demonstrate QE using the multilingual BART model, which has not yet been utilized, and conduct comparative experiments and analyses with cross-lingual language models (XLMs), multilingual BERT, and XLM-RoBERTa.
Efficient quantum programming using EASE gates on a trapped-ion quantum computer

Parallel operations in conventional computing have proven to be an essential tool for efficient and practical computation, and the story is not different for quantum computing. Indeed, there exists a large body of works that study advantages of parallel implementations of quantum gates for efficient quantum circuit implementations. Here, we focus on the recently invented efficient, arbitrary, simultaneously entangling (EASE) gates, available on a trapped-ion quantum computer. Leveraging its flexibility in selecting arbitrary pairs of qubits to be coupled with any degrees of entanglement, all in parallel, we show an $n$-qubit Clifford circuit can be implemented using $6\log(n)$ EASE gates, an $n$-qubit multiply-controlled NOT gate can be implemented using $3n/2$ EASE gates, and an $n$-qubit permutation can be implemented using six EASE gates. We discuss their implications to near-term quantum chemistry simulations and the state of the art pattern matching algorithm. Given Clifford + multiply-controlled NOT gates form a universal gate set for quantum computing, our results imply efficient quantum computation by EASE gates, in general.
BGM-Net: Boundary-Guided Multiscale Network for Breast Lesion Segmentation in Ultrasound

Automatic and accurate segmentation of breast lesion regions from ultrasonography is an essential step for ultrasound-guided diagnosis and treatment. However, developing a desirable segmentation method is very difficult due to strong imaging artifacts e.g., speckle noise, low contrast and intensity inhomogeneity, in breast ultrasound images. To solve this problem, this paper proposes a novel boundary-guided multiscale network (BGM-Net) to boost the performance of breast lesion segmentation from ultrasound images based on the feature pyramid network (FPN). First, we develop a boundary-guided feature enhancement (BGFE) module to enhance the feature map for each FPN layer by learning a boundary map of breast lesion regions. The BGFE module improves the boundary detection capability of the FPN framework so that weak boundaries in ambiguous regions can be correctly identified. Second, we design a multiscale scheme to leverage the information from different image scales in order to tackle ultrasound artifacts. Specifically, we downsample each testing image into a coarse counterpart, and both the testing image and its coarse counterpart are input into BGM-Net to predict a fine and a coarse segmentation maps, respectively. The segmentation result is then produced by fusing the fine and the coarse segmentation maps so that breast lesion regions are accurately segmented from ultrasound images and false detections are effectively removed attributing to boundary feature enhancement and multiscale image information. We validate the performance of the proposed approach on two challenging breast ultrasound datasets, and experimental results demonstrate that our approach outperforms state-of-the-art methods.
Distance Learning Advantages and Disadvantages: Teaching Experience Analysis at the University with the Basis on Different Informational-Communicative Technologies

The transition to distance learning during the coronavirus pandemic is seen as a natural pedagogical experiment, which became more manageable for all participants of educational process in autumn 2020. Comprehending work experience in spring, as well as training in the field of informational and communication technologies (ICT), allows the teacher not only to expand their capabilities when working online, but also to experiment with using different digital tools (Teams, Zoom, Moodle, Kahoot), analyzing their application success and the reasons for difficulties occurred. The article presents different courses’ students survey results and forms of education regarding distance learning features which they note. The analysis of attitudes towards certain distance learning aspects of by students of different ages (and, accordingly, different generations - Z and Y) is carried out. It is shown that the hypotheses regarding more positive perception of online learning and greater satisfaction with the use of digital tools by the teacher that ensure the interactive classes nature (holding meetings in subgroups in separate channels, organizing joint work using the One Note tool, quizzes by discipline using the Kahoot digital tool) by junior students was not confirmed: senior students note more positive aspects of distance learning and, with the same satisfaction as generation Z students, perceive the use of ICT by the teacher to organize interaction within the educational process. The paper analyzes the results obtained, as well as outlines the prospects for further research in the field of distance learning: overcoming the problems of insufficient (loss) information) when using ICT, overcoming limitations in the transfer of personal knowledge, studying the influence of group dynamics when organizing interaction using ICT.
Design of a quantum repeater using quantum circuits and benchmarking its performance on an IBM quantum computer

Quantum communication relies on the existence of entanglement between two nodes of a network. However, due to its fragile nature, it is nearly impossible to establish entanglement at large distances through the direct transmission of qubits. Quantum repeaters have been proposed to solve this problem, which split up the network to create small-scale entangled links and then connect them up to create the large-scale link. As researchers race to establish entanglement over larger and larger distances, it becomes essential to gauge the performance and robustness of different protocols that have been proposed to design a quantum repeater, before deploying them in real life. Currently available noisy quantum computers are ideal for this task, as they can emulate the noisy environment in a quantum communication channel, and provide a measure for how the protocols will perform on real-life hardware. In this paper, we report the circuit-level implementation of the complete architecture of a quantum repeater and benchmark this protocol on IBM's cloud quantum computer—IBMQ. Our experiments indicate a 26% fidelity of shared bell pairs for a complete on-chip quantum repeater with a yield of 49%. We also compare these results with simulation data from IBM Qiskit. The results of our experiments provide a quantitative measure for the fidelity of entanglement that currently available repeaters can establish. In addition, the proposed circuit implementation provides a robust benchmark for state-of-the-art quantum computing hardware.
Consistent depth of moving objects in video

We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera. We seek a geometrically and temporally consistent solution to this underconstrained problem: the depth predictions of corresponding points across frames should induce plausible, smooth motion in 3D. We formulate this objective in a new test-time training framework where a depth-prediction CNN is trained in tandem with an auxiliary scene-flow prediction MLP over the entire input video. By recursively unrolling the scene-flow prediction MLP over varying time steps, we compute both short-range scene flow to impose local smooth motion priors directly in 3D, and long-range scene flow to impose multi-view consistency constraints with wide baselines. We demonstrate accurate and temporally coherent results on a variety of challenging videos containing diverse moving objects (pets, people, cars), as well as camera motion. Our depth maps give rise to a number of depth-and-motion aware video editing effects such as object and lighting insertion.
Application of tilt correlation statistics to anisoplanatic optical turbulence modeling and mitigation

Atmospheric optical turbulence can be a significant source of image degradation, particularly in long range imaging applications. Many turbulence mitigation algorithms rely on an optical transfer function (OTF) model that includes the Fried parameter. We present anisoplanatic tilt statistics for spherical wave propagation. We transform these into 2D autocorrelation functions that can inform turbulence modeling and mitigation algorithms. Using these, we construct an OTF model that accounts for image registration. We also propose a spectral ratio Fried parameter estimation algorithm that is robust to camera motion and requires no specialized scene content or sources. We employ the Fried parameter estimation and OTF model for turbulence mitigation. A numerical wave-propagation turbulence simulator is used to generate data to quantitatively validate the proposed methods. Results with real camera data are also presented.
Automated Risk Assessment of COVID-19 Patients at Diagnosis Using Electronic Healthcare Records

COVID-19 causes significant morbidity and mortality and early intervention is key to minimizing deadly complications.Available treatments, such as monoclonal antibody therapy, may limit complications, but only when given soon after symptom onset.Unfortunately, these treatments are often expensive, in limited supply, require administration within a hospital setting, and should be given before the onset of severe symptoms.These challenges have created the need for early triage of patients likely to develop life-threatening complications.To meet this need, we developed an automated patient risk assessment model using a real-world hospital system dataset with over 17,000 COVIDpositive patients.Specifically, for each COVID-positive patient, we generate a separate risk score for each of four clinical outcomes including death within 30 days, mechanical ventilator use, ICU admission, and any catastrophic event (a superset of dangerous outcomes).We hypothesized that a deep learning binary classification approach can generate these four risk scores from electronic healthcare records data at the time of diagnosis.Our approach achieves significant performance on the four tasks with an area under receiver operating curve (AUROC) for any catastrophic outcome, death within 30 days, ventilator use, and ICU admission of 86.7%, 88.2%, 86.2%, and 87.8%, respectively.In addition, we visualize the sensitivity and specificity of these risk scores to allow clinicians to customize their usage within different clinical outcomes.We believe this work fulfills a clear clinical need for early detection of objective clinical outcomes and can be used for early screening for treatment intervention.
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks

Various adversarial attack methods pose a threat to secure machine learning models. Pre-processing-based defense against adversarial input was not adequate, and they are vulnerable to adaptive attacks. Our study proposed a dynamic pre-process-based defense technique leveraging a Genetic Algorithm that can defend against traditional adaptive attacks. We described our methodology and performed experiments using multiple datasets tested with several adversarial attacks. Our thorough empirical experiments exhibited encouraging outcomes indicating that the procedure can efficiently utilize an adversarial defense for any learning model.
An Optical Flow Based Left-Invariant Metric for Natural Gradient Descent in Affine Image Registration

Accurate spatial alignment is essential for any population neuroimaging study, and affine (12 parameter linear/translation) or rigid (6 parameter rotation/translation) alignments play a major role. Here we consider intensity based alignment of neuroimages using gradient based optimization, which is a problem that continues to be important in many other areas of medical imaging and computer vision in general. A key challenge is robustness. Optimization often fails when transformations have components with different characteristic scales, such as linear versus translation parameters. Hand tuning or other scaling approaches have been used, but efficient automatic methods are essential for generalizing to new imaging modalities, to specimens of different sizes, and to big datasets where manual approaches are not feasible. To address this we develop a left invariant metric on these two matrix groups, based on the norm squared of optical flow induced on a template image. This metric is used in a natural gradient descent algorithm, where gradients (covectors) are converted to perturbations (vectors) by applying the inverse of the metric to define a search direction in which to update parameters. Using a publicly available magnetic resonance neuroimage database, we show that this approach outperforms several other gradient descent optimization strategies. Due to left invariance, our metric needs to only be computed once during optimization, and can therefore be implemented with negligible computation time.
Phase-stable source of high-quality three-photon polarization entanglement by cascaded down-conversion

Stable sources of entangled photons are important requirements for quantum communications. In recent years, cascaded downconversion has been demonstrated as an effective method of directly producing three-photon entanglement. However, to produce polarization entanglement these sources have until now relied on intricate active phase stabilization schemes, thus limiting their robustness and usability. In this work, we present a completely phase-stable source of three-photon entanglement in the polarization degree of freedom. With this source, which is based on a cascade of two pair sources based on Sagnac configurations, we produce states with over 96\% fidelity with an ideal GHZ state. Moreover, we demonstrate the stability of the source over several days without any ongoing optimization. We expect this source to be a useful tool for applications requiring multiphoton entanglement, such as quantum secret sharing and producing heralded entangled photon pairs.
Proportionate response to the COVID‐19 threat? Use of apps and other technologies for monitoring employees under the European Union's data protection framework

This article explores the potential uses by employers of contact-tracing apps and other monitoring technologies to mitigate the spread of COVID-19, and the potential concerns that these raise in the context of the European Union's General Data Protection Regulation. Given the imbalance of power in the employment relationship, the authors call for national laws to strengthen employees' ability to refuse the use of such apps and technologies after the end of the COVID-19 pandemic. When such tools are no longer needed to keep employees safe, additional regulations and guidance will be necessary to prevent future problems, such as function creep and other misuse by employers.