Reviewerly/exHarmony huggingface dataset:
Demystifying the Python-Processing Landscape: An Overview of Tools Combining Python and Processing

Processing is composed of a programming language and an editor for writing and compiling code, providing a collection of special commands to draw, animate, and handle user input using Java. Python Mode for Processing (also referred to as Processing.py) leverages Jython, a Java implementation of Python, to interface with Processing’s Java core. One can install and activate Python Mode in Processing using a button in the IDE interface. Python Mode enables Python syntax in the IDE (instead of Java) but has its limitations: it is source-compatible with Python 2.7 (not 3+) and does not support CPython libraries (such as NumPy). Several promising new Python-Processing tools have emerged, but this proliferation of alternatives can confuse would-be users. This talk maps out the Python-Processing landscape, offering insight into the different options and providing direction for beginners, teachers, and more accomplished programmers keen to explore Python as a tool for creative coding projects.
Advances in Spatial Hashing: A Pragmatic Approach towards Robust, Real-time Light Transport Simulation

Spatial hashing is a battle-tested technique for efficiently storing sparse spatial data. Originally designed to optimize secondary light bounces in path tracing, it has been extended for real-time ambient occlusion and diffuse environment lighting. We complement spatial hashing by introducing support for view-dependent effects using world-space temporal filtering. Optimizing the hash key generation, we improve performance using a much better cache coherence and aliasing reduction. Finally, we enhance the sampling quality using methods including visibility-aware environment sampling.
Training a Deep Remastering Model

The success of video streaming platforms has pushed studios to make available TV shows from legacy catalog, and there is an increased demand for remastering this content. Ideally, film reels are re-scanned with modern devices directly into high quality digital format. However this is not always possible as parts of the original film reels can be damaged or missing, and the content is then available in its entirety only in the broadcast version, typically NTSC. In this work, we present a deep learning solution to bring the NTSC version to the new scan quality levels, which would be otherwise impossible with existing tools.
Integrating Mobile Games in Arabic Orthography Classrooms

Considerable advances in the capabilities of modern mobile devices have enabled their use as powerful educational tools. Today, mobile learning games are widely used as creative platforms for teaching and learning, offering enjoyable and ubiquitous educational content. This study describes the design and evaluation of a mobile Arabic orthography game aimed at improving Arabic orthographical skills among young learners. In particular, 52 female fourth-grade students participated in this study to answer the following research questions: what impact does the use of mobile Arabic orthography games have on students’ performance, and how do students perceive this learning approach? A mixed-method research design was adopted to answer these research questions, including pre-and post-tests, interviews, and classroom observations. Analysis of the data revealed that, although there were a few challenges involved in using mobile games as a learning tool, significant improvements were found in students’ performance and engagement, and positive attitudes were developed towards using the mobile game. In addition, there was an overall increase in students’ motivation and interaction. The pedagogical implications of these findings can be linked to the gamification of the teaching and learning environments. Teachers are encouraged to consider integrating mobile educational games into their instructional approaches, as they can serve as great incentives to learning (especially for young students).
. Improving the Supporting Business Processes of Higher Educational Institutions to Improve the Efficiency of Educational Service Organizations

The article examines the processes and systems of employee motivation in the achievement of their KPI’s as an element of the implementation of university development programs. The following business processes in employee motivation systems of seven higher educational institutions were analyzed: determination of key performance indicators of employees, collection of data on the fact of the implementation of measures affecting the implementation of key performance indicators of employees, data verification (confirmation of the fact of the implementation of measures) by responsible persons, resolution of conflict situations related to the need to revise the results of data verification, calculation of allowances. Five diagrams in UML 2.0 notation describing these processes were compiled for them. The conclusion was made about the similarity of the mentioned motivation systems. Based on the above, it is possible to automate emerging processes by developing a special information system. The results of the study formed the basis for the development of a system of motivation and planning for the implementation of KPIs by university employees, which was implemented at the National Research University of MEI, which showed efficiency due to automation of management.
Variational message passing (VMP) applied to LDA

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) is the original inference mechanism for LDA. Many variants of VB for LDA, as well as for VB in general, have been developed since LDA's inception in 2013, but standard VB is still widely applied to LDA. Variational message passing (VMP) is the message passing equivalent of VB and is a useful tool for constructing a variational inference solution for a large variety of conjugate exponential graphical models (there is also a non conjugate variant available for other models). In this article we present the VMP equations for LDA and also provide a brief discussion of the equations. We hope that this will assist others when deriving variational inference solutions to other similar graphical models.
Microbiological Analysis of Liquid Lipstick Products Used by Makeup Artists

AbstractLipstick is a non-sterile pharmaceutical product that is used for make-up on the lips. Liquid lipstick used by makeup artists may have a high level of microbial contamination because they are applied to different clients in turn. This study aims to determine the microbiological quality of liquid lipstick used by makeup artists based on the result of the Bacterial Total Plate Count (TPC), Yeast and Mold Count (YMC), and the presence of pathogenic microbes. Three samples of liquid lipstick were taken from three different makeup artists and one sample was a new liquid lipstick product. The Bacterial TPC test was carried out on TSA media and the YMC test was carried out on SDA with the spread plate technique. The presence of microbial pathogens P. aeruginosa, S. aureus, and C. albicans were carried out on selective media. The results of bacterial TPC was 9.0 × 103 to 3.0 × 104 CFU/mL, while the YMC was 1.1 × 103 to 3.1 × 103 CFU/mL. The pathogenic microbes detected were S. aureus in 2 samples and C. albicans in 1 sample. The bacterial TPC and YMC values exceed the contamination limit based on BPOM Regulation No. 12/2019.
Data Breaches in Healthcare Security Systems

Providing security to Health Information is considered to be the topmost priority when compared to any other field. After the digitalization of the patient's records in the medical field, the healthcare/medical field has become a victim of several internal and external cyberattacks. Data breaches in the healthcare industry have been increasing rapidly. Despite having security standards such as HIPAA (Health Insurance Portability and Accountability Act), data breaches still happen on a daily basis. All various types of data breaches have the same harmful impact on healthcare data, especially on patients' privacy. The main objective of this paper is to analyze why healthcare data breaches occur and what is the impact of these breaches. The paper also presents the possible improvements that can be made in the current standards, such as HIPAA, to increase security in the healthcare field.
Multi-Task Meta-Learning Modification with Stochastic Approximation

Meta-learning methods aim to build learning algorithms capable of quickly adapting to new tasks in low-data regime. One of the main benchmarks of such an algorithms is a few-shot learning problem. In this paper we investigate the modification of standard meta-learning pipeline that takes a multi-task approach during training. The proposed method simultaneously utilizes information from several meta-training tasks in a common loss function. The impact of each of these tasks in the loss function is controlled by the corresponding weight. Proper optimization of these weights can have a big influence on training of the entire model and might improve the quality on test time tasks. In this work we propose and investigate the use of methods from the family of simultaneous perturbation stochastic approximation (SPSA) approaches for meta-train tasks weights optimization. We have also compared the proposed algorithms with gradient-based methods and found that stochastic approximation demonstrates the largest quality boost in test time. Proposed multi-task modification can be applied to almost all methods that use meta-learning pipeline. In this paper we study applications of this modification on Prototypical Networks and Model-Agnostic Meta-Learning algorithms on CIFAR-FS, FC100, tieredImageNet and miniImageNet few-shot learning benchmarks. During these experiments, multi-task modification has demonstrated improvement over original methods. The proposed SPSA-Tracking algorithm shows the largest accuracy boost that is competitive against the state-of-the-art meta-learning methods. Our code is available online.
Using Natural Language Processing to Understand Reasons and Motivators  Behind Customer Calls in Financial Domain

In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.
Quantum Computational Quantification of Protein-Ligand Interactions

We have demonstrated a prototypical hybrid classical and quantum computational workflow for the quantification of protein-ligand interactions. The workflow combines the Density Matrix Embedding Theory (DMET) embedding procedure with the Variational Quantum Eigensolver (VQE) approach for finding molecular electronic ground states. A series of $\beta$-secretase (BACE1) inhibitors is rank-ordered using binding energy differences calculated on the latest superconducting transmon (IBM) and trapped-ion (Honeywell) Noisy Intermediate Scale Quantum (NISQ) devices. This is the first application of real quantum computers to the calculation of protein-ligand binding energies. The results shed light on hardware and software requirements which would enable the application of NISQ algorithms in drug design.
Semantics-aware Attention Improves Neural Machine Translation

The integration of syntactic structures into Transformer machine translation has shown positive results, but to our knowledge, no work has attempted to do so with semantic structures. In this work we propose two novel parameter-free methods for injecting semantic information into Transformers, both rely on semantics-aware masking of (some of) the attention heads. One such method operates on the encoder, through a Scene-Aware Self-Attention (SASA) head. Another on the decoder, through a Scene-Aware Cross-Attention (SACrA) head. We show a consistent improvement over the vanilla Transformer and syntax-aware models for four language pairs. We further show an additional gain when using both semantic and syntactic structures in some language pairs.
Towards Time-Optimal Tunnel-Following for Quadrotors

Minimum-time navigation within constrained and dynamic environments is of special relevance in robotics. Seeking time-optimality, while guaranteeing the integrity of time-varying spatial bounds, is an appealing trade-off for agile vehicles, such as quadrotors. State of the art approaches, either assume bounds to be static and generate time-optimal trajectories offline, or compromise time-optimality for constraint satisfaction. Leveraging nonlinear model predictive control and a path parametric reformulation of the quadrotor model, we present a real-time control that approximates time-optimal behavior and remains within dynamic corridors. The efficacy of the approach is evaluated according to simulated results, showing itself capable of performing extremely aggressive maneuvers as well as stop-and-go and backward motions.
The QICK (Quantum Instrumentation Control Kit): Readout and control for  qubits and detectors

We introduce a Xilinx RFSoC-based qubit controller (called the Quantum Instrumentation Control Kit, or QICK for short) which supports the direct synthesis of control pulses with carrier frequencies of up to 6 GHz. The QICK can control multiple qubits or other quantum devices. The QICK consists of a digital board hosting an RFSoC (RF System-on-Chip) FPGA \cite{zcu111}, custom firmware and software and an optional companion custom-designed analog front-end board. We characterize the analog performance of the system, as well as its digital latency, important for quantum error correction and feedback protocols. We benchmark the controller by performing standard characterizations of a transmon qubit. We achieve an average Clifford gate fidelity of $\mathcal{F}_{avg}=99.93\%$. All of the schematics, firmware, and software are open-source \cite{QICKrepo}.
Prediction of Feasibility of Entrepreneurial Proposals in Student Creativity Program

Student Creativity Program is a program organized by the Directorate of Learning and Student Affairs, Directorate General of Higher Education, Research, and Technology, Ministry of Education, Culture, Research and Technology as a national level student creativity event as an effort to grow, accommodate, and realize students' creative and innovative ideas. Based on 2017-2021 data, each year an average of 63,337 proposals are received, administrative and substance evaluations involve complex assessment components and are carried out manually so that it takes a relatively long time in the calculation process. Then a special method is needed that speeds up the processing of assessment data. This research was conducted on the substance of the Entrepreneurship Sector to predict the feasibility of a proposal to get funding applying data mining with the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (K-NN) algorithms with a comparison between Euclidean Distance and Manhattan Distance. From the results, it is known that NBC produces 96.49% accuracy and 0.912 Kappa. K-NN with the largest Euclidean Distance calculation in K-5, K-7 and K9 with an accuracy of 99.04% and Kappa 0.975, K-NN Manhattan Distance calculation produces the greatest accuracy of all the methods used by researchers, namely 100% and Kappa 1,00 categorized as Excellent. So the conclution is that the K-NN method with K-5 which produces the greatest accuracy and Kappa can be recommended to PKM stakeholders in funding feasibility algorithms.
Nyquist-Sampling and Degrees of Freedom of Electromagnetic Fields

A signal-space approach is presented to study the Nyquist sampling and number of degrees of freedom of an electromagnetic field under arbitrary propagation conditions. Conventional signal processing tools such as the multidimensional sampling theorem and Fourier theory are used to build a linear system theoretic interpretation of electromagnetic wave propagations and revisit classical electromagnetic theory results, e.g., bandlimited property of an electromagnetic field, from a signal processing perspective. Scalar electromagnetic fields are considered for simplicity, which physically correspond to acoustic propagation in general or electromagnetic propagation under certain conditions. The developed approach is extended to study ensembles of a stationary random electromagnetic field that is representative of different propagation conditions.
Energy Extrapolation in Quantum Optimization Algorithms

Quantum optimization algorithms are promising to find the ground state of a target Hamiltonian on near-term quantum devices. However, it is necessary to limit the evolution time or the circuit depth as much as possible since otherwise decoherence will degrade the computation. Even when this is done, there always exists a non-negligible estimation error in the ground state energy. Here we show a scalable extrapolation approach to mitigate this error. With an appropriate regression, we can significantly improve the estimation accuracy for quantum annealing, variational quantum eigensolver and quantum imaginary time evolution for fixed evolution time or circuit depth. The inference is achieved by extrapolating the annealing time to infinity or extrapolating the variance to zero. For Hamiltonians that only involve few-body interactions, the additional overhead is an increase in the number of measurements by a constant factor. We verified the validity of our method via numerical simulation and experiments on an IBM quantum computer. The method is robust to noise, and the techniques are applicable to other physics problems. Analytic derivations for the quadratic convergence feature of the residual energy in quantum annealing and the linear convergence feature of energy variance are given. Our work paves a promising new way for enhancing near-term quantum computing with classical post-processing.
A Bayesian semi-parametric approach for modeling memory decay in dynamic  social networks

In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the quantity of past interactions and the time that elapsed since the past interactions occurred affect the actors' decision-making to interact with other actors in the network. Recently occurred events generally have a stronger influence on current interaction behavior than past events that occurred a long time ago--a phenomenon known as "memory decay". Previous studies either predefined a short-run and long-run memory or fixed a parametric exponential memory using a predefined half-life period. In real-life relational event networks however it is generally unknown how the memory of actors about the past events fades as time goes by. For this reason it is not recommendable to fix this in an ad hoc manner, but instead we should learn the shape of memory decay from the observed data. In this paper, a novel semi-parametric approach based on Bayesian Model Averaging is proposed for learning the shape of the memory decay without requiring any parametric assumptions. The method is applied to relational event history data among socio-political actors in India.
Multi-Task Self-Training for Learning General Representations

Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision. Here we introduce multi-task self-training (MuST), which harnesses the knowledge in independent specialized teacher models (e.g., ImageNet model on classification) to train a single general student model. Our approach has three steps. First, we train specialized teachers independently on labeled datasets. We then use the specialized teachers to label an unlabeled dataset to create a multi-task pseudo labeled dataset. Finally, the dataset, which now contains pseudo labels from teacher models trained on different datasets/tasks, is then used to train a student model with multi-task learning. We evaluate the feature representations of the student model on 6 vision tasks including image recognition (classification, detection, segmentation)and 3D geometry estimation (depth and surface normal estimation). MuST is scalable with unlabeled or partially labeled datasets and outperforms both specialized supervised models and self-supervised models when training on large scale datasets. Lastly, we show MuST can improve upon already strong checkpoints trained with billions of examples. The results suggest self-training is a promising direction to aggregate labeled and unlabeled training data for learning general feature representations.
Quantum Markov monogamy inequalities

Markovianity lies at the heart of communication problems. This in turn makes the information-theoretic characterization of Markov processes worthwhile. Data processing inequalities are ubiquitous in this sense, assigning necessary conditions for all Markov processes. We address here the problem of the information-theoretic analysis of constraints on Markov processes in the quantum regime. We show the existence of a novel class of quantum data processing inequalities called here quantum Markov monogamy inequalities. This new class of necessary conditions on quantum Markov processes is inspired by its counterpart for classical Markov processes, and thus providing a strong link between classical and quantum constraints on Markovianity. We go on to construct a family of multitime quantum Markov monogamy inequalities, based on the process tensor formalism and that exploits multitime correlations. We then show, by means of an explicit example, that the Markov monogamy inequalities can be stronger than the usual quantum data processing inequalities.
Temporal Knowledge Consistency for Unsupervised Visual Representation  Learning

The instance discrimination paradigm has become dominant in unsupervised learning. It always adopts a teacher-student framework, in which the teacher provides embedded knowledge as a supervision signal for the student. The student learns meaningful representations by enforcing instance spatial consistency with the views from the teacher. However, the outputs of the teacher can vary dramatically on the same instance during different training stages, introducing unexpected noise and leading to catastrophic forgetting caused by inconsistent objectives. In this paper, we first integrate instance temporal consistency into current instance discrimination paradigms, and propose a novel and strong algorithm named Temporal Knowledge Consistency (TKC). Specifically, our TKC dynamically ensembles the knowledge of temporal teachers and adaptively selects useful information according to its importance to learning instance temporal consistency. Experimental result shows that TKC can learn better visual representations on both ResNet and AlexNet on linear evaluation protocol while transfer well to downstream tasks. All experiments suggest the good effectiveness and generalization of our method.