Reviewerly/exHarmony huggingface dataset:
SocialInteractionGAN: Multi-Person Interaction Sequence Generation

Prediction of human actions in social interactions has important applications in the design of social robots or artificial avatars. In this paper, we focus on a unimodal representation of interactions and propose to tackle interaction generation in a data-driven fashion. In particular, we model human interaction generation as a discrete multi-sequence generation problem and present SocialInteractionGAN, a novel adversarial architecture for conditional interaction generation. Our model builds on a recurrent encoder-decoder generator network and a dual-stream discriminator, that jointly evaluates the realism of interactions and individual action sequences and operates at different time scales. Crucially, contextual information on interacting participants is shared among agents and reinjected in both the generation and the discriminator evaluation processes. Experiments show that albeit dealing with low dimensional data, SocialInteractionGAN succeeds in producing high realism action sequences of interacting people, comparing favorably to a diversity of recurrent and convolutional discriminator baselines, and we argue that this work will constitute a first stone towards higher dimensional and multimodal interaction generation. Evaluations are conducted using classical GAN metrics, that we specifically adapt for discrete sequential data. Our model is shown to properly learn the dynamics of interaction sequences, while exploiting the full range of available actions.
BC-store: A program for MGISEQ barcode sets analysis

Here we present the devised BC-store–a program for analyzing and selecting sets of barcodes for sequencing on platforms manufactured by MGI Tech (China). The app is available as an open source in Python3 and as a desktop version. The application allows analyzing the compatibility of barcodes on a single lane of a flow cell in a set in the case of equal and arbitrary fractions. In addition, with the help of this tool barcodes can be added to an existing set with custom share options. In this paper we describe how BC-store works for different tasks and consider the effectiveness of using BC-store in sequence lab routine tasks.
Soft on errors? The correcting mechanism of a Skolt Sami speller

The article discusses correcting of typos due to erroneous use of the so-called soft sign in Skolt Sami, one of the most common orthographic symbols, and the most common source of typographic errors. The discussion is based upon the suggestion mechanism of an existing open source Skolt Sami speller. The discussion shows that with an improved suggestion mechanism, the speller is able to restore a single soft sign error in over 97 % of the cases, and remove a hypercorrect soft sign as first correction in 90 % of the cases. Allowing the target form to be within top-5, the correction performance is well above 99 %. Improving the suggestion mechanism also had a positive impact of its overall performance, rising the percentage of target forms within top-5 from 74.1 % to 84.7 %.
Ethical issues in empirical studies using student subjects: Re-visiting practices and perceptions

Using student subjects in empirical studies has been discussed extensively from a methodological perspective in Software Engineering (SE), but there is a lack of similar discussion surrounding ethical aspects of doing so. As students are in a subordinate relationship to their instructors, such a discussion is needed. We aim to increase the understanding of practices and perceptions SE researchers have of ethical issues with student participation in empirical studies. We conducted a systematic mapping study of 372 empirical SE studies involving students, following up with a survey answered by 100 SE researchers regarding their current practices and opinions regarding student participation. The mapping study shows that the majority of studies does not report conditions regarding recruitment, voluntariness, compensation, and ethics approval. In contrast, the majority of survey participants supports reporting these conditions. The survey further reveals that less than half of the participants require ethics approval. Additionally, the majority of participants recruit their own students on a voluntary basis, and use informed consent with withdrawal options. There is disagreement among the participants whether course instructors should be involved in research studies and if they should know who participates in a study. It is a positive sign that mandatory participation is rare, and that informed consent and withdrawal options are standard. However, we see immediate need for action, as study conditions are under-reported, and as opinions on ethical practices differ widely. In particular, there is little regard in SE on the power relationship between instructors and students.
Sorald: Automatic Patch Suggestions for SonarQube Static Analysis Violations

Previous work has shown that early resolution of issues detected by static code analyzers can prevent major costs later on. However, developers often ignore such issues for two main reasons. First, many issues should be interpreted to determine if they correspond to actual flaws in the program. Second, static analyzers often do not present the issues in a way that is actionable. To address these problems, we present Sorald: a novel system that uses metaprogramming templates to transform the abstract syntax trees of programs and suggests fixes for static analysis warnings. Thus, the burden on the developer is reduced from interpreting and fixing static issues, to inspecting and approving full fledged solutions. Sorald fixes violations of 10 rules from SonarJava, one of the most widely used static analyzers for Java. We evaluate Sorald on a dataset of 161 popular repositories on Github. Our analysis shows the effectiveness of Sorald as it fixes 65% (852/1,307) of the violations that meets the repair preconditions. Overall, our experiments show it is possible to automatically fix notable violations of the static analysis rules produced by the state-of-the-art static analyzer SonarJava.
DEVELOPMENT OF AN ADAPTIVE LEARNING MODEL BASED ON DIGITAL TECHNOLOGIES AND THE WORK OF THE TEACHER

Many Russian and international educational institutions (from primary to higher education) are trying to introduce elements of adaptive learning into the educational process, following the global trend of transition from mass to individual education. Over the last 10-15 years, the active introduction of digital educational technologies has accelerated this process and led to the development and wide spread of various adaptive learning models, especially in higher education.The main objective of adaptive learning is to build a logical individual educational trajectory, to increase the efficiency of the learner by using his or her strengths and existing knowledge to obtain the necessary competences in the best way for each student.The article considers the features of the existing models of adaptive learning implementation: individual offline learning with a teacher, exclusively online learning and mixed systems of adaptive learning.The aim of the work is to study the advantages and disadvantages of the existing models of implementing adaptive learning and to develop an adaptive learning model, which involves the use of digital technologies and teacher's work, which combines the advantages and disadvantages of online learning and individual work with the teacher. The peculiarity of this model is synchronisation of the educational process, mandatory fixing of the term of study taking into account the individual pace of the student's learning of course materials. The presence of fixed digital traces of the educational process makes it possible to track the individual path of a student, analyse and eliminate competency gaps as quickly as possible, forming an individual educational trajectory for each student. In this model, the teacher plays the role of a motivator of the student's learning activity, having the opportunity to use active teaching methods, advise the student, adjust the learning process to the individual characteristics of the student. Adaptation is formed by combining the results of the digital footprint with the characteristics of the student's behaviour during learning. The educational process is organised in the following way: the platform, analysing the results of the digital student footprint, offers educational content to the teacher, but the final decision on the use of content is made by the teacher, bringing the necessary element of irrationality into the system, stimulating the motivational component of the individual educational process.
Detecting initial system-environment correlations in open systems

Correlations between a system and its environment lead to errors in an open quantum system. Detecting those correlations would be valuable for avoiding and/or correcting those errors. Here we show that we can detect correlations by only measuring the system itself if we know the cause of the interaction between the two, for example in the case of a dipole-dipole interaction. We investigate the unitary $U$ which is associated with the exchange Hamiltonian and examine the ability to detect initial correlations between a system and its environment for various types of initial states. The states we select are motivated by realistic experimental conditions and we provide bounds for when we can state with certainty that there are initial system-environment correlations given experimental data.
Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints

In this paper, we propose a general method to detect outliers from contaminated estimates of various image estimation applications. The method does not require any prior knowledge about the purpose, theory or hardware of the application but simply relies on the law of edge consistency between sources and estimates. The method is termed as ALRe (anchored linear residual) because it is based on the residual of weighted local linear regression with an equality constraint exerted on the measured pixel. Given a pair of source and contaminated estimate, ALRe offers per-pixel outlier likelihoods, which can be used to compose the data weights of post-refinement algorithms, improving the quality of refined estimate. ALRe has the features of asymmetry, no false positive and linear complexity. Its effectiveness is verified on four applications, four post-refinement algorithms and three datasets. It demonstrates that, with the help of ALRe, refined estimates are better in the aspects of both quality and edge consistency. The results are even comparable to model-based and hardware-based methods. Accuracy comparison on synthetic images shows that ALRe could detect outliers reliably. It is as effective as the mainstream weighted median filter at spike detection and is significantly better at bad region detection.
Otomatisasi Pakan Kucing Berbasis Mikrokontroller Intel Galileo Dengan Interface Android

To raise cats, feeding is one of the main factors and should be given according to schedule. If the direct feed with a large portion can result in the cat not being eaten because it is not too deep to eat and is wasted. As a result of this problem, anxiety arises from the point of view of the health and development of cats and leads to spending on excess cat feed. The results of this study are a tool that can help feed cats in real time with 2 modes of feeding, namely for adult cats and kittens with feeding schedules at 8 AM, 12 PM and 6 PM. Control of this tool can be accessed wirelessly with an Android smartphone so that it can make it easier to monitor the working status of this tool. The benefits obtained from the results of making this tool when viewed from the cat are able to maintain their diet, prevent malnutrition, prevent obesity and prevent cats stress due to deposition. When viewed from the point of view of the benefits to cat owners or users, namely it can reduce performance and minimize forgetting to feed and can save on buying cat feed because portions have been arranged in such a way as to save expenditure..
Bidirectional Information Flow Quantum State Tomography

The exact reconstruction of many-body quantum systems is one of the major challenges in modern physics, because it is impractical to overcome the exponential complexity problem brought by high-dimensional quantum many-body systems. Recently, machine learning techniques are well used to promote quantum information research and quantum state tomography has also been developed by neural network generative models. We propose a quantum state tomography method, which is based on a bidirectional gated recurrent unit neural network, to learn and reconstruct both easy quantum states and hard quantum states in this study. We are able to use fewer measurement samples in our method to reconstruct these quantum states and to obtain high fidelity.
Partial Correctness of a Fibonacci Algorithm

Summary In this paper we introduce some notions to facilitate formulating and proving properties of iterative algorithms encoded in nominative data language [19] in the Mizar system [3], [1]. It is tested on verification of the partial correctness of an algorithm computing n -th Fibonacci number: i := 0 s := 0 b := 1 c := 0 while (i &lt;&gt; n) c := s s := b b := c + s i := i + 1 return s This paper continues verification of algorithms [10], [13], [12] written in terms of simple-named complex-valued nominative data [6], [8], [17], [11], [14], [15]. The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2], [4] with partial pre- and post-conditions [16], [18], [7], [5].
Principal Manifold Estimation Via Model Complexity Selection

We propose a framework of principal manifolds to model high-dimensional data. This framework is based on Sobolev spaces and designed to model data of any intrinsic dimension. It includes principal component analysis and principal curve algorithm as special cases. We propose a novel method for model complexity selection to avoid overfitting, eliminate the effects of outliers, and improve the computation speed. Additionally, we propose a method for identifying the interiors of circle-like curves and cylinder/ball-like surfaces. The proposed approach is compared to existing methods by simulations and applied to estimate tumor surfaces and interiors in a lung cancer study.
Knowledge-Aware Hypergraph Neural Network for Recommender Systems

Knowledge graph (KG) has been widely studied and employed as auxiliary information to alleviate the cold start and sparsity problems of collaborative filtering in recommender systems. However, most of the existing KG-based recommendation models suffer from the following drawbacks, i.e., insufficient modeling of high-order correlations among users, items, and entities, and simple aggregation strategies which fail to preserve the relational information in the neighborhood. In this paper, we propose a Knowledge-aware Hypergraph Neural Network (KHNN) framework to tackle the above issues. First, the knowledge-aware hypergraph structure, which is composed of hyperedges, is employed for modeling users, items, and entities in the knowledge graph with explicit hybrid high-order correlations. Second, we propose a novel knowledge-aware hypergraph convolution method to aggregate different knowledge-based neighbors in hyperedge efficiently. Moreover, it can conduct the embedding propagation of high-order correlations explicitly and efficiently in knowledge-aware hypergraph. Finally, we apply the proposed model on three real-world datasets, and the empirical results demonstrate that KHNN can achieve the best improvements against other state-of-the-art methods.
Определение весовых коэффициентов для аддитивной фитнес-функции генетического алгоритма

The paper presents a solution for the problem of choosing a method for analytical determining of weight factors for a genetic algorithm additive fitness function.This algorithm is the basis for an evolutionary process, which forms a stable and effective query population in a search engine to obtain highly relevant results.The paper gives a formal description of an algorithm fitness function, which is a weighted sum of three heterogeneous criteria.The selected methods for analytical determining of weight factors are described in detail.It is noted that expert assessment methods are impossible to use.The authors present a research methodology using the experimental results from earlier in the discussed project "Data Warehouse Support on the Base Intellectual Web Crawler and Evolutionary Model for Target Information Selection".There is a description of an initial dataset with data ranges for calculating weights.The calculation order is illustrated by examples.The research results
Scattering in Terms of Bohmian Conditional Wave Functions for Scenarios with Non-Commuting Energy and Momentum Operators

Without access to the full quantum state, modeling quantum transport in mesoscopic systems requires dealing with a limited number of degrees of freedom. In this work, we analyze the possibility of modeling the perturbation induced by non-simulated degrees of freedom on the simulated ones as a transition between single-particle pure states. First, we show that Bohmian conditional wave functions (BCWFs) allow for a rigorous discussion of the dynamics of electrons inside open quantum systems in terms of single-particle time-dependent pure states, either under Markovian or non-Markovian conditions. Second, we discuss the practical application of the method for modeling light–matter interaction phenomena in a resonant tunneling device, where a single photon interacts with a single electron. Third, we emphasize the importance of interpreting such a scattering mechanism as a transition between initial and final single-particle BCWF with well-defined central energies (rather than with well-defined central momenta).
Machine Learning Algorithms for Prediction of Credit Card Defaulters—A Comparative Study

The prime goal of this study is to predict the accuracy of the classifiers predicting the default credit card customers in Taiwan. Since the last few years, the transactional companies are providing loans to their customers on their credibility, but they suffer from the default customers’ payments. It is difficult to predict the accuracy of real credit card customers who are going to be the next default. So, various classification methods including boosting methods and some other methods to predict the probability of default are studied. The accuracy of different classifiers is calculated by using the confusion matrix and area under the curve (AUC) and compared with other classification techniques.
Joint image enhancement and localization framework for vehicle model recognition in the presence of non-uniform lighting conditions

Recognizing the model of a vehicle in natural scene images is an important and challenging task for real-life applications. Current methods perform well under controlled conditions, such as frontal and horizontal view-angles or under optimal lighting conditions. Nevertheless, their performance decreases significantly in an unconstrained environment, that may include extreme darkness or over illuminated conditions. Other challenges to recognition systems include input images displaying very low visual quality or considerably low exposure levels. This paper strives to improve vehicle model recognition accuracy in dark scenes by using a deep neural network model. To boost the recognition performance of vehicle models, the approach performs joint enhancement and localization of vehicles for non-uniform-lighting conditions. Experimental results on several public datasets demonstrate the generality and robustness of our framework. It improves vehicle detection rate under poor lighting conditions, localizes objects of interest, and yields better vehicle model recognition accuracy on low-quality input image data.
Quantum resources of the steady-state of three coupled qubits: Microscopic versus phenomenological model

Quantum resources, such as entanglement, steering, and Bell nonlocality, are evaluated for three coupled qubits in the steady-state configuration. We employ the phenomenological master equation and the microscopic master equation to probe such quantum resources, which provide very different results depending on the system configuration. In particular, steering and Bell nonlocality are null within the phenomenological model, while they reach considerable values within the microscopic model. These results show that the phenomenological approach is not able to capture all quantum resources of the system. We also provide an analytical expression for the steady-state and quantum resources of the system composed of three coupled qubits in the zero temperature limit. Such results demonstrate that quantum resources between two qubits are strongly affected by the third qubit in a nontrivial way.
Time-frequency manifold representation for separating and classifying frequency modulation signals

In high density communication and radar environments, radio frequency (RF) signal processing for receivers faces significant challenges. Receivers may receive the overlapped or pulse-on-pulse (POP) RF signals transmitted by cochannel and co-site RF emitters. Detecting, separating, and classifying these POP signals are valuable, but difficult due to the overlapping in time and frequency. In this paper, we propose a time-frequency manifold representation to solve these challenging problems. Using time frequency analysis, we show that a frequency modulation RF (FMRF) signal can be represented as a one dimensional manifold embedded in a two dimensional time frequency space. Using graph theory, we propose a path finding approach to extract this time frequency manifold. With both theoretical analysis and experiments, we show that the proposed approach can extract both simple single and complicated pulse-on-pulse FMRF signals
Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction

Term weighting schemes are widely used in Natural Language Processing and Information Retrieval.In particular, term weighting is the basis for keyword extraction.However, there are relatively few evaluation studies that shed light about the strengths and shortcomings of each weighting scheme.In fact, in most cases researchers and practitioners resort to the wellknown tf-idf as default, despite the existence of other suitable alternatives, including graphbased models.In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction.Our analysis reveals some interesting findings such as the advantages of the lessknown lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods.Finally, based on our findings we discuss and devise some suggestions for practitioners. 1
Numerical Gaussian process Kalman filtering

In this manuscript we introduce numerical Gaussian process Kalman filtering (GPKF). Numerical Gaussian processes have recently been developed to simulate spatiotemporal models. The contribution of this paper is to embed numerical Gaussian processes into the recursive Kalman filter equations. This embedding enables us to do Kalman filtering on infinite-dimensional systems using Gaussian processes. This is possible because i) we are obtaining a linear model from numerical Gaussian processes, and ii) the states of this model are by definition Gaussian distributed random variables. Convenient properties of the numerical GPKF are that no spatial discretization of the model is necessary, and manual setting up of the Kalman filter, that is fine-tuning the process and measurement noise levels by hand is not required, as they are learned online from the data stream. We showcase the capability of the numerical GPKF in a simulation study of the advection equation.