Reviewerly/exHarmony huggingface dataset:
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation

Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaningpreserving restriction.However, a potential pitfall for this approach is that we cannot decide whether the generated examples are adversarial to the target NMT model or the auxiliary backward one, as the reconstruction error through the RTT can be related to either.To remedy this problem, we propose a new criterion for NMT adversarial examples based on the Doubly Round-Trip Translation (DRTT).Specifically, apart from the sourcetarget-source RTT, we also consider the targetsource-target one, which is utilized to pick out the authentic adversarial examples for the target NMT model.Additionally, to enhance the robustness of the NMT model, we introduce the masked language models to construct bilingual adversarial pairs based on DRTT, which are used to train the NMT model directly.Extensive experiments on both the clean and noisy test sets (including the artificial and natural noise) show that our approach substantially improves the robustness of NMT models.
Deep Video Inpainting Guided by Audio-Visual Self-Supervision

Humans can easily imagine a scene from auditory information based on their prior knowledge of audio-visual events. In this paper, we mimic this innate human ability in deep learning models to improve the quality of video inpainting. To implement the prior knowledge, we first train the audio-visual network, which learns the correspondence between auditory and visual information. Then, the audio-visual network is employed as a guider that conveys the prior knowledge of audio-visual correspondence to the video inpainting network. This prior knowledge is transferred through our proposed two novel losses: audio-visual attention loss and audio-visual pseudo-class consistency loss. These two losses further improve the performance of the video inpainting by encouraging the inpainting result to have a high correspondence to its synchronized audio. Experimental results demonstrate that our proposed method can restore a wider domain of video scenes and is particularly effective when the sounding object in the scene is partially blinded.
Certifiable Robot Design Optimization using Differentiable Programming

There is a growing need for computational tools to automatically design and verify autonomous systems, especially complex robotic systems involving perception, planning, control, and hardware in the autonomy stack.Differentiable programming has recently emerged as powerful tool for modeling and optimization.However, very few studies have been done to understand how differentiable programming can be used for robust, certifiable end-to-end design optimization.In this paper, we fill this gap by combining differentiable programming for robot design optimization with a novel statistical framework for certifying the robustness of optimized designs.Our framework can conduct end-to-end optimization and robustness certification for robotics systems, enabling simultaneous optimization of navigation, perception, planning, control, and hardware subsystems.Using simulation and hardware experiments, we show how our tool can be used to solve practical problems in robotics.First, we optimize sensor placements for robot navigation (a design with 5 subsystems and 6 tunable parameters) in under 5 minutes to achieve an 8.4x performance improvement compared to the initial design.Second, we solve a multi-agent collaborative manipulation task (3 subsystems and 454 parameters) in under an hour to achieve a 44% performance improvement over the initial design.We find that differentiable programming enables much faster (32% and 20x, respectively for each example) optimization than approximate gradient methods.We certify the robustness of each design and successfully deploy the optimized designs in hardware.
Less is More: Reweighting Important Spectral Graph Features for Recommendation

As much as Graph Convolutional Networks (GCNs) have shown tremendous success in recommender systems and collaborative filtering (CF), the mechanism of how they, especially the core components (\textit{i.e.,} neighborhood aggregation) contribute to recommendation has not been well studied. To unveil the effectiveness of GCNs for recommendation, we first analyze them in a spectral perspective and discover two important findings: (1) only a small portion of spectral graph features that emphasize the neighborhood smoothness and difference contribute to the recommendation accuracy, whereas most graph information can be considered as noise that even reduces the performance, and (2) repetition of the neighborhood aggregation emphasizes smoothed features and filters out noise information in an ineffective way. Based on the two findings above, we propose a new GCN learning scheme for recommendation by replacing neihgborhood aggregation with a simple yet effective Graph Denoising Encoder (GDE), which acts as a band pass filter to capture important graph features. We show that our proposed method alleviates the over-smoothing and is comparable to an indefinite-layer GCN that can take any-hop neighborhood into consideration. Finally, we dynamically adjust the gradients over the negative samples to expedite model training without introducing additional complexity. Extensive experiments on five real-world datasets show that our proposed method not only outperforms state-of-the-arts but also achieves 12x speedup over LightGCN.
Resource-Aware Classification via Model Management Enabled Data Stream Optimization

The integration of machine learning (ML) approaches in sensor-based applications in the field of pervasive computing is becoming increasingly prominent due to the increasing number of sensor-based applications in general and the continuous adaption of ML approaches to new domains. Several ML models are used within a processing pipeline that operates on the same sensor data. Still, the cloud computing approach is a straightforward solution where all sensor data is sent to the cloud before processing, which is inefficient according to resource utilization. Appropriate management of the different processing tasks for ML models enhances resource utilization.This paper proposes an architecture for resource-aware classification empowered by an ML model management (MLMM) framework and a distributed data stream management system (DDSMS). First, the classification pipeline is decomposed and implemented as data stream operators. Second, ML models are retrieved from an MLMM framework considering preprocessing, segmentation, and feature alignment to enable an effective redundancy elimination. Finally, the classification pipeline is deployed using resource-aware operator placement optimization. The evaluation results on a real-world scenario of a sensor-based activity classification pipeline for dairy cows show that our approach can reduce network utilization up to 98.9%.
Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning

Interference classification plays an important role in anti-jamming communication. Although the existing interference signal recognition methods based on deep learning have a higher accuracy than traditional methods, these have poor robustness while rejecting interference signals of unknown classes in interference open-set recognition (OSR). To ensure the classification accuracy of the known classes and the rejection rate of the unknown classes in interference OSR, we propose a new hollow convolution prototype learning (HCPL) in which the inner-dot-based cross-entropy loss (ICE) and the center loss are used to update prototypes to the periphery of the feature space so that the internal space is left for the unknown class samples, and the radius loss is used to reduce the impact of the prototype norm on the rejection rate of unknown classes. Then, a hybrid attention and feature reuse net (HAFRNet) for interference signal classification was designed, which contains a feature reuse structure and hybrid domain attention module (HDAM). A feature reuse structure is a simple DenseNet structure without a transition layer. An HDAM can recalibrate both time-wise and channel-wise feature responses by constructing a global attention matrix automatically. We also carried out simulation experiments on nine interference types, which include single-tone jamming, multitone jamming, periodic Gaussian pulse jamming, frequency hopping jamming, linear sweeping frequency jamming, second sweeping frequency jamming, BPSK modulation jamming, noise frequency modulation jamming and QPSK modulation jamming. The simulation results show that the proposed method has considerable classification accuracy of the known classes and rejection performance of the unknown classes. When the JNR is −10 dB, the classification accuracy of the known classes of the proposed method is 2–7% higher than other algorithms under different openness. When the openness is 0.030, the unknown class rejection performance plateau of the proposed method reaches 0.9883, while GCPL is 0.9403 and CG-Encoder is 0.9869; when the openness is 0.397, the proposed method is more than 0.89, while GCPL is 0.8102 and CG-Encoder is 0.9088. However, the rejection performance of unknown classes of CG-Encoder is much worse than that of the proposed method under low JNR. In addition, the proposed method requires less storage resources and has a lower computational complexity than CG-Encoder.
MODIFICATION OF THE QUARTZ LOADING CONTACT CLARIFIERS IN WATER TREATMENT PROCESSES

In the article are considered the influence of modification of the quartz loading contact clarifiers on the degree of structural and mechanical hydration of aluminum hydroxide in water treatment processes. Modification of the molecular structure of the surface of quartz grains of the filter charge increases the dirt capacity and increases the performance of structures. For the modification of filter materials can be used coagulants. Modification of filter materials allows with minimal cost to increase the performance of station of water treatment plants, to improve the quality of filtrate and reduce the cost of water treatment by reducing the consumption of reagents and increase the speed of filtration. The essence of the method is to increase the adhesion of the filter materials by the method of processing it with a solution of the reagent. The process of adhesion of suspended particles usually with a negative charge is more intense relative to the modified loading than the unmodified. It is established that the use of modified quartz loading allows to reduce the degree of structural and mechanical hydration of aluminum hydroxide, increase the proportion of coagulated impurities, which ultimately allows to intensify water purification processes, namely to reduce reagent costs by an average of 40…50 % with water required quality, while the cost of clarified water is reduced by 25…30 %.
Scientometrics in the sociohumanistic sciences: Problems and solutions

Scientometrics is a valuable asset in science management. However, application of scientometric indicators in the sociohumanistic sciences reveals some drawbacks. The problems of assessing researcher efficiency are analyzed and several solutions are proposed. The number of citations in the sociohumanistic sciences and STEMS are analyzed in terms of languages presented in the Web of Science. The author agrees with the expert opinion that research activities in the social sciences and the humanities cannot be correctly assessed with scientometric indicators. On the basis of the study, the author formulates the proposals on how to eliminate these problems. The study covers the wide time period (over 40 years) revealing citation trends in the social sciences, STEM and the humanities. The proposals can be applied in science management in universities and academic organizations.
Anomaly Detection Using Spatio-Temporal Context Learned by Video Clip Sorting

Previous studies on anomaly detection in videos have trained detectors in which reconstruction and prediction tasks are performed on normal data so that frames on which their task performance is low will be detected as anomalies during testing. This paper proposes a new approach that involves sorting video clips, by using a generative network structure. Our approach learns spatial contexts from appearances and temporal contexts from the order relationship of the frames. Experiments were conducted on four datasets, and we categorized the anomalous sequences by appearance and motion. Evaluations were conducted not only on each total dataset but also on each of the categories. Our method improved detection performance on both anomalies with different appearance and different motion from normality. Moreover, combining our approach with a prediction method produced improvements in precision at a high recall.
The role of moral values in the professional self-realization of technical university students

Introduction. The article examines the moral sphere of technical university students and various aspects of socialization, as well as the development of the personality of undergraduate students from the first to the fourth year. The authors focus on the contradictory and crisis situation in the field of education of young generation in modern Russia and emphasize the need to create a strategy of education at the university taking into account modern conditions. The article looks at the interdependent influence of terminal and instrumental values in the professional self-realization of technical university students with the help of valid and reliable psychodiagnostic methods of personality research. An in-depth statistical analysis reveals the hidden relationship between multiple human values. The authors provide the map of the value landscape of students’ personality and analyze the relevance of the leading values such as active way of life, life wisdom, health, interesting work, beauty of nature and art, love, financially secure life, the presence of good and true friends, public recognition, knowledge, intellectual development, etc. This makes it possible to understand the features of professional self-realization of students. Problem statement. The evaluation of the motivational component of the values under study provides an opportunity to discover the resources of the educational process. Methodology of the study. Grouping of numerous values into certain factors and blocks is aimed at a more detailed study of the main aspects that facilitate the process of self-knowledge and professional self-realization of students of a technical university. Results. The research material makes it possible to detect competing values, as well as contradictions between the declared and real values. Moreover, it helps to find resources and give recommendations on eliminating possible obstacles in professional self-realization, formation and adaptation of a competent specialist to all participants of the educational process. Conclusions. The generalized results can be successfully used in practical work, in particular, in motivational trainings for students at any stage of training and the creation of new technologies in the professional orientation of first-year students. Working with values is supposed to optimize the development of students’ personal potential.
Research Support as One of the Activity of a Scientific Library

The purpose of the article is to present theoretical generalizations on library and information research as aspects of the development of information support for research and development: a classification of a complex of relevant works, products and services and a model that allows determining the level of development for each library based on its tasks and human resources. The methodological possibilities of the developed model are shown: it can be used for personnel training and for the organization of a new library at a research institute, research university under certain requirements.
Heed the noise in performance evaluations in neural architecture search

Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise. Due to stochastic factors in neural network initialization, training, and the chosen train/validation dataset split, the performance evaluation of a neural network architecture, which is often based on a single learning run, is also stochastic. This may have a particularly large impact if a dataset is small. We therefore propose to reduce this noise by evaluating architectures based on average performance over multiple network training runs using different random seeds and cross-validation. We perform experiments for a combinatorial optimization formulation of NAS in which we vary noise reduction levels. We use the same computational budget for each noise level in terms of network training runs, i.e., we allow less architecture evaluations when averaging over more training runs. Multiple search algorithms are considered, including evolutionary algorithms which generally perform well for NAS. We use two publicly available datasets from the medical image segmentation domain where datasets are often limited and variability among samples is often high. Our results show that reducing noise in architecture evaluations enables finding better architectures by all considered search algorithms.
Copula-based bivariate finite mixture regression models with an application for insurance claim count data

Abstract Modeling bivariate (or multivariate) count data has received increased interest in recent years. The aim is to model the number of different but correlated counts taking into account covariate information. Bivariate Poisson regression models based on the shock model approach are widely used because of their simple form and interpretation. However, these models do not allow for overdispersion or negative correlation, and thus, other models have been proposed in the literature to avoid these limitations. The present paper proposes copula-based bivariate finite mixture of regression models. These models offer some advantages since they have all the benefits of a finite mixture, allowing for unobserved heterogeneity and clustering effects, while the copula-based derivation can produce more flexible structures, including negative correlations and regressors. In this paper, the new approach is defined, estimation through an EM algorithm is presented, and then different models are applied to a Spanish insurance claim count database.
Exploring BERT for Aspect-based Sentiment Analysis in Portuguese Language

Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing (NLP) task that extracts referred aspects from text and assigns polarities to opinions about those aspects. Most research on ABSA focuses on English. Only a few ABSA works deal with the Portuguese language. In this work, we used BERTimbau to create a Question-Answer approach to ABSA in Portuguese. First, we post-trained this model with text from the same domain as our target corpus. Then, we constructed an auxiliary sentence from the aspect and converted ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). Our experiments show that ABSA based on BERT for Portuguese achieved Balanced Accuracy (BACC) of 77% on a corpus of reviews about the accommodation sector using a post-trained model with QA approach.
Gaze-enhanced Crossmodal Embeddings for Emotion Recognition

Emotional expressions are inherently multimodal -- integrating facial behavior, speech, and gaze -- but their automatic recognition is often limited to a single modality, e.g. speech during a phone call. While previous work proposed crossmodal emotion embeddings to improve monomodal recognition performance, despite its importance, an explicit representation of gaze was not included. We propose a new approach to emotion recognition that incorporates an explicit representation of gaze in a crossmodal emotion embedding framework. We show that our method outperforms the previous state of the art for both audio-only and video-only emotion classification on the popular One-Minute Gradual Emotion Recognition dataset. Furthermore, we report extensive ablation experiments and provide detailed insights into the performance of different state-of-the-art gaze representations and integration strategies. Our results not only underline the importance of gaze for emotion recognition but also demonstrate a practical and highly effective approach to leveraging gaze information for this task.
Contribution to the Improvement of the Petroleum Products Delivery Policy by the Implementation of a Computer System Based on the Dijkstra Method

The distribution and marketing of petroleum products are the sources of energy for the various activities of human society and household supply.We spent our time researching the competitive factors to reduce the time of distribution of petroleum products, by setting up a computer system based on Dijkstra's algorithm our contribution will solve a real problem of delivery with direct targeting of gas stations.We set up a tool to help to map based on the method of operational research starting with the identification of delivery point, mapping and then applying the algorithm.Mainly the graph theory and the optimal path in a network by a short path using the method of Dijkstra for the design we used the UML language, the programming we used the JAVA language with very appreciable results to finish with a conclusion.
On the Decoding of Lattices Constructed via a Single Parity Check

This paper investigates the decoding of a remarkable set of lattices: We treat in a unified framework the Leech lattice in dimension 24, the Nebe lattice in dimension 72, and the Barnes-Wall lattices. A new interesting lattice, named <inline-formula> <tex-math notation="LaTeX">$L_{3\cdot 24}$ </tex-math></inline-formula>, is constructed as a simple application of the single parity check on the Leech lattice. The common aspect of these lattices is that they can be obtained via a single parity check or via the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-ing construction. We exploit these constructions to introduce a new efficient paradigm for decoding. This leads to efficient list decoders and quasi-optimal decoders on the Gaussian channel. Both theoretical and practical performance (point error probability and complexity) of the new decoders are provided.
Computation of bifurcations: Automatic provisioning of variational equations

In the conventional implementations for solving bifurcation problems, Jacobian matrix and its partial derivatives regarding the given problem should be provided manually. This process is not so easy, thus it often induces human errors like computation failures, typing error, especially if the system is higher order. In this paper, we develop a preprocessor that gives Jacobian matrix and partial derivatives symbolically by using SymPy packages on the Python platform. Possibilities about the inclusion of errors are minimized by symbolic derivations and reducing loop structures. It imposes a user only on putting an expression of the equation into a JSON format file. We demonstrate bifurcation calculations for discrete neuron dynamical systems. The system includes an exponential function, which makes the calculation of derivatives complicated, but we show that it can be implemented simply by using symbolic differentiation.