Reviewerly/exHarmony huggingface dataset:
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models

Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. As a result, these models fail to generalize to real-world out-of-distribution data. In this work, we show that the words in the NLU training set can be modeled as a long-tailed distribution. There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training. These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample. Based on this shortcut measurement, we propose a shortcut mitigation framework LTGR, to suppress the model from making overconfident predictions for samples with large shortcut degree. Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models. Experimental analysis further indicates that LTGR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.
Coherence dynamics induced by attenuation and amplification Gaussian channels

Quantum Gaussian channels play a key role in quantum information theory. In particular, the attenuation and amplification channels are useful to describe noise and decoherence effects on continuous variables systems. They are directly associated with the beam splitter and two-mode squeezing operations, which have operational relevance in quantum protocols with bosonic models. An important property of these channels is that they are Gaussian completely positive maps, and the action on a general input state depends on the parameters characterizing the channels. In this work, we study the coherence dynamics introduced by these channels on input Gaussian states. We derive explicit expressions for the coherence depending on the parameters describing the channels. By assuming a displaced thermal state with initial coherence as input state, for the attenuation case it is observed a revival of the coherence as a function of the transmissivity coefficient, whereas for the amplification channel the coherence reaches asymptotic values depending on the gain coefficient. Further, we obtain the entropy production for these classes of operations, showing that it can be reduced by controlling the parameters involved. We write a simple expression for computing the entropy production due to the coherence for both channels. This can be useful to simulate many processes in quantum thermodynamics, as finite-time driving on bosonic modes.
Path and Trajectory Planning for an Automated Carrier Vehicle Equipped with two Conveyor Belts used in Manufacturing Supply

The AGVs or mobile robots are well used in today's manufacturing supply technologies and also can be used in engineering's education.The motion controlling and simulation of such vehicles are a crucial question.This paper introduces the steps of motion planning for a driverless carrier vehicle from the positions initially available to the speed of the wheels.The vehicle is located in the High-Tech Logistics Systems Laboratory of the Logistics Institute of the University of Miskolc.For motion controlling and simulation between two points the further modules are necessary: 1. path planner, 2. trajectory planner, 3. velocity-voltage converter using velocities gained from trajectory planner, 4. motion controlling and simulation of a motor dynamical model using voltages from the converter, 5. simulation of the path and 6. data processing.In this paper the first two modules are detailed, i.e. the path planning and then the trajectory planning.Path planning is based on a new approach, using Bezier-curves and Hermite curves.The trajectory planning tends to the mininum energy, which can be carried out by the examining the current consumption created in the other modules.The smaller consumption originated from the two curves determines the final path and trajectory.
Dynamic Weighted Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to improve the classification performance on an unlabeled target domain by leveraging information from a fully labeled source domain. Recent approaches explore domain-invariant and class-discriminant representations to tackle this task. These methods, however, ignore the interaction between domain alignment learning and class discrimination learning. As a result, the missing or inadequate tradeoff between domain alignment and class discrimination are prone to the problem of negative transfer. In this paper, we propose Dynamic Weighted Learning (DWL) to avoid the discriminability vanishing problem caused by excessive alignment learning and domain misalignment problem caused by excessive discriminant learning. Technically, DWL dynamically weights the learning losses of alignment and discriminability by introducing the degree of alignment and discriminability. Besides, the problem of sample imbalance across domains is first considered in our work, and we solve the problem by weighing the samples to guarantee information balance across domains. Extensive experiments demonstrate that DWL has an excellent performance in several benchmark datasets.
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution

Existing color-guided depth super-resolution (DSR) approaches require paired RGB-D data as training samples where the RGB image is used as structural guidance to recover the degraded depth map due to their geometrical similarity. However, the paired data may be limited or expensive to be collected in actual testing environment. Therefore, we explore for the first time to learn the cross-modality knowledge at training stage, where both RGB and depth modalities are available, but test on the target dataset, where only single depth modality exists. Our key idea is to distill the knowledge of scene structural guidance from RGB modality to the single DSR task without changing its network architecture. Specifically, we construct an auxiliary depth estimation (DE) task that takes an RGB image as input to estimate a depth map, and train both DSR task and DE task collaboratively to boost the performance of DSR. Upon this, a cross-task interaction module is proposed to realize bilateral cross task knowledge transfer. First, we design a cross-task distillation scheme that encourages DSR and DE networks to learn from each other in a teacher-student role-exchanging fashion. Then, we advance a structure prediction (SP) task that provides extra structure regularization to help both DSR and DE networks learn more informative structure representations for depth recovery. Extensive experiments demonstrate that our scheme achieves superior performance in comparison with other DSR methods.
Improved Constructions for Secure Multi-Party Batch Matrix Multiplication

This paper investigates the problem of Secure Multi-party Batch Matrix Multiplication (SMBMM), where a user aims to compute the pairwise products $\mathbf{A}\divideontimes\mathbf{B}\triangleq(\mathbf{A}^{(1)}\mathbf{B}^{(1)},\ldots,\mathbf{A}^{(M)}\mathbf{B}^{(M)})$ of two batch of massive matrices $\mathbf{A}$ and $\mathbf{B}$ that are generated from two sources, through $N$ honest but curious servers which share some common randomness. The matrices $\mathbf{A}$ (resp. $\mathbf{B}$) must be kept secure from any subset of up to $X_{\mathbf{A}}$ (resp. $X_\mathbf{B}$) servers even if they collude, and the user must not obtain any information about $(\mathbf{A},\mathbf{B})$ beyond the products $\mathbf{A}\divideontimes\mathbf{B}$. A novel computation strategy for single secure matrix multiplication problem (i.e., the case $M=1$) is first proposed, and then is generalized to the strategy for SMBMM by means of cross subspace alignment. The SMBMM strategy focuses on the tradeoff between recovery threshold (the number of successful computing servers that the user needs to wait for), system cost (upload cost, the amount of common randomness, and download cost) and system complexity (encoding, computing, and decoding complexities). Notably, compared with the known result by Chen et al., the strategy for the degraded case $X= X_{\mathbf{A}}=X_{\mathbf{B}}$ achieves better recovery threshold, amount of common randomness, download cost and decoding complexity when $X$ is less than some parameter threshold, while the performance with respect to other measures remain identical.
Identifying Duplicate Questions in Community Question Answering Forums Using Machine Learning Approaches

Quora and Stack Exchange are information sharing stages where individuals ask questions with expectations of high-quality answers. Frequently, questions that have been asked in this data sharing may have been asked in prior. Organizations like Stack Exchange or Quora can improve client experience by distinguishing these duplicate questions. This would empower users to discover answers to the questions that have just been replied and keep community members from responding to a similar inquiry multiple times. The words in the sentence or questions may be different, but they have the same intent. In this paper to overcome the problem of duplicate question identification, we are using natural language processing (NLP) techniques to retrieve the question pair with different intents between the words, and classification is done by using machine learning (ML) techniques to learn from the human-labeled data, and predict whether a new question pair is duplicate or not. This paper also discusses how the proposed duplicate detection approach can expand to large collections, through the usage of NLP and ML techniques, like text preprocessing, embedding, logistic regression, classification techniques and neural networks.
Self-Imitation Learning by Planning

Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration. However, in long-horizon motion planning tasks, a challenging problem in deploying IL and RL methods is how to generate and collect massive, broadly distributed data such that these methods can generalize effectively. In this work, we solve this problem using our proposed approach called {self-imitation learning by planning (SILP)}, where demonstration data are collected automatically by planning on the visited states from the current policy. SILP is inspired by the observation that successfully visited states in the early reinforcement learning stage are collision-free nodes in the graph-search based motion planner, so we can plan and relabel robot's own trials as demonstrations for policy learning. Due to these self-generated demonstrations, we relieve the human operator from the laborious data preparation process required by IL and RL methods in solving complex motion planning tasks. The evaluation results show that our SILP method achieves higher success rates and enhances sample efficiency compared to selected baselines, and the policy learned in simulation performs well in a real-world placement task with changing goals and obstacles.
Aspects of physical training lessons at the educational institutions of the Ministry of Internal Affairs of Russia

&amp;nbsp; The research subject is physical training as an integral, dynamic and targeted pedagogical process. The research object is the students of the professional training faculties of educational institutions of the Ministry of Internal Affairs of Russia. The author considers such aspects of physical training lessons as the inclusion of exercises aimed at the improvement of coordination, the negotiation of psychological difficulties, and the usage of the students&amp;rsquo; best skills. Special attention is given to the issues of the improvement of physical culture of students of the educational institutions of the Ministry of Internal Affairs of Russia, and the inclusion of various exercises along with the method of professional situations modeling. The main conclusions of the research are: the necessity to change regulating documents connected with the age peculiarities of the human body in accordance with physical training methods within the professional training program for the students aged 24 - 32, and the usage of the situation modeling method. The author&amp;rsquo;s special contribution to the research of the issue is the application of the author&amp;rsquo;s teaching technique based on the method of modeling, and its impact on the effectiveness of professional activity. The academic novelty of the research consists in the fact that the author develops the physical training lessons technique based on the complex of various exercises and the method of modeling.&amp;nbsp;
Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networks

We introduce Larq Compute Engine, the world's fastest Binarized Neural Network (BNN) inference engine, and use this framework to investigate several important questions about the efficiency of BNNs and to design a new state-of-the-art BNN architecture. LCE provides highly optimized implementations of binary operations and accelerates binary convolutions by 8.5 - 18.5x compared to their full-precision counterparts on Pixel 1 phones. LCE's integration with Larq and a sophisticated MLIR-based converter allow users to move smoothly from training to deployment. By extending TensorFlow and TensorFlow Lite, LCE supports models which combine binary and full-precision layers, and can be easily integrated into existing applications. Using LCE, we analyze the performance of existing BNN computer vision architectures and develop QuickNet, a simple, easy-to-reproduce BNN that outperforms existing binary networks in terms of latency and accuracy on ImageNet. Furthermore, we investigate the impact of full-precision shortcuts and the relationship between number of MACs and model latency. We are convinced that empirical performance should drive BNN architecture design and hope this work will facilitate others to design, benchmark and deploy binary models.
Provenance studies using lead isotopy: contribution of the consideration of geological contexts in archaeological databases

The identification of mineral supply sources and trade routes are at the heart of the archaeological issues. The tracing of sources of metal production via lead isotopy has been used since the 1980s to identify the deposits from which the metal constituting the archaeological objects came. Such studies are based on mineral signature repositories and archaeologists have thus built up databases containing thousands of ore deposit analyses. The databases, however, only very rarely include geological information and are limited to geographic information. But considering only geographical data leads to many limitations of studies, including the overlapping of signatures between remote regions. This problem could nevertheless be circumvented by taking into account precise ore deposit data that enables to think in terms of restricted mineralized subsets. We illustrate this through the example of data collected in the Alps by Marcoux (1986) and Nimis et al. (2012). Taking into account geological data (and more specifically, gitological data) could thus considerably improve the accuracy of provenance interpretations and enable multivariate statistical processing to be carried out (these statistical treatments are inconclusive if they are carried out on purely geographical data). However, remains the problem of the thousands of ore analyses carried out without having defined their mineralization context. Although still imperfect (some contexts are not individualized), the use of a statistical treatment could nevertheless be used to identify the gitological contexts.
The adjunct’s initial level of training and creativity property as factors of a Ph.D. thesis successful defence

The article presents the results of studying the influence of the adjuncts’ initial level of training and creative abili-ties on the timeliness of writing and the success of defending candidate theses. Forecasting this indicator of the suc-cessful completing training in postgraduate studies has been determined.
The libraries in students’ and undergraduates’ educational reading

The goal of the study was to reveal distinction between the reading practices at different educational levels and to specify the libraries’ place in these practices. The reading practices are determined not only by the new technological (digital) environment but also by the institutional and organizational context and social relationship within the educational environment. The case study of educa-tional institutions of Novosibirsk Academic Center (namely Novosibirsk State Uni-versity and the University’s specialized Academic Research Center) was accom-plished. The data was acquired through structured interviews, questionnaire-based survey supplemented with the statistical data of RAS SB State Public Scien-tific and Technological Library. The majority of high-graders use the library for textbooks and/or if recommended by the teacher. The university library is less involved in the educational process; the students visit the library occasionally (e.g. searching for rare publications or lacking alternatives). The university is not their source of digital documents or full texts either; the students prefer not to scruti-nize the specificity of every database and access procedure and turn to searchable Internet-resources. The library, in student’s opinion, needs modernization for less formal and disciplinary regulations, for more comfort, and polyfunctional envi-ronment of reading rooms.
Indonesian Mathematics Teachers’ Responses Towards the Implementation of Distance Learning During Covid-19 Pandemic

In Indonesia, Covid-19 pandemic has brought unprecedented disruption in the education sector.This paper examines the responses of 415 Indonesian Mathematics Teachers' towards the implementation of distance learning during Covid-19 pandemic.It identifies online learning platforms used, type of online learning that dominates their learning, and learning approach implemented during the distance learning.Data collected through an online survey showed that 9.9% of the respondents unable to facilitate distance learning during this pandemic.Regarding the learning platforms used in distance learning, the data showed that the average of the number of online learning platforms used by teachers in Java, the most developed island of Indonesia, is approximately only three types of online learning platforms.The majority of teachers who facilitate remote learning used Whatsapp as their online learning platforms.The main reasons for using online learning platforms were the teachers can operate the platforms well and the applications are practical and simple for teachers and students.In terms of the type of online learning mode, teachers tended to use asynchronous learning.Furthermore, based on learning activities that dominate distance learning practices, it is shown that the majority of the teachers tended to use teacher-centered learning during the early pandemic.
Self-Supervised Training Enhances Online Continual Learning

In continual learning, a system must incrementally learn from a non-stationary data stream without catastrophic forgetting. Recently, multiple methods have been devised for incrementally learning classes on large-scale image classification tasks, such as ImageNet. State-of-the-art continual learning methods use an initial supervised pre-training phase, in which the first 10% - 50% of the classes in a dataset are used to learn representations in an offline manner before continual learning of new classes begins. We hypothesize that self-supervised pre-training could yield features that generalize better than supervised learning, especially when the number of samples used for pre-training is small. We test this hypothesis using the self-supervised MoCo-V2, Barlow Twins, and SwAV algorithms. On ImageNet, we find that these methods outperform supervised pre-training considerably for online continual learning, and the gains are larger when fewer samples are available. Our findings are consistent across three online continual learning algorithms. Our best system achieves a 14.95% relative increase in top-1 accuracy on class incremental ImageNet over the prior state of the art for online continual learning.
Poster: Analysis of the Differences in Adaptation to Higher Education of the First-Year Engineering and Humanities Students

The Bologna process gives unlimited opportunities for modern youth to continue their studies in different countries and increases the mobility of students, yet at the same time creates a problem for quick adaptation and mobility of the student environment. The speed and effectiveness of students’ adaptation largely determines the success of the learning process. The objective of this research is to define the differences in adaptation processes of first-year students of engineering and of humanities to university education. Our hypothesis is that there exists a difference between adaptation of engineering students and adaptation process of students of humanities because of the difference in original skills and competencies. As this research has shown, the lowest score of general adaptation was observed among the male engineering students. Self-assessment of adaptation level among the engineering students (regardless of the gender) is lower than among the humanities students across all the adaptation scales.
Engineering Secure Self-Adaptive Systems with Bayesian Games

Abstract Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. Game theory approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components . Under security attacks, the architecture model of the system is translated into a Bayesian multi-player game , where each component is explicitly modeled as an independent player while security attacks are encoded as variant types for the components. The optimal defensive strategy for the system is dynamically computed by solving the pure equilibrium (i.e., adaptation response) to achieve the best possible system utility, improving the resiliency of the system against security attacks. We illustrate our approach using an example involving load balancing and a case study on inter-domain routing.