Reviewerly/exHarmony huggingface dataset:
METHODS AND TECHNOLOGIES FOR PROFESSIONAL SELF-DETERMINATION OF STUDENTS IN THE USA

The article analyses the methods and technologies of professional self-determination of students in the US education system. It has been found that the theory and practice of preparing students for professional self-determination has accumulated some positive experience in the development and use of activating career guidance methods and the formation and adaptation of career guidance programs with elements of activation of high school students. Also, the article presents the analysis of basic career guidance methods and technologies in the American high school.&#x0D; Keywords: professional self-determination, student youth, USA, methods, technologies, career choice, career guidance games and exercises, activating career guidance questionnaires.
Mirror3D: Depth Refinement for Mirror Surfaces

Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors. To address this problem, we create the Mirror3D dataset: a 3D mirror plane dataset based on three RGBD datasets (Matterport3D, NYUv2 and ScanNet) containing 7,011 mirror instance masks and 3D planes. We then develop Mirror3DNet: a module that refines raw sensor depth or estimated depth to correct errors on mirror surfaces. Our key idea is to estimate the 3D mirror plane based on RGB input and surrounding depth context, and use this estimate to directly regress mirror surface depth. Our experiments show that Mirror3DNet significantly mitigates errors from a variety of input depth data, including raw sensor depth and depth estimation or completion methods.
Online trajectory planning for UCAV performing ground attacks based on threat modeling

The paper studies how to plan the trajectories of an unmanned combat aerial vehicle (UCAV) that releases its airborne weapons and presents an online trajectory planning method based on threat modeling. Firstly, it analyzes the aerodynamic characteristics, engine thrust and fuel characteristics of the UCAV and builds its dynamic and kinematic models. Secondly, the trajectory planning model of the UCAV is formulated with flight performance constraints and battlefield threat constraints considered. To improve the accuracy of ground attacks, the envelope of a guided bomb's acceptable region of weapon release is studied, and the release center and posture of the guided bomb work as terminal planning conditions. Thirdly, an online trajectory planning method is proposed. With the help of threat modeling, the complicated trajectory planning problem is transformed into a simplified situation classification. Finally, the simulation results demonstrate that the online planning method proposed in the paper can provide feasible trajectories for a UCAV to succeed in releasing its airborne weapons.
Mungojerrie: Reinforcement Learning of Linear-Time Objectives

Reinforcement learning synthesizes controllers without prior knowledge of the system. At each timestep, a reward is given. The controllers optimize the discounted sum of these rewards. Applying this class of algorithms requires designing a reward scheme, which is typically done manually. The designer must ensure that their intent is accurately captured. This may not be trivial, and is prone to error. An alternative to this manual programming, akin to programming directly in assembly, is to specify the objective in a formal language and have it "compiled" to a reward scheme. Mungojerrie (https://plv.colorado.edu/mungojerrie/) is a tool for testing reward schemes for $ω$-regular objectives on finite models. The tool contains reinforcement learning algorithms and a probabilistic model checker. Mungojerrie supports models specified in PRISM and $ω$-automata specified in HOA.
Developing of the electronic educational and methodological complex “Automotive design” for the professional training of future teachers of vocational training in the field of transport

The article analyzes the essence of the concept of “electronic educational and methodological complex” and defines its structure, which consists of interconnected components that ensure the process of assimilation and consolidation of educational information and the implementation of automated pedagogical monitoring. The main didactic principles of designing electronic educational and methodological complexes are determined, in particular: science, clarity, accessibility and feasibility, systematicity and consistency, strength of acquired knowledge, problems, independence and activity of learning. The main requirements for electronic educational software, which have been also taken into account when creating the EEMC “Automotive Design”, are outlined: scientific content, purposefulness, openness, motivation, individualization of learning, creativity and more. Four stages of designing EEMC of the discipline “Automotive Design” are described, which consists of educational and methodological, control and diagnostic, and information and searching modules, has a relatively stable means of communication between them and provides a systematic learning process of students through additional independent or distance mastering of educational material with the help of a computer. It is noted that the implementation in the software of educational purposes of modern computer graphics, a variety of visual aids forms and develops visual thinking of the future teachers of vocational training. The use of EEMC contributes to the formation of information culture of the students, readiness for the widespread use of ICT tools in future professional activities.
Determinants of residents’ support for Tourism development- A Pilot Study

The aim of this pilot study is to test the reliability and validity of the survey instrument designed to measure the residents’ support for tourism. Since the study uses an adapted questionnaire the need to assess the reliability and validity appears to be desirable. The questionnaire was distributed to altogether 70 residents of Huna Valley. Initially the content and face validity was authenticated by field experts and later on the internal construct validity was calculated through various measures. Hence inter-item correlation shows that all the variables are correlated to each other at significant level. Secondly, construct validity results show that all the constructs used by study are reliable and met the level of acceptability. Therefore, the results validated that modified instrument is valid and reliable in the context of the social lab selected i.e. residents of Huna Velly and a full large scale study can be carried out using this instrument.
VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots

In this paper, we investigate the task of hallucinating an authentic high-resolution (HR) human face from multiple low-resolution (LR) video snapshots. We propose a pure transformer-based model, dubbed VidFace, to fully exploit the full-range spatio-temporal information and facial structure cues among multiple thumbnails. Specifically, VidFace handles multiple snapshots all at once and harnesses the spatial and temporal information integrally to explore face alignments across all the frames, thus avoiding accumulating alignment errors. Moreover, we design a recurrent position embedding module to equip our transformer with facial priors, which not only effectively regularises the alignment mechanism but also supplants notorious pre-training. Finally, we curate a new large-scale video face hallucination dataset from the public Voxceleb2 benchmark, which challenges prior arts on tackling unaligned and tiny face snapshots. To the best of our knowledge, we are the first attempt to develop a unified transformer-based solver tailored for video-based face hallucination. Extensive experiments on public video face benchmarks show that the proposed method significantly outperforms the state of the arts.
Learning under the assessment of the higher-level student, in times of COVID-19

Learning through virtual scenarios is today the way in which education has to reach higher level students, however it contracts, multiple scenarios, which do not only integrate the teaching or guide of the current subject, it also encompasses the tasks , practices, exercises, among others, that could value learning, however, it is there where the student's perception becomes valuable when considering the aspects that strengthen knowledge, learning and skills, under the modality that the conditions of health allow, considering the presence of the pandemic derived from the SARS-CoV2 virus. The purpose of this document is to analyze the perception of the higher-level student regarding the guidance provided regarding the subjects taken in time of the COVID-19 pandemic as a result of the transition from face-to-face classes to online teaching, considering the learning achieved by the student. Among the results that were obtained, it can be mentioned that face-to-face learning is today the scenario that is longed for among students, however, it is the scenario that today cannot be accessed, being valued the level of learning, skills and knowledge acquired in person with a greater presence by more than 90% of the study subjects. Contrary to this, the orientation, and solution of doubts under the various virtual scenarios, the doubts generated and the clarification of the doubts present regarding the tasks, practices, among others, are correctly and positively perceived, that is, they were solved with an arithmetic mean between both aspects of 88%.
The Training Program Promoting Students’ Self-Realization in the Integrated Educational Environment

The article presents the training program (for personality-oriented training) developing psychological qualities and personal resources of students’ self-realization in the integrated educational environment: psychological hardiness, value-semantic and motivational resources (meaningfulness of life, motives for self-development, values of self-realization, time perspective); resources of self-regulation (self-attitude, self-efficacy, autonomy); active coping strategies. The study purpose was to reveal the content of the training program promoting students’ self-realization in the integrated educational environment and the features of its implementation. The correlation analysis was used to identify links among the components of self-realization of students with disabilities and their personal characteristics – self-attitudes, life values and meanings, basic beliefs, psychological hardiness, time perspective, used coping strategies. The comprehensive empirical study involved 325 students with disabilities and 225 students without disabilities studying in integrated university groups (Kyiv). The targets for psychological influence and meaningful parts of the proposed training promoting self-realization were identified. The training program was a personality-oriented training and included a set of psychological techniques and methods promoting continuous personal growth, activating psychological mechanisms of self-development, self-improvement, self-activation and self-realization of potential capabilities. The training program was developed on the principles of humanistic and positive psychology; the proactive conceptual model of disability; the resource-oriented approach used in psychological counselling and psychotherapy; the concept of self-determined behaviour. The purpose of the proposed training program was to promote students’ self-realization via development of their psychological qualities and personal resources: psychological hardiness, value-semantic and motivational resources (meaningfulness of life, motives for self-development, values of self-realization, time perspective); resources of self-regulation (self-attitude, self-efficacy, autonomy); active coping strategies. The training program was based on self-realization components selected by us and consisted of three meaningfully related parts: «I and my inner world» (psychological resources of personal self-realization) including three modules; «I and the Other» (social resources of personal self-realization) with two modules; «I am in the World and Life» (instrumental resources maintaining life quality and psychological well-being) with two modules. Approbation of the program has showed its high efficiency. The analysed components of students’ self-realization – psychological hardiness, self-attitude, self-acceptance, meaningful life goals, active coping – showed their significant growth, and as a consequence, improved students’ psychological well-being.
Counterfactual Explanations Can Be Manipulated

Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical applications (e.g. law enforcement, financial lending), it becomes important to ensure that we clearly understand the vulnerabilities of these methods and find ways to address them. However, there is little understanding of the vulnerabilities and shortcomings of counterfactual explanations. In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated. More specifically, we show counterfactual explanations may converge to drastically different counterfactuals under a small perturbation indicating they are not robust. Leveraging this insight, we introduce a novel objective to train seemingly fair models where counterfactual explanations find much lower cost recourse under a slight perturbation. We describe how these models can unfairly provide low-cost recourse for specific subgroups in the data while appearing fair to auditors. We perform experiments on loan and violent crime prediction data sets where certain subgroups achieve up to 20x lower cost recourse under the perturbation. These results raise concerns regarding the dependability of current counterfactual explanation techniques, which we hope will inspire investigations in robust counterfactual explanations.
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks

Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the accuracy gains are often based on specialized model designs using additional 32-bit components. Furthermore, almost all previous BNNs use 32-bit for feature maps and the shortcuts enclosing the corresponding binary convolution blocks, which helps to effectively maintain the accuracy, but is not friendly to hardware accelerators with limited memory, energy, and computing resources. Thus, we raise the following question: How can accuracy and energy consumption be balanced in a BNN network design? We extensively study this fundamental problem in this work and propose a novel BNN architecture without most commonly used 32-bit components: \textit{BoolNet}. Experimental results on ImageNet demonstrate that BoolNet can achieve 4.6x energy reduction coupled with 1.2\% higher accuracy than the commonly used BNN architecture Bi-RealNet. Code and trained models are available at: https://github.com/hpi-xnor/BoolNet.
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST and CIFAR-10 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.
Bayesian Inference for Gamma Models

We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in statistics and machine learning, including Multinomial-Dirichlet distributions, Negative binomial regression, Poisson-Gamma hierarchical models, Extreme value models, to name but a few. All of those models include a gamma function which does not admit a natural conjugate prior distribution providing a significant challenge to inference and prediction. To provide a data augmentation strategy, we construct and develop the theory of the class of Exponential Reciprocal Gamma distributions. This allows scalable EM and MCMC algorithms to be developed. We illustrate our methodology on a number of examples, including gamma shape inference, negative binomial regression and Dirichlet allocation. Finally, we conclude with directions for future research.
Finding and Fixing Spurious Patterns with Explanations.

Machine learning models often use spurious patterns such as relying on presence of a person to detect a tennis racket, which do not generalize. In this work, we present an end-to-end pipeline for identifying and mitigating spurious patterns for image classifiers. We start by finding patterns such as the model's prediction for tennis racket changes 63% of time if we hide people. Then, if a pattern is spurious, we mitigate it via a novel form of data augmentation. We demonstrate that this approach identifies a diverse set of spurious patterns and that it mitigates them by producing a model that is both more accurate on a distribution where spurious pattern is not helpful and more robust to distribution shift.
Learning and Planning in Complex Action Spaces

Many important real-world problems have action spaces that are high-dimensional, continuous or both, making full enumeration of all possible actions infeasible. Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement. In this paper, we propose a general framework to reason in a principled way about policy evaluation and improvement over such sampled action subsets. This sample-based policy iteration framework can in principle be applied to any reinforcement learning algorithm based upon policy iteration. Concretely, we propose Sampled MuZero, an extension of the MuZero algorithm that is able to learn in domains with arbitrarily complex action spaces by planning over sampled actions. We demonstrate this approach on the classical board game of Go and on two continuous control benchmark domains: DeepMind Control Suite and Real-World RL Suite.
Generating Custom Set Theories with Non-set Structured Objects

Set theory has long been viewed as a foundation of mathematics, is pervasive in mathematical culture, and is explicitly used by much written mathematics. Because arrangements of sets can represent a vast multitude of mathematical objects, in most set theories every object is a set. This causes confusion and adds difficulties to formalising mathematics in set theory. We wish to have set theory’s features while also having many mathematical objects not be sets. A generalized set theory (GST) is a theory that has pure sets and may also have non-sets that can have internal structure and impure sets that mix sets and non-sets. This paper provides a GST-building framework. We show example GSTs that have sets and also (1) non-set ordered pairs, (2) non-set natural numbers, (3) a non-set exception object that can not be inside another object, and (4) modular combinations of these features. We show how to axiomatize GSTs and how to build models for GSTs in other GSTs.
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics

Deep neural networks excel at image classification, but their performance is far less robust to input perturbations than human perception. In this work we explore whether this shortcoming may be partly addressed by incorporating brain-inspired recurrent dynamics in deep convolutional networks. We take inspiration from a popular framework in neuroscience: 'predictive coding'. At each layer of the hierarchical model, generative feedback 'predicts' (i.e., reconstructs) the pattern of activity in the previous layer. The reconstruction errors are used to iteratively update the network's representations across timesteps, and to optimize the network's feedback weights over the natural image dataset-a form of unsupervised training. We show that implementing this strategy into two popular networks, VGG16 and EfficientNetB0, improves their robustness against various corruptions and adversarial attacks. We hypothesize that other feedforward networks could similarly benefit from the proposed framework. To promote research in this direction, we provide an open-sourced PyTorch-based package called Predify, which can be used to implement and investigate the impacts of the predictive coding dynamics in any convolutional neural network.
EFFICIENT CNN-BASED PROFILED SIDE CHANNEL ATTACKS

Profiled side-channel attacks are now considered as powerful forms of attacks used to break the security of cryptographic devices. A recent line of research has investigated a new profiled attack based on deep learning and many of them have used convolution neural network (CNN) as deep learning architecture for the attack. The effectiveness of the attack is greatly influenced by the CNN architecture. However, the CNN architecture used for current profiled attacks have often been based on image recognition fields, and choosing the right CNN architectures and parameters for adaption to profiled attacks is still challenging. In this paper, we propose an efficient profiled attack for on unprotected and masking-protected cryptographic devices based on two CNN architectures, called CNNn, CNNd respectively. Both of CNN architecture parameters proposed in this paper are based on the property of points of interest on the power trace and further determined by the Grey Wolf Optimization (GWO) algorithm. To verify the proposed attacks, experiments were performed on a trace set collected from an Atmega8515 smart card when it performs AES-128 encryption, a DPA contest v4 dataset and the ASCAD public dataset