Reviewerly/exHarmony huggingface dataset:
SCIENTIFIC RESEARCH ACTIVITIES OF FUTURE TEACHERS IN THE PROCESS OF PROFESSIONAL TRAINING

The article reveals the main areas of scientific research activity of future teachers in higher education institutions. Research activity is a means of training a new type of specialist and an important condition for the development of a creative, proactive future teacher with an orientation towards constant knowledge, a desire to determine the essence of any phenomenon, search for the best means of solving professional problems, constant experimentation, self-analysis and self-evaluation. Participation in research activities develops in future teachers a reflection of their own life and professional needs and interests, which is carried out in the process of professionally oriented research tasks, which ensures objectivity and forms a critical and constructive attitude towards oneself, helps to realize one's compliance with the requirements of the profession and scientifically -research activity, when the future teacher acquires the skills of independent understanding, finds meaning and ways of self-improvement in this activity. Research activity satisfies the needs of the future teacher in achieving success, self-knowledge, self-realization, in understanding the content of his activity, in a systematic vision of his prospects. The shift of emphasis from teaching to the organization of student learning as an independent systematic self-educational activity, which is only accompanied and supported by the teacher, becomes one of the most important guidelines for substantiating the content of the training of highly qualified pedagogical staff, defining its strategic goals and guidelines. The shift of emphasis from teaching to the organization of student learning as an independent systematic self-educational activity, which is only accompanied and supported by the teacher, becomes one of the most important guidelines for substantiating the content of the training of highly qualified pedagogical staff, defining its strategic goals and guidelines. Therefore, it is necessary to promote the strengthening of the role of scientific activity in the professional training of future teachers; to ensure the joint participation of students and teachers in the performance of specific research works; create conditions for creative scientific growth of students; to stimulate their development of sustainable professional interest in scientific work; integration of educational and research activities of students; intensification and individualization of the educational process.
TRANSVERSAL SKILLS BELONGING TO KEY COMPETENCES: CONDITIONS AND WAYS OF FORMATION IN TECHNOLOGY LESSONS

The article is devoted to the disclosure of the conditions and ways of forming end-to-end skills that are inherent in all key competencies of those acquiring education in technology classes (on-the-job training). The theoretical basis of the research was the study of the pedagogical experience and the general European principles of building educational standards and national state standards, in particular the State Standard of Primary Education (2018) and the State Standard of Basic Secondary Education (2020) of Ukraine. These documents are based on the competency approach, learning outcomes, and cross-cutting skills common to all competencies. According to the authors, soft skills are consistent with the formulation of cross-cutting skills of the State Standard, which are inherent in all key competencies. Such cross-cutting skills include: reading with understanding; expressing one's own opinion orally and in writing; thinking critically and systematically; logically justifying the position; acting creatively, which involves creative thinking; production of new ideas; showing initiative; constructively managing emotions, which consists of the ability to recognize one's own emotions and the emotional state of others; assess risks; make a decision; solve problems; cooperate with others. The authors considered in detail the potential opportunities of the technological educational field and the technology subject of the same name in the formation of cross-cutting skills inherent in all the key competencies of the New Ukrainian School. Characterization of skills was carried out by taking into account the peculiarities of the subject of technology, the use of project and interactive technologies, and the corresponding methodological features of the work of a teacher of labor training and technologies. It is noted that each comprehensive skill needs separate attention and research in order to develop a special methodology for its formation in male and female students. The authors recommend developing a system of special tasks based on interactive exercises in combination with students' project activities, which for labor training (technology) lessons have established design stages.
DESIGNING SEWING PRODUCTS IN THE CONDITIONS OF THE EDUCATIONAL PROCESS

Social processes taking place in society put forward new requirements for the formation and development of personality. The urgent need of the hour is the manifestation of creativity, the search for rational, non-standard ways of solving tasks in any field of activity, increasing the intellectual potential of every member of society, and maximizing the development of human creative abilities. Our research makes it possible to assert that the effectiveness of the development of students' creative abilities in labor training lessons can be significantly increased if the educational process organized by the teacher is specially focused on the formation of a whole complex of personality properties and qualities that give it the opportunity to be creative in any way human activity. If the lessons are built in such a way that the same design technique is repeated for a long time in action, then children lose interest in knowledge. The monotony of activity is tiring, and this contradicts the psychological characteristics of teenagers. It is not possible to achieve high professionalism in design and construction activities even when performing several works (within the same design) in a row. That is why the system of creative tasks developed by us is built in such a way that cyclic repetition of constructions is carried out, technical modeling is performed at a higher level of complexity. In addition, in the process of developing a model at all successive stages of design activity (designing a model, developing a design and performing technical modeling of a product), the students' skills are constantly improved and improved. It is necessary to constantly monitor that the formation of knowledge, abilities and skills is carried out through the personal interest of schoolchildren. For this purpose, we have developed and proposed, in addition to the main, a set of variable tasks, unconventional for performing model design, as part of the school program. The following generalized directions have been established, according to which we propose to evaluate the peculiarities of the organization of special activities of schoolchildren in the lessons of labor training: the organization of activities that promotes the development of creative abilities of the individual at each stage of design activity; organization of independent participation of schoolchildren in special activities; creation of conditions for activation of creative abilities; humanistic approach to the educational process.
Depth Is All You Need for Monocular 3D Detection

A key contributor to recent progress in 3D detection from single images is monocular depth estimation. Existing methods focus on how to leverage depth explicitly, by generating pseudo-pointclouds or providing attention cues for image features. More recent works leverage depth prediction as a pretraining task and fine-tune the depth representation while training it for 3D detection. However, the adaptation is insufficient and is limited in scale by manual labels. In this work, we propose to further align depth representation with the target domain in unsupervised fashions. Our methods leverage commonly available LiDAR or RGB videos during training time to fine-tune the depth representation, which leads to improved 3D detectors. Especially when using RGB videos, we show that our two-stage training by first generating pseudo-depth labels is critical because of the inconsistency in loss distribution between the two tasks. With either type of reference data, our multi-task learning approach improves over the state of the art on both KITTI and NuScenes, while matching the test-time complexity of its single task sub-network.
A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics

In this paper, we propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. This problem is usually encountered when multiple users have legitimate access to shared computers and accounts, where, at times, one user can inadvertently be logged in on another user's account. Since the login processes are usually bypassed at this stage, we rely on keystroke dynamics in order to tell users apart. Our algorithm uses the quantile transform and techniques from localization to classify and identify users. Specifically, we use an algorithm known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal data obtained from comparing distance proxies, by "locating" users in a reduced PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.
Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translation

Language modelling and machine translation tasks mostly use subword or character inputs, but syllables are seldom used. Syllables provide shorter sequences than characters, require less-specialised extracting rules than morphemes, and their segmentation is not impacted by the corpus size. In this study, we first explore the potential of syllables for open-vocabulary language modelling in 21 languages. We use rule-based syllabification methods for six languages and address the rest with hyphenation, which works as a syllabification proxy. With a comparable perplexity, we show that syllables outperform characters and other subwords. Moreover, we study the importance of syllables on neural machine translation for a non-related and low-resource language-pair (Spanish--Shipibo-Konibo). In pairwise and multilingual systems, syllables outperform unsupervised subwords, and further morphological segmentation methods, when translating into a highly synthetic language with a transparent orthography (Shipibo-Konibo). Finally, we perform some human evaluation, and discuss limitations and opportunities.
Bayesian probability updates using Sampling/Importance Resampling: Applications in nuclear theory

We review an established Bayesian sampling method called sampling/importance resampling and highlight situations in nuclear theory when it can be particularly useful. To this end we both analyse a toy problem and demonstrate realistic applications of importance resampling to infer the posterior distribution for parameters of $Δ$NNLO interaction model based on chiral effective field theory and to estimate the posterior probability distribution of target observables. The limitation of the method is also showcased in extreme situations where importance resampling breaks.
Glowing in the Dark Uncovering IPv6 Address Discovery and Scanning Strategies in the Wild

In this work we identify scanning strategies of IPv6 scanners on the Internet. We offer a unique perspective on the behavior of IPv6 scanners by conducting controlled experiments leveraging a large and unused /56 IPv6 subnet. We selectively make parts of the subnet visible to scanners by hosting applications that make direct or indirect contact with IPv6- capable servers on the Internet. By careful experiment design, we mitigate the effects of hidden variables on scans sent to our /56 subnet and establish causal relationships between IPv6 host activity types and the scanner attention they evoke. We show that IPv6 host activities e.g., Web browsing, membership in the NTP pool and Tor network, cause scanners to send a magnitude higher number of unsolicited IP scans and reverse DNS queries to our subnet than before. DNS scanners focus their scans in narrow regions of the address space where our applications are hosted whereas IP scanners broadly scan the entire subnet. Even after the host activity from our subnet subsides, we observe persistent residual scanning to portions of the address space that previously hosted applications
ForestQB: An Adaptive Query Builder to Support Wildlife Research

This paper presents ForestQB, a SPARQL query builder, to assist Bioscience and Wildlife Researchers in accessing Linked-Data. As they are unfamiliar with the Semantic Web and the data ontologies, ForestQB aims to empower them to benefit from using Linked-Data to extract valuable information without having to grasp the nature of the data and its underlying technologies. ForestQB is integrating Form-Based Query builders with Natural Language to simplify query construction to match the user requirements. Demo available at https://iotgarage.net/demo/forestQB
Conditional Feature Importance for Mixed Data

Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical perspective, a major distinction is between analyzing a variable's importance before and after adjusting for covariates - i.e., between $\textit{marginal}$ and $\textit{conditional}$ measures. Our work draws attention to this rarely acknowledged, yet crucial distinction and showcases its implications. Further, we reveal that for testing conditional FI, only few methods are available and practitioners have hitherto been severely restricted in method application due to mismatching data requirements. Most real-world data exhibits complex feature dependencies and incorporates both continuous and categorical data (mixed data). Both properties are oftentimes neglected by conditional FI measures. To fill this gap, we propose to combine the conditional predictive impact (CPI) framework with sequential knockoff sampling. The CPI enables conditional FI measurement that controls for any feature dependencies by sampling valid knockoffs - hence, generating synthetic data with similar statistical properties - for the data to be analyzed. Sequential knockoffs were deliberately designed to handle mixed data and thus allow us to extend the CPI approach to such datasets. We demonstrate through numerous simulations and a real-world example that our proposed workflow controls type I error, achieves high power and is in line with results given by other conditional FI measures, whereas marginal FI metrics result in misleading interpretations. Our findings highlight the necessity of developing statistically adequate, specialized methods for mixed data.
Cloud Hopping; Navigating in 3D Uneven Environments via Supervoxels and Control Lyapunov Function

This paper presents a novel feedback motion planning method for mobile robot navigation in 3D uneven terrains. We take advantage of the \textit{supervoxel} representation of point clouds, which enables a compact connectivity graph of traversable regions on the point cloud maps. Given this graph of traversable areas, our approach navigates the robot to any reachable goal pose using a control Lyapunov function (cLf) and a navigation function. The cLf ensures the kinodynamic feasibility and target convergence of the generated motion plans, while the navigation function optimizes the resulting feedback motion plans. We carried out navigation experiments in real and simulated 3D uneven terrains. In all circumstances, the experimental findings show that our approach performs superior to the baselines, proving the approach's efficiency and adaptability to navigate a robot in challenging uneven 3D terrains. The proposed method can also navigate a robot with a particular objective, e.g., shortest-distance or least-inclined plan. We compared our approach to well-established sampling-based motion planners in which our method outperformed all other planners in terms of execution time and resulting path length. Finally, we provide an open-source implementation of the proposed method to benefit the robotics community.
Hello Quantum World! A rigorous but accessible first-year university course in quantum information science

Addressing workforce shortages within the Quantum Information Science and Engineering (QISE) community requires attracting and retaining students from diverse backgrounds early on in their undergraduate education. Here, we describe a course we developed called Hello Quantum World! that introduces a broad range of fundamental quantum information and computation concepts in a rigorous way but without requiring any knowledge of mathematics beyond high-school algebra nor any prior knowledge of quantum mechanics. Some of the topics covered include superposition, entanglement, quantum gates, teleportation, quantum algorithms, and quantum error correction. The course is designed for first-year undergraduate students, both those pursuing a degree in QISE and those who are seeking to be `quantum-aware'.
Quantum computation of dynamical quantum phase transitions and entanglement tomography in a lattice gauge theory

Strongly-coupled gauge theories far from equilibrium may exhibit unique features that could illuminate the physics of the early universe and of hadron and ion colliders. Studying real-time phenomena has proven challenging with classical-simulation methods, but is a natural application of quantum simulation. To demonstrate this prospect, we quantum compute non-equal time correlation functions and perform entanglement tomography of non-equilibrium states of a simple lattice gauge theory, the Schwinger model, using a trapped-ion quantum computer by IonQ Inc. As an ideal target for near-term devices, a recently-predicted [Zache et al., Phys. Rev. Lett. 122, 050403 (2019)] dynamical quantum phase transition in this model is studied by preparing, quenching, and tracking the subsequent non-equilibrium dynamics in three ways: i) overlap echos signaling dynamical transitions, ii) non-equal time correlation functions with an underlying topological nature, and iii) the entanglement structure of non-equilibrium states, including entanglement Hamiltonians. These results constitute the first observation of a dynamical quantum phase transition in a lattice gauge theory on a quantum computer, and are a first step toward investigating topological phenomena in nuclear and high-energy physics using quantum technologies.
VIMA: General Robot Manipulation with Multimodal Prompts

Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens. Accordingly, we develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and a four-level evaluation protocol for systematic generalization. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. VIMA features a recipe that achieves strong model scalability and data efficiency. It outperforms alternative designs in the hardest zero-shot generalization setting by up to $2.9\times$ task success rate given the same training data. With $10\times$ less training data, VIMA still performs $2.7\times$ better than the best competing variant. Code and video demos are available at https://vimalabs.github.io/
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!

We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario. Our work builds upon the iWildCam dataset, and, to the best of our knowledge, we are the first to propose such an approach for visual data. We empirically validate that environment-aware methods perform better in such cases when compared with the basic Empirical Risk Minimization (ERM). We next propose an extension for generating positive samples for contrastive methods that considers the environment labels when training, improving the ERM baseline score by 8.7%.
Self-Distillation for Further Pre-training of Transformers

Pre-training a large transformer model on a massive amount of unlabeled data and fine-tuning it on labeled datasets for diverse downstream tasks has proven to be a successful strategy, for a variety of vision and natural language processing tasks. However, direct fine-tuning of the pre-trained model may be suboptimal if there exist large discrepancies across data domains for pre-training and fine-tuning. To tackle this issue, several previous studies have proposed further pre-training strategies, where we continue to pre-train the model on the target unlabeled dataset before fine-tuning. However, all of them solely focus on language models and we empirically find that a Vision Transformer is vulnerable to overfitting as we continue to pretrain the model on target unlabeled data. In order to tackle this limitation, we propose self-distillation as a regularization for a further pre-training stage. Specifically, we first further pre-train the initial pre-trained model on the target unlabeled data and then consider it as a teacher for self-distillation. Then we take the same initial pre-trained model as a student and enforce its hidden representations to be close to those of the teacher while optimizing the student with a masked auto-encoding objective. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks. Experimentally, we show that our proposed method outperforms all the relevant baselines. Theoretically, we analyze the proposed method with a simplified model to understand how self-distillation for further pre-training can potentially help improve the performance of the downstream tasks.
Multiview Contextual Commonsense Inference: A New Dataset and Task

Contextual commonsense inference is the task of generating various types of explanations around the events in a dyadic dialogue, including cause, motivation, emotional reaction, and others. Producing a coherent and non-trivial explanation requires awareness of the dialogue's structure and of how an event is grounded in the context. In this work, we create CICEROv2, a dataset consisting of 8,351 instances from 2,379 dialogues, containing multiple human-written answers for each contextual commonsense inference question, representing a type of explanation on cause, subsequent event, motivation, and emotional reaction. We show that the inferences in CICEROv2 are more semantically diverse than other contextual commonsense inference datasets. To solve the inference task, we propose a collection of pre-training objectives, including concept denoising and utterance sorting to prepare a pre-trained model for the downstream contextual commonsense inference task. Our results show that the proposed pre-training objectives are effective at adapting the pre-trained T5-Large model for the contextual commonsense inference task.