Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Open-Source Serverless Platforms for Kubernetes

Serverless computing has grown massively in popularity over the last few years, and has provided developers with a way to deploy function-sized code units without having to take care of the actual servers or deal with logging, monitoring, and scaling of their code. High-performance computing (HPC) clusters can profit from improved serverless resource sharing capabilities compared to reservation-based systems such as Slurm. However, before running self-hosted serverless platforms in HPC becomes a viable option, serverless platforms must be able to deliver a decent level of performance. Other researchers have already pointed out that there is a distinct lack of studies in the area of comparative benchmarks on serverless platforms, especially for open-source self-hosted platforms. This study takes a step towards filling this gap by systematically benchmarking two promising self-hosted Kubernetes-based serverless platforms in comparison. While the resulting benchmarks signal potential, they demonstrate that many opportunities for performance improvements in serverless computing are being left on the table.
On Causally Disentangled Representations

Representation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability. Initially consisting of unsupervised models with independence assumptions, more recently, weak supervision and correlated features have been explored, but without a causal view of the generative process. In contrast, we work under the regime of a causal generative process where generative factors are either independent or can be potentially confounded by a set of observed or unobserved confounders. We present an analysis of disentangled representations through the notion of disentangled causal process. We motivate the need for new metrics and datasets to study causal disentanglement and propose two evaluation metrics and a dataset. We show that our metrics capture the desiderata of disentangled causal process. Finally we perform an empirical study on state of the art disentangled representation learners using our metrics and dataset to evaluate them from causal perspective.
Motivational priorities of bachelor degree course graduates

Introduction. The political and economic changes, along with the technological progress of the last decades, have significantly changed the worldview, value orientations and motivational priorities of young people. Since motivation has a decisive influence on any activity, including learning, it is necessary to understand properly the students’ motivational priorities in order to enhance the efficiency of higher education. Research goal: identifying motivational priorities of bachelor degree course graduates. Materials and methods. The study involved 102 graduate students of the Financial University under the Government of the Russian Federation. The method of questioning was used. The event-driven approach and reflexive-type method were used in the research: the respondents independently identified the future events they sought to achieve and the ones they wanted to avoid. The respondents as well ranked these events in terms of importance. The survey was conducted anonymously. Results. The study highlighted some integral constructs – the students’ motivational priorities, with presentation of their substantive content. The six main motivational priorities in the descending order, as to their popularity and importance, are as follows: private life and personal relationships; work and career in organisations; wellbeing; self-actualisation; learning and development; freedom of relocation round the world and own country. Discussion and conclusion. The implemented study has made it possible to empirically establish or confirm some facts that are either not noted by the researchers or are on the periphery of their attention, or are controversial. The high importance of work and career for university graduates has been confirmed; the treatment of financial wellbeing as an auxilary factor, rather than an end in itself, has been confirmed. The presence of motivation towards travelling freely round the country and the world, along with the lack of motivation for healthy lifestyle, has been established. Teachers need to link the learning outcomes in students’ minds with satisfaction of their personal needs and expectations identified during the study. They should draw students’ attention to the fact that education expands opportunities for choice of interesting and well-paid jobs, increases the chance for successful career, promotes one’s social status, extends the opportunities for self-realisation.
Educational potential of English-language extracurricular activities with elements of palaeontology at secondary school

Introduction. The relevance of the research is accounted for by the search of new pedagogical technologies for the formation, preservation and reproduction of traditional cultural values in the socio-cultural space. The aim of the article is to analyse the educational potential of extracurricular activities in the English language involving the elements of palaeontology for secondary school students. Materials and methods: 1) study of due normative documents and scientific methodological literature; 2) organisation of two expert interviews with worldclass palaeontologists; 3) questioning encompassing sixty-four 8th-year pupils of two schools of Karaganda region, Kazakhstan; 4) experimental work aimed at introduction of a series of extracurricular activities in the English language with elements of palaeontology; 5) methods of mathematical processing of research results: Yule’s coefficient of association (Q), Pearson’s contingency coefficient (Cc), Fisher’s angular transformation criterion (φ), McNemar test (χ2). Results. A number of factors were revealed testifying that the palaeontological discoveries on the territory of Kazakhstan represent a cultural component which heightens the interest to the history of the native land, develops patriotism, improves the quality of education, as well as draws attention to the problem of imbalance between the interest in palaeontological finds of Kazakhstan on the part of the world scientific community and the lack of knowledge in Kazakh young people about the richness of the country’s ancient flora and fauna. The study of the regulatory framework revealed some inconsistencies and gaps in the biology curriculum for 7th-9th year school learners, which were filled only with the help of extracurricular activities in the English language with the inclusion of elements of palaeontology. The cumulative assessment formed by the means of analysis of two expert interviews makes one conclude on the interest of the global palaeontological community in the ancient flora and fauna of Kazakhstan as well as on the motivational, educational and socio-cultural potential of palaeontological content in school’s educational activities. Some statistically significant differences were revealed in respect of preference of extracurricular activity types in 8th-year learners (χ2exp =7.719; p≤0.05), on the ways to increase motivation to participate in such extracurricular activities (φemp=2.353 with p≤0.05) and regarding the extension of vocabulary on palaeontology in English (χ2 = 9.524 with p≤0.05). Conclusions. The evidential base of the educational potential of palaeontological elements in extracurricular activities at classes of English at secondary middle school can be used in interdisciplinary extracurricular activities at all educational levels.
Learning Disentangled Classification and Localization Representations for Temporal Action Localization

A common approach to Temporal Action Localization (TAL) is to generate action proposals and then perform action classification and localization on them. For each proposal, existing methods universally use a shared proposal-level representation for both tasks. However, our analysis indicates that this shared representation focuses on the most discriminative frames for classification, e.g., ``take-offs" rather than ``run-ups" in distinguishing ``high jump" and ``long jump", while frames most relevant to localization, such as the start and end frames of an action, are largely ignored. In other words, such a shared representation can not simultaneously handle both classification and localization tasks well, and it makes precise TAL difficult. To address this challenge, this paper disentangles the shared representation into classification and localization representations. The disentangled classification representation focuses on the most discriminative frames, and the disentangled localization representation focuses on the action phase as well as the action start and end. Our model could be divided into two sub-networks, i.e., the disentanglement network and the context-based aggregation network. The disentanglement network is an autoencoder to learn orthogonal hidden variables of classification and localization. The context-based aggregation network aggregates the classification and localization representations by modeling local and global contexts. We evaluate our proposed method on two popular benchmarks for TAL, which outperforms all state-of-the-art methods.
Boosting Active Learning via Improving Test Performance

Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless, it remains unclear how selected data impacts the test performance of the task model used in AL. In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in a better test performance. However, due to the lack of label information, directly computing gradient norm for unlabeled data is infeasible. To address this challenge, we propose two schemes, namely expected-gradnorm and entropy-gradnorm. The former computes the gradient norm by constructing an expected empirical loss while the latter constructs an unsupervised loss with entropy. Furthermore, we integrate the two schemes in a universal AL framework. We evaluate our method on classical image classification and semantic segmentation tasks. To demonstrate its competency in domain applications and its robustness to noise, we also validate our method on a cellular imaging analysis task, namely cryo-Electron Tomography subtomogram classification. Results demonstrate that our method achieves superior performance against the state of the art. We refer readers to https://arxiv.org/pdf/2112.05683.pdf for the full version of this paper which includes the appendix and source code link.
AI Snap! Blocks for Speech Input and Output, Computer Vision, Word Embeddings, and Neural Net Creation, Training, and Use

We will demonstrate blocks integrated into Snap! capable of a wide range of AI services, interactive AI programming guides, and a selection from thirty sample projects. Sessions and workshops in both school settings and informal learning contexts have been held in many countries. The full version of this paper includes descriptions of the Snap! blocks and unpublished descriptions of student experiences in India.
Multirotor Unmanned Aerial Vehicle Configuration Optimization Approach for Development of Actuator Fault-Tolerant Structure

Presently, multirotor unmanned aerial vehicles (UAV) are utilized in numerous applications. Their design governs the system’s controllability and operation performance by influencing the achievable forces and moments produced. However, unexpected causalities, such as actuator failure, adversely affect their controllability, which raises safety concerns about their service. On the other hand, their design flexibility allows further design optimization for various performance requirements, including actuator failure tolerance. Thus, this study proposed an optimization framework that can be employed to design a novel actuator fault-tolerant multirotor UAV configuration. The approach used an attainable moment set (AMS) to evaluate the achievable moment from a multirotor configuration; similarly, standard deviation geometries (SDG) were employed to define performance requirements. Therefore, given a UAV configuration, actuator fault situation, and SDG derived from the designed mission requirement, the suggested optimization framework maximizes the scaling factor of SDG and fits it into the AMS by adjusting the design parameters up to a sufficient margin. The framework is implemented to optimize selected parameters of the Hexacopter-type of parcel delivery multirotor UAV developed by the PNU drone, and a simulation was conducted. The result showed that the optimized configuration of the UAV achieved actuator fault tolerance and operation-performing capability in the presence of a failed actuator.
UNISON: Unpaired Cross-Lingual Image Captioning

Image captioning has emerged as an interesting research field in recent years due to its broad application scenarios. The traditional paradigm of image captioning relies on paired image-caption datasets to train the model in a supervised manner. However, creating such paired datasets for every target language is prohibitively expensive, which hinders the extensibility of captioning technology and deprives a large part of the world population of its benefit. In this work, we present a novel unpaired cross-lingual method to generate image captions without relying on any caption corpus in the source or the target language. Specifically, our method consists of two phases: (1) a cross-lingual auto-encoding process, which utilizing a sentence parallel (bitext) corpus to learn the mapping from the source to the target language in the scene graph encoding space and decode sentences in the target language, and (2) a cross-modal unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to language modality. We verify the effectiveness of our proposed method on the Chinese image caption generation task. The comparisons against several existing methods demonstrate the effectiveness of our approach.
PYLON: A PyTorch Framework for Learning with Constraints

Deep learning excels at learning task information from large amounts of data, but struggles with learning from declarative high-level knowledge that can be more succinctly expressed directly. In this work, we introduce PYLON, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained models with declaratively specified knowledge. PYLON lets users programmatically specify constraints as Python functions and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. PYLON includes both exact as well as approximate compilers to efficiently compute the loss, employing fuzzy logic, sampling methods, and circuits, ensuring scalability even to complex models and constraints. Crucially, a guiding principle in designing PYLON is the ease with which any existing deep learning codebase can be extended to learn from constraints in a few lines code: a function that expresses the constraint, and a single line to compile it into a loss. Our demo comprises of models in NLP, computer vision, logical games, and knowledge graphs that can be interactively trained using constraints as supervision.
Preparation of future teachers for the formation of personal self-determination of students in a virtual socializing environment

Introduction. In the conditions of the modern socializing environment, which acquires a virtual character and is characterized by a high degree of uncertainty, the problem of preparing future teachers for the formation of students' ability to self-determination is actualized. The purpose of our article is to analyze the difficulties that future teachers have in forming self-determination actions among students in a virtual socializing environment and substantiate the necessary changes in the content of pedagogical education. Methods. An online survey of 84 students of the Faculty of Primary School Teacher Training of the South Ural State Humanitarian Pedagogical University was conducted to identify existing competencies and difficulties in the formation of the ability of younger schoolchildren to self-determination. The method of content analysis of the content of 29 sample educational programs in pedagogical and psychological-pedagogical areas of higher education is used from the standpoint of reflecting in them the planned results of training future teachers to solve the problem of self-determination of the individual. Results. Dissatisfaction of 60% of students with the degree of readiness for the process under study was revealed, their difficulties were identified. The indicators of the orientation of educational programs to the formation of appropriate professional actions among students are determined. The lowest frequency coefficients of manifestation have indicators related to the skills of safe behavior and interaction with the subjects of the educational process in the world of virtual reality (0.1 and 0.06, respectively). A comprehensive solution to the problem of vocational training is proposed through integration with the processes of formation of critical thinking, functional and media-information literacy of students. Conclusion. The data obtained in the course of the study can be used to improve the process of preparing future teachers for professional activity, taking into account the opportunities and risks of a virtual socializing environment. This will ensure the formation of such educational actions among students as making a reasonable moral choice based on ethical norms; finding contradictions in the facts under consideration, recognizing reliable and unreliable information, etc.
Learning Bayesian Networks in the Presence of Structural Side Information

We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the structure of a general BN is both computationally and statistically challenging. However, often in many applications, side information about the underlying structure can potentially reduce the learning complexity. In this paper, we develop a recursive constraint-based algorithm that efficiently incorporates such knowledge (i.e., side information) into the learning process. In particular, we study two types of structural side information about the underlying BN: (I) an upper bound on its clique number is known, or (II) it is diamond-free. We provide theoretical guarantees for the learning algorithms, including the worst-case number of tests required in each scenario. As a consequence of our work, we show that bounded treewidth BNs can be learned with polynomial complexity. Furthermore, we evaluate the performance and the scalability of our algorithms in both synthetic and real-world structures and show that they outperform the state-of-the-art structure learning algorithms.
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds

In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. The proposed method, referred to as 3D DetecTrack, enables the detector and tracker to cooperate to generate a spatio-temporal representation of the camera and LiDAR data, with which 3D object detection and tracking are then performed. The detector constructs the spatio-temporal features via the weighted temporal aggregation of the spatial features obtained by the camera and LiDAR fusion. Then, the detector reconfigures the initial detection results using information from the tracklets maintained up to the previous time step. Based on the spatio-temporal features generated by the detector, the tracker associates the detected objects with previously tracked objects using a graph neural network (GNN). We devise a fully-connected GNN facilitated by a combination of rule-based edge pruning and attention-based edge gating, which exploits both spatial and temporal object contexts to improve tracking performance. The experiments conducted on both KITTI and nuScenes benchmarks demonstrate that the proposed 3D DetecTrack achieves significant improvements in both detection and tracking performances over baseline methods and achieves state-of-the-art performance among existing methods through collaboration between the detector and tracker.
Weakly Supervised Neural Symbolic Learning for Cognitive Tasks

Despite the recent success of end-to-end deep neural networks, there are growing concerns about their lack of logical reasoning abilities, especially on cognitive tasks with perception and reasoning processes. A solution is the neural symbolic learning (NeSyL) method that can effectively utilize pre-defined logic rules to constrain the neural architecture making it perform better on cognitive tasks. However, it is challenging to apply NeSyL to these cognitive tasks because of the lack of supervision, the non-differentiable manner of the symbolic system, and the difficulty to probabilistically constrain the neural network. In this paper, we propose WS-NeSyL, a weakly supervised neural symbolic learning model for cognitive tasks with logical reasoning. First, WS-NeSyL employs a novel back search algorithm to sample the possible reasoning process through logic rules. This sampled process can supervise the neural network as the pseudo label. Based on this algorithm, we can backpropagate gradients to the neural network of WS-NeSyL in a weakly supervised manner. Second, we introduce a probabilistic logic regularization into WS-NeSyL to help the neural network learn probabilistic logic. To evaluate WS-NeSyL, we have conducted experiments on three cognitive datasets, including temporal reasoning, handwritten formula recognition, and relational reasoning datasets. Experimental results show that WS-NeSyL not only outperforms the end-to-end neural model but also beats the state-of-the-art neural symbolic learning models.
Learning to Learn Transferable Attack

Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversarial perturbations are easily overﬁtting with a single surrogate model and speciﬁc data pattern. In this paper, we propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. For data augmentation, we adopt simple random resizing and padding. For model augmentation, we randomly alter the back propagation instead of the forward propagation to eliminate the effect on the model prediction. By treating the attack of both speciﬁc data and a modiﬁed model as a task, we expect the adversarial perturbations to adopt enough tasks for generalization. To this end, the meta-learning algorithm is further introduced during the iteration of perturbation generation. Empirical results on the widely-used dataset demonstrate the effectiveness of our attack method with a 12.85% higher success rate of transfer attack compared with the state-of-the-art methods. We also evaluate our method on the real-world online system, i.e., Google Cloud Vision API, to further show the practical potentials of our method.