Reviewerly/exHarmony huggingface dataset:
High-fidelity quantum control by polychromatic pulse trains

We introduce a quantum control technique using polychromatic pulse sequences (PPS), consisting of pulses with different carrier frequencies, i.e. different detunings with respect to the qubit transition frequency. We derive numerous PPS, which generate broadband, narrowband, and passband excitation profiles for different target transition probabilities. This makes it possible to create high-fidelity excitation profiles which are either (i) robust to deviations in the experimental parameters, which is attractive for quantum computing, or (ii) more sensitive to such variations, which is attractive for cross talk elimination and quantum sensing. The method is demonstrated experimentally using one of IBM's superconducting quantum processors, in a very good agreement between theory and experiment. These results demonstrate both the excellent coherence properties of the IBM qubits and the accuracy, robustness and flexibility of the proposed quantum control technique. They also show that the detuning is as efficient control parameter as the pulse phase that is commonly used in composite pulses. Hence the method opens a variety of perspectives for quantum control in areas where phase manipulation is difficult or inaccurate.
Efficient Geometry-aware 3D Generative Adversarial Networks

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent; the former limits quality and resolution of the generated images and the latter adversely affects multi-view consistency and shape quality. In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. We introduce an expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry. By decoupling feature generation and neural rendering, our framework is able to leverage state-of-the-art 2D CNN generators, such as StyleGAN2, and inherit their efficiency and expressiveness. We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats, among other experiments.
A Survey of Historical Document Image Datasets

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.
Block-STM: Scaling Blockchain Execution by Turning Ordering Curse to a Performance Blessing

Block-STM is a parallel execution engine for smart contracts, built around the principles of Software Transactional Memory. Transactions are grouped in blocks, and every execution of the block must yield the same deterministic outcome. Block-STM further enforces that the outcome is consistent with executing transactions according to a preset order, leveraging this order to dynamically detect dependencies and avoid conflicts during speculative transaction execution. At the core of Block-STM is a novel, low-overhead collaborative scheduler of execution and validation tasks. Block-STM is implemented on the main branch of the Diem Blockchain code-base and runs in production at Aptos. Our evaluation demonstrates that Block-STM is adaptive to workloads with different conflict rates and utilizes the inherent parallelism therein. Block-STM achieves up to $110k$ tps in the Diem benchmarks and up to $170k$ tps in the Aptos Benchmarks, which is a $20$x and $17$x improvement over the sequential baseline with $32$ threads, respectively. The throughput on a contended workload is up to $50k$ tps and $80k$ tps in Diem and Aptos benchmarks, respectively.
The school of thinking, nobility of philosophical spirit and civil courage (to the 75-th anniversary of H.S. Skovoroda Institute of Philosophy, National Academy of Sciences of Ukraine)

The article emphasizes the cultural and educational importance of H. Skovoroda Institute of Philosophy for the spiritual development of the Ukrainian society, especially in the direction of democracy and establishment of the worldview culture as a requirement for the culture of freedom. From the position of the included observer the author of the article describes some episodes of relationship in the scientist’s communities which can be defined as justice and solidary community. On the basis of the Heidegerian scheme, some dangers are analyzed as demands addressed to philosophical thinking. This scheme is used by structuring of the article. The metaphoric of these dangers indicates the contextual problems which are important for producing of philosophical ideas. The experience of the Institute of Philosophy gained by solving of problems is very remarkable for the fulfillment of philosophical studies and educational practices especially by mentoring of the postgraduate students. The fact of naming the Institute after H. Skovoroda is regarded as a symbolic act with a great creative potential and energy which can be used for reviving of national traditions of philosophical thinking. The role of lyric in activating of philosophical ideas is showed. The symbiotic coexisting of the poetry, art, journalism and philosophy is one of the original features of the Skovorodian tradition caring by the well-known representatives of the Institute. All these characteristics joined with the civic culture, which was demonstrated by all generations of the Institute, allow defining it as a metaphysical place with the ability both to follow on the road of the way of light and to represent it on the landscapes of philosophical science and education. The history of the Institute, especially, in the period of its post-totalitarian transformations, gives the opportunity of taking comparison with the Institute for Social Research Frankfurt. The civic courage of the Ukrainian intellectual elites was always initiated, supported and generated by philosophers gathering in Institute of Philosophy.
ScienceWorld: Is your Agent Smarter than a 5th Grader?

We present ScienceWorld, a benchmark to test agents' scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum. Despite the transformer-based progress seen in question-answering and scientific text processing, we find that current models cannot reason about or explain learned science concepts in novel contexts. For instance, models can easily answer what the conductivity of a known material is but struggle when asked how they would conduct an experiment in a grounded environment to find the conductivity of an unknown material. This begs the question of whether current models are simply retrieving answers by way of seeing a large number of similar examples or if they have learned to reason about concepts in a reusable manner. We hypothesize that agents need to be grounded in interactive environments to achieve such reasoning capabilities. Our experiments provide empirical evidence supporting this hypothesis -- showing that a 1.5 million parameter agent trained interactively for 100k steps outperforms a 11 billion parameter model statically trained for scientific question-answering and reasoning from millions of expert demonstrations.
E-Learning course design and implementation in fuzzy logic

The goal of e-learning in fuzzy logic courses is to assist students in the learning process, create menu structures and simple operation techniques, and create prototypes for e-learning in fuzzy logic courses. The difficulty of students in implementing what they have learned stems from the fact that the advent of e-learning can make it simpler for students to access material that they do not comprehend. In this study, an analytic learning prototype was used as the research approach. The outcomes of e-learning products based on online apps in this fuzzy logic course can be used as learning material. The menu structure developed in this e-learning is a home page with an introduction to e-learning, a site page with participants, calendars, and notes. Pages that can be used to grow the network and courses are the most significant aspects of e-learning. E-learning includes material, discussion, forums, quizzes, and other activities. The findings of the validation by media specialists on this e-learning application are pretty good, indicating that it is suitable for use. According to the results of material expert validation, the material used is excellent, suggesting that it is ideal for use in fuzzy logic courses. The limited test results for Informatics study program students were in the very good category, indicating that this e-learning tool was simple to use.
Improved and Efficient Conversational Slot Labeling through Question Answering

Transformer-based pretrained language models (PLMs) offer unmatched performance across the majority of natural language understanding (NLU) tasks, including a body of question answering (QA) tasks. We hypothesize that improvements in QA methodology can also be directly exploited in dialog NLU; however, dialog tasks must be \textit{reformatted} into QA tasks. In particular, we focus on modeling and studying \textit{slot labeling} (SL), a crucial component of NLU for dialog, through the QA optics, aiming to improve both its performance and efficiency, and make it more effective and resilient to working with limited task data. To this end, we make a series of contributions: 1) We demonstrate how QA-tuned PLMs can be applied to the SL task, reaching new state-of-the-art performance, with large gains especially pronounced in such low-data regimes. 2) We propose to leverage contextual information, required to tackle ambiguous values, simply through natural language. 3) Efficiency and compactness of QA-oriented fine-tuning are boosted through the use of lightweight yet effective adapter modules. 4) Trading-off some of the quality of QA datasets for their size, we experiment with larger automatically generated QA datasets for QA-tuning, arriving at even higher performance. Finally, our analysis suggests that our novel QA-based slot labeling models, supported by the PLMs, reach a performance ceiling in high-data regimes, calling for more challenging and more nuanced benchmarks in future work.
Explaining away results in more robust visual tracking

Abstract Many current trackers utilise an appearance model to localise the target object in each frame. However, such approaches often fail when there are similar-looking distractor objects in the surrounding background, meaning that target appearance alone is insufficient for robust tracking. In contrast, humans consider the distractor objects as additional visual cues, in order to infer the position of the target. Inspired by this observation, this paper proposes a novel tracking architecture in which not only is the appearance of the tracked object, but also the appearance of the distractors detected in previous frames, taken into consideration using a form of probabilistic inference known as explaining away. This mechanism increases the robustness of tracking by making it more likely that the target appearance model is matched to the true target, rather than similar-looking regions of the current frame. The proposed method can be combined with many existing trackers. Combining it with SiamFC, DaSiamRPN, Super_DiMP, and ARSuper_DiMP all resulted in an increase in the tracking accuracy compared to that achieved by the underlying tracker alone. When combined with Super_DiMP and ARSuper_DiMP, the resulting trackers produce performance that is competitive with the state of the art on seven popular benchmarks.
Cloud Computing in a Distributed Environment Implemented with Networking Technologies

With the advent of cloud computing, data can be distributed in remote servers. Data is distributed in remote servers instead of keeping all the data in the local server, and the principles of cloud computing are applied to all the servers that hold data. This paper uses a cloud simulator that works in a distributed environment using two server models that holds data from the Computer Science and Engineering department of our college. Then, we apply the dogma of cloud computing to the data present in the servers. Distributed cloud environment reduces overheads of communication, latencies and costs by offering efficient storage resources and orderly computation. This paper tries a build a model of networking as service that gives a virtual and flexible environment to the users for doing computation work. It also helps the users to dispatch their requirements using this virtual domain.
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detection, a distinguished category of methods relies on predicting masked information (e.g. patches, future frames, etc.) and leveraging the reconstruction error with respect to the masked information as an abnormality score. Different from related methods, we propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. The proposed self-supervised block is generic and can easily be incorporated into various state-of-the-art anomaly detection methods. Our block starts with a convolutional layer with dilated filters, where the center area of the receptive field is masked. The resulting activation maps are passed through a channel attention module. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video, providing empirical evidence that shows considerable performance improvements on MVTec AD, Avenue, and ShanghaiTech. We release our code as open source at https://github.com/ristea/sspcab.
Computer science and information vision of the world from the standpoint of the principle of materialistic monism

The subject of this study is the problem of the failure of attempts by the scientific community to come to a common understanding of what exactly information can be as something encoded into material structures and moved along with them. At the same time, the following aspects of this problem are considered in detail: what is the immediate cause of the information problem; what are the objective and subjective prerequisites for its appearance; why the unresolved nature of this problem does not interfere with the creation and development of communication systems, control and other "smart" devices; is there any general guideline for finding solutions to such problems, and what is it; what role did the philosophy of information play in turning the problem of information from a private scientific problem into a problem of the ideological level. The main results of the conducted research are as follows: the immediate cause of the information problem has been identified and a comprehensively substantiated solution to this problem has been given; it has been revealed that the incompleteness of the materialistic theory of cognition has become a fertile ground for the appearance of this problem; the circumstances that prevented its completion have been identified; the natural mechanism of control and cognition has been revealed, as a result of which the materialistic theory of cognition has been further developed the source of the ideal in the material world is revealed; it is shown that the mechanistic idea of information coding made the concept of information incompatible with the presence of any objective content; it is revealed what is actually hidden behind the words about information coding and its measurement; the nature of signals and signs is revealed; a kind of bias of the philosophy of information and a general error is revealed all known concepts of information.
Single Model Ensemble for Subword Regularized Models in Low-Resource Machine Translation

Subword regularizations use multiple subword segmentations during training to improve the robustness of neural machine translation models.In previous subword regularizations, we use multiple segmentations in the training process but use only one segmentation in the inference.In this study, we propose an inference strategy to address this discrepancy.The proposed strategy approximates the marginalized likelihood by using multiple segmentations including the most plausible segmentation and several sampled segmentations.Because the proposed strategy aggregates predictions from several segmentations, we can regard it as a single model ensemble that does not require any additional cost for training.Experimental results show that the proposed strategy improves the performance of models trained with subword regularization in low-resource machine translation tasks.
Learning Representations Robust to Group Shifts and Adversarial Examples

Despite the high performance achieved by deep neural networks on various tasks, extensive studies have demonstrated that small tweaks in the input could fail the model predictions. This issue of deep neural networks has led to a number of methods to improve model robustness, including adversarial training and distributionally robust optimization. Though both of these two methods are geared towards learning robust models, they have essentially different motivations: adversarial training attempts to train deep neural networks against perturbations, while distributional robust optimization aims at improving model performance on the most difficult "uncertain distributions". In this work, we propose an algorithm that combines adversarial training and group distribution robust optimization to improve robust representation learning. Experiments on three image benchmark datasets illustrate that the proposed method achieves superior results on robust metrics without sacrificing much of the standard measures.
Student character building activities as a component of higher education primary educational program

The paper provides a solution to the task of implementing student’s character building activities as a component of the main educational program of higher education in a technical university through the common universal competences formation, the educational process is considered as a single focused process of education and training in the interests of a person, family, society and the state, where education is an activity aimed at personal development, creating conditions for self-determination and socialization of the student.
Knowledge-Enhanced Multi-task Learning for Course Recommendation

Knowledge tracing (KT) aims to model learners’ knowledge level and predict future performance given their past interactions in learning applications. Adaptive learning systems mainly generate course recommendations based on learner’s knowledge level acquired by KT. However, for KT tasks, learners’ forgetting has not been well modeled. In addition, learner’s individual differences also influence the accuracy of knowledge level prediction. While for recommendation tasks, most of methods are conducted separately from KT tasks, ignoring the deep connection between them. In this paper, we are motivated to propose a Knowledge-Enhanced Multi-task Learning model for Course Recomme-ndation (KMCR), which regards the improved knowledge tracing task (IKTT) as an auxiliary task to assist the primary course recommendation task (CRT). Specifically, in IKTT, for assessing dynamic evolving knowledge level, we not only design a personalized controller to enhance the deep knowledge tracing model for modeling learner’s forgetting behavior, but also use personality to model the individual differences based on the theory of cognitive psychology. In CRT, we adaptively combine learner’s knowledge level obtained by IKTT with their sequential behavior to generate learners’ representation. The experimental results on real-world datasets demonstrate that our approach outperforms related methods in terms of recommendation accuracy.
Self-confidence and digital proficiency: Determinants of digital skills perceptions among young people in Spain

The necessity of introducing telework and online education due to the COVID-19 crisis has drawn attention to the problem of digital inequalities among young people. Besides access and connectivity problems, the lockdown has shown a gap between young people’s self-perceived level of digital skills (which is generally high) and their actual digital performance, they tend to experience important difficulties associated with the second-level digital divide. Most of the empirical evidence about digital skills relies on index construction based on variables about digital tasks or practices that people can fulfill. Nevertheless, there is no evidence about the sociological and biographical determinants that influence subjects’ self-perception of their level of digital skills, particularly among young people, since this generational group is generally associated with a high level of digital skills. Therefore, in this article we will measure the influence of different sociological and biographical aspects (gender, age, education, forms of digital accessibility, digital practices, itineraries of digital literacy along their life, etc.) over declaring a certain level of digital skills. The outcomes show the importance of socio-demographic factors and of the processes of appropriation of technology when young people perceive their level of digital performance, which are only partially related to socioeconomic situations, since digital vulnerability is mediated by acquired cultural patterns over the use of technology.