Reviewerly/exHarmony huggingface dataset:
2P-Kt: A logic-based ecosystem for symbolic AI

To date, logic-based technologies are either built on top or as extensions of the Prolog language, mostly working as monolithic solutions tailored upon specific inference procedures, unification mechanisms, or knowledge representation techniques.Instead, to maximise their impact, logic-based technologies should support and enable the general-purpose exploitation of all the manifold contributions from logic programming.Accordingly, we present 2P-Kt, a reboot of the tuProlog project offering a general, extensible, and interoperable ecosystem for logic programming and symbolic AI.
Isolation Forest and Local Outlier Factor for Credit Card Fraud Detection System

Fraud identification is a crucial issue facing large economic institutions, which has caused due to the rise in credit card payments. This paper brings a new approach for the predictive identification of credit card payment frauds focused on Isolation Forest and Local Outlier Factor. The suggested solution comprises of the corresponding phases: pre-processing of data-sets, training and sorting, convergence of decisions and analysis of tests. In this article, the behavior characteristics of correct and incorrect transactions are to be taught by two kinds of algorithms local outlier factor and isolation forest. To date, several researchers identified different approaches for identifying and growing such frauds. In this paper we suggest analysis of Isolation Forest and Local Outlier Factor algorithms using python and their comprehensive experimental results. Upon evaluating the dataset, we received Isolation Forest with high accuracy compared to Local Outlier Factor Algorithm
Sorting through the noise: Testing robustness of information processing in pre-trained language models

Pre-trained LMs have shown impressive performance on downstream NLP tasks, but we have yet to establish a clear understanding of their sophistication when it comes to processing, retaining, and applying information presented in their input.In this paper we tackle a component of this question by examining robustness of models' ability to deploy relevant context information in the face of distracting content.We present models with cloze tasks requiring use of critical context information, and introduce distracting content to test how robustly the models retain and use that critical information for prediction.We also systematically manipulate the nature of these distractors, to shed light on dynamics of models' use of contextual cues.We find that although models appear in simple contexts to make predictions based on understanding and applying relevant facts from prior context, the presence of distracting but irrelevant content has clear impact in confusing model predictions.In particular, models appear particularly susceptible to factors of semantic similarity and word position.The findings are consistent with the conclusion that LM predictions are driven in large part by superficial contextual cues, rather than by robust representations of context meaning.
QuantImPy: Minkowski functionals and functions with Python

The Minkowski functionals and functions are a family of morphological measures and can be used to describe both the morphology (shape) and topology (connectedness) of a system. This paper presents the QuantImPy Python package which can compute both the Minkowski functionals and functions. In addition, this package can efficiently perform basic morphological operations and compute their distance maps. QuantImPy is easy to install, well-documented, integrated with existing Python packages, and open source.
A New Watershed Algorithm for Pothole Image Segmentation

The road casualties caused by poor road conditions, especially potholed roads, amount to a high percentage of 13% per year in Asia.In order for the government to carry out repair works, they must know the surface area of the potholed road, the depth of the potholes, and the position of the potholes.In this paper, with the purpose of determining the surface area of the potholed road, segmentation was carried out first.The segmentation method used here is a new watershed algorithm.This algorithm includes six processes: smoothing, calculating the gradient value, automatic determination of minimum flooding value, combining the catchment basins, erasing the remaining watershed lines, and creating a watershed curve.The proposed algorithm is evaluated and compared with previously analysed watershed segmentation methods.This study shows that this algorithm is effective for reducing the effects of excessive segmentation.The proposed algorithm obtained a high accuracy of 95.34%, which is higher than that of the existing watershed segmentation methods.
Comparison of the Effectiveness of Deep Learning Methods for Face Mask Detection

The usage of mask is necessary for the prevention and control of COVID-19 which is a respiratory disease that passes from person to person by contact and droplets from the respiratory tract. It is an important task to identify people who do not wear face mask in the community. In this study, performance comparison of the automated deep learning based models including the ones that use transfer learning for face mask detection on images was performed. Before training deep models, faces were detected within images using multi-task cascaded convolutional network (MTCNN). Images obtained from face mask detection dataset, COVID face mask detection dataset, mask detection dataset, and with/without mask dataset were used for training and testing the models. Face areas that are detected with MTCNN were used as input for convolutional neural network (CNN), MobileNetV2, VGG16 and ResNet50. VGG16 showed best performance with 97.82% accuracy. MobileNetV2 showed the worst performance for detecting faces without mask with 72.44% accuracy. Comparison results show that VGG16 can be used effectively to detect faces without mask. This system can be used in crowded public areas to warn people without mask that may help the reduce the risk of pandemic.
Graph Pointer Neural Networks

Graph Neural Networks (GNNs) have shown advantages in various graph-based applications. Most existing GNNs assume strong homophily of graph structure and apply permutation-invariant local aggregation of neighbors to learn a representation for each node. However, they fail to generalize to heterophilic graphs, where most neighboring nodes have different labels or features, and the relevant nodes are distant. Few recent studies attempt to address this problem by combining multiple hops of hidden representations of central nodes (i.e., multi-hop-based approaches) or sorting the neighboring nodes based on attention scores (i.e., ranking-based approaches). As a result, these approaches have some apparent limitations. On the one hand, multi-hop-based approaches do not explicitly distinguish relevant nodes from a large number of multi-hop neighborhoods, leading to a severe over-smoothing problem. On the other hand, ranking-based models do not joint-optimize node ranking with end tasks and result in sub-optimal solutions. In this work, we present Graph Pointer Neural Networks (GPNN) to tackle the challenges mentioned above. We leverage a pointer network to select the most relevant nodes from a large amount of multi-hop neighborhoods, which constructs an ordered sequence according to the relationship with the central node. 1D convolution is then applied to extract high-level features from the node sequence. The pointer-network-based ranker in GPNN is joint-optimized with other parts in an end-to-end manner. Extensive experiments are conducted on six public node classification datasets with heterophilic graphs. The results show that GPNN significantly improves the classification performance of state-of-the-art methods. In addition, analyses also reveal the privilege of the proposed GPNN in filtering out irrelevant neighbors and reducing over-smoothing.
METHODOLOGICAL APPROACH TO DETERMINING THE MAIN FACTORS FOR THE DEVELOPMENT OF CREATIVE THINKING IN STUDENTS OF CREATIVE PROFESSIONS

The phenomenon of a person’s creative thinking, as well as the mechanisms of its development,are in the problematic field of research of numerous scientists for many centuries. The relevance of these problems was reinforced in many respects by the desire of a person to understand his capabilities, to use them in order to create a comfortable environment around him. Interest in this problem at this stage of socio-historical development has its own special features.The questions that are raised in the study are what factors have the most significant influence on the development of creative thinking in students of creative professions. The main purpose of the article is to determine and compile the key factors for the development of creative thinking in students of creative professions as the main guarantee of their success. To do this, we have applied the methodology of the graph connections between the main factors, which will allow us to form a methodological approach to the hierarchical ordering of the most important factors in the development of creative thinking in students of creative professions as the main guarantee of their success. The subject of the research is the factors of influence on the creative thinking of students of creative specialties. The results of the study led to the formation of the key factors influencing the development of creative thinking among students of creative professions through expert analysis and a survey of specialists in this field and literature. The practical value of the study is to determine the key factors of influence on the development of creative thinking in students and the possibility of using the proposed methodological approach, in particular. The study has limitations and they consist because the process of determining the key factors for the development of creative thinking in students of creative professions took place only considering the specifics of the activities of educational institutions in Ukraine.
Syndrome-based Min-sum vs OSD-0 decoders: FPGA Implementation and Analysis for Quantum LDPC codes

Quantum processors need to improve their reliability to scale up the number of qubits and increase the number of algorithms that can execute. To reduce the logical error rate of the quantum systems, the use of error correction codes and decoders has been established as a low-cost and feasible approach, with good results from a theoretical perspective, for mid and long-term architectures. While most of the authors are focused on the algorithms to improve the correction capability of quantum computers, without taking into account a fundamental implementation aspect for their deployment in a real system, i.e., their latency must be bounded to avoid the qubit decoherence, only a few propose hardware architectures and they just include time estimations of their decoding latency. However, a real implementation has not been shown yet. In this work, we analyze from the point of view of hardware implementation two algorithmic options based on quantum low-density parity-check (QLDPC) codes: a) belief propagation min-sum decoders combined with codes with good error-floor behavior and b) belief propagation min-sum decoders concatenated with ordered statistics decoders (OSDs) for codes with early error-floor. The bounds for the maximum clock frequency required by the decoders to decode within the qubit coherence time are established as a parameter to show if a practical implementation is possible with the present or near future FPGA technology. Furthermore, real implementation results for a Xilinx FPGA device are provided, showing that some solutions can meet the timing constraints set up by the state-of-the-art quantum processors.
Personalized Recommendation via Multi-dimensional Meta-paths Temporal Graph Probabilistic Spreading

● We propose a personalized recommendation method via multi-dimensional meta-paths temporal graph probabilistic spreading (MD-MP-TGPS). ● We construct temporal multi-dimensional graphs, taking the interest drift of users, obsolescence and popularity of items, and dynamic update of interaction behavior data into full consideration. ● We propose a dimension-free temporal graph probabilistic spreading framework via multi-dimensional meta-paths. ● We propose two personalized recommendation strategies to automatically learn the priority and importance of these multi-dimensional meta-paths at the user-level granularity. ● Comprehensive experiments show that the proposed MD-MP-TGPS method transcends the compared state-of-the-art methods in accuracy, diversity, and novelty of recommendation. Since meta-paths have the innate ability to capture rich structure and semantic information, meta-path-based recommendations have gained tremendous attention in recent years. However, how to composite these multi-dimensional meta-paths? How to characterize their dynamic characteristics? How to automatically learn their priority and importance to capture users' diverse and personalized preferences at the user-level granularity? These issues are pivotal yet challenging for improving both the performance and the interpretability of recommendations. To address these challenges, we propose a personalized recommendation method via Multi-Dimensional Meta-Paths Temporal Graph Probabilistic Spreading (MD-MP-TGPS). Specifically, we first construct temporal multi-dimensional graphs with full consideration of the interest drift of users, obsolescence and popularity of items, and dynamic update of interaction behavior data. Then we propose a dimension-free temporal graph probabilistic spreading framework via multi-dimensional meta-paths. Moreover, to automatically learn the priority and importance of these multi-dimensional meta-paths at the user-level granularity, we propose two boosting strategies for personalized recommendation. Finally, we conduct comprehensive experiments on two real-world datasets and the experimental results show that the proposed MD-MP-TGPS method outperforms the compared state-of-the-art methods in such performance indicators as precision, recall, F1-score, hamming distance, intra-list diversity and popularity in terms of accuracy, diversity, and novelty.
The Quality of Training Future Teachers during the COVID-19 Pandemic: A Case from TNPU

This article describes the experience of the communicative competence formation of future teachers in a blended form of education as a dominant one in the period of the COVID-19 pandemic. The main features of such terms as “education quality” and “communicative competence” are revealed; the peculiarities of their components and structure are described. Using the results of a survey the authors draw conclusions about the advantages and disadvantages of online and offline learning and the efficacy of technical learning tools. The article also describes the educational platforms and technical means most frequently used by teachers and students in their everyday life. The results of testing in philological and linguodidactic disciplines after online and offline learning are presented in the paper. Conclusions are made concerning the peculiarities of online and offline training by means of Moodle and Google Classroom learning platforms, as well as Viber, Telegram, Email, Zoom Cloud Meetings and other platforms. Considering the results of the survey of students on the usage of technical means in online learning, the authors provide suggestions on improving the quality of training future teachers. The discussion part of the article presents the questions for discussion concerning a blended form of training as an effective one during the COVID-19 pandemic. The conclusion states the advantages and disadvantages of mixed forms of learning.
The Phenomenon of Arabic-English Translation of Foreign Language Classes During The Pandemic

The outbreak of the Covid-19 compelled the education sector to teach remotely. This study attempts to investigate the translation strategies used by Arabic-speaking students in EFL Saudi during online learning. The study used a questionnaire to gather the data. The participants of the study were the 120 EFL learners who majored in English at the PSU. The participant's language is Arabic to find the Phenomenon of Arabic-English Translation of Foreign Language. SPSS was used to analyze the data. The findings are encouraging. And supportive for Arabic-English translation of Foreign Language classes in online teaching. Participants used numerous translation strategies during online learning. They expressed a wide range of opinions about translation to help them in the online learning environment. The outcome demonstrates that they feel one must be immersed in an English-speaking environment for some time before thinking fluently in English. The translation was the most often utilized tool by Saudi EFL students to learn English words and phrases, write, and read English during online learning. The translation had no negative impact on participants' EFL learning. Arabic-English Translation helped in the comprehension of the subject as it is lareners’ mother tongue. Also, it is supported tremendously in EFL learning
MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding

Multimodal pre-training with text, layout, and image has made significant progress for Visually Rich Document Understanding (VRDU), especially the fixed-layout documents such as scanned document images.While, there are still a large number of digital documents where the layout information is not fixed and needs to be interactively and dynamically rendered for visualization, making existing layout-based pre-training approaches not easy to apply.In this paper, we propose MarkupLM for document understanding tasks with markup languages as the backbone, such as HTML/XMLbased documents, where text and markup information is jointly pre-trained.Experiment results show that the pre-trained MarkupLM significantly outperforms the existing strong baseline models on several document understanding tasks.The pre-trained model and code will be publicly available at https:// aka.ms/markuplm.
Squeaky wheels: Missing data, disability, and power in the smart city

Data about the accessibility of United States municipalities is infrastructure in the smart city. What is counted and how, reflects the sociotechnical imaginary (norms and values) of a time or place. In this paper we focus on features identified by people with disabilities as promoting or hindering safe pedestrian travel. We use a regionally stratified sample of 178 cities across the United States. The municipalities were scored on two factors: their open data practices (or lack thereof), and the degree to which they cataloged the environmental features that persons with disabilities deemed critical for safe movement through urban spaces. In contradiction to the dominating narrative of too much data and not enough analyses, we find that when it comes to data points that might be useful to persons with disabilities, data are lacking. This data gap has consequences both politically and materially—on one hand data could help enforce compliance with the Americans with Disabilities Act, on the other they would allow for safe route planning. We find that reading these data formats and collection patterns from the perspective of critical disability studies—particularly those whose work disrupts notions of “normal” —helps answer questions about potential benefits and harms of data practices. This lens has the potential to promote analysis that is as disruptive to injustices as it is practical.
Quantum Fisher information and skew information correlations in dipolar spin system

Quantum Fisher information (QFI) and skew information (SI) plays a key role in the quantum resource theory. Understanding these measures in the physical system has practical significance in the state parameter estimation and quantum metrology. In this article, we consider a pair of spin-1/2 particles coupled with dipolar and Dzyaloshinsky-Moriya (DM) interactions, serving as the physical carrier of quantum information. We examine the bipartite nonlocal correlations of pair of spin-1/2 particle system for the thermal equilibrium states, characterized by local quantum uncertainty (LQU) and local quantum Fisher information (lQFI). The effects of dipolar coupling constants on quantum correlation quantifiers are studied. The DM interaction greatly enhances the quantum correlation in the system whereas the temperature tends to annihilate the amount of quantum correlations.
Beyond Quadratic Speedups in Quantum Attacks on Symmetric Schemes

In this paper, we report the first quantum key-recovery attack on a symmetric block cipher design, using classical queries only, with a more than quadratic time speedup compared to the best classical attack. We study the 2XOR-Cascade construction of Gaži and Tessaro (EUROCRYPT 2012). It is a key length extension technique which provides an n-bit block cipher with $$\frac{5n}{2}$$ bits of security out of an n-bit block cipher with 2n bits of key, with a security proof in the ideal model. We show that the offline-Simon algorithm of Bonnetain et al. (ASIACRYPT 2019) can be extended to, in particular, attack this construction in quantum time $$\widetilde{\mathcal {O}}\left( 2^n \right) $$ , providing a 2.5 quantum speedup over the best classical attack. Regarding post-quantum security of symmetric ciphers, it is commonly assumed that doubling the key sizes is a sufficient precaution. This is because Grover's quantum search algorithm, and its derivatives, can only reach a quadratic speedup at most. Our attack shows that the structure of some symmetric constructions can be exploited to overcome this limit. In particular, the 2XOR-Cascade cannot be used to generically strengthen block ciphers against quantum adversaries, as it would offer only the same security as the block cipher itself.
Entropic barriers as a reason for hardness in both classical and quantum algorithms

We study both classical and quantum algorithms to solve a hard optimization problem, namely 3-XORSAT on 3-regular random graphs. By introducing a new quasi-greedy algorithm that is not allowed to jump over large energy barriers, we show that the problem hardness is mainly due to entropic barriers. We study, both analytically and numerically, several optimization algorithms, finding that entropic barriers affect in a similar way classical local algorithms and quantum annealing. For the adiabatic algorithm, the difficulty we identify is distinct from that of tunneling under large barriers, but does, nonetheless, give rise to exponential running (annealing) times.
Sharpness-Aware Minimization Improves Language Model Generalization

The allure of superhuman-level capabilities has led to considerable interest in language models like GPT-3 and T5, wherein the research has, by and large, revolved around new model architectures, training tasks, and loss objectives, along with substantial engineering efforts to scale up model capacity and dataset size.Comparatively little work has been done to improve the generalization of these models through better optimization.In this work, we show that Sharpness-Aware Minimization (SAM), a recently proposed optimization procedure that encourages convergence to flatter minima, can substantially improve the generalization of language models without much computational overhead.We show that SAM is able to boost performance on SuperGLUE, GLUE, Web Questions, Natural Questions, Trivia QA, and TyDiQA, with particularly large gains when training data for these tasks is limited.