Reviewerly/exHarmony huggingface dataset:
Searching B-Smooth Numbers Using Quantum Annealing: Applications to Factorization and Discrete Logarithm Problem

Integer factorization and discrete logarithm problem, two problems of classical public-key cryptography, are vulnerable to quantum attacks, especially polynomial-time Shor’s algorithm, which has to be run on the general-purpose quantum computer. On the other hand, one can make quantum computations using quantum annealing, where every problem has to be transformed into an optimization problem, for example, the QUBO problem. Currently, the biggest available quantum annealer, D-Wave advantage, has almost 6,000 physical qubits, and therefore it can solve bigger problems than using general-purpose quantum computers. Even though it is impossible to run Shor’s algorithm on a quantum annealer, several methods allow one to transform factorization or discrete logarithm problems into the QUBO problem. Using a D-Wave quantum annealer, the biggest factored integer had 20 bits, and the biggest field, on which it was possible to compute a discrete logarithm problem using any quantum method, had 6 bits. This paper shows how to transform searching for B-smooth numbers, an important part of the quadratic sieve method for factorization and index calculus for solving discrete logarithm problems, to the QUBO problem and then solve it using D-Wave Advantage quantum solver. The linear algebra step for integer factorization and index calculus methods has been solved using classical computations. Using our method, we factorized the 26-bit integer and computed the discrete logarithm problem over the 18-bit prime field. Therefore we broke the current records in factorization using quantum annealing by 6 bits and in discrete logarithm problem, using any quantum method, by 12 bits.
Feasibility and Performance Benefits of Directional Force Fields for the Tactical Conflict Management of UAVs

As we move towards scenarios where the adoption of unmanned aerial vehicles (UAVs) becomes massive, smart solutions are required to efficiently solve conflicts in the flight trajectories of aircraft so as to avoid potential collisions. Among the different possible approaches, adopting virtual force fields is a possible solution acknowledged for being simple, distributed, and yet effective. In this paper, we study the feasibility of a directional force field (D-FFP) approach, preliminary assessing its performance benefits compared to a standard force field protocol (FFP) using Matlab simulation. Results show that, in typical scenarios associated with aerial traffic corridors, the proposed approach can reduce the flight time overhead by 32% (on average) while maintaining the required flight safety distances between aircraft.
Video: APIs for Papermill detection (ConTech API Series)

Recently, I gave a presentation on the APIs for Papermill Detection offered by Clear Skies Ltd.I also touch on a newer service called the Clear Skies Standard Report. More on that in future posts… :)Here’s the video:https://medium.com/media/ee5a04aaed9fc53d2748e64516178ebe/hrefWould you like to know more?There are a few more presentations coming up:I’ll be speaking with Ebru Cucen from Open Credo about some work we did on detecting unusual co-authorships based on data shared by Anna...
Educational Game-Based Thematic Learning Media to Improve Student Learning Competence

The limitations of interesting learning media on thematic subjects impact low student learning competence. To improve student learning competence, digital learning media is needed that is easy for students to access. This study aims to develop educational game-based thematic learning media to improve the learning competence of grade IV students. This research is developmental research using the Hannafin &amp; Peck model. The research subjects were one content expert, one design expert, and one learning media expert, then three individual trials, six small group trials, and fifteen field trials. Data collection in this study was carried out using questionnaires and tests, with research instruments in the form of product validity test questionnaires and student learning outcomes tests. The data obtained in the study were then analyzed using quantitative descriptive analysis techniques and qualitative descriptive analysis methods. The research analysis results showed that the products developed were valid based on the results of content experts obtained at 95.50%, learning design experts obtained at 88.00%, learning media experts at 94.67%, individual trials at 92.67%, small group trials at 95.17%, and field trials 95.70%. Based on these results, it can be concluded that developing educational games in thematic subjects in class IV is classified as a very good qualification and is declared suitable for use in the learning process.
Local Explanations for Reinforcement Learning

Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood by domain users has received much less attention. In this paper, we propose a novel perspective to understanding RL policies based on identifying important states from automatically learned meta-states. The key conceptual difference between our approach and many previous ones is that we form meta-states based on locality governed by the expert policy dynamics rather than based on similarity of actions, and that we do not assume any particular knowledge of the underlying topology of the state space. Theoretically, we show that our algorithm to find meta-states converges and the objective that selects important states from each meta-state is submodular leading to efficient high quality greedy selection. Experiments on four domains (four rooms, door-key, minipacman, and pong) and a carefully conducted user study illustrate that our perspective leads to better understanding of the policy. We conjecture that this is a result of our meta-states being more intuitive in that the corresponding important states are strong indicators of tractable intermediate goals that are easier for humans to interpret and follow.
Learning Semantic Degradation-Aware Guidance for Recognition-Driven Unsupervised Low-Light Image Enhancement

Low-light images suffer severe degradation of low lightness and noise corruption, causing unsatisfactory visual quality and visual recognition performance. To solve this problem while meeting the unavailability of paired datasets in wide-range scenarios, unsupervised low-light image enhancement (ULLIE) techniques have been developed. However, these methods are primarily guided to alleviate the degradation effect on visual quality rather than semantic levels, hence limiting their performance in visual recognition tasks. To this end, we propose to learn a Semantic Degradation-Aware Guidance (SDAG) that perceives the low-light degradation effect on semantic levels in a self-supervised manner, which is further utilized to guide the ULLIE methods. The proposed SDAG utilizes the low-light degradation factors as augmented signals to degrade the low-light images, and then capture their degradation effect on semantic levels. Specifically, our SDAG employs the subsequent pre-trained recognition model extractor to extract semantic representations, and then learns to self-reconstruct the enhanced low-light image and its augmented degraded images. By constraining the relative reconstruction effect between the original enhanced image and the augmented formats, our SDAG learns to be aware of the degradation effect on semantic levels in a relative comparison manner. Moreover, our SDAG is general and can be plugged into the training paradigm of the existing ULLIE methods. Extensive experiments demonstrate its effectiveness for improving the ULLIE approaches on the downstream recognition tasks while maintaining a competitive visual quality. Code will be available at https://github.com/zheng980629/SDAG.
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks

Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However, current work typically models graph dynamics with recurrent neural networks (RNNs), making them suffer seriously from computation and memory overheads on large temporal graphs. So far, scalability of dynamic graph representation learning on large temporal graphs remains one of the major challenges. In this paper, we present a scalable framework, namely SpikeNet, to efficiently capture the temporal and structural patterns of temporal graphs. We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs. As a low-power alternative to RNNs, SNNs explicitly model graph dynamics as spike trains of neuron populations and enable spike-based propagation in an efficient way. Experiments on three large real-world temporal graph datasets demonstrate that SpikeNet outperforms strong baselines on the temporal node classification task with lower computational costs. Particularly, SpikeNet generalizes to a large temporal graph (2.7M nodes and 13.9M edges) with significantly fewer parameters and computation overheads.
POEM: Polarization of Embeddings for Domain-Invariant Representations

Handling out-of-distribution samples is a long-lasting challenge for deep visual models. In particular, domain generalization (DG) is one of the most relevant tasks that aims to train a model with a generalization capability on novel domains. Most existing DG approaches share the same philosophy to minimize the discrepancy between domains by finding the domain-invariant representations. On the contrary, our proposed method called POEM acquires a strong DG capability by learning domain-invariant and domain-specific representations and polarizing them. Specifically, POEM co-trains category-classifying and domain-classifying embeddings while regularizing them to be orthogonal via minimizing the cosine-similarity between their features, i.e., the polarization of embeddings. The clear separation of embeddings suppresses domain-specific features in the domain-invariant embeddings. The concept of POEM shows a unique direction to enhance the domain robustness of representations that brings considerable and consistent performance gains when combined with existing DG methods. Extensive simulation results in popular DG benchmarks with the PACS, VLCS, OfficeHome, TerraInc, and DomainNet datasets show that POEM indeed facilitates the category-classifying embedding to be more domain-invariant.
A Data Source for Reasoning Embodied Agents

Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datasets for fine-tuning. In this work, to further pursue these advances, we introduce a new data generator for machine reasoning that integrates with an embodied agent. The generated data consists of templated text queries and answers, matched with world-states encoded into a database. The world-states are a result of both world dynamics and the actions of the agent. We show the results of several baseline models on instantiations of train sets. These include pre-trained language models fine-tuned on a text-formatted representation of the database, and graph-structured Transformers operating on a knowledge-graph representation of the database. We find that these models can answer some questions about the world-state, but struggle with others. These results hint at new research directions in designing neural reasoning models and database representations. Code to generate the data and train the models will be released at github.com/facebookresearch/neuralmemory
Aligned deep neural network for integrative analysis with high-dimensional input

Deep neural network (DNN) techniques have demonstrated significant advantages over regression and some other techniques. In recent studies, DNN-based analysis has been conducted on data with high-dimensional input such as omics measurements. In such analysis, regularization, in particular penalization, has been applied to regularize estimation and distinguish relevant input variables from irrelevant ones. A unique challenge arises from the "lack of information" attributable to high dimensionality of input and limited size of training data. For many data/studies, there exist other data/studies that may be relevant and can potentially provide additional information to boost performance.In this study, we conduct integrative analysis of multiple independent datasets/studies, with the goal of borrowing information across each other and improving overall performance. Significantly different from regression-based integrative analysis (where alignment can be easily achieved based on covariates), alignment across multiple DNNs can be nontrivial. We develop ANNI, an Aligned DNN technique for Integrative analysis with high-dimensional input. Penalization is applied for regularized estimation, selection of important input variables, and, equally importantly, information borrowing across multiple DNNs. An effective computational algorithm is developed.Extensive simulations demonstrate competitive performance of the proposed technique. The analysis of cancer omics data further establishes its practical utility.
A Study on High School Teachers' Perceptions of the Operation of Career Option Courses and Reflection of Achievement Evaluation

This study aimed to find out the perceptions of high school teachers about the factors influencing the selection of caree-related subject in order to seek guidance on career elective courses in high school and how to utilize college entrance exams. A questionnaire survey and individual FGI questionnaires were conducted to find out about the perception of the operation of career elective courses and the recognition of evaluation methods, and to collect opinions. Career option courses should be systematically prepared to provide students with opportunities to complete various elective courses according to their career path and aptitude, guaranteeing students the right to choose their courses and realizing customized education so that they can be connected to their majors. In order to operate this, high schools must open and operate various subjects according to the demand of students, evaluate them, and provide them as evaluation materials for college entrance exams. In addition, since universities can be used as evaluation data for selection and recruitment in connection with high school education, information on the university's curriculum must also be shared. This study was conducted from the perspective of high school teachers, and it is necessary to explore the factors influencing career choice course selection from various perspectives in future research.
The Effects of Korean Subjects Integrated Career Education Program on Career Decision Making Self-efficacy and Dysfunctional Career Thinking of Middle School Students

The purpose of this study is to find out how it affects students’ career decision-making self-efficacy and career thinking by devising a career education program that integrates Korean language subjects and integrating the contents of Korean language classes with career education that enables students to develop their dreams. there is. The research method is as follows.&#x0D; First, the need for career education integrated with Korean subjects was examined for the third year of middle school. Second, a career education program integrated with Korean language subjects was conceived by converging Korean language subject achievement standards and career-related learning elements. Third, the Korean language integrated career education program was applied to the 8th class for an experimental group of 63 out of 127 third-year middle school students in Seoul.&#x0D; As a career decision self-efficacy test tool, the Career Decision-Making Self-Efficacy Scale-SF developed by Taylor and Betz (1983) was validated by Lee Eunkyung(2001) for middle and high school students. used The self-efficacy scale for career decision-making consists of a total of 25 questions of 4 sub-factors: goal selection, job information, problem solving, and future planning (Lee Eunkyung 2001:49).&#x0D; The Career Thought Inventory (CTI) tool developed by Sampson et al. (1996) and validated by factor analysis by Jang Seon-Cheol(2003) was used. This questionnaire measures career-related dysfunctional career thinking and consists of a total of 26 questions in three sub-factors: generalized decision-making confusion, performance anxiety, and distorted identity regarding the career decision-making process.&#x0D; The collected data was verified for homogeneity between the two groups through an independent sample t-test using SPSS 25.0, followed by a paired samples t-test. As a result, the experimental group's total pre-average score for career decision self-efficacy was 84.35 points, and the post-average average score was 98.90 points. (t=-7.644, p&lt;.001).&#x0D; In addition, the overall mean score of the career thinking test in the experimental group was 54.21 points, and the mean post-test score was 46.84 points. (t=3.430, p&lt;.01).&#x0D; As such, it was confirmed that the overall self-efficacy in career decision-making improved and dysfunctional career thinking decreased after the implementation of the integrated Korean language career education program in the experimental group compared to before the implementation.&#x0D; This study has the following significance. First, the career decision-making self-efficacy of students was improved through the career education program integrated with Korean subjects. Second, it reduced the dysfunctional career thinking that interferes with students’ career exploration.
The Impact of Transformational Leadership and Transactional Leadership on Voice Behavior and Creative Behavior through Job Crafting

Purpose - In the face of an increasingly competitive business environment and the growing complexity and dynamism of work activities, employee voice and creative behaviors are essential to organizational growth and survival. Consequently, this study examines how to increase voice and creative behaviors in organizational life using transformational leadership and transactional leadership as the entry points, and selecting job crafting as the mediating variable.&#x0D; Design/Methodology/Approach - A hypothesized model was developed to examine the relationship between the constructs. The sample for the study comprised 364 Chinese employees. Data were analyzed using SPSS and structural equation modelling in AMOS.&#x0D; Findings - Transformational leadership and transactional leadership positively impact job crafting, voice behavior, and creative behavior, complementing the existing theory. Furthermore, this study finds that through job crafting, transformational leadership has an enhanced impact on voice and creative behaviors.&#x0D; Research Implications - Transformational leadership and transactional leadership are two separate and complementary types of leadership. Therefore, organizations should develop leaders that can flexibly use both styles to improve leadership effectiveness. Furthermore, the organization should pay more attention to employee job crafting, voice behavior, and creative behavior. Organizations or supervisors should appropriately grant employees work autonomy to respond to the rapidly changing external environment.
The meaning of the teaching life experience of high-career national and public kindergarten teachers for more than 30 years

Objectives The purpose of this study was to study the life and teaching experiences of high-career kindergarten teachers in national and public kindergartens and explore their meaning.&#x0D; Methods To this end, in-depth interviews were conducted for 5 teachers with more than 30 years of experience in national and public kindergartens in Gyeongsangnamdo for two sessions each from early January ~ early April 2023, and additional data were collected through phone calls and messages and the interview data of the research participants were conducted in open coding using the nomadic analysis method. Afterward it was categorized by topic, and the process of research results and interpretation was carried out through an iterative comparison process and an inductive process.&#x0D; Results The participants experienced setbacks and conflicts as they began a grueling first-time teaching career without a mentor, but they found it rewarding and grew as teachers. In response to the change from an authoritarian era to an era where individual rights are valued, high-career national and public kindergarten teachers have made efforts to adapt to changes in their relationships, curricula, and educational environment with the people they meet in the educational field, and sometimes their professionalism is shaken. It was also seen that institutional support needed to be provided to overcome problems such as the fall of the teaching authority and the heavy administrative workload.&#x0D; Conclusions The teaching life experience and significance of high-career national and public kindergarten teachers is the history of the educational field in Korea, and it was the basis for understanding the field of early childhood education and what kind of efforts and institutional support are needed for the development of kindergarten teachers.
Analysis of Research Trends in Korean Language Education Using Metaverse

Objectives The purpose of this study is to analyze the trend of Korean language education research using the metaverse.&#x0D; Methods Domestic academic papers published before April 1, 2023 were searched as keywords related to metaverse. The contents of the papers were reviewed and 26 papers were selected excluding duplicate papers. Next, research trends were analyzed and the results were discussed by setting analysis criteria such as year, research method, research object, research subject, and use of metaverse.&#x0D; Results Research on Korean language education using metaverse began in 2013 and the number of studies increased rapidly in 2022. Integrated research methods were most frequently used, and studies on non-human subjects and Korean learners at all levels for general purposes were actively conducted. The most research was conducted on the subject of teaching and learning methods, and there were many studies related to real-time online classes using the virtual world metaverse platform.&#x0D; Conclusions It is necessary to study how to educate various learners on language knowledge and language functions using metaverse and develop general design principles. In addition, research on the effectiveness of Korean language education using metaverse should be conducted, and discussions on teachers' TPCK(Technology Pedagogical Content Knowledge) and teacher education are needed. Finally, research on educational materials, curriculum, and evaluation suitable for the characteristics of the metaverse should be conducted.