Reviewerly/exHarmony huggingface dataset:
Implementation of Supervision by the Secretary in Efforts to Achieve Effectiveness of Archives Management at the Secretariat of the Cooperatives and SMEs Office of North Sumatra Province

In connection with the preparation of the research entitled "Implementation of Supervision by the Secretary in Efforts to Achieving Effectiveness of Archives Management at the Secretariat of the Cooperatives and SMEs Office of North Sumatra Province" the author conducted a study to find out how far the relationship between supervision and archive management is at the Secretariat of the Cooperatives and SMEs Office of North Sumatra Province which is a one of the indicators that can be used as a benchmark for the success of the implementation of tasks and functions at the Service. The research method used is descriptive analysis method, which is a research method that systematically describes the implementation of supervision by the secretary in an effort to achieve the effectiveness of archive management at the Secretariat of the Office of Cooperatives and SMEs of North Sumatra Province. The implementation phase of the research was carried out by means of observation and interviews. Based on the author's observations during the research at the Department of Cooperatives and SMEs of North Sumatra Province, it turns out that there are still some indications of problems that indicate that the effectiveness of archive management at the agency has not been maximized. The problem is suspected because the Secretary of the North Sumatra Province Cooperatives and SMEs Office in the implementation of supervision has not been optimal.&#x0D;
Simulation modeling in digital transformation projects of business processes

The advantages and features of the use of simulation modeling in digital transformation projects of business processes are considered. The interrelation of business processes models with information technologies that automate the execution of business processes at enterprises in real time is shown. The aspects of planning experiments within the framework of a stochastic simulation model of a business process based on statistical modeling methods to obtain adequate simulation results are considered. The principles of constructing models of business processes are formulated and the possibilities that simulation modeling provides in the context of transforming business processes of an enterprise are revealed.
State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd  Technical Challenge

In this paper, we describe our proposed methodology to approach the predict+optimise challenge introduced in the IEEE CIS 3rd Technical Challenge. The predictive model employs an ensemble of LightGBM models and the prescriptive analysis employs mathematical optimisation to efficiently prescribe solutions that minimise the average cost over multiple scenarios. Our solutions ranked 1st in the optimisation and 2nd in the prediction challenge of the competition.
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks

Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with differentiable operators from fuzzy or real-valued logic that are parameter-free thus diminishing their capacity to fit the data, other approaches are only loosely based on logic making it difficult to interpret the learned "rules". In this paper, we propose learning rules with the recently proposed logical neural networks (LNN). Compared to others, LNNs offer strong connection to classical Boolean logic thus allowing for precise interpretation of learned rules while harboring parameters that can be trained with gradient-based optimization to effectively fit the data. We extend LNNs to induce rules in first-order logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable and can achieve comparable or higher accuracy due to their flexible parameterization.
A Study on the Building of Lifelong Education Platforms Based on Digital Literacy Education

Digital transformation is being promoted most rapidly by the Fourth Industrial Revolution. The tools (language) that move and communicate the world are changing. Lifelong education must also learn new languages and communicate through them. It is an era in which the level of national digital literacy is the adaptability and competitiveness of the future society. In line with these times, studies on various literacy education and programs are being actively conducted in the field of lifelong education. However most of them are fragmentary event education or one-time programs. It is difficult to say that it is an education for continuous improvement of digital literacy capabilities. Lifelong education must realize social responsibility and public values. And we must foster the right citizens of the future society. For this it is necessary to change the paradigm of lifelong education and change communication tools and contents. In this study we propose a methodology for establishing a lifelong education platform based on digital literacy education.
Artificial Cognitively-inspired Generation of the Notion of Topological  Group in the Context of Artificial Mathematical Intelligence

The new computational paradigm of conceptual computation has been introduced in the research program of Artificial Mathematical Intelligence. We provide the explicit artificial generation (or conceptual computation) for the fundamental mathematical notion of topological groups. Specifically, we start with two basic notions belonging to topology and abstract algebra, and we describe recursively formal specifications in the Common Algebraic Specification Language (CASL). The notion of conceptual blending between such conceptual spaces can be materialized computationally in the Heterogeneous Tool Set (HETS). The fundamental notion of topological groups is explicitly generated through three different artificial specifications based on conceptual blending and conceptual identification, starting with the concepts of continuous functions and mathematical groups (described with minimal set-theoretical conditions). This constitutes in additional heuristic evidence for the third pillar of Artificial Mathematical Intelligence.
METHODOLOGICAL PREPARATION OF FUTURE TEACHERS OF COMPUTER SCIENCE FOR EXTRA-EDUCATIONAL ACTIVITIES

The article deals with the formation of cognitive interest in the subject of informatics, as well as the skills of independent work on the acquisition of new knowledge, the development of thinking, memory, attention. A modern Informatics teacher is called upon not only to educate and educate schoolchildren, to develop students' interest in the subject, which has a great effect in the formation of knowledge and contributes to the activation of the process of knowledge perception and mental activity, but must also be able to organize extracurricular activities. The relevance of this topic meets the new requirements of education in the field of Informatics. The article describes the experience of carrying out extracurricular work in the study of teaching methods of Informatics. Therefore, targeted methodological training of future teachers of informatics for extracurricular work on the subject significantly increases their overall methodological training.
Designing digital educational resources for information and digital training of masters

The article considers the use of computer modeling technologies for visualization of learning elements of engineering disciplines in order to improve understanding of the structure and functioning of complex technical objects and technological systems. The principles of digital didactics and their influence on the formation of the content of educational resources are analyzed. An education blog is presented as a modern tool for information and digital training of vocational education teachers. The scientific novelty of the work lies in the fact that the use of computer modeling technologies as meta-technologies for organizing information and digital training of modern masters allows us to take a fresh look at the process of digitalization of education and at the design of its content. The practical significance of the study lies in the fact that inclusion of computer modeling technologies in educational practice allows not only to improve its quality, but also to prepare a graduate to enter the innovative digital system of an educational organization.
Fully probabilistic design of strategies with estimator

The axiomatic fully probabilistic design (FDP) of decision strategies strictly extends Bayesian decision making (DM) theory. FPD also models the closed decision loop by a joint probability density (pd) of all inspected random variables, referred as behaviour. FPD expresses DM aims via an ideal pd of behaviours, unlike the usual DM. Its optimal strategy minimises Kullback–Leibler divergence (KLD) of the joint, strategy-dependent, pd of behaviours to its ideal twin. A range of FPD results confirmed its theoretical and practical strength. Curiously, no guide exists how to select a specific ideal pd for an estimator design. The paper offers it. It advocates the use of the closed-loop state notion and generalises dynamic programming so that FPD is its special case. Primarily, it provides an explorative optimised feedback that “naturally” diminishes exploration (gained in learning) as the learning progresses.
Compositional Transformers for Scene Generation

We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural priors, to reflect the compositional nature of visual scenes, and synthesizes images through a sequential process. It operates in two stages: a fast and lightweight planning phase, where we draft a high-level scene layout, followed by an attention-based execution phase, where the layout is being refined, evolving into a rich and detailed picture. Our model moves away from conventional black-box GAN architectures that feature a flat and monolithic latent space towards a transparent design that encourages efficiency, controllability and interpretability. We demonstrate GANformer2's strengths and qualities through a careful evaluation over a range of datasets, from multi-object CLEVR scenes to the challenging COCO images, showing it successfully achieves state-of-the-art performance in terms of visual quality, diversity and consistency. Further experiments demonstrate the model's disentanglement and provide a deeper insight into its generative process, as it proceeds step-by-step from a rough initial sketch, to a detailed layout that accounts for objects' depths and dependencies, and up to the final high-resolution depiction of vibrant and intricate real-world scenes. See https://github.com/dorarad/gansformer for model implementation.
Can uncertainty boost the reliability of AI-based diagnostic methods in  digital pathology?

Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions. We compare the effectiveness of model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are compared. Our experiments focus on two domain shift scenarios: a shift to a different medical center and to an underrepresented subtype of cancer. Our results show that uncertainty estimates can add some reliability and reduce sensitivity to classification threshold selection. While advanced metrics and deep ensembles perform best in our comparison, the added value over simpler metrics and TTA is small. Importantly, the benefit of all evaluated uncertainty estimation methods is diminished by domain shift.
A Survey on Deep learning based Document Image Enhancement

Digitized documents such as scientific articles, tax forms, invoices, contract papers, historic texts are widely used nowadays. These document images could be degraded or damaged due to various reasons including poor lighting conditions, shadow, distortions like noise and blur, aging, ink stain, bleed-through, watermark, stamp, etc. Document image enhancement plays a crucial role as a pre-processing step in many automated document analysis and recognition tasks in particular when those tasks are dealing with degraded documents. With recent advances in deep learning, many methods are proposed to enhance the quality of these document images. In this paper, we review deep learning-based methods, datasets, and metrics for six main document image enhancement tasks, including binarization, debluring, denoising, defading, watermark removal, and shadow removal. We summarize the recent works for each task and discuss their features, challenges, and limitations. We introduce multiple document image enhancement tasks that have received little to no attention, including over and under exposure correction, super resolution, and bleed-through removal. We identify several promising research directions and opportunities for future research.
Design and Development of AR Teaching System for Cardiac Physical Examination

Cardiopulmonary physical examination is an important part of the professional learning of rehabilitation treatment technology in higher vocational schools. Through the application of AR technology, students can be better guided to complete the professional skills of applying the knowledge they have learned to practical case diagnosis. Therefore, our team designed and developed an AR teaching system for cardiac physical examination to assist teaching.
Diagonal unitary and orthogonal symmetries in quantum theory II:  Evolution operators

We study bipartite unitary operators which stay invariant under the local actions of diagonal unitary and orthogonal groups. We investigate structural properties of these operators, arguing that the diagonal symmetry makes them suitable for analytical study. As a first application, we construct large new families of dual unitary gates in arbitrary finite dimensions, which are important toy models for entanglement spreading in quantum circuits. We then analyze the non-local nature of these invariant operators, both in discrete (operator Schmidt rank) and continuous (entangling power) settings. Our scrutiny reveals that these operators can be used to simulate any bipartite unitary gate via stochastic local operations and classical communication. Furthermore, we establish a one-to-one connection between the set of local diagonal unitary invariant dual unitary operators with maximum entangling power and the set of complex Hadamard matrices. Finally, we discuss distinguishability of unitary operators in the setting of the stated diagonal symmetry.
Image Denoising in FPGA using Generic Risk Estimation

The generic risk estimator addresses the problem of denoising images corrupted by additive white noise without placing any restriction on the statistical distribution of the noise. In this paper, we discuss an efficient FPGA implementation of this algorithm. We use the undecimated Haar wavelet transform with shrinkage parameters for each sub-band as the denoising function. The computational complexity and memory requirement of the algorithm is first analyzed. To optimize the performance, a combination of convolution and recursion is employed to realize Haar filter bank and gradient descent algorithm is used to find the shrinkage parameters. A fully pipelined and parallel architecture is developed to achieve high throughput. The proposed design achieves an execution time of 3.5ms for an image of size 512x512. We also show that the recursive implementation of Haar wavelet is more expensive than the direct implementation in terms of hardware utilization.
Improving scripts with a memory of natural feedback

How can an end-user provide feedback if a deployed structured prediction model generates incorrect output? Our goal is to allow users to correct errors directly through interaction, without retraining, by giving feedback on the model's output. We create a dynamic memory architecture with a growing memory of feedbacks about errors in the output. Given a new, unseen input, our model can use feedback from a similar, past erroneous state. On a script generation task, we show empirically that the model learns to apply feedback effectively (up to 30 points improvement), while avoiding similar past mistakes after deployment (up to 10 points improvement on an unseen set). This is a first step towards strengthening deployed models, potentially broadening their utility.
Tensor Deblurring and Denoising Using Total Variation

We consider denoising and deblurring problems for tensors. While images can be discretized as matrices, the analogous procedure for color images or videos leads to a tensor formulation. We extend the classical ROF functional for variational denoising and deblurring to the tensor case by employing multi-dimensional total variation regularization. Furthermore, the resulting minimization problem is calculated by the FISTA method generalized to the tensor case. We provide some numerical experiments by applying the scheme to the denoising, the deblurring, and the recoloring of color images as well as to the deblurring of videos.
2-D Topology Optimization of the Breech Flat-Plate Bus Bar of the Electromagnetic Railgun

The optimization of the breech bus bar has an important influence on the lightweight research of the electromagnetic railgun. A topology optimization model considering volume constraints is established for the breech flat-plate bus bar. The bus bar is fed through sixteen coaxial cables. The optimal structure of the bus bar is calculated and given under the requirement of 50&#x0025; volume constraint. In addition, the influence of four control parameters is also analyzed. These four parameters are respectively the prescribed volume fraction, the positioning dimension of cable arrangement, the filter radius, and the penalization power. For the first parameter, the optimal structures of the bus bar are given and compared when it is respectively 0.4, 0.6, 0.7, and 0.8. For the second parameter, the results show that the larger the positioning dimension, the smaller the size of all parts of the bus bar. For the third parameter, it is suggested to use the surrounding two-layer elements of each element to conduct sensitivity filtering. For the fourth parameter, it is reasonable to choose 3.0 as the penalization power value. It is meaningful for the structural design of the bus bar in the initial design stage.
Analisis keefektifan pengabdian masyarakat pada kegiatan workshop AKM Guru SMP Mapel Bahasa Indonesia Kabupaten Semarang

The ideal condition is that teachers must be competent in conditioning students to be ready to face AKM. The current condition, the ability of teachers in developing assessment instruments is still low. They prefer shortcuts using a collection of questions sold in the market. Therefore, this community service activity is focused on providing guidance for junior high school teachers in preparing tests to face the AKM. The method used to achieve the objectives of this activity is a lecture with an in-on-in pattern. Documents that need to be prepared so that the service runs effectively are preparing powerpoint materials and supporting materials (observation sheets and questionnaires). All participants' responses to the AKM surgical workshop showed a positive response (with a percentage above 50%), except for one variable that received a low score, namely the login problem. The results of the documentation analysis showed that all participants collected the complete set of questions completely. The accuracy of writing the grid is in the range of 69% - 75% and the accuracy of writing the questions lies in the range of 84% - 100%. Based on the results of the questionnaire and the results of document analysis, it can be concluded that community service in AKM workshop activities is quite effective.
Emotion-Cause Pair Extraction in Customer Reviews

Emotion-Cause Pair Extraction (ECPE) is a complex yet popular area in Natural Language Processing due to its importance and potential applications in various domains. In this report , we aim to present our work in ECPE in the domain of online reviews. With a manually annotated dataset, we explore an algorithm to extract emotion cause pairs using a neural network. In addition, we propose a model using previous reference materials and combining emotion-cause pair extraction with research in the domain of emotion-aware word embeddings, where we send these embeddings into a Bi-LSTM layer which gives us the emotionally relevant clauses. With the constraint of a limited dataset, we achieved . The overall scope of our report comprises of a comprehensive literature review, implementation of referenced methods for dataset construction and initial model training, and modifying previous work in ECPE by proposing an improvement to the pipeline, as well as algorithm development and implementation for the specific domain of reviews.
Collaborative Learning over Wireless Networks: An Introductory Overview

In this chapter, we will mainly focus on collaborative training across wireless devices. Training a ML model is equivalent to solving an optimization problem, and many distributed optimization algorithms have been developed over the last decades. These distributed ML algorithms provide data locality; that is, a joint model can be trained collaboratively while the data available at each participating device remains local. This addresses, to some extend, the privacy concern. They also provide computational scalability as they allow exploiting computational resources distributed across many edge devices. However, in practice, this does not directly lead to a linear gain in the overall learning speed with the number of devices. This is partly due to the communication bottleneck limiting the overall computation speed. Additionally, wireless devices are highly heterogeneous in their computational capabilities, and both their computation speed and communication rate can be highly time-varying due to physical factors. Therefore, distributed learning algorithms, particularly those to be implemented at the wireless network edge, must be carefully designed taking into account the impact of time-varying communication network as well as the heterogeneous and stochastic computation capabilities of devices.
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies

Autonomous driving has the potential to revolutionize mobility and is hence an active area of research. In practice, the behavior of autonomous vehicles must be acceptable, i.e., efficient, safe, and interpretable. While vanilla reinforcement learning (RL) finds performant behavioral strategies, they are often unsafe and uninterpretable. Safety is introduced through Safe RL approaches, but they still mostly remain uninterpretable as the learned behaviour is jointly optimized for safety and performance without modeling them separately. Interpretable machine learning is rarely applied to RL. This paper proposes SafeDQN, which allows to make the behavior of autonomous vehicles safe and interpretable while still being efficient. SafeDQN offers an understandable, semantic trade-off between the expected risk and the utility of actions while being algorithmically transparent. We show that SafeDQN finds interpretable and safe driving policies for a variety of scenarios and demonstrate how state-of-the-art saliency techniques can help to assess both risk and utility.