Reviewerly/exHarmony huggingface dataset:
Collection of tasks for the development of Natural Science competence of students in the format of PISA (part 1)

Анотація (англійською мовою) The collection of tasks for the development of natural and scientific competence of students in the PISA format includes the author's competence-oriented tasks, test questions on natural and scientific literacy based on OECD PISA trial test questions designed to develop the natural and scientific competence of students. The collection can be used by primary and high school students, teachers of general secondary education institutions, teachers of specialized secondary education institutions, students of pedagogical higher educational institutions. The publication was carried out within the Jean Monnet Module (620287-EPP-1-2020-1-UA-EPPJMO-MODULE) 2020‒2023 at the Institute of Pedagogy of the National Academy of Pedagogical Sciences of Ukraine. The European Commission's endorsement of this publication does not imply endorsement of its content, which reflects only the opinion of the participants. The commission cannot be held responsible for any use of the information contained in the collection.
Effective healthcare service recommendation with network representation learning: A recursive neural network approach

Recently, recommender systems have been combined with healthcare systems to recommend needed healthcare items for both patients and medical staff. By monitoring the patients' states, healthcare services and their consumed smart medical objects can be recommended to a medical team according to the patient's critical situation and requirements. However, a common drawback of the few existing solutions lies in the limited modeling of the healthcare information network. In addition, current solutions do not consider the typed nature of healthcare items. Moreover, existing healthcare recommender systems lack flexibility, and none of them offers re-configurable healthcare workflows to medical staff. In this paper, we take advantage of collaborative filtering and representation learning principles, by proposing a method for the recommendation of healthcare services. These latter follow a predefined execution pattern, i.e. treatment/medication workflow, that is determined by our framework depending on the patient's state. To achieve this goal, we model the healthcare information network as a knowledge graph. This latter, based on an incremental learning method, is then transformed into a cuboid space to facilitate its processing. That is by learning latent representations of its content (e.g., smart objects, healthcare services, patients symptoms, etc.). Finally, a collaborative recommendation method is defined to select the high-quality healthcare services that will be composed and executed according to a determined workflow model. Experimental results have proven the efficiency of our solution in terms of recommended services' quality.
Tuning Arrays with Rays: Physics-Informed Tuning of Quantum Dot Charge States

Many methods to automatically tune silicon spin qubits are limited by reliability and data efficiency, which makes them less likely to be scalable. The authors demonstrate a reliable, efficient, physics-informed tuning algorithm (PIT) for navigating to a target charge configuration⏤a prerequisite to forming qubits. This tuning method combines machine learning and physical intuition with an algorithm that leverages one-dimensional scans (rays) and conventional peak-finding to navigate from a coarse, unknown device state to a desired charge occupation efficiently and effectively. PIT enables the transformation of an uncalibrated circuit to a functioning quantum processor.
Pretraining the Vision Transformer Using Self-Supervised Methods for Vision Based Deep Reinforcement Learning

The Vision Transformer architecture has shown to be competitive in the computer vision (CV) space where it has dethroned convolution-based networks in several benchmarks. Nevertheless, convolutional neural networks (CNN) remain the preferential architecture for the representation module in reinforcement learning. In this work, we study pretraining a Vision Transformer using several state-of-the-art self-supervised methods and assess the quality of the learned representations. To show the importance of the temporal dimension in this context we propose an extension of VICReg to better capture temporal relations between observations by adding a temporal order verification task. Our results show that all methods are effective in learning useful representations and avoiding representational collapse for observations from the Atari Learning Environment (ALE) which leads to improvements in data efficiency when we evaluated in reinforcement learning (RL). Moreover, the encoder pretrained with the temporal order verification task shows the best results across all experiments, with richer representations, more focused attention maps and sparser representation vectors throughout the layers of the encoder, which shows the importance of exploring such similarity dimension. With this work, we hope to provide some insights into the representations learned by ViT during a self-supervised pretraining with observations from RL environments and to understand which properties arise in the representations that lead to the best-performing agents.
Negation of German polar words and expressions in automated analysis of text tonality

The aim of the study is to describe various groups of negative words in the German language and the features of their functioning in text tonality analysis in the framework of automated natural language processing (NLP). Scientific analysis focuses on the ability of negative words to modify polar words and expressions, i.e., to change their connotation within an utterance. The study is novel in that it is the first to present in a systematic manner the syntactic-morphological relationship between certain groups of linguistic units of negation and polar words, which must be taken into account in NLP when carrying out sentiment analysis of German texts based on lexico-grammatical rules. As a result, all German language means of negation were divided into groups depending on the extent of their impact on the polar words in the sentence: tonality modification for only the adjoining polar element or the ability to interact with the parts of an utterance that are located at a distance of several tokens. For automatic tonality analysis of German texts, it is necessary to mark the morphological and syntactic features reflecting the peculiarities of both the selected groups of negation words and polar words and expressions at the stage of feature extraction.
Banach Space Valued Univariate Neural Network Approximation Based on Parametrized Error Activation Function

Here we research the univariate quantitative approximation of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. We perform also the related Banach space valued fractional approximation. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivaties. Our operators are defined by using a density function induced by a parametrized error function. The approximations are pointwise and with respect to the uniform norm.
MaxSAT-Based Inconsistency Measurement

Inconsistency measurement aims at obtaining a quantitative assessment of the level of inconsistency in knowledge bases. While having such a quantitative assessment is beneficial in various settings, inconsistency measurement of propositional knowledge bases is under most existing measures a significantly challenging computational task. In this work, we harness Boolean satisfiability (SAT) based solving techniques for developing practical inconsistency measurement algorithms. Our algorithms—some of which constitute, to the best of our knowledge, the first practical approaches for specific inconsistency measures—are based on using natural choices of SAT-based techniques for the individual inconsistency measures, ranging from direct maximum satisfiability (MaxSAT) encodings to MaxSAT-based column generation techniques making use of incremental computations. We show through an extensive empirical evaluation that our approaches scale well in practice and significantly outperform recently-proposed answer set programming approaches to inconsistency measurement.
Graph-Based Abstractive Summarization of Extracted Essential Knowledge for Low-Resource Scenarios

Although current summarization models can process increasingly long text sequences, they still struggle to capture salient related information spread across the lengthy size of inputs with few labeled training instances. Today’s research still relies on standard input truncation without considering graph-based modeling of multiple semantic units to summarize only crucial facets. This paper proposes G-SEEK, a graph-based summarization of extracted essential knowledge. By representing the long source with a heterogeneous graph, our method extracts and provides salient sentences to an abstractive summarization model to generate the summary. Experimental results in low-resource scenarios, distinguished by data scarcity, reveal that G-SEEK consistently improves both the long- and multi-document summarization performance and accuracy across several datasets.
A STOCK PREDICTION SYSTEM USING TEKNIKAL INDICATORS WITH THE LSTM METHOD

The capital market industry in Indonesia is developing in a better direction so that the growth of new investors is also increasing. Until the end of February 2021, operational data from the Indonesian Stock Exchange (IDX) and data from the Indonesian Central Securities Depository (KSEI) recorded that the number of new capital market investors had increased by 16.35% or 634,350 investors, from the previous 3,880,753 investors. to 4,515,103 investors. The development of the capital market industry in Indonesia, which has increased investor interest in investing, is expected to mobilize public funds to support national economic development. Some companies that are familiar to the community are BCA, BNI, BRI and MANDIRI. This study attempts to forecast banking stock prices on the LQ45 index, using the Long Short-Term Memory (LSTM) method. LSTM is one of the Recurrent Neural Networks (RNN) which has good accuracy in predictions. The identified fields are Close, Open, RSI, MACD and MA. The evaluation method used in this prediction system is MAPE in the form of percent output.
A Hybrid Planning Method for 3D Autonomous Exploration in Unknown Environments With a UAV

This article investigates the autonomous exploration problem of an unmanned aerial vehicle (UAV) in a fully unknown three-dimensional (3D) space, subject to the constraints of collision avoidance, energy-saving, and computation consumption. To tackle this problem, a hybrid planning algorithm named FSHP is proposed. The algorithm consists of a novel local planner designed to explore unknown space within the onboard camera’s field of view (FoV) faster and less computationally. The local planner is a combination of the frontier-based and sampling-based methods, overcoming the bottlenecks of high computational time for the former and non-heuristics for the latter. Furthermore, the algorithm incorporates a global planner based on historical information to enhance performance in larger and more complex scenarios. The global planner includes a historical road map (HRM) using the rapidly-exploring random tree (RRT) and a historical tree (HST) based on the k-dimension (k-d) tree, built simultaneously. When no informative viewpoints are nearby, the planner replans trajectories globally to unexplored space. Finally, the proposed approach is evaluated in both simulations and real-world experiments, demonstrating the effectiveness and efficiency of the FSHP. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper stems from the need to develop a fast and efficient autonomous exploration algorithm for a UAV for practical applications such as 3D reconstruction, search-and-rescue and military reconnaissance. Frontier-based and sampling-based methods are widely used to solve this problem due to their heuristics and low computational effort, respectively. However, either method can not meet the requirements related to exploration efficiency arising from increasingly complex and diverse tasks. To speed up the exploration process, reduce the exploration time and shorten the exploration path length, we propose this new method FSHP. It combines the advantages of global exploration (frontier-based methods) and local exploration (sampling-based methods) with random sampling in the frontiers. Furthermore, the replanning target selection and waypoints optimization schemes helps in reducing the path. Overall, this novel framework, FSHP, enables efficient and effective autonomous exploration tasks.
D-PFA: A Discrete Metaheuristic Method for Solving Traveling Salesman Problem Using Pathfinder Algorithm

The Traveling Salesman Problem (TSP) which is a theoretical computer science and operations research problem, has several applications even in its purest formulation, such as the manufacture of microchips, planning, and logistics. There are many methods proposed in the literature to solve TSP with gains and losses. We propose a discrete metaheuristic method called D-PFA to solve this problem more efficiently. Initially, the Pathfinder Algorithm (PFA) was presented to handle issues involving continuous optimization, where it worked effectively. In recent years, there have been various published variants of PFA, and it has been frequently employed to address engineering challenges. In this study, the original PFA algorithm is broken into four sub-algorithms and every sub-algorithm is discretized and coupled to form a new algorithm. The proposed algorithm has a high degree of flexibility, a quick response time, strong exploration and exploitation. To validate the significant advantages of the proposed D-PFA, 34 different instances with different sizes are used in simulation results. The proposed method was also compared with 12 State-of-the-Art algorithms. Results indicate that the suggested approach is more competitive and resilient in solving TSP than other algorithms in different aspects. A conclusion and an outlook on future studies and applications are given at the end of the paper.
Flexible Unfolding of Circular Structures for Rendering Textbook-Style Cerebrovascular Maps

Comprehensive, contiguous visualizations of the main cerebral arteries and the surrounding parenchyma offer considerable potential for improving diagnostic workflows in cerebrovascular disease, e.g., for fast assessment of vascular topology and lumen in stroke patients. Unfolding the brain vasculature into a 2D overview is, however, infeasible using common Curved Planar Reformation (CPR) due to the circular structure of the Circle of Willis (CoW) and the spatial configuration of the vessels typically rendering them unsuitable for mapping onto simple geometric primitives. We propose CeVasMap, extending the As-Rigid-As-Possible (ARAP) deformation by a smart initialization of the required mesh to map the CoW as well as a merging of neighboring vessels depending on the resulting degree of distortion. Otherwise, vessels are unfolded and attached individually, creating a textbook-style overview image. We provide an extensive distortion analysis, comparing the vector fields of individual and merged unfoldings of each vessel to their CPR results. In addition to enabling unfolding of circular structures, our method is on par in terms of incurred distortions to optimally oriented CPRs for individual vessels and comparable to unfavorable CPR orientations when merging the complete CoW with a median distortion of 65 $$\upmu $$ m/mm.
Digital Transformation in University: Enterprise Architecture and Blockchain Technology

Implementing digital transformation in higher education is now required for effective adaptation to rapid technological advances. Utilizing Enterprise Architecture (EA) with blockchain technology is a recommended strategic approach for implementing digital transformation. The college first ascertains the University's digital transformation requirements and goals, which include data management, security, operational efficiency, and transparency. In addition, formulating a strategic plan to determine the optimal integration of blockchain technology within the college's architectural framework, including the judicious selection of the most suitable blockchain platform, is essential. In addition, developing a company's architecture must prioritize seamless integration with existing systems while upholding data security and consistency principles. Consideration should be given to the significance of training and awareness building among faculty and students. In addition, the implementation process must be conducted in phases with consistent monitoring and evaluation. The success of this project is contingent upon the formation of partnerships and collaborations with blockchain technology companies, as well as a thorough understanding of the applicable regulatory framework. By adopting this methodology, the University can increase operational efficiency, bolster data security measures, and improve the educational experience. This research aims to increase efficiency, data security, transparency, and educational innovation in universities using Blockchain Technology and University Enterprise Architecture.
Exploring the Fusion of Blockchain and AI for Enhanced Practices in IoT Ecosystems: Opportunities and Challenges

The rapid expansion of the Internet of Things (IoT) has ushered in an era of unprecedented data generation, offering transformative potential across industries. Yet, this vast data landscape brings forth challenges related to security, privacy, trust, and intelligent data analysis. In response to these challenges, the fusion of blockchain technology and artificial intelligence (AI) within IoT ecosystems has emerged as a promising solution. This paper embarks on a comprehensive exploration of this fusion, delving into its opportunities and challenges. We provide an overview of IoT's evolution, blockchain technology's fundamental principles, and the significance of AI in data analysis and decision-making. Our focus lies in elucidating how the integration of blockchain fortifies data security, trust, and transparency in IoT applications, while AI augments data analysis, predictive maintenance, and automation. Furthermore, we discuss the challenges and considerations that accompany the integration of AI and blockchain in IoT environments, including scalability, privacy concerns, interoperability, and ethical considerations. By examining the intricate interplay of these technologies, this paper contributes to a deeper understanding of how the fusion of blockchain and AI can usher in a new era of secure, intelligent, and efficient IoT practices.
Ethno-Religious Diversity in Nigeria Academic libraries: Role of swarm intelligence on teams

This article explores how Swarm intelligence (SI) addresses academic library team limitations, amplifies library teams' intelligence and facilitates better library service decisions. It demonstrates how libraries can use SI to harness the diverse perspectives that individual personnel bring to groups and facilitate convergence upon decisions. A thorough analysis of literature was conducted. Discoveries from literature were then used to construct a Swarm intelligence model applicable to the library. Findings indicate that librarians must continue to strategize through Swarm intelligence to position themselves for better decision-making, increased innovation and increased productivity. Swarm intelligence is a vital parameter for managing teams in a diverse library. Due to the striking collective feats that many animals can produce, the study of animal swarm intelligence is a crucial area for inspiration that offers the library a unique potential for transformation. The paper is valuable as it explores the application of swarm intelligence on teams in diverse library settings
Memristor-Based Neural Network Circuit of Associative Memory With Occasion Setting

Classical and operant conditioning are rarely studied together in the neural network based on memristor. But in real life, they often happen at the same time. This paper proposes a neural network circuit based on the memristor of associative memory with occasion setting. The designed circuit includes four modules: an operant synapse module, a response module, a classical synapse module, and a perceptron module. The first two modules constitute operant conditioning, and the remaining two modules constitute classical conditioning. The designed circuit combines operant conditioning with classical conditioning to realize a special associative memory through an occasion setting. Besides, it implements the function of learning, forgetting, and long-term memory on the basis of associative memory with occasion setting. The neural network circuit of associative memory with occasion setting is closer to the real situation and provides a reference for classical and operant conditioning.
Blockchain-based E-tendering Evaluation Framework

Tendering &amp; Bidding generally involve many construction units participating in a project bidding. The purchaser selects the best bidder with a short construction period, low cost, high quality, and good reputation. There are huge economic benefits and points of concern behind it. However, tendering suffers an increased risk of data tampering due to the centralized database architecture, resulting in huge financial losses and unnecessary consumption of human resources. Blockchain technology has gained increasing attention in the construction industry because of its high transparency, information traceability, nonrepudiation, and non-tampering characteristics. Initial explorations have been carried out to discuss blockchain benefits in the e-tendering process. However, existing studies are limited to conducting qualitative analysis or proposing conceptual frameworks. A workable blockchain- enabled solution that supports basic functions in the e-tendering process still lacks. Therefore, as an initial exploration, this paper presents a secure blockchain- based framework for tender evaluation. Besides, technical components such as tendering smart contracts are developed to support framework functionalities. The framework feasibility is validated in a tendering evaluation example. Results show that the proposed framework guarantees secure tender evaluation by keeping data traceability and immutability.
Normalization by evaluation for modal dependent type theory

Abstract We present the Kripke-style modal type theory, Mint , which combines dependent types and the necessity modality. It extends the Kripke-style modal lambda-calculus by Pfenning and Davies to the full Martin-Löf type theory. As such it encompasses dependently typed variants of system K , T , K 4, and S 4. Further, Mint seamlessly supports a full universe hierarchy, usual inductive types, and large eliminations. In this paper, we give a modular sound and complete normalization-by-evaluation (NbE) proof for Mint based on an untyped domain model, which applies to all four aforementioned modal systems without modification. This NbE proof yields a normalization algorithm for Mint, which can be directly implemented. To further strengthen our results, our models and the NbE proof are fully mechanized in Agda and we extract a Haskell implementation of our NbE algorithm from it.