Reviewerly/exHarmony huggingface dataset:
Multi-disciplinary Research: Open Science Data Lake

Open Science aims to establish an interdisciplinary exchange between researchers through knowledge sharing and open data. However, this interdisciplinary exchange requires exchanges between different research domains and there is currently no simple computerized solution to this problem. Although the data lake adapts well to the constraints of variety and volume offered by the Open Science context, it is necessary to adapt this solution to (1) the accompaniment of data with metadata having a specific metadata model depending on the domain and community of origin, (2) the cohabitation of open and closed data within the same open data management platform, and (3) a wide diversity of pre-existing research data management platforms to deal with. We propose to define the Open Science Data Lake (OSDL) by adapting the Data Lake to this particular context and allowing interoperability with pre-existing research data management platforms. We propose a functional architecture that integrates multi-model metadata management, virtual integration of externally stored (meta)data and security mechanisms to manage the openness of the platforms and data. We propose an open-source and plug-and-play technical architecture that makes adoption as easy as possible. We set up a proof-of-concept experiment to evaluate our solution with different users from the research community and show that OSDL can meet the needs of transparent multidisciplinary data research.
Databases and Information Systems: Contributions from ADBIS 2023 Workshops and Doctoral Consortium

The 27th European Conference on Advances in Databases and Information Systems (ADBIS) aims at providing a forum where researchers and practitioners in the fields of databases and information systems can interact, exchange ideas and disseminate their accomplishments and visions.
Mining Totally Ordered Sequential Rules to Provide Timely Recommendations

In this paper we show the importance of mining totally ordered sequential rules, and in particular we propose an extension of sequential rules where not only the antecedent precedes the consequent, but their itemsets are labelled with an explicit representation of their relative order. This allows us to provide more precise timely recommendations. Our technique has been applied to a real-world scenario regarding the provision of tailored suggestions for supermarket shopping activities.
Data Literacy

In this contribution, Valentin Unger, Michael Beck, and Vera Husfeldt critically examine the construct of "data literacy" and provide a historical overview of the emergence of the term. They also suggest ways to promote data literacy in higher education.
Comparative Analysis of Student Understanding Levels Using Lecture, Group Discussion and Information Technology- Based Methods of Postpartum Care Education at 'Aisyiyah University Surakarta

Postpartum care is one of the most essential subjects in midwifery program which student needs to obtain. This provides students with abilities based on concepts, attitudes, skills and the results of critical thinking as well as an evidence base in practice. This study aims to analyze the comparison of students' understanding levels using information technology-based, lecture and group discussion methods about postpartum care education at 'aisyiyah Surakarta University, especially during the Covid-19 pandemic. The method used was data collection ,form of interviews and questionnaires, related to midwifery care material/competencies using lecture, group discussion and information technology-based methods with a total of 37 students as respondents. Calculation the level of understanding using a Likert Scale, Validity Test (T-count, T-table) and Reliability Test, to find out the results of a comparison of learning models with lecture, group discussion and information technology-based methods. Results : The level of understanding using the lecture method is 81%, 94% in the group discussion method and 100% in information technology-based methods. Conclusion : It can be concluded that the learning model which is most in demand by students and has a strongly high level of comprehension was using information technology-based methods.
Model-Based Reinforcement Learning using Model Mediator in Dynamic Multi-Agent Environment

Centralised training and decentralised execution (CTDE) is one of the most effective approaches in multiagent reinforcement learning (MARL). However, these CTDE methods still require large amounts of interaction with the environment, even to reach the same performance as very simple heuristic-based algorithms. Although modelbased RL is a prominent approach to improve sample efficiency, its adaptation to a multi-agent setting combining existing CTDE methods has not been well studied in the literature. The few existing studies only consider settings with relaxed restrictions on the number of agents and observable range. In this paper, we consider CTDE settings where some information about each agent’s observations (e.g. each agent’s visibility, number of agents) are changed dynamically. In such a setting, the fundamental challenge is how to train models that accurately generate each agent’s observations with complex transitions in addition to the central state, and how to use it for sample efficient policy learning. We propose a multi-agent model based RL algorithm based on the novel model architecture consisting of global and local prediction models with model mediator. We evaluate our model-based RL approach applied to an existing CTDE method on challenging StarCraft II micromanagement tasks and show that it can learn an effective policy with fewer interactions with the environment.
Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center

Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical grade digital pathology adoption and patient safety. In this work, we describe the development of a structured digital pathology laboratory quality management system (QMS) for the clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The Digital Scan Team in conjunction with the Department of Pathology and Laboratory Medicine Quality Team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the pre-scan, scan, and post-scan processes, instrumentation, and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology related documents were subject to regulatory review and approval by Departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials (DPQE) framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large scale digital pathology operations in clinical settings.
Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

Abstract Early attack detection is essential to ensure the security of complex networks, especially those in critical infrastructures. This is particularly crucial in networks with multi-stage attacks, where multiple nodes are connected to external sources, through which attacks could enter and quickly spread to other network elements. Bayesian attack graphs (BAGs) are powerful models for security risk assessment and mitigation in complex networks, which provide the probabilistic model of attackers’ behavior and attack progression in the network. Most attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring, which is unrealistic given the ever-growing complexity of threats. This paper derives the optimal minimum mean square error (MMSE) attack detection and monitoring policy for the most general form of BAGs. By exploiting the structure of BAGs and their partial and imperfect monitoring capacity, the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman filtering. An adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined value. Exact and efficient matrix-form computations of the proposed policies are provided, and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.
Tourism Destination Article Search Features using TF-IDF and Cosine similarity

In the current digital era, the increasing public interest in searching for information about travel destinations necessitates an effective and accurate search system. However, search results for travel destination articles often yield irrelevant or inadequate outcomes. To address this issue, this paper proposes applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and Cosine Similarity in the search feature for travel destination articles. By employing these algorithms, the search system is anticipated to deliver more relevant and accurate results according to user needs.&#x0D; This research contributes to developing an effective search system for travel destination articles, assisting users in obtaining relevant and high-quality information about the destinations they are searching for. The methodology involves collecting data on travel destination articles, implementing the TF-IDF algorithm to evaluate term importance, and utilizing Cosine Similarity to measure the similarity between articles and user queries.&#x0D; The study results demonstrate that implementing the TF-IDF algorithm and Cosine Similarity in the search feature for travel destination articles enhances the accuracy and relevance of search results. Users can quickly discover articles that align with their queries, improving their search experience. In conclusion, this research highlights that applying the TF-IDF algorithm and Cosine Similarity in the search feature significantly improves the accuracy and relevance of search results for travel destination articles. This enhances the search experience for users seeking information about travel destinations.
A double-sided surface scanning platform for sapphire substrate quality assessment

With excellent mechanical and optical properties, sapphire substrate becomes a very important industrial material especially for LED manufacturing. Its quality control requires two tasks, double-sided surface profiling and defect detection. In this paper, a 3D surface scanning system is proposed as a platform for sapphire substrate inspection. During the scanning process, the height information of both upper and lower surfaces is measured by a chromatic confocal probe. For lateral positioning feedback, an in-house developed sensor, namely, image grating, is introduced in this paper. For these inspection processes, there are three challenges: 1) the motion error in the vertical direction, 2) simultaneous measurement of both upper and lower surfaces, and 3) lateral positioning error in a large scanning area. To address these challenges, three key technologies were developed: 1) real-time motion error compensation based on a fringe interferometer, 2) a double-sided measurement for transparent specimen without the pre-knowledge of its refractive index, and 3) planar image grating pair for accurate 2D displacement feedback. The experimental results showed that the proposed measurement system was able to achieve 50 nm accuracy of the standard deviation when measuring the step height of gauge blocks. When the system measured 4-inch double-sided polished sapphire substrate, the measurement repeatability of TTV (Total Thickness Variation) was within 60 nm; and that of warpage was within 10 nm. As a side product, refractive index can also be measured using the proposed system, and experimental results showed respectable consistency.
Multi-Target Multi-Camera Pedestrian Tracking System for Non-Overlapping Cameras

The goal of the Multi-Target Multi-Camera (MTMC) pedestrian tracking task is to simultaneously track multiple target individuals using multiple cameras. Current methods mostly use exponential moving averages to store features and perform multi-camera person matching using these features. Apparently, it will cause the issue of poor long-term feature storage, in this paper, we propose a new method to address the issue, which often leads to ID-switching when individuals change clothes or when lighting conditions change significantly, and also improve the ID-switching problem that occurs during single-camera tracking. To evaluate our method, we created our owns dataset. The dataset was included approximately 40000 frames from 1080p, 30fps videos, whitch were recorded by these cameras. Experimental results show that our method outperforms existing methods in both single-camera and multi-camera tracking, with single-camera tracking improved by 7.06% and multi-camera tracking improved by 12.51%.
Identical Twins Verification with Fine-grained Recognition

Facial recognition technology has been increasingly applied to daily life; however, differentiating identical twins remains a challenging task. This paper investigates the performance of facial recognition models on identical twins and introduces fine-grained image classification as a potential solution. We created a dataset of 54 pairs of twin images and tested various models on three datasets (LFW, SLLFW, and our homemade twins dataset) with different degrees of similarity. The Facenet model was chosen as the backbone network for our fine-tuned model due to its outstanding performance. The fine-tuned model showed improved performance in distinguishing negative pairs compared to the pretrained model and had slightly better accuracy than human recognition.
Myths and Facts about a Career in Software Testing: The Perspectives of Students and Practitioners

Testing is an indispensable part of software development. However, a career in software testing is reported to be unpopular among technology students. This can potentially create a shortage of testers in the software industry in the future. The question is, whether the perception that undergraduate students have about software testing is accurate and whether it differs from the experience reported by those who work in testing activities in the software development industry. We obtained 63 answers from practitioners in the software industry, and previous surveys analyzed the perceptions of 648 students from software engineering. This investigation demonstrates that a career in software testing is more exciting and rewarding, as reported by professionals working in the field, than students may believe. Therefore, in order to guarantee a workforce focused on software quality, the academy and the software industry need to work together to better inform students about software testing and its essential role in software development. In particular, courses in testing automation are expected to increase the popularity of the area among students.
Fast Digit Recognition Using Lightweight Neural Network Model

The E13B font recognition method achieved fast and accurate recognition but had certain drawbacks. One of which was the lack of confidence information for character prediction. When the decision tree encounters an issue, a closer answer cannot be recovered, leading to the failure of the entire check recognition. Although this event is highly improbable, it could still occur. Therefore, we needed to continue optimizing the recognition process, and this study proposed a novel framework to address this issue. This study incorporated a model trained by a lightweight neural network to predict the result when the decision tree encounters an anomaly. This resolves the previous flaw of failing to provide a prediction when the tree encounters a problem. The trained model can also meet the computational requirements of embedded devices and complete the recognition of a character within 1.8 ms.
OLM<sup>2</sup>: Automatic Optimal Strategy Generating for Large-Scale Model Training with Limited-Memory

The scale of model parameters and the amount of training data is exponentially increasing. It requires more GPU memory with the exponential increasement of model parameters. Recomputation and swapping are two main memory optimization methods that have been extensively studied, and there are also optimization strategies that combine the two methods. However, most of them are based on heuristic search strategies, which do not explore the complete solution space and can’t guarantee the optimality of the solution results. An optimal search strategy with tensor-level recomputation and swapping is expected in large-scale model training. In this paper, we propose an optimal strategy searching algorithm combining tensor-based recomputation and swapping. Specifically, the memory swapping strategy is reformulated as an optimization problem, which converts the memory constraints into mixed integer programming, to find the optimal memory optimization strategy. By leveraging the advantages of both recomputation and swapping, this approach minimizes computation consumption without exceeding the available memory limitation. Experimental results show that our method exhibits about 60% reduction in memory requirements during the training process. Furthermore, our method can reduce the overall training time beyond the existing algorithms. Compared to Checkmate, our approach achieves about 0.3–0.9% reduction in computation cost per iteration.
Design of Path Planning Algorithm for Tourist Attractions Based on Deep Learning

There is no reasonable tourism planning route, the number of individual tourist attractions is too large, resulting in a low tourist safety factor, and the number of tourists in a few scenic spots is scarce, which has become the biggest problem that plagues tourism companies and tourists. DL (Deep learning) based on ANN (artificial neural network), a DL structure is constructed by using multi-layer perceptron with multiple hidden layers. In this paper, the path planning algorithm of tourist attractions based on DL is designed. Aiming at the defects of BPNN (BP neural network) such as slow convergence speed and easy to fall into local minimum points, the ACO (ant colony optimization) with good global search ability is proposed. The global update formula is further optimized in the strategy based on the combination of global and local update pheromones in the improved scheme, and this global and local update pheromone scheme is applied to BPNN. The research results show that the algorithm in this paper is more effective and the optimization efficiency is higher, and the ACO-optimized BPNN not only greatly saves the complexity of operation but also improves the efficiency, and efficiently obtains the specific route of each path, providing convenient services for travel enthusiasts.
Intelligent Hybrid System of Collective Architectural Design Based on Genetic Algorithm

Due to its advantages of appropriate scale and high overall cost performance, the collective building occupies a very important position in the housing market, especially in small and medium-sized cities, where multi-storey houses almost dominate the whole country. Based on the analysis and research of the basic knowledge such as expert system, knowledge base, language, etc., an intelligent hybrid system of collective architectural design is established, which is mainly composed of expert system and interface standard of knowledge base. The definition of fitness function of GA (Genetic Algorithm) takes into account the classification accuracy of the constructed fuzzy classification system and the number of fuzzy rules at the same time. On this basis, the rule redundancy caused by data distribution is continuously deleted to obtain the final classification system. The GA set comprehensive optimization model of architectural decision-making design scheme is established, and the effectiveness of this method in architectural decision-making design is verified by an example.
Revised stratigraphic sequence and type sections of the Southern Bida Basin, Nigeria

A reassessment of the stratigraphy of the Southern Bida Basin has been attempted among various disagreements. For this work, field geological mapping and stratigraphic descriptions of exposed lithologic types on outcrop sections were used. Section-by-section descriptions of important outcrops based on field observations aided in identifying lithologic units and delineating the boundaries of the sub-basin's three formations (Lokoja, Ahoko, and Agbaja). The type sections for formations in the sub-basin were not properly erected, so Jones (1958) type section has been supplemented by a more comprehensive hypostratotype, and the name Patti Formation, which is considered a misnomer in this context, has been replaced with Ahoko Formation proposed by Rahaman et al. (2019). Ahoko village is the type locality. The Agbaja Plateau, where the Agbaja Formation is best exposed, has been presented as the lectostratotype for the formation, while the revised stratigraphic succession of the Lokoja region shows the Lokoja Formation directly encompassing the Paleozoic Basement terrain, with the Maastrichtian Agbaja and Ahoko Formations (former Patti) overlying it sequentially.