Reviewerly/exHarmony huggingface dataset:
Making paper labels smart for augmented wine recognition

Abstract An invisible layer of knowledge is progressively growing with the emergence of situated visualizations and reality-based information retrieval systems. In essence, digital content will overlap with real-world entities, eventually providing insights into the surrounding environment and useful information for the user. The implementation of such a vision may appear close, but many subtle details separate us from its fulfillment. This kind of implementation, as the overlap between rendered virtual annotations and the camera’s real-world view, requires different computer vision paradigms for object recognition and tracking which often require high computing power and large-scale datasets of images. Nevertheless, these resources are not always available, and in some specific domains, the lack of an appropriate reference dataset could be disruptive for a considered task. In this particular scenario, we here consider the problem of wine recognition to support an augmented reading of their labels. In fact, images of wine bottle labels may not be available as wineries periodically change their designs, product information regulations may vary, and specific bottles may be rare, making the label recognition process hard or even impossible. In this work, we present augmented wine recognition, an augmented reality system that exploits optical character recognition paradigms to interpret and exploit the text within a wine label, without requiring any reference image. Our experiments show that such a framework can overcome the limitations posed by image retrieval-based systems while exhibiting a comparable performance.
Deep Learning-Based Efficient Analysis for Encrypted Traffic

To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security. As a result, the precise classification of encrypted network traffic has become a crucial problem in network security. In light of this, our paper proposes an encrypted traffic identification method based on the C-LSTM model for encrypted traffic recognition by leveraging the power of deep learning. This method can effectively extract spatial and temporal features from encrypted traffic, enabling accurate identification of traffic types. Through rigorous testing and evaluation, our system has achieved an impressive accuracy rate of 96.4% on the widely used ISCXVPN2016 dataset. This achievement demonstrates the effectiveness and reliability of our method in accurately classifying encrypted network traffic. By addressing the challenges posed by encrypted traffic identification, our research contributes to enhancing network security and privacy protection.
Sensor Selection for Robotic Platform

Knowledge of the driving environment is an essential prerequisite for an autonomous machine to perform its intended task. The autonomous and automated machines receive the essential knowledge of the environment via different sensors. In fact, the sensors act as a sensory organ for the robot to receive information similar to the five primary senses in the human being (Svechtarova et al., 2016) e.g., visual, tactile, auditory, taste and smell. The robot can have sensors in all these sensing ranges except taste.
Subsampling of Frequent Words in Text for Pre-training a Vision-Language Model

In this paper, we introduce Subsampling of frequent Words for Contrastive Language-Image Pre-training (SW-CLIP), a novel approach for the training Vision-Language Models (VLMs). SW-CLIP uses frequency-based subsampling of words that has been previously proposed to train skip-gram models in natural language processing and applies it to the textual training data of VLMs. We report on experiments that demonstrate the ability of frequency-based subsampling to speed up training and also to deliver a substantial improvement in accuracy in a number of downstream zero-shot (i.e., transfer) classification tasks. We notice that the classification test sets on which SW-CLIP seems to be particularly effective are those in which the labels of the classes occur infrequently as words in the training data, and thus have a high probability of being retained during frequency-based subsampling of the model training data. Overall, the advantages of SW-CLIP demonstrated in this paper serves to motivated further future work in text subsampling for the training of VLMs. Our code and pre-trained weights are available at https://github.com/Anastasiais-ml/sw_clip.git
Formation of axiological competence as a psychological and pedagogical problem

The relevance and necessity of a permanent systemic axiological approach in the educational process of the modern education system is revealed. The problem of formation of axiological competence was studied from the point of view of various approaches to its solution. It is noted that modern society is experiencing the most dangerous crisis of values, humanity, loss of ideals. A way out of the crisis is presented, which is possible through the formation and spread of global values as the foundation of humanity. A significant increase in the attention of scientists and practitioners to the education of modern youth in accordance with the humanistic values of society and the world was noted. Emphasis is placed on the presence of a close connection between values and education, culture and education, which is revealed in the improvement of a person’s personality. Emphasis is placed on the growing significance of the value approach in the field of education, which is aimed at the formation of key competencies of the individual, in particular axiological. The views of scientists on the essence of the concept of “axiological competence” are analyzed. It was found that there are different points of view regarding the definition of the studied concept. On the one hand, the concept of “axiological competence” is presented as a component of subject historical competence, which should be gradually and systematically developed and improved during primary school education. On the other hand, the concept of “axiological competence” is highlighted from the point of view of a component of professional competence in the context of training specialists in various fields. On the basis of the conducted analysis, the definition of “axiological competence” was formulated, which we understand as a complex multi-component personal education, which is the result of value education and is based on theoretical knowledge and objective ideas about the diversity of humanistic values of society and the world, which are implemented through skills, abilities and models of behaviour that ensure successful life in society.
General theory of quantum holography based on two-photon Interference

As a kind of quantum phenomenon, Hong-Ou-Mandel (HOM) interference is more robust against phase noise. Because of this feature, robust quantum holography emerges, through which wave function of interested photon can be retrieved according to HOM interference pattern. For better understanding and developing this method, we derive a theoretical framework of robust HOM holography. In the quantum holography scheme, test state and reference state interfere at beam splitter (BS). Then, degree of freedom (DOF) resolved detections (such as spatial resolved detection, temporal resolved detection or spectrum resolved detection) are used at the BS output ports, respectively. Based on the single photon detection results, the DOF resolved coincidence counts are postselected, producing interference patterns. The information of the test states is retrieved from the patterns. According to different test states and reference states, four combinations are analysed, including measuring the wave function of single photon state by using standard single photon state or coherent state and measuring the wave function of coherent state through using standard single photon state or coherent state. In all cases, information of the test states is reflected in normalized second-order correlation function or interference patterns in similar forms. Specially, the wave function of test states can be directly retrieved from the interference patterns, with no complex algorithm required. Besides, phase noise from environment has no influence on this kind quantum holography. Comparison between traditional holography and quantum holography is made and analysed.
Reactive-Based and Scalable-Driven Architecture for Mobile Development

The aim of this paper is to show and explain the implementation of this custom design pattern in developing iOS mobile applications, in regards to adopting the SOLID [14, 15] principles. We also do not rely on any interface builder tool, we code the user interface elements. One of the main purposes of this research was to find and assess the best combination of the existing software design patterns based on the ease of understanding, granularity, maintainability, modularity and scalability. The existing design patterns used in this custom implementation are Singleton, Factory, Builder, MVC, MVVM and Coordinator, all wrapped around the Rx-Swift reactive framework [1]. The study results in the performance analysis of this custom design pattern using the Swift programming language [17]. The mobile application used for case study is UBOOKER [16] which domain is business and modelling, representing the usage of algorithms, multimedia content (photos) and ample data.
The Influence of Organizational Factors on Design Employees' Innovation Ability: The Mediating Role of Team Learning

Innovative design has promoted the improvement of the design ability of the manufacturing industry, realized the transformation of design itself from concept to method, and promoted the progress of the training mode of design talents. However, the core problem that currently restricts the continuous improvement of China's manufacturing design capabilities lies in the shortage of design talents. At present, due to the rigidity of the organizational structure, the defects of the organizational system, the insufficient stock of organizational knowledge, the inertia of the organization, and the weak organizational culture, etc., the innovation ability of the employees of the enterprise is seriously affected. How to improve behaviour, optimize team system, and enhance organizational adaptability to achieve innovation goals on the basis of team learning has become a problem that enterprise managers must seriously consider.
Learning Characteristics and Teaching Strategies of Vocational College Students in the Context of Blended Teaching

With the rapid development of intelligence and digital technology, blended learning is increasingly receiving high attention from the education community, key research from teachers, and widespread popularity among students. In the context of blended learning, it is of great significance to conduct in-depth research on the learning characteristics of vocational college students and continuously adjust and improve teaching strategies based on their learning characteristics, in order to improve the quality of education and teaching.
The Dataset-Similarity-Based Approach to Select Datasets for Evaluation in Similarity Retrieval

Most papers on similarity retrieval present experiments executed on an assortion of complex datasets. However, no work focuses on analyzing the selection of datasets to evaluate the techniques proposed in the related literature. Ideally, the datasets chosen for experimental analysis should cover a variety of properties to ensure a proper evaluation; however, this is not always the case. This paper introduces the dataset-similarity-based approach, a new conceptual view of datasets that explores how they vary according to their characteristics. The approach is based on extracting a set of features from the datasets to represent them in a similarity space and analyze their distribution in this space. We present an instantiation of our approach using datasets gathered by surveying the dataset usage in papers published in relevant conferences on similarity retrieval and sample analyses. Our analyses show that datasets often used together in experiments are more similar than they seem to be at first glance, reducing the variability. The proposed representation of datasets in a similarity space allows future works to improve the choice of datasets for running experiments in similarity retrieval.
Peer review declaration

All papers published in this volume of IOP Conference Series: Earth and Environmental Science have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. Type of peer-: Double-blind We use a double-blind type of peer review process. The author and reviewer identities are hidden to each other. The papers that pass the plagiarism check, then proceed to review process. Review process was conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. Our reviewers are the eminent experts, prominent scientists and researchers. We use a double-blind type of peer-review process. We provide reviewers an article grading form for each paper. The article grading form contains general comments and also specific suggestions and feedbacks for each section in the paper. The reviewer also asked to make a decision regarding the feasibility of publishing a paper along with the scientific reason behind it, such as substance suitability and data eligibility. Articles will not be processed further, if the results of the review state that the article is not eligible with the reviewer's notes on the assessment form. We send the paper to the reviewer, for one until two weeks, to be reviewed. Then, we forwarded the results of the review to the author so that it could be improved according to the suggestions and notes of the reviewer. Next, we sent the results of the improvements from the authors back to the reviewers to be followed up, whether they have been well elaborated or still need improvement. When the revised paper still needed some improvement, the steps repeated until the reviewer verified that the article is feasible and ready to be processed to the final stage by the editor (layout and proofread). Conference submission management system: All the ISOSS 2022 papers were processed by MORRESSIER system and also by personal message between committees and authors. Number of submissions received: 82 Number of submissions sent for review: 77 Number of submissions accepted: 68 Acceptance Rate: 82,93% Average number of reviews per paper: 2 Total number of reviewers involved: 63 reviewers Any additional info on review process: We only accepted paper that has a plagiarism value below 20% exclude bibliography. Contact person for queries: Dr. Beginer Subhan E-mail: beginersubhan@apps.ipb.ac.id Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University, Dramaga, Bogor 16680, West Java, Indonesia Phone: +62 812 8347 579
Pedagogical Activities of Graduate (1916) of Riga Polytechnic Institute Jānis Rupais (1889–1974) in Latvia

The research on Jānis Rupais (1889–1974), engineer, pedagogue, and graduate of the Department of Chemistry (1916) of Riga Polytechnic Institute (RPI), was conducted using the documents of the National Archives of Latvia, the Latvian State Historical Archive and the Latvian State Archives, as well as Gulbene Municipality Museum of History and Art. The article about the well-known pedagogue in Latvia in the 20th century – the long-term and only Principal of Gulbene State Commercial School and Vocational School (1926– 1944) J. Rupais also reflects on his activities after World War II at Riga Industrial Polytechnicum. It describes both the educational institutions, the principal and pedagogue J. Rupais and his work methods, and the contribution he made to Gulbene and Latvia.
LUNCH: an Answer Set Programming System for Course Scheduling

Timetable scheduling is a known NP-hard problem; despite this, there have been many efforts to enable fast and efficient algorithms and heuristics for such a challenging task. Within the realm of timetable scheduling lies the particularly complex problem of Course Scheduling (CS). The goal in CS is to find an optimal timetable configuration of courses within the constraints set by faculty, course requirements and departmental functions. Answer Set Programming (ASP) is a declarative logic programming paradigm for solving combinatorial search tasks; instead of explicitly writing the solution to the problem, ASP programs define the problem’s constraints and knowledge in a high-level language, leaving the model search to a highly optimized solver. In this work, we construct and showcase LUNCH, an easily extensible, free and open-source system for course scheduling. Notably, we study the use of ASP for course scheduling within the specific context of the Computer Science Department at the University of São Paulo, showing how LUNCH fares against the manual scheduling done in previous years.
Managerial competence and innovation performance: The mediating role of willingness to change, entrepreneurial orientation and culture

Small and medium-sized enterprises (SMEs) significantly influence Saudi Arabia’s economic stability and growth. However, this industry faces significant managerial competence (MC) and innovation performance (IP) obstacles. Based on the findings of previous studies, i.e., Kellermanns and Eddleston (2006), De Clercq et al. (2015), Vafaei-Zadeh et al. (2019) and Renwarin et al. (2023) and the dire need for further exploration, the researchers developed the conceptual framework and model for confirmation in the context of Saudi Arabia. The study aims to investigate the direct and indirect linkages between MC and IP, mediated by a willingness to change (WTC), entrepreneurial orientation (EO), and entrepreneurial culture (EC) among SMEs in Saudi Arabia. The study used responses from 368 managers of Saudi Arabian SMEs. The structural equation modeling (SEM) analysis results confirmed that MC has a significant positive effect on WTC, IP, EO and EC. Besides, the factors such as WTC, EO, and EC positively and significantly affect IP. Finally, the mediation analysis through path analysis suggests a mediating role of WTC, EO and EC between MC and IP. The findings of this study can assist SME managers in making informed resource allocation decisions that can lead to maximum outcomes for their businesses. Besides, this study can help SME managers overcome challenges while improving their organizations’ innovation performance.
Poor Man's Line Scan – a simple tool for the acquisition of high-resolution, undistorted drill core photos

Abstract. The analysis and presentation of drill cores, an essential part of geoscientific research, requires the acquisition of high-quality core photos. Typically, core photos are either taken by hand, which often results in poor and inconsistent image quality and perspective distortions, or with large, heavy, and thus inflexible as well as expensive line scan setups. We present a simple, portable “Poor Man's Line Scan” setup that turns a customary smartphone into a semi-automatic core scanner utilising its panoramic photo function while guided on a rail in order to record undistorted core photographs at high resolution. The resulting images, although affected by some minor artefacts, are clearly superior in quality and resolution to single photos taken by hand and are comparable to images taken with commercial line scan cameras. The low cost (∼ EUR 100) and high flexibility, including the potential for modifications, of our tool make it an interesting alternative to the classical line scan setup.
Quantum Computing in Insurance Capital Modelling under Reinsurance Contracts

In this study, we design an algorithm to work on gate-based quantum computers. Based on the algorithm, we construct a quantum circuit that represents the surplus process of a cedant under a reinsurance agreement. This circuit takes into account a variety of factors: initial reserve, insurance premium, reinsurance premium, and specific amounts related to claims, retention, and deductibles for two different non-proportional reinsurance contracts. Additionally, we demonstrate how to perturb the actuarial stochastic process using Hadamard gates to account for unpredictable damage. We conclude by presenting graphs and numerical results to validate our capital modelling approach.
Application of Deep Learning Techniques to Depth Images for Person Tracking and Detection

Nowadays, using neural networks for image processing and tracking of individuals/objects is a highly popular subject that can be applied to various real-world issues. However, for such cases, the image often needs to possess good quality and exhibit distinct features to aid in object detection, posing challenges in environments with low or no illumination. In our work, we present a comparative study on the performance of leading convolutional neural networks for the detection and tracking of individuals through depth color images generated by infrared sensors. Additionally, we aim to demonstrate the usability of the YOLO (You Only Look Once) architecture as an alternative for identifying objects in images generated by sensors that do not rely on illumination. Experimental results showcase that the approach using YOLO Tiny improves accuracy by approximately 9% and processes around 8 times more frames per second (FPS).
Simple Additive Weight in Placement of Community Service Program Student Locations

Community Service Program is a mandatory activity for student to implement the Tree Pillars of Higher Education as well as being a creative and innovative space for students to cannel knowledge and knowledge to find a solution for problems faced by the village community. It is hoped that the placement of the location of the community service program students will be right on target and not be a polemic for Community Service Program students who feel infair with the distribution of locations. The selection of Community Service Program locations for students in this case study uses the Simple Additive Weight (SAW) Algorithm by reviewing several student criteria, such a students having babies, student health conditions, wheather the student is pregnant or not, students have a vehicle or not, where students live, Grade Point Average (GPA) of students, organizational participation, taking courses, field experience practice, as well as the criteria for the location of the Community Service Program is the location of the Community Service Program, illiteracy, population density, and village area. The results of ranking using SAW, , the results obtained for student 1 with a value of 5.866667 are suggested to occupy Village 1 with a value of 2.833333, Student 2 with a value of 7.133333 occupies Village 2 with a ranking value of 2.916667, and Student 3 with a value of 7.2 occupies Village 3 with a value of 3.75.