Open World Research Articles

The Person Beneath the Hair: Hair Discrimination, Health, and Well-Being.

Discrimination toward black hair is pervasive in today's society. Hair discrimination is negative bias manifested toward black natural or textured hair styles typically worn by persons of African descent. This commentary discusses the potential effects of hair discrimination on the health and well-being of persons of African descent. Specifically, it explores the mental and physical health implications of hair discrimination and situates it within the broader context of social determinants of health. The Creating a Respectful and Open World for Natural Hair Act has been recently passed in the United States House of Representatives, but more work is needed to eliminate hair discrimination and its negative effects.

Otwarty świat i narracja rozproszona w grze Elden Ring studia FromSoftware


 
 
 The article Open World and Dispersed Narrative in FromSoftware’s “Elden Ring” tackles a number of design issues associated with open world design in video games. Building upon Tim Ingold’s dis- crepancy of two distinct modes of exploring the reality (wayfaring and trail-following), the author proceeds with analysing how the categories in question might have influenced the design of Elden Ring’s ‘The Lands Between’, which is torn between two different ambitions. One is to create a high fantasy narrative in allotopia, whilst the other is to sustain the principles of procedural rhetoric typical for soulsborne games, well-known for leaning towards a dark fantasy design style both in terms of their aesthetics and narrative content. Consequently, the main goal of this analysis is to propose a potential way of avoiding such inconsequence in open world game design, which is termed here a “ludotopian dissonance”.
 
 

International Marine Tourism

In today’s globalised and open world, international marine tourism needs renewal, reorganisation and definition of perspectives, including in the context of the concept of sustainable development. In order to determine the state and possible prospects for the development of international marine tourism in the context of sustainable development priorities, a bibliometric and contextual analysis of the conclusions and recommendations of selected scientific publications was carried out. Relevant publications since 1987 were searched using the Dimensions research database search engine. This analysis identified the trend in the number of publications, the most numerous research categories according to Fields of Research and the Sustainable Development Goals, the most productive journals and the network of authors. As a result of the contextual analysis of the selected articles, the need for (1) management reforms and (2) scientific research at all levels, in accordance with the concept of sustainable development and the Global Code of Ethics in Tourism, (3) clustering and externalization of marine tourism in accordance with the orientation towards specific community and (4) further development of the blue economy and protection and restoration of natural resources of coastal areas as a basis for marine tourism activities. The results of the research can be useful for the scientific community, the general public and specialists in the field of bibliography.

Planning and Learning for Reliable Autonomy in the Open World

Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge. My research focuses on developing planning and learning algorithms that support reliable autonomy in fully and partially observable environments, in the presence of uncertainty, limited information, and limited resources. This talk covers a summary of my recent research towards reliable autonomy.

Planning and Learning for Non-markovian Negative Side Effects Using Finite State Controllers

Autonomous systems are often deployed in the open world where it is hard to obtain complete specifications of objectives and constraints. Operating based on an incomplete model can produce negative side effects (NSEs), which affect the safety and reliability of the system. We focus on mitigating NSEs in environments modeled as Markov decision processes (MDPs). First, we learn a model of NSEs using observed data that contains state-action trajectories and severity of associated NSEs. Unlike previous works that associate NSEs with state-action pairs, our framework associates NSEs with entire trajectories, which is more general and captures non-Markovian dependence on states and actions. Second, we learn finite state controllers (FSCs) that predict NSE severity for a given trajectory and generalize well to unseen data. Finally, we develop a constrained MDP model that uses information from the underlying MDP and the learned FSC for planning while avoiding NSEs. Our empirical evaluation demonstrates the effectiveness of our approach in learning and mitigating Markovian and non-Markovian NSEs.

The Concept of Ta'dib According to An-Naquib Al-Attas And Humanism According to Paulo Freire and their Relevance with Islamic Education

A Naquib Al-Attas, with the idea of ta' dib, talks about to plan exercises to strengthen oneself in examining this open world. A Naquib Al- Attas is one of the figures who have impressive thoughts and figures in the improvement of contemporary Islamic training thought. Paulo Freire, with the concept of humanism, is interested in achieving the primary goal of education that humanizes humanity so that the potential within the subject of education can be developed. This study aims to describe and find essential points of thought of the concept of education, according to An-Naquib Al-Attas and Paulo Freire, and explain the relevance of the two figures' educational concepts with Islamic education. This research is qualitative. Furthermore, the type of research the writer chooses is library research with descriptive analysis. The primary data sources in this study are An-Naquib Al-Attas and Paulo Freire books that have been translated into Indonesian. Secondary data sources are taken from books relevant to the two educational concepts. Techniques of collecting data using techniques Implementing data that is analyzed with content. This exploration yielded results on the idea of ta' dib A Naquib Al-Attas and Paulo Freire's humanism that are applicable to Islamic instruction, while Freire's innovativeness despite everything bolsters the solid with common and training in Indonesia. No. 20 of 2003 can be affirmed.

Cloud & IoT based indoor positioning solution with intuitive AR guidance

GPS, the most frequently used outdoor navigation system used in the contemporary world in peoples mobile phones, however, does not prove to be reliable in indoor settings. Neither do some wireless protocols such as Bluetooth and WIFI with RSSI do an effective job due to their susceptibility to physical obstacles such as walls and objects in the building. Our team came up with a solution utilizing UWB wireless communication protocol with SS-TWA to help users with an AR device, or similarly an AR application on smartphones, to navigate in indoor settings with clear graphic guidance, similar to the guiding systems already used by people in contemporary society. The map of the indoor setting and related calculations during the navigation process are all based on clouds, and we will show in detail how we implement our design. Finally, we discussed how our solution projects about indoor positioning can also be related to fields like ML, Open World and Metaverse.

Evaluation of deep learning-based algorithms on person re-identification

At present, CCTVs and multifunctional cameras have been well equipped everywhere in our daily lives. However, most of the public sectors especially the department of public safety are still using the traditional approach, analysing these data by humans. This research presents a summary of the current technologies and algorithms based on deep learning in person Re-identification (Re-id), which aims to recognize a particular individual in several camera views. This paper firstly described the current challenges in using deep learning for person Re-id. Then, a brief introduction of standard person Re-id including feature learning and metric learning is given. Furthermore, the open world Re-id problem is examined from three aspects: person re-id with occlusions, unsupervised learning-based method, and cross-modal person re-Id. The author then evaluated the regularly used datasets and analysed the performance of the algorithms from recent times. Finally, some potential future directions of research for Re-id are proposed.

KRL_MLCCL: Multi-label classification based on contrastive learning for knowledge representation learning under open world

Knowledge representation learning(KRL) transforms knowledge graph(KG) from symbol space to vector space. However, KRL under open world assumption(OWA) is deeply trapped in the dilemma of lack of labels due to difficulty or high cost in labeling. To address this problem, we propose KRL_MLCCL:Multi-Label Classification based on Contrastive Learning(CL) Knowledge Representation Learning method. Specifically, (1)we formalize a problem of solving true knowledge graph objects(KGOs) matchings(KGOMs) under the OWA in the original KGOM sample space(KGOMSS)(multi-label classification with one known true matching(positive-example)). (2)we solve the problem in the new KGOMSS, generated through augmenting the true matching according to CL’s idea(multi-label classification with multiple known true matching). (3)we score the true matchings based on hermitian inner product and softmax and minimize a negative logarithm likelihood loss to establish KRL_MLCCL model preliminarily. (4)we migrate the learned model back to the original KGOMSS to solve the true matching problem. We creatively design and apply a positive-example augmentation way of CL enabling KRL_MLCCL with back migration ability: “pulling KGOs in true matching close and pushing KGOs in false matching away”, which helps KRL out of the labels shortage dilemma faced in modeling. We also propose a negative-example noise filtering algorithm to enhance this ability. The open world entity prediction(OWEP) experiment on dataset FB15K-237-OWE shows that the performance of KRL_MLCCL is increased by 3% in Hits@10 and 1.32% in MRR compared with the state-of-the-art in the baselines. The experiments of OWEP in KG also show that KRL_MLCCL has a better back migration ability.

Lifelong nnU-Net: a framework for standardized medical continual learning

As the enthusiasm surrounding Deep Learning grows, both medical practitioners and regulatory bodies are exploring ways to safely introduce image segmentation in clinical practice. One frontier to overcome when translating promising research into the clinical open world is the shift from static to continual learning. Continual learning, the practice of training models throughout their lifecycle, is seeing growing interest but is still in its infancy in healthcare. We present Lifelong nnU-Net, a standardized framework that places continual segmentation at the hands of researchers and clinicians. Built on top of the nnU-Net—widely regarded as the best-performing segmenter for multiple medical applications—and equipped with all necessary modules for training and testing models sequentially, we ensure broad applicability and lower the barrier to evaluating new methods in a continual fashion. Our benchmark results across three medical segmentation use cases and five continual learning methods give a comprehensive outlook on the current state of the field and signify a first reproducible benchmark.

Dual-stream coupling network with wavelet transform for cross-resolution person re-identification

Person re-identification is a prevalent technology deployed on intelligent surveillance. There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution, yet such models are not applicable to the open world. In real world, the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent. When low-resolution (LR) images in the query set are matched with high-resolution (HR) images in the gallery set, it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images. To address the above issues, we present a dual-stream coupling network with wavelet transform (DSCWT) for the cross-resolution person re-identification task. Firstly, we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images, which is applied to restore the lost detail information of LR images. Then, we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions. Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.

Open world recognition of communication jamming signals

To improve the recognition ability of communication jamming signals, Siamese Neural Network-based Open World Recognition (SNN-OWR) is proposed. The algorithm can recognize known jamming classes, detect new (unknown) jamming classes, and unsupervised cluseter new classes. The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset. On the one hand, the network is required to have the ability to distinguish whether two samples are from the same class. On the other hand, the latent distribution of known class is forced to approach their own unique Gaussian distribution, which is prepared for the subsequent open set testing. During the test, the unknown class detection process based on Gaussian probability density function threshold is designed, and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes. The simulation results show that when the jamming-to-noise ratio is more than 0dB, the accuracy of SNN-OWR algorithm for known jamming classes recognition, unknown jamming detection and unsupervised clustering of unknown jamming is about 95%. This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

Independent society publishers and the path to open scholarship

Independent society publishers and the path to open scholarship

Quantum X-entropy in generalized quantum evidence theory

Generalized quantum evidence theory (GQET) is effective for uncertainty reasoning from the view of the quantum framework in an open world. However, how to construct the quantum model of GQET to bridge evidence theory and quantum theory remains an open issue. In this paper, a new quantum model of GQET is proposed, which provides a new perspective to express and handle the generalized quantum mass function with more explicit physical meanings. Specifically, a new quantum model for the generalized quantum mass function in GQET is first proposed, which represents the generalized quantum mass function as a mixed quantum system of quantum pure states. In addition, belief and plausibility measurement functions are newly defined in terms of the generalized quantum mass function in GQET, which represent the upper and lower bounds to support decision-making by means of the new quantum model. In addition, a new quantum X-entropy is proposed to measure the uncertainty of the generalized quantum mass function in GQET. The proposed quantum X-entropy can be viewed as the generalized model of classical von Neumann entropy. In particular, when the basic belief amplitude assignment of a GQBBA is in accordance with a probability distribution, quantum X-entropy degenerates into von Neumann entropy. Numerical examples demonstrate the effectiveness of the new quantum model of GQET as well as quantum X-entropy.

Open Source is Good for AI But, Is AI Good for Open Source?

Abstract Terence Eden CITP MBCS MIET, Chair of the BCS Open Source Specialist Group, takes Martin Cooper MBCS on a tour of the open source world and explains why writing software is both an art and a science.

Factors Influencing Information Adoption From Social Media About COVID-19 by Saudi Citizens.

Social networking sites comprise an open world where anyone can share different types of information. During the Covid-19 pandemic, people's social isolation made social media particularly important, and it has emerged as an important platform for sharing health information. The credibility of the information, however, is questionable. Therefore, this study aims to understand attitudes towards using social media as an information source among social media users in Saudi Arabia. Specifically, it seeks to examine the propensity of individuals towards using health information from social media platforms and whether individuals demographic factors (such as gender) impact information adoption. Based on using an online questionnaire incorporating a novel vignette design to measure participants’ perceptions of information sources on social media platforms. The findings revealed that there is a high use of social media platforms among the Saudi population and that verified accounts on social media platforms, same gender and nationality positively affect the propensity towards adopting information.

Meaningful Place: A Phenomenological Approach to the Design of Spatial Experience in Open-world Games

With the development of game engines and graphics technology, open-world games have gained popularity all over the world recently. One of the key features of this genre is a realistic open world space for the players to explore, to the extent that some even deemed it as the prototype of virtual world and the Metaverse. However, in comparison with the growing world size, the player's spatial experience is a downward spiral. Interaction in an ever-increasing game space suffered from a repetitive design pattern which rendered the world grandeur on the surface but hollow deep down. How to find meaning in this seemingly hollow world and what game designers can learn from it? From a phenomenological perspective, this paper distinguishes “place” from “space,” and, according to this, proposes a three-dimensional meaning structure of place, that is “orientation-identification-time,” as a design approach to reconstruct a meaningful spatial experience in the game world.

Manajemen pastoral yang inovatif berbasis kecerdasan kultural

Globalization in this communication and information technology era has brought many challenges and new needs, including in the field of church pastoral care. Church leaders must deal with a congregation that is highly diverse and comes from various cultural backgrounds. This means that models of service and pastoral care must be designed so that the proclamation of the Good News can effectively reach the people of God. In the past decade, one attribute variable that has been proven effective as a medium for enhancing service performance is cultural intelligence. This study aims to examine the contribution of cultural intelligence to pastoral management in the context of a multicultural world. The method used is a systematic literature review of various literature on cultural intelligence. The results of the study show that cultural intelligence has a significant contribution to pastoral care. Innovative and practical pastoral design should consider the multicultural aspects of the people of God by utilizing specific theological models/paradigms. Effective pastoral strategies include management, organization, and delivery strategies. AbstrakGlobalisasi di era teknologi komunikasi dan informasi abad ini telah membawa banyak tantangan dan kebutuhan baru termasuk dalam bidang pastoral atau penggembalaan gereja. Para pemimpin jemaat harus berurusan dengan umat yang sudah sangat terbuka dan berasal dari berbagai latar belakang budaya. Ini berarti model pelayanan dan penggembalaan harus dirancang sedemikian rupa sehingga pewartaan Kabar Baik dapat menjangkau umat Allah secara efektif. Dalam satu dasawarsa terakhir, salah satu variabel atribut yang terbukti efektif dalam sebagai medium untuk meningkatkan kinerja layanan adalah kecerdasan kultural. Penelitan ini bertujuan untuk mengkaji sumbangan kecerdasan kultural terhadap manajemen pastoral dalam konteks dunia yang multikultur. Metode yang digunakan adalah tinjauan literatur sistematik terhadap berbagai literatur tentang kecerdasan kultural. Hasil kajian memperlihatkan bahwa kecerdasan kultural memiliki kontribusi yang penting bagi pelayanan pastoral. Desain pastoral yang inovatif dan berdaya guna hendaknya mempertimbangkan aspek-aspek umat Allah yang multikultur dengan memanfaatkan model/paradigma teologis tertentu. Strategi pastoral yang efektif mencakup strategi pengelolaan, pengorganisasian dan penyampaian.

CPSS-FAT: A consistent positive sample selection for object detection with full adaptive threshold

Recent CNN-based methods for object detection largely focus on training the backbone of the object detector, neglecting the key part of selecting positive/negative samples. Instead, based on our analyses about the limitations and inconsistency of existing positive sample selection, we propose a novel Consistent Positive Sample Selection (CPSS) method to select the positive samples automatically, which joints Box-IoU and normalized central distance of object-anchor by mutual weighting. Meanwhile, we employ a consistent approach to location quality evaluation for suppressing the false-positive predicted box when inferencing. Furthermore, an auxiliary Full Adaptive Threshold (FAT) post-processing is also adopted according to the objects’ occlusion level to improve the recall ratio. We implement the proposed CPSS-FAT detector using MS COCO 2017 and CityScapes datasets, the comparing results indicate that our approaches are effective and robust to the different objects in an open world. Especially, we achieve 52.2% AP and 43.1% ARS, outperforming most existing detectors.

사무직 중년 여성의 퇴직과 진로구성 이야기

Objectives This study was conducted to explore the retirement experiences of middle-aged women in office work and the career composition process after retirement.
 Methods The narrative inquiry method was used as a research method to best understand the retirement and career composition process of four middle-aged women in their 50s who have retired two to five years after working as a lifelong job. Field texts were collected through individual in-depth interviews with research participants, and research texts were created while analyzing the data.
 Results Office women were more influenced by the external variables of the times and family situations than office men in the process of employment, career development, and retirement. In a male-centered organizational society, they have lived as conductors such as work, housework, and parenting, despite the lack of connections, time, and physical limitations. In such multiple roles, work-family conflict has been an important obstacle to women's career development. While doing new jobs after retirement, they regain their masters in life and are leading and active in both work and relationships. After retirement, the theme of their new job is to regain their place of ownership / fulfill their wishes / run to the open world / meet people in the square.
 Conclusions Starting from retirement, retired office women achieve work-related integrated self-development as the weight of their work decreases, and their self-directedness increases. Even within office women, the impact of retirement varies depending on how much they are identified with work, and the attitude toward post-retirement work varies depending on the actual impersonation.