Reviewerly/exHarmony huggingface dataset:
Current and potential use of augmented reality in (geographic) citizen science projects: a survey

Augmented Reality (AR) offers new opportunities for Citizen Science (CS) projects regarding data visualization, data collection, and training of participants. Since limited research on the usage of AR in CS projects exists, an online survey is conducted in this study by reaching out to CS project managers to determine the extent of its current use. The survey can identify areas where CS project managers themselves see the greatest potential for AR in their projects and reasons that exist against the use of AR. A total of 53 CS project managers participated in the survey and shared their opinions and concerns. Of all participating CS projects, only three are currently using AR. However, 27 CS projects indicated that AR could be beneficial for their project. Especially projects with a geographic focus, in which participants are involved in the process of collecting spatial data, expressed this opinion. Particularly in the areas “data visualization” and “attraction/motivation of participants” the projects identified potential for AR. Arguments against the use of AR named by 23 CS projects include remote study areas, financial considerations, and the lack of a practical use case. This study shows initial trends regarding the use of AR in CS projects and highlights specific use cases for the application of AR.
Background Instance-Based Copy-Paste Data Augmentation for Object Detection

In supervised deep learning object detection, the quantity of object information and annotation quality in a dataset affect model performance. To augment object detection datasets while maintaining contextual information between objects and backgrounds, we proposed a Background Instance-Based Copy-Paste (BIB-Copy-Paste) data augmentation model. We devised a method to generate background pseudo-labels for all object classes by calculating the similarity between object background features and image region features in Euclidean space. The background classifier, trained with these pseudo-labels, can guide copy-pasting to ensure contextual relevance. Several supervised object detectors were evaluated on the PASCAL VOC 2012 dataset, achieving a 1.1% average improvement in mean average precision. Ablation experiments with the BlitzNet object detector on the PASCAL VOC 2012 dataset showed an improvement of mAP by 1.19% using the proposed method, compared to a 0.18% improvement with random copy-paste. Images from the MS COCO dataset containing objects of the same classes as in PASCAL VOC 2012 were also selected for object pasting experiments. The contextual relevance of pasted objects demonstrated our model’s effectiveness and transferability between datasets with same class of objects.
Symmetric Graph-Based Visual Question Answering Using Neuro-Symbolic Approach

As the applications of robots expand across a wide variety of areas, high-level task planning considering human–robot interactions is emerging as a critical issue. Various elements that facilitate flexible responses to humans in an ever-changing environment, such as scene understanding, natural language processing, and task planning, are thus being researched extensively. In this study, a visual question answering (VQA) task was examined in detail from among an array of technologies. By further developing conventional neuro-symbolic approaches, environmental information is stored and utilized in a symmetric graph format, which enables more flexible and complex high-level task planning. We construct a symmetric graph composed of information such as color, size, and position for the objects constituting the environmental scene. VQA, using graphs, largely consists of a part expressing a scene as a graph, a part converting a question into SPARQL, and a part reasoning the answer. The proposed method was verified using a public dataset, CLEVR, with which it successfully performed VQA. We were able to directly confirm the process of inferring answers using SPARQL queries converted from the original queries and environmental symmetric graph information, which is distinct from existing methods that make it difficult to trace the path to finding answers.
Behavioural Psychology Towards Artificial Intelligence in Cybersecurity

More than 99% of cybercriminal attacks exploit human characteristics and behaviour (people's vulnerabilities) rather than vulnerabilities in computer systems. When it comes to cybersecurity, it is crucial to consider human behaviour on the part of the user. Cognitive aspects such as attention, memory, and reasoning can be affected by conditions such as fatigue, illness, or injury and are pointed out as human characteristics that are at the origin of several incidents. Users' stress and anxiety may be correlated with making mistakes that compromise cybersecurity. Thus, behavioural psychology should be used to develop effective educational strategies and encourage safe and conscious behaviour in relation to cybersecurity. By understanding how users interact and respond to AI, more effective and aware cybersecurity systems can be created. This chapter's overall objective is to map the literature regarding the psychology of behaviour towards AI in cybersecurity, using a scoping review methodology to provide input into the behavioural psychology aspects regarding AI and cybersecurity.
vim: Research on OWL-Based Vocabulary Ontology Construction Method for Units of Measurement

The advent of the digital era has put forward an urgent demand for the digitization of units of measurement, and the construction of unit ontology is an important method to realize the digitization of units of measurement. However, the existing unit ontology is at the preliminary research stage, especially the bilingual unit of measurement suitable for the construction of Digital China. Based on the Web Ontology Language (OWL), a bilingual unit of measurement ontology, vim, is designed and constructed using the Seven Steps to Ontology Development approach. vim provides a standardized, interoperable, and unified architecture to realize the bilingual digital representation of units in the International Vocabulary of Metrology—Basic and general concepts (VIM) and from the Chinese metrological technical specification JJF 1001-2011 General Terms in Metrology and Their Definitions. The ontology was verified for machine readability, knowledge reasoning capability, and semantic retrieval and applied. The experimental results show that the vim ontology can achieve machine readability with correct syntax, logical consistency, and validity, and can facilitate data communication and sharing. Furthermore, a comparison between vim, OM, and QUDT was conducted. OM and QUDT serve as representative instances in the field of ontology for units. The construction of this ontology lays the foundation for realizing the digitization and standardization of China’s unit of measurement, as well as the machine-readability, interoperability, and sharing of domestic and foreign metrology test data and metrology certificates.
A Hybrid Approach for WebRTC Video Streaming on Resource-Constrained Devices

This paper introduces thorough comparative and interoperability analyses involving a browser-based P2P video streaming approach utilizing WebRTC (web real-time communication), along with WebRTC hybrid solutions developed using the React Native framework (for mobile) and the electron framework (for desktop and Raspberry Pi). The assessment is carried out based on various metrics, including CPU (central processing unit) load, RAM (random access memory) utilization, and network data consumption. The obtained findings highlight the potential of integrating WebRTC P2P video streaming into hybrid applications as a promising alternative for real-time video streaming applications in embedded systems; given that, compared to the current mainstreams, e.g., Chrome or Firefox, the proposed approach has superiority performance in terms of CPU load, RAM usage, and network occupancy. The results also demonstrated that interoperability is ensured between the two types of applications (web-based and hybrid-based one).
Error-tolerant oblivious transfer in the noisy-storage model

The noisy-storage model of quantum cryptography allows for information-theoretically secure two-party computation based on the assumption that a cheating user has at most access to an imperfect, noisy quantum memory, whereas the honest users do not need a quantum memory at all. In general, the more noisy the quantum memory of the cheating user, the more secure the implementation of oblivious transfer, which is a primitive that allows universal secure two-party and multiparty computation. For experimental implementations of oblivious transfer, one has to consider that also the devices held by the honest users are lossy and noisy, and error correction needs to be applied to correct these trusted errors. The latter are expected to reduce the security of the protocol, since a cheating user may hide themselves in the trusted noise. Here we leverage entropic uncertainty relations to derive tight bounds on the security of oblivious transfer with a trusted and untrusted noise. In particular, we discuss noisy storage and bounded storage, with independent and correlated noise.
Cybercrime and Cybersecurity Laws in Current and Future Contexts With Evolving Crimes Across National Boundaries

This chapter intends to discuss cybercrime and cybersecurity laws in their current and future contexts. The initial part of this chapter discusses terms and issues related to the cyber world. This portion has touched areas where the requirement of cyber laws is increasing with the advancement of time, technology, and types of cybercrimes. The later section of this chapter will look at existing and widely used cybersecurity laws from around the world. We have emphasized on Cyber Security Laws in India, the US, and the UK. The reason for focusing on these countries is that there are many multinational organizations that have offices spread across these regions. However, it should be noted that cybercrime is not limited by national or geographical boundaries and once committed, is dealt with under international law and in accordance with the trade agreements between the countries involved. The authors have included two famous trans boundary cyber cases that were brought to justice.
Optical encryption using a sparse-data-driven framework

The issue of integration of optical cryptosystems and deep learning (DL) algorithms have attracted a great deal of attention recently. When neural network models pre-trained by a series of known plaintext-ciphertext pairs is utilized for decryption, however, all the physical secret keys become useless for most of reported optical encryption systems, and the security of those systems consequently relies too heavily on the network models. In this paper, we propose a sparse-data-driven framework for speckle-based optical encryption, which add an extra layer of security to DL-assisted optical cryptosystems. Two networks are trained in the encryption, one is associated with secret keys and the other is driven by sparse data. The framework we build can provide flexibility by allowing the cryptosystem to adapt the generation of cyphertext to a changeable strategy. The effectiveness and security of the proposed framework for speckle-based optical encryption is verified with different experimental results. We hope the considerations presented in this work could promote the application of DL in different types of optical cryptosystems.
Sketching an ontology for Leichte Sprache and using it to represent information in Wikidata

Leichte Sprache 1 (Easy Language in Germany) was developed for people with reading disabilities, cognitive disabilities, hearing impairments or dementia, among others. It was intended to give them access to written language and enable them to participate in social life (Bredel/Maaß 2016). In order to satisfy the huge demand for Leichte Sprache, and to advance the state-of-affairs, additional cross-domain collaboration and software can help. Communication is key to collaboration, and ontological knowledge is of high importance for building powerful software.
Plain Language and text accessibility in health communication

According to Busch-Lauer (2001), “[m]edicine is one of the highly competitive international fields of research and practice where knowledge transfer and effective communication require a particular sensitivity of specialists towards language [both L1 and Lx], culture, individuum, and community” (849). As a matter of fact, this specialized domain is characterized by epistemic – and thus also power – asymmetry1 between communication act participants, i.e. expert and layperson. If communication is not handled properly, these dissymmetries may lead to both the so-called expert knowledge barrier (‘Fachbarriere’) and expert language barrier (‘Fachsprachenbarriere’) according to the terminology used by Rink (2019, 2020) (building on Schubert 2016 and Jekat et al. 2014) in her categorization of communication barriers.2
Analyzing sentence alignment for automatic simplification of German texts

Simplified language1 is a variety of standard language characterized by reduced lexical and syntactic complexity, the addition of explanations for difficult concepts, and a clearly structured layout. It is used to enhance comprehensibility for the readers, allowing them to understand information or concepts more clearly. Among the target groups of simplified language are people with cognitive impairment and autism spectrum disorder, prelingually deaf and functionally illiterate persons, and sometimes also foreign language learners and children (Bredel & Maaß, 2016, p. 140–142). Simplified German has received increasing attention in research due to a number of movements in society and politics, as well as legal developments in German-speaking countries.
The role of language technology in accessible communication research

“Language Technologies (LTs) study and develop the means by which computer programs or data processing devices can analyze, produce, modify or respond to texts and human speech” (EU Host Paper 2019: 1). It is a broad field that includes various natural language processing (NLP) methods such as machine translation (MT) and speech technologies, as well as multilingual content management. LTs and computational linguistics have been steadily gaining popularity over the past 25 years, becoming both an exciting area of scientific research and practical technology that is increasingly being incorporated into consumer products (Hirschberg/Manning 2015).
Using contrastive inferences to learn about new words and categories

In the face of unfamiliar language or objects, description is one cue people can use to learn about both. Beyond narrowing potential referents to those that match a descriptor (e.g., "tall"), people could infer that a described object is one that contrasts with other relevant objects of the same type (e.g., "the tall cup" contrasts with another, shorter cup). This contrast may be in relation to other objects present in the environment (this cup is tall among present cups) or to the referent's category (this cup is tall for a cup in general). In three experiments, we investigate whether people use such contrastive inferences from description to learn new word-referent mappings and learn about new categories' feature distributions. People use contrastive inferences to guide their referent choice, though size - and not color - adjectives prompt them to consistently choose the contrastive target over alternatives (Experiment 1). People also use color and size description to infer that a novel object is atypical of its category (Experiments 2 and 3): utterances like "the blue toma" prompt people to infer that tomas are less likely to be blue in general. However, these two inferences do not trade off substantially: people infer a described referent is atypical even when the descriptor was necessary to establish reference. We model these experiments in the Rational Speech Act (RSA) framework and find that it predicts both of these inferences. Overall, people are able to use contrastive inferences from description to resolve reference and make inferences about a novel object's category, letting them learn more about new things than literal meaning alone allows.
Speech Modulation for Image Watermarking

In modern society, digital images are easily shared employing diverse platforms such as Instagram, Facebook, WhatsApp, and so on.
Stacking of BERT and CNN Models for Arabic Word Sense Disambiguation

We propose a new approach for Arabic Word Sense Disambiguation (AWSD) by hybridization of single-layer Convolutional Neural Network (CNN) with contextual representation (BERT). WSD is the task of automatically detecting the correct meaning of a word used in a given context. WSD can be performed as a classification task, and the context is generally a short sentence. Kim [ 26 ] proved that combining a CNN with an RNN (recurrent neural network) provides a good result for text classification. Here, we use a concatenation of BERT models as a word embedding to get simultaneously the target and context representation. Our approach improves the performance of WSD in Arabic languages. The experimental results show that our model outperforms the state-of-the-art approaches and improves the accuracy of 96.42% on the Arabic WordNet dataset.
Secure Image Watermarking Based on LWT and SVD

Digital information is easy to distribute, duplicate, and modify, which leads to the need for copyright protection techniques.
Transformation of Information Systems in the Tasikmalaya City Communication and Information Department

Statistical data and information regarding government and regional development is a right of the community. But unfortunately, this has not been fully fulfilled in the community. This research then tries to look at the implementation of factors in managing information systems at the Tasikmalaya City Communication and Information Service. This research will be carried out using qualitative methods through case studies. The data used in this study came from observations and interviews. The results of this study then found that the management of information systems at the Tasikmalaya Office was still ineffective. Unprofessional executors and inadequate infrastructure have resulted in this. Several factors that can be useful in supporting information transformation at the Tasikmalaya City Service are data collection, data processing, data analysis and assessment, data presentation and dissemination, and documentation. Some of the inhibiting factors for this transformation are professional staff, infrastructure, and technology.
Semantic Collaborative Learning for Cross-Modal Moment Localization

Localizing a desired moment within an untrimmed video via a given natural language query, i.e., cross-modal moment localization, has attracted widespread research attention recently. However, it is a challenging task because it requires not only accurately understanding intra-modal semantic information, but also explicitly capturing inter-modal semantic correlations (consistency and complementarity). Existing efforts mainly focus on intra-modal semantic understanding and inter-modal semantic alignment, while ignoring necessary semantic supplement. Consequently, we present a cross-modal semantic perception network for more effective intra-modal semantic understanding and inter-modal semantic collaboration. Concretely, we design a dual-path representation network for intra-modal semantic modeling. Meanwhile, we develop a semantic collaborative network to achieve multi-granularity semantic alignment and hierarchical semantic supplement. Thereby, effective moment localization can be achieved based on sufficient semantic collaborative learning. Extensive comparison experiments demonstrate the promising performance of our model compared with existing state-of-the-art competitors.
Path Planning in Dynamic Environments Based on Q-Learning

With the rapid progress of science and technology, the scope of applications for mobile robots is growing. Path planning in dynamic environment is always a challenging task for mobile robot, which shows significant impacts in the medical field and the military field. Q-learning, a model-free reinforcement learning algorithm, can recognize its surroundings and demonstrate a system making decisions for itself about how this algorithm learns to make accurate decisions about achieving optimal target. Therefore, a path planning algorithm was proposed for robots in dynamic environments based on Q-learning. This algorithm can successfully generate several viable paths in environments with both static obstacles, dynamic obstacles and target point. First, three environments with different levels of complexity were created. Afterwards, to generate several optimal paths, the path planning algorithm was conducted in multiple times in different environments. Finally, the experimental results were collected and visualized to illustrate information. The effectiveness of the proposed algorithm is validated by experiment results in solving problems of path planning in dynamic environments with required point.
The Application of Music Games in Elementary School Classes

Music games have been a teaching method in school music classrooms in recent years. The essence of music games is games, with the aim of experiencing and perceiving music. The essence of music games is to stimulate students' attention, interest, and physical activity, which is in line with the psychological characteristics of children's strong curiosity, short attention span, and playfulness. Music games mainly use physical movements, loud reading, and classroom games to experience the basic components of music, such as the strength of beats and the segmentation of music. Music games enable students to become the main body of music cognition, fully mobilize their enthusiasm, liberate them from the originally dull music classroom, and thus improve the classroom efficiency of music classes. The theme of this article is mainly divided into two parts. The first part focuses on the classification of music games, the actual classroom design of music games, and the basic principles that music games need to follow. The second part mainly introduces the experience summary after practice in the music classroom. Through the analysis and summary of music games, explore the connection between music games and the content in textbooks, and explore the connotation of music games to make students the main body of music. Understand music from the structure and motivation of music, and achieve the goal of easy and enjoyable learning.