Reviewerly/exHarmony huggingface dataset:
Radar–Camera Fusion Vehicle Detection Based on Feature Weighting and Visual Enhancement

To improve the vehicle detection accuracy under the condition of low light and long-distance detection requirements on expressway, a radar–camera fusion vehicle target detection method based on visual enhancement and feature weighting is proposed. First, starting with the fusion of the radar–camera data layer, the spatial location of the potential target based on millimeter wave radar is characterized, and the characterization result is used for the division of long-distance target areas in visual images. Then, the images of the divided area are reconstructed, detected, and restored to improve visual detection accuracy of the long-distance target. Next, the radar–camera detection feature layer is fused and modeling is conducted. Considering the difference in the contribution of different layers to feature detection, the weight parameters of different feature maps are obtained through model training, and the features of different layers are fused and calculated according to the weight to enhance the feature information of the target. Next, a branch network is added, and different receptive field information in the feature map is extracted by using convolutional layers of different sizes, and the branch output results are fused to obtain a stronger image representation ability, which can achieve the goal of improving the detection accuracy in low light. Finally, combining the feature weighting radar–camera framework and the vision enhancement based on millimeter wave radar spatial preprocessing, the radar–camera fusion detection network based on YOLOv4-tiny is designed and the verification system is built. The results show that (1) the average precision (AP) of the proposed algorithm in the low-light environment increased by 20% compared with that of YOLOv4, and the AP increased by 5% compared with that of the radar–camera fusion algorithm RVNet; (2) in the test of detection performance at different distances, when detecting a 120-meter target, the AP of the proposed algorithm is 73% higher than that of the YOLOv4 algorithm, and 63% higher than that of RVNet, which improved the coverage distance and low-light detection accuracy of vehicle detection in intelligent transportation systems (ITS).
Cybersecurity: Young People’s Awareness and Risk Management

The paper focuses on the problem of cybersecurity among young Internet users. We present the most famous classifications of risks and threats. The paper contains a description of the analysis of foreign studies on the development of new methods of risk management in the digital space and the results of a qualitative study among students at the Tomsk universities. The main research methods are qualitative and include a semi-structured essay, content analysis, and a focus group. The paper aims to identify the peculiarities of young people’s awareness of cybersecurity and risk and cyber threats management. As a result, we reveal the main ways of risk management and identify three cyber threats noted by students: psychological, financial, and technical. We divided the research participants into four groups depending on the level of threat awareness: (1) conscious and serious attitudes to cyber threats and cybersecurity; (2) appraisal of the problem of cyber threats and risks but ignoring prevention and self-defense; (3) cybersecurity appraisal characterized as limited and incomplete (self-restraint and self-control); and (4) no appraisal of the problems of cybersecurity (independence and indifference to the Internet in all its manifestations). As a rule, when using network Internet resources, students follow a strategy of self-restraint and a strategy of readiness for destructive media content. Creating and introducing new educational practices for students’ cybersecurity culture are of great importance.
Online Harmful Content: A Study on Violent Fan Communities on Social Media

People use social media globally. Currently, social media has become increasingly responsible for growing rates of deviant behavior, violence, and hatred. The authors analyze online social network communities of individuals who admire and glorify aggression, violence, and school shootings. The mentioned fan communities employ a wide range of strategies to survive, including creating “mirror communities” on social networks. Therefore, machine search methods based on linguistic markers aimed to identify communities containing harmful content do not always work. The paper aims to present an alternative methodology for identifying violent fan communities on social media.
An Approach to Decentralized Hybrid Question Answering Systems

Question answering (QA) systems are sequence-to-sequence model based programs that find the most accurate answer to a query by using given texts. In today’s world, QA system software is run on different platforms by various institutions. This means that users have to use different QA system software, developed with different technologies on various platforms, to find the most accurate answer to their queries. However, there is a lack of systems that allow these types of systems to be used in a hybrid manner on a single interface. To address this issue, this research investigates a software architecture that will enable QA systems trained with different datasets on various platforms to be used with a single programming interface. To demonstrate the feasibility and usefulness of the proposed hybrid question answering system framework software architecture, a prototype software has been developed. The performance of the developed prototype software has been compared with standalone QA system software based on the execution time performance metric. The obtained results demonstrate that the proposed framework software architecture has negligible processing overheads.
Community Networks in Kenya: Characteristics and Challenges

Community networks are set up by communities through pooling of resources. Such networks may also be initiated by third parties such as non-profit organizations (NPOs) or volunteers with involvement of the target community in a certain area. Municipalities can also roll out a community network by setting up free or inexpensive Internet access hotspots. Community networks are set up in order to bring affordable Internet connectivity to an area or to bring Internet connectivity to an area where there is no connectivity at all. Community networks in Kenya have been growing every year since 2015 when the first community network was founded. There are currently four community networks in Kenya. Despite the growth of community networks in Kenya, there is currently no existing critical analysis done on community networks in Kenya. This paper, hence, presents a study on community networks that are currently operating in Kenya. This paper presents the challenges currently hampering the growth of community networks in Kenya. This paper also provides possible solutions to those challenges and make some practical recommendations that can be adopted by community networks in Kenya. The paper further discusses the characteristics of community networks in Kenya and the critical success factors for a community network based on the experience of two community networks in Kenya. The applied methodology are qualitative exploratory research and desktop reviews.
Modelling and Multi-agent Simulation of Urban Road Network

In many African cities, there is a constant increase in road network usage, while we notice an absence of proper means of traffic regulation. Decision-makers should make decisions about road network improvement to ease the management and availability of the road network. They must consider the smart city’s concept because of its features that will enable smart mobility. The most popular and tedious method to provide mobility data is the traditional traffic count, which is used in many cities. However, this method does not make it possible to assess mobility according to travel scenarios and requires significant financial and human resources compared to a computer simulations-based way. We propose, a simulation tool based on multi-agent technology to facilitate the testing of mobility scenarios and to help in decision-making about traffic regulation. The tool we designed has been applied to the city of Cotonou to simulate mobility and to have a fully functional representation of the existing road network. This virtual representation can help to identify the key metrics decision-makers can leverage to improve the traffic and get an insight into the city road network. To test it, we simulated the traffic by considering a travel scenario: the home-work journey of the citizens of Cotonou. The results help in decisions making to improve mobility under this scenario. Even though our example is applied to the city of Cotonou, our model from its design is flexible enough to support the peculiarity of African cities.
Impact of Text Pre-processing on Classification Accuracy in Polish

Natural language processing (NLP) like other Machine Learning (ML), Deep Learning (DL) and data processing tasks, requires a large amount of data to be effective. Thus, one of the most significant challenges confronting ML/DL tasks, including NLP, is a lack of data. This is especially noticeable in the case of text data for niche languages like Polish. Manual collection and labelling of text data is the primary method for obtaining language-specific data. However, this is a lengthy and labour-intensive process. As a result, researchers use a variety of other solutions, such as machine translation from another, more developed language in the field, to obtain data more quickly and affordably. For these reasons the suitable experiments, described in the paper, were carried out, resulting in a machine translation model that translates texts from English into Polish. Its results were compared with those of a pre-trained model and translations were subjected to human testing. Moreover, the paper presents the influence of different pre-processing stages on the final result of text classification in Polish in terms of one of six emotions: anger, fear, joy, love, sadness, surprise.
An Investigative Approach of Context in Internet of Behaviours (IoB)

In the context of language and communication, context refers to the circumstances or background information that surrounds a situation or event and helps to give it meaning. Context can include the time and place of an event, the people involved, the cultural and social norms, and the larger issues and events that may be related. Understanding the context of a situation or event can help to provide deeper insight and understanding. In the context of computing and technology, context can refer to the information and settings that are relevant to a specific task or application. In this context, context may include data, preferences, and other factors that help to shape the way a task or application operates. So for the interaction between humans to machines, we need to explore the concept of Context. So, Context means any information of a person, place, object, things, activity or entity. It has been used in almost all domains like Internet of Things, Internet of Behaviours, pervasive computing, intelligent environment, ubiquitous computing, etc. In such a domain to sense data according to the Behaviours of an entity and then adapting Behaviours or context information. There are various techniques for gathering data and then processing information. In embedded system, we can collect data using sensors and sensed data can be place, person, pattern, and activity of humans, so we need the type of context. So, in this paper, we explored the taxonomy of context in different scenarios.
Deciphering Barriers and Facilitators of eHealth Adoption in Uganda Using the Systems Thinking Approach - A Systematic Review

Several studies have explored the barriers and facilitators of eHealth adoption in Uganda but hardly any literature investigating these factors using a Systems Thinking perspective, which is the purpose of this study. A search for articles was done in several databases and only 35 articles were fit for the study. Five group model-building sessions were conducted to synthesize the data. As a result, six causal loop diagrams were constructed to provide a theoretical insight of the causation between the different factors. The study revealed that training users in Information and Communication Technology (ICT) improves their digital literacy and general awareness about ICT, which improves their knowledge of telemedicine and subsequently changes their attitude towards the use of eHealth systems. The eHealth trajectory in Uganda is still in its infancy hence, it is imperative for key players in the sector to painstakingly consider the contextual barriers of eHealth and capitalize on the facilitators if eHealth ensue.
Rules’ Quality Generated by the Classification Method for Independent Data Sources Using Pawlak Conflict Analysis Model

The study concerns classification based on dispersed data, more specifically data collected independently in many local decision tables. The paper proposes an approach in which coalitions of local tables are generated using Pawlak’s conflict analysis model. Decision trees are built based on tables aggregated within the coalitions The paper examines the impact of the stop criterion (determined by the number of objects in a node) on the quality classification and on the rules’ quality generated by the model. The results are compared with the baseline approach, in which decision trees are built independently based on each of the decision tables. The paper shows that using the proposed model, the generated decision rules have much greater confidence than the rules generated by the baseline method. In addition, the proposed model gives better classification quality regardless of the stop criterion compared to the non-coalitions approach. Moreover, the use of higher values of the stop criterion for the proposed model significantly reduces the length of the rules while maintaining the classification accuracy and the rules’ confidence at a high level.
Radius Estimation in Angiograms Using Multiscale Vesselness Function

This paper presents a new method for estimating the radius of blood vessels using vesselness functions computed at multiple scales. The multiscale vesselness technique is commonly used to enhance blood vessels and reduce noise in angiographic images. The corrected and binarized image resulting from this technique is then used to construct a 3D vector model of the blood vessel tree. However, the accuracy of the model and consequently the accuracy of radii estimated from the model may be limited by the image voxel spacing. To improve the accuracy of the estimated vessel radii, the method proposed in this study makes use of the vesselness functions that are already available as by-products of the preceding enhancement procedure. This approach speeds up the estimation process and maintains sub-voxel accuracy. The proposed method was validated and compared with two other state-of-the-art methods. The quantitative comparison involved artificially generated images of tubes with known geometries, while the qualitative assessment involved analyzing a real magnetic resonance angiogram. The results obtained demonstrate the high accuracy and usefulness of the proposed method. The presented algorithm was implemented, and the source code was made freely available to support further research.
Cerebral Vessel Segmentation in CE-MR Images Using Deep Learning and Synthetic Training Datasets

This paper presents a novel architecture of a convolutional neural network designed for the segmentation of intracranial arteries in contrast-enhanced magnetic resonance angiography (CE-MRA). The proposed architecture is based on the V-Net model, however, with substantial modifications in the bottleneck and the decoder part. In order to leverage multiscale characteristics of the input vessel patterns, we postulate to pass the network embeddings generated on the encoder path output through the atrous spatial pyramid pooling block. We motivate that this mechanism allows the decoder part to rebuild the segmentation mask based on local features, however, determined at various ranges of voxels neighborhoods. The ASPP outputs are aggregated using a simple gated recurrent unit which, on the other hand, facilitates the learning of feature maps’ relevance with respect to the final output. We also propose to enrich the global context information provided to the decoder by including a vessel-enhancement block responsible for filtering out background tissues. In this study, we also aimed to verify if it is possible to train an effective deep-learning vessel segmentation model based solely on synthetic data. For that purpose, we reconstructed 30 realistic cerebral arterial tree models and used our previously developed MRA simulation framework.
Volume Introduction – Global Perspectives on Higher Education: From Crisis to Opportunity

Recent events worldwide have necessitated rapid transformation within the areas of teaching, learning, assessment, administration, and academic support systems in today’s higher educational institutions globally. The 33 chapters in this volume provide evidence of these transformations and discuss the innovative ways in which university educators and emerging teachers have addressed the challenges brought on by a crisis. A crisis, in this context, refers to a predicament or a turning point that necessitates a response in the form of adaptation or innovation. The chapters in this volume capture the personal experiences of educators, educational support professionals, and emerging teachers who encountered crises in the form of disruptions (i.e., COVID-19 pandemic), advancements (i.e., technology), and transformations (i.e., practices, procedures). In addition, the contributing authors provide a glimpse into the professional challenges they faced and discuss how they embraced the challenges brought on by a crisis as an opportunity to enhance existing practices or introduce innovative approaches.
Seventh International Scientific and Practical Conference “Current Trends in Researches of Mass Consciousness”

On March 24–25, 2023, the Seventh International Scientific and Practical Conference “Actual Problems of the Study of Mass Consciousness” was held. The organizers were Penza State University and Kostanay Regional University named after. Akhmet Baitursynov (Kazakhstan). The conference was held in a hybrid format – in person and online. The participants discussed key issues related to the problematic field of mass consciousness, focusing on the multi-level and inconsistency of this phenomenon. As a result of the conference, a collection of materials was published, revealing a wide range of research by modern psychologists in the field of psychology of mass consciousness.
Computational Models of Learning: Deepening Care and Carefulness in AI in Education

The field of Artificial Intelligence in Education (AIED) cares by supporting the needs of learners with technology, and does so carefully by leveraging a broad set of methodologies to understand learners and instruction. Recent trends in AIED do not always live up to these values, for instance, projects that simply fit data-driven models without quantifying their real world impact. This work discusses opportunities to deepen careful and caring AIED research by developing theories of instructional design using computational models of learning. A narrow set of advances have furthered this effort with simulations of inductive and abductive learning that explain how knowledge can be acquired from experience, initially produce mistakes, and become refined to mastery. In addition to being theoretically grounded, explainable, and empirically aligned with patterns in human data, these systems show practical interactive task learning capabilities that can be leveraged in tools that interactively learn from natural tutoring interactions. These efforts present a dramatically different perspective on machine-learning in AIED than the current trends of data-driven prediction.
Item Difficulty Constrained Uniform Adaptive Testing

Computerized adaptive testing tends to select and present items frequently with high discrimination parameters because these items can discriminate examinees’ abilities in a wide range. Unfortunately, that tendency leads to bias of item exposure. To address this shortcoming, we propose item difficulty constrained uniform adaptive testing. During the initial stage, an optimal item is selected and presented from a uniform item group generated by a modern uniform test assembly method. The method switches to the secondary stage when the examinee’s ability converges. It selects and presents the optimal item with a difficulty parameter value near the examinee’s ability estimate from the whole item pool. Empirical experiments demonstrate that the proposed method mitigates the bias of item exposure while maintaining low measurement error by reducing the number of presented items with high discrimination parameters which are likely to be presented frequently by earlier CAT methods.
Towards Extracting Adaptation Rules from Neural Networks

Defining adaptation rules is an important step in the design of adaptive systems. This paper proposes using a constrained multi-modal neural network to extract adaptation rules. The proposed approach enhances a serious game’s adaptive capability, which aims to help learners improve their socio-moral reasoning skills. The neural network takes learners’ multimodal data as input and predicts how it will answer an exercise. The rules extraction is based on reading and interpreting weights learned by the trained network to determine the players’ attributes and the system’s elements that play an important role in predicting the knowledge involved. The extracted rules are then validated using a decision tree. This validation shows that the proposed technique can support the production of adaptation rules in adaptive systems.
Classification of Brain Signals Collected During a Rule Learning Paradigm

We propose incorporating biophysical data with behavioral data to inform digital learning environments on an individual’s current cognitive state and how it relates to their learning. We used a rule learning paradigm drawn from cognitive psychology to define phases of rule learning across multiple domains. This paradigm can simulate an inductive reasoning framework seen during mathematics education while reducing the number of covariates compared to real-world settings. We combined the time series brain data with behavioral and contextual data in machine learning models for prediction of rule learning phases with the aim of developing approaches to incorporate a mixture of behavioral and neural data into digital learning designs.
Deidentifying Student Writing with Rules and Transformers

As education increasingly takes place in technologically mediated settings, it has become easier to collect student data that would be valuable to researchers. However, much of this data is not available due to concerns surrounding the protection of student privacy. Deidentification of student data is a partial solution to this problem, but student-generated text, a form of unstructured data, is a major challenge for deidentification strategies. In response to this problem, we develop and evaluate two approaches for the automatic detection of student names. We develop one system using a rule-based approach and one using a transformer-based approach that relies on finetuning a pretrained large language model. Our findings indicate that the transformer-based approach to student name detection shows more promise, especially when there is a high degree of variation between texts in a dataset.